Advances in Geophysics: Satellite Oceanic Remote Sensing

ADVANCES IN

GEOPHYSICS

VOLUME 27

Contributors to This Volume JOHN c. ALISHOUSE J. R. APEL ROSWELLW. AUSTIN DONALDE. BARRICK PETERG. BLACK JOHNT. BRUCKS W. J. CAMPBELL VINCENTJ. CARDONE D. J . CAVALIERI DENNISK . CLARK L. S. FEDOR R. CECILGENTRY P. GLOERSEN E I. GONZALEZ HOWARDR. GORDON CECILIAGIRZGRIFFITH JEFFREYD. HAWKINS NANCYJ. HOOPER

WARRENA. Hovis WILBERB. HUSTON R. MICHAELLAURS BELINDAJ. LIPA E. PAULMCCLAIN RONALDD. MCPHERSON N. M. MOGNARD BRUCEH. NEEDHAM JAMES OVERLAND S. PETEHERYCH JR. WILLARD J. PIERSON, DUNCANB. Ross C. L. RUFENACH I11 JOHNW. SHERMAN, C. T. Swim JOHNC. WILKERSON CHARLES S. YENTSCH Yu TSANN-WANG H. J. ZWALLY

Advances in

GEOPHYSICS VOLUME 27

Satellite Oceanic Remote Sensing Edited by

BARRY SALTZMAN Department of Geology and Geophysics Yale University New Haven, Connecticut

1985

Academic Press, Inc. (Harcourf Brace Jovanovich, Publishers)

Orlando San Diego New York London Toronto Montreal Sydney Tokyo

COPYRIGHT 8 1985, BY ACADEMIC PRESS, INC. ALL RIGHTS RESERVED. NO PART OF THIS PUBLICATION MAY BE REPRODUCED OR TRANSMITTED IN ANY FORM OR BY ANY MEANS,ELECTRONIC OR MECHANICAL, INCLUDING PHOTOCOPY, RECORDING, OR ANY INFORMATION STORAGE AND RETRIEVAL SYSTEM, WITHOUT PERMISSION IN WRITING FROM THE PUBLISHER.

ACADEMIC PRESS, INC. Orlando, Florida 32887

Unitcd Kingdom Edition published by ACADEMIC PRESS INC. (LONDON) LTD. 24-28 Oval Road, London NWI 7DX

LIBRARYOF CONGRESS CATALOG CARD NUMBER:52-12266 ISBN: 0-12-018827-9 PRINTED IN THE UNlTED STATES OF AMERICA 85868788

9 8 7 6 5 1 3 2 1

CONTENTS CONTRIBUTORS ............................................................................... FOREWORD .................................................................................... PREFACE .......................................................................................

ix

...

xiii

xv

1 . Introduction JOHN W. SHERMAN. Ill

:.

I . Purpose and Scope .................. ................................................... 2 . Historical Background .................................................................. 3 . Approach and Organization ............................................................ References ................................................................................ 2. The 1978 Oceanic Trilogy: Seasat. Nimbus-7. and TIROS-N

JOHN W. SHERMAN . I11 1. Introduction ............................................................. ........................... 2 . The Seasat Sensors and Results .... 3. The Nimbus-7 Sensors and Results ................................................... 4 . The TIROS-N Series and Results .. ........................... 5 . Summary .................................................................................. References ............................ ...........................

11

12 47 52 56 59

3 . Analysis and Interpretation of Altimeter Sea Echo DONALD E. BARRICK AND BELINDA J . LIPA 1 . Introduction .....................................

.......................... 2 . The Convolutional Representation of the Signal and Its Use ...................... 3. Model Fits of Recovered Sea Surface Probability Density ......................... 4 . The Study of Altimetric Biases Using Models ....................................... 5 . Electromagnetic Bias .................................................................... 6. A General, Improved Deconvolution Algorithm .................................... 7. Conclusions ............................................................................... Appendix ......... ................................................................... References .........................................................

V

61 62 68 73 86 93 96 97 99

vi

CONTENTS

4 . Oceanic Surface Winds

DUNCAN B . Ross. VINCENT J . CARDONE. JAMESOVERLAND. RONALD D . MCPHERSON . WILLARD J . RERSON. JR.,AND TSANN-WANG Yu 1 . Introduction ............................................................................... 2 . Mechanism for Measurement of Surface Wind Speed Using Microwave Systems ..................................................................... 3. The Marine Surface Boundary Layer ................................................. 4 . Development of a Model Function Relating SASS Data to Surface Wind Speed .................................................................. 5 . Global Data Assimilation Experiments ............................................... 6 . Conclusions ............................................................................... References ................................................................................

101 103 105 109 121 137 138

5 . Surface and Internal Ocean Wave Observations

c. L . RUFENACH.L . S . FEDOR. J . R . APEL.AND F . I . GONZALEZ 1. Introduction ............................................................................... 2 . Ocean Surface Waves ................................................................... 3. Ocean Internal Waves ................................................................... 4 . Summary and Conclusions ............................................................. References ................................................................................

141

142 176

192 193

6. Seasat Microwave Wind and Rain Observations in Severe Tropical and Midlatitude Marine Storms PETERG . BLACK.R . CECILGENTRY.VINCENT J . CARDONE. AND JEFFREY D . HAWKINS 1 . Introduction ............................................................................... 2 . The Nature of Severe Marine Storms ................................................. 3. Microwave Measurements in Severe Marine Storms ............................... 4 . Seasat Observations of Rain Rate and Microwave Attenuation in Tropical Cyclones ..................................................................... 5. Seasat Surface Wind Observations in Tropical Cyclones ........................... 6. Analysis of Individual Storms and Comparison of Seasat Data with “Surface-Truth’ Data .............................................................. 7 . Seasat Observations of Sea Surface Temperature Near Tropical Cyclones ....... 8. Application of Seasat Observations to Operational Marine Storm Forecasting Needs ....................................................................... 9. Conclusions ............................................................................... References ................................................................................

198 200 206 208

215 218 264 265 273 274

vii

CONTENTS

7. Sea Surface Temperature Determinations

JOHNc . ALISHOUSE AND E. PAUL MCCLAIN I. 2. 3. 4.

Introduction to Remote Sensing of Sea Surface Temperature ..................... Microwave Sensing of Sea Surface Temperature .......................... ................... Infrared Sensing of Sea Surface Temperature ..... Summary .............................. ...................................... References ........ ................................

279 28 1 286 292 294

8 . Ocean Color Measurements

HOWARD R. GORDON, ROSWELL W. AUSTIN, DENNIS K. CLARK, WARREN A. HOVIS.AND CHARLES s. YENTSCH

......................... ...................... 297 303 ............................. 2. The CZCS System ................. ................ 306 3. Response to Oceanic and Atmospheric Conditions ..... 1 . Introduction .......

4. Remote Sensing of the Phytoplankton Pigment 5. Remote Sensing of the Diffuse Attenuation Co ................... 6. Summary and Conclusions .......................... References .................. ................................

313 322 33 1 332

9. Observations of the Polar Regions from Satellites Using Active and Passive Microwave Techniques

C. T. SWIFT.W. J. CAMPBELL, D. J . CAVALIERI, L. S. FEDOR.P. GLOERSEN. S. PETEHERYCH. AND H. J. ZWALLY N. M. MOGNARD, I. 2. 3. 4. 5.

Introduction .......................... .................................. .................. Sea-Ice Observations by Seasat: A .................................... Sea-Ice Observations by Nimbus-7 ... Observations of Ocean Waves in the Antarctic ................. Seasat Altimeter Observations of Ice Sheets ........ ................... References .................................... .............................

336 337 361 373 379 390

10. Precipitation in Tropical Cyclones

CECILIA GIRZGRIFFITH AND L. S. FEDOR 1. Introduction ................................. .............. 2. Rain Rate Estimates from the VIRR ...................................... ................................... ..... 3. Future Studies ....... ................................ References ..................................

393 394 415 416

viii

CONTENTS

11. Living Marine Resources Applications

R . MICHAEL LAURSAND JOHN T. BRUCKS 419 1 . Introduction ............................................................................... 2 . Satellite Ocean Remote-Sensing Applications in Fisheries Research ............. 421 3 . Utilization of Satellite Data in Fisheries-Aids Products Distribution 443 to Fishermen .............................................................................. 450 References ................................................................................

Appendix A

.

instruments

A.1. Introduction ............................................................................ A.2. Radar Altimeter ....................................................................... A.3. Seasat Scatterometer System ........................................................ A.4. Scanning Multichannel Microwave Radiometer .................................. A S . Synthetic Aperture Radar ............................................................ A.6. Coastal Zone Color Scanner .........................................................

453 453

453 457 451 457

Appendix B. Seasat Validation Program JOHN C . WILKERSON

B .1 . B.2. B.3. B.4. B .5. B.6. B .I. B.8.

Introduction ............................................................................ Weather Conditions ................................................................... Plan of Operations (General) ........................................................ OSS Oceanographer .................................................................. Ocean Weather Station PAPA ....................................................... NOAA Data Buoys ................................................................... Aircraft ................................................................................. Total Data Sets ........................................................................

463 466 467 469 474 474 471 477

Appendix C . Data Availability BRUCEH . NEEDHAM C .I . Seasat Data Archive and Distribution .............................................. C.2. Nimbus-7 Coastal Zone Color Scanner (CZCS) .................................. C.3. Ordering Data .........................................................................

Appendix D. Glossary of Acronyms

...................

481 487 493 495

WILBERB . HUST~NAND NANCYJ . HOOPER

Appendix E. Glossary of Symbols

....................

499

INDEX ..........................................................................................

505

WILBER B. HUSTON

CONTRIBUTORS Numbers in parentheses indicate the pages on which the authors’ contributions begin.

JOHN C. ALISHOUSE, NOAAINESDISISEL, Washington, D.C. 20233 (279)

,J. R. APEL,Applied Physics Laboratory, The Johns Hopkins University,Laurel, Maryland 20707 (141)

ROSWELLW. AUSTIN,Visibility Laboratory, Scripps Institution of Oceanography, La Jolla, California 92093 (297) DONALDE. BARRICK, Ocean Surface Research, Boulder, Colorado 80303 (61) PETERG . BLACK,Atlantic Oceanographicand Meteorological Laboratory, Hurricane Research Division, Miami, Florida 33149 (197) JOHN T. BRUCKS,National Marine Fisheries Service, Fishery Engineering and Development Division, National Space Technology Laboratories, NSTL Station, Mississippi 39529 (419) W. J. CAMPBELL, United States Geological Survey, University of Puget Sound, Tacoma, Washington 98416 (335)

VINCENT J. CARDONE,Oceanweather, Inc., White Plains, New York (101, 197) D. J. CAVALIERI, National Aeronautics and Space Administration, Goddard Space Flight Center, Greenbelt, Maryland 20771 (335) DENNISK. CLARK,National Oceanic and AtmosphericAdministration, National Environmental Satellite, Data, and Information Service, Washington, D.C. 20233 (297) 1

L. S. FEDOR,National Oceanic and AtmosphericAdministration, WavePropagation Laboratory, Environmental Research Laboratories, Boulder, Colorado 80303 (141,335, 393) R . CECILGENTRY,Department of Physics and Astronomy, Clemson University, Clemson, South Carolina 29631 (197)

P. GLOERSEN, National Aeronautics and Space Administration, Goddard Space Flight Center, Greenbelt, Maryland 20771 (335) ’

F. I. GONZALEZ, National Oceanic and Atmospheric Administration, Pacific Marine Environmental Laboratory, Environmental Research Laboratories, Seattle, Washington 98115 (141)

ix

CONTRIBUTORS

X

HOWARDR. GORDON, Department of Physics, University of Miami, Coral Gables, Florida 33124 (297)

CECILIAGIRZ GRIFFITH,National Oceanic and Atmospheric Administration, Weather Research Program, Environmental Research Laboratories, Boulder, Colorado 80303 (393) JEFFREY D. HAWKINS ,* Atlantic Oceanographic and Meteorological Laboratory, Hurricane Research Division, Miami, Florida 33149 (197) NANCYJ. HOOPER,Metrics, Incorporated, Atlanta, Georgia 30339 (495)

WARRENA. HOVIS, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, Washington, D.C. 20233 (297) WILBER

B. HUSTON,OAO Corporation, Greenbelt, Maryland 20770 (495,499)

R. MICHAELLAURS,National Marine Fisheries Service, Southwest Fisheries Center, La Jolla, California 92037 (419) \

BELINDAJ.

LIPA,

Ocean Surface Research, Woodside, California 94062 (61)

E . PAULMCCLAIN, NOAAINESDISICESL, Washington, D.C. 20233 (279)

. RONALDD. MCPHERSON,

National Oceanic and Atmospheric Administration, National Meteorological Center, Washington, D. C. 20233 (101) N. M. MOGNARD, Groupe de Recherche de Geodesic Spatiales, Centre Nacional d’etudes Spatiales, Toulouse, Cedex, France 31055 (335) BRUCEH. NEEDHAM, NOAAINESDISINCDC, Satellite Data Services Division, Washington, D.C. 20233 (481)

JAMES OVERLAND, National Oceanic and Atmospheric Administration, Pacific Marine Environmental Laboratories, Seattle, Washington 98115 (101)

\

S . PETEHERYCH, Atmospheric Environment Service, Downsview, Ontario, Canada M3H 5T4 (335) \

WILLARD J. PIERSON, JR., CUNY Institute of Marine and Atmospheric Sciences, The City College of New York, New York, New York 10031 (101)

,DUNCANB. ROSS, National Oceanic and Atmospheric Administration, Atlantic Oceanographic and Meteorological Laboratory, Miami, Florida 33149 (101)

*Present address: Naval Ocean Research and Development Activity (NORDA), Bay St. Louis, Mississippi.

CONTRlB UTORS

xi

C . L. RUFENACH,National Oceanic and Atmospheric Administration, Wave Propagation Laboratory, Environmental Research Laboratories, Boulder, Colorado 80303 (141)

JOHN W. SHERMAN, 111, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, Washington, D.C. 20233 (1, 11) C. T. SWIFT,Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, Massachusetts 01002 (335)

JOHN C. WILKERSON, NOAAINESSIOceanic Sciences Branch, Washington, D.C. 20233 (463) CHARLESS. YENTSCH,Bigelow Laboratory for Ocean Sciences, West Boothbay Harbor, Maine 04575 (297) TSANN-WANG Yu,National Oceanic and Atmospheric Administration, National Meteorological Center, Washington, D.C. 20233 (101)

H . J. ZWALLY,National Aeronautics and Space Administration, Goddard Space Flight Center, Greenbelt, Maryland 20771 (335)

This Page Intentionally Left Blank

FOREWORD With the rapid refinement of satellite remote sensing techniques over the last decade, we have witnessed a virtual step-function increase in the acquisition of oceanic data; it is not an overstatement to say that these new techniques have opened a new era in oceanographic research. In this special volume of Advances in Geophysics we are pleased to present a review of the development, and main results, of the pioneering studies conducted primarily by the National Oceanic and Atmospheric Administration (NOAA) following from the operation of three satellite systems launched by the National Aeronautics and Space Administration (NASA) during 1978: Seasat, Nimbus-7, and TIROS-N. It is clear that all aspects of oceanographic research, including the study of surface winds, waves, currents, thermal structure, ice coverage, and biota, have been advanced considerably by the unique new data sets generated. We hope that this review volume will provide a useful summary of the accomplishments and, as developmentsin this area proceed at their almost breathless current pace, that this review will remain as a landmark of the early breakthroughs. The editor and publisher are grateful to all of the authors for their contributions, and especially to John W. Sherman, 111, who was the driving force behind the volume and main coordinator of all of the contributions. One of the contributors to the volume communicated the following remarks concerning John Sherman: There are many individuals who no doubt are justly deserving of some sort of acknowledgement for contributions made to satellite-oceanography. But, in my opinion, there’s one who stands out-Jack Sherman. I believe the science of satellite-oceanography has made the progress it has largely because of Jack’s efforts. Jack has worked and fought tirelessly and unselfishly for more and better sensors, and for funding and research programs to ensure the development of satellite-oceanography; I would guess that virtually every U.S. scientist who has conducted research in satellite-oceanography during the past I % decades has at one time or another received funding as a direct or indirect result of Jack’s efforts.

It is a pleasure to acknowledge these efforts with the publication of this volume.

BARRYSALTZMAN

xiii

This Page Intentionally Left Blank

PREFACE Don’t try to describe the ocean if you’ve never seen it. . . . Jimmy Buffet

From ancient mariners to modern troubadours mankind has sought to describe the vast beauty, serenity, and awe of the sea. Many observations by early mariners were maintained orally in order to protect unique routes used for commerce. The spectrum of written descriptions ranges from chastushkas to highly mathematical papers. Over the years the methods of description have shifted from sight, sound, and smell, to lead lines and Nansen bottles, and on to current techniques of science. Each new method has provided another view of the sea. In 1785 marine chronometers were perfected to measure longitude as well as the traditional latitude. The addition of longitude greatly assisted navigation for commerce and allowed the birth of oceanology wherein measurements could be located in time and space. During the intervening 200-year period navigational improvements were extended, so that by 1985 satellite techniques provide global, three-dimensional navigational accuracies on the order of meters. In addition to contributions to navigation, earth-orbiting satellites have had major scientific success in communications and environmental observations; indeed, so successful that some satellites have become operational systems. Both research and operational environmental satellites have modified and extended the understanding and knowledge of many global phenomena, including concepts about the ocean. These satellite observations of the ocean describe, at best, the upper tens of meters of the sea surface. However, this is the most dynamic portion of the ocean requiring the greatest frequency of observations. For a large portion of the global oceans, seasonal effects do not occur below 200 meters. Thus oceanic remote sensing opportunities afforded by satellites to view the upper ocean are not competitive with those in situ systems used on ships and buoys, but free these in situ systems to describe other, deeper portions of the ocean. During this same 200-year period, commerce has become even more dependent on the seas for many types of resources, recreation, and transportation. Because of this importance, the 1984/1985 time period has been recognized by Presidential Proclamation as the Year of the Ocean. xv

xv i

PREFACE

Further, the Silver Anniversary of earth-observing satellites is April 1, 1985, when 25 years earlier the first Television and InfraRed Observation Satellite (TIROS-1) was launched by the National Aeronautics and Space Administration. In this evolutionary period for satellites, remote sensing techniques for oceanic observations developed as a significant scientific program. The purpose of this volume of Advances in Geophysics is to provide a summary of the results of this scientific achievement which provides yet another description of mankind’s view of the ocean. JOHN W. SHERMAN, I11

INTRODUCTION JOHNW. SHERMAN, I11 National Oceanic and Atmospheric Administration National Environmental Satellite. Data. and Information Service Washington, D. C .

1. Purpose and Scope . . . . . . . . . . . . . . . . . . . . . . . 2. Historical Background . . . . . . . . . . . . . . . . . . . . . . 3. Approach and Organization . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . .

1 1 8 9

1. PURPOSE AND SCOPE

During 1978 three satellite systems were launched by the National Aeronautics and Space Administration (NASA) that provided both a new view of the earth’s oceans and a revised picture of the nature and dynamics of the behavior of the ocean surface and its interaction with the atmosphere and land. The three 1978 satellite systems were Seasat, a California Institute of Technology’s Jet Propulsion Laboratory (JPL) development, launched June 26; and two satellites developed by NASA Goddard Space Flight Center (GSFC)-TIROS-N, launched October 13 to serve as the prototype third-generation operational satellite for NOAA, and Nimbus-7, launched October 24. These three satellites provided an ensemble of ocean-unique sensors which covered the microwave, infrared, and visible portions of the electromagnetic spectrum. The purpose of this volume of Advances in Geophysics is to summarize the results and accomplishments from these three satellites and to illustrate selected applications of the operational use of satellites for environmental monitoring of the global oceans. The summary is based on both the results of the authors and the efforts of many other researchers. The scope is limited to techniques for the remote observation of the ocean surface or near surface from satellites. 2. HISTORICAL BACKGROUND

While the oceans have been observed visually for winds, waves, color, and color change for centuries, it has been only within the last 100 years or so that quantitative measurements have been undertaken. The Beaufort scale for 1 ADVANCES IN GEOPHYSICS, VOLUME 2’1 ISBN 0-12-018827-9

2

JOHN W.SHERMAN, 111

winds originated in 1805, the Forel-Ule scale for color evolved with the Secchi disk between 1865 and 1900, and the Douglas scales for waves were first proposed in 1921 (Fairbridge, 1966). Many optical, infrared, and microwave systems were given impetus by the needs of World War 11. As an outgrowth for environmental applications, aircraft infrared measurements of sea surface temperature (SST) were begun in the early 1950s (Stommel et al., 1953), and microwave studies of the ocean surface also were begun in about the same time period (Skolnik, 1962). Even though aircraft remote-sensing applications to oceanology and marine research were still in their infancy in the early 1950s, the first known paper to discuss earth observations from satellites was published prior to the advent of earth-orbiting spacecraft (Stehling, 1953). The first earth-observing environmental satellite was NASA’s Television and InfraRed Observation Satellite (TIROS-1) launched for meteorological observations on April 1, 1960. The first satellite observations of ocean features began with the TIROS-2 detection of SST differences in 1960/1961. The first satellite oceanic experiment involved the TIROS-4 (launched February 8,1962) observation of sea ice and icebergs, and was designated the TIROS Ice Experiment (TIREX). During this same time period TIROS-8 (launched December 21, 1963) carried the first Automatic Picture Transmission (APT) system, an optical vidicon sensor which allowed real-time readout of local cloud features using relatively inexpensive shipboard receivers. This continuously transmitted picture permitted direct observation of marine weather in the vicinity of ships to support at-sea operations. With the potential of satellites to address synoptic oceanic problems illustrated by these early satellites,NASA sponsored a summer study in 1964 to consider the application of satellites to oceanic research and operational needs (Ewing, 1965). At that time, no satellite sensors had been specifically designed for oceanic measurements,so the study drew primarily on the stateof-the-art technology being used in airborne remote sensing of oceans and sea ice. The study was far-reaching in its view of potential uses and served as a long-term guide for NASA research and development, ranging from optical colorimeters for water mass detection and chlorophyll concentration to microwave altimeters for sea level, tsunamis, and sea state. Further reinforcement of satellite altimetry for studies of oceanic processes was made in a subsequent NASA-sponsored study at Williams College during 1969, now designated as the “Williamstown” study (Kaula, 1970). This 1969 study is regarded as providing the primary impetus (Barrick and Swift, 1980) for the altimeter systems flown on Skylab in 1973 and the third Geodynamic Experimental Ocean Satellite (GEOS-3), launched April 9, 1975. The Skylab altimeter measured the distance between the spacecraft and the surface to a precision of 1 to 2 m. The GEOS-3 altimeter was three to four times more

1. INTRODUCTION

3

accurate with precision of 30 to 40 cm. The GEOS-3 results are documented in a special issue of Marine Geodesy (Saxena, 1980). The early 1970s also provided the time in which temperature detection advanced to providing global SST maps with precision approaching 2"C, but more importantly, on a local and regional scale, provided details on temperature gradients not previously observed. Particularly, the Scanning Radiometer (SR) on the Improved TIROS Operational Satellite (ITOS), launched January 23, 1970, provided the first attempt at operational SST products. The development of polar-orbiting satellites was complemented by the development of earth-observing geostationary satellites. The visible and infrared scanners, first on the NASA Synchronous Meteorological Satellites (SMS-1, launched May 17,1974)and subsequently on the NOAA Geostationary Operational Environmental Satellites (GOES-1, launched November 16, 1976), proved invaluable in monitoring severe weather conditions over both land (tornadoes) and oceans (hurricanes and typhoons). Presently, the two GOES satellites cover the United States in the region approximately & 55" latitude and 20"W to 180"Wlongitude (nominally one satellite positioned at 75"W longitude and the other at 135"W), with each satellite providing coverage every 30 min of weather systems over and surrounding the United States. Through international cooperation with the USSR, Japan, and the European Space Agency (ESA), near-global coverage(to -C- 55" latitude) is now acquired using geostationary satellites. ESA uses Meteosat at O"E, Japan occupies a position at 14D"W, and the USSR is planning to operate at 70"E. The NOAA GOES satellite infrared systems have been used to demonstrate the dynamics of major ocean currents such as the Gulf Stream, but have also provided data on the propagation of equatorial long waves in the eastern equatorial Pacific (Legeckis et al., 1983). While such a wave mechanism had been postulated, it was the GOES system that first provided for the detection and quantification of the long-wave characteristics. Except for GEOS-3, no satellite sensors had been designed specifically for oceanic observations prior to 1978. During the early 1970s the capabilities for oceanological applications of microwave altimetry, scatterometry and radar, microwave and infrared radiometry, and optical-imaging spectrometers matured, along with satellite technology to provide for specific oceanic sensor design. Thus, the NASA-sponsored Seasat, Nimbus-7, and TIROS-N launches marked the beginning of a new era in oceanic observations,just over 100 years after the sailing of the HMS Challenger in 1872. It is anticipated that these satellites will mold the thinking about the dynamics of the global oceans just as the HMS Challenger revised the state of scientific knowledge about the earth's oceans.

4

JOHN W.SHERMAN, 111

The understanding of oceanic surface dynamics is tantamount to further improvement in life at sea and the efficient use of the world's oceans for transportation, energy, and food. Just as communication and navigation improvements have been made with satellites, global satellite-derived information on ocean surface winds, temperature, waves, currents, ice, and biological features has been determined feasible by Seasat, Nimbus-7, and TIROS-N. This volume of Advances in Geophysics focuses on the environmental information provided by these satellites. To borrow the thought of Dr. Gifford C. Ewing, who chaired the 1964 NASA summer study on oceanography from space, the length, breadth, and depth of the world's total ocean are approximately proportional to the length, breadth, and depth of a single page from an average newspaper. Satellites,as Dr. Ewing saw it, offered the opportunity for the first time to read the dynamics of global oceanic behavior. The summary of U.S. civil satellites is contained in Tables 1-111. The list is not all-inclusive but demonstrates a unique earth-monitoring capability. These satellites form the heritage and legacy for current and future marine satellites. The United States is now entering its third generation of earthobserving satellites, in terms of both time and technology. Not shown are the NASA Skylab and Space Transportation System, which have also contributed to oceanic remote sensing. TABLE I. CHRONOLOGY OF THE FIRST GENERATION OF U.S. CrviL ENVIRONMENTAL SATELLITES Satellite"

Purposeb

Launch date

Satellite"

Purposeb

Launch date

TIROS-I TIROS-I1 TIROS-I11 TIROS-IV TIROS-V TIROS-VI TIROS-VII TIROS-VIII Nimbus-1 TIROS-IX TIROS-X ESSA 1 ESSA 2

Research Research Research Research Research Research Research Research Research Research Research Operations Operations

04/01/60 11/23/60 07112/61 02/08/62 06119/62 09/18/62 06/19/63 12/21/63 08/28/64 01/22/65 07/02/65 02/03/66 02/28/66

Nim bus-2 ESSA 3 ATS-1 ESSA 4 ATS-2 ESSA 5 ATS-3 ESSA 6 ESSA 7 ESSA a ESSA 9 Nimbus-3 ATS-5

Research Operations Research Operations Research Operations Research Operations Operations Operations Operations Research Research

05/15/66 10/02/66 12/06/66 0 1/26/67 04/05/67 04/20/67 11/05/67

11/10/67 08/16/68 12115/68 02/26/69 04/14/69 08/ 12/69

" Acronyms: TIROS, Television and EnfraRed Observation Satellite; ESSA, Environmental Science Services Administration (NOAA predecessor); ATS, Applications Technology Satellite; Nimbus, not an acronym; derived from the Latin rainstorm or cloud. Research satellites are NASA and operational satellites are NOAA or its predecessor ESSA. All first-generation satellites were primarily for meteorology/weather-related observations using visible and IR imaging sensors.

'

TABLE 11. CHRONOLOGY AND APPLICATIONS OF TIE SECOND-GENERATION U.S. CIVIL ENVIRONMENTAL SATELLITES Orbital characteristics Satellite"

Primary applicationb

Launch date

Altitude (km)

Inclination (deg)

Primary sensorsc

ITOS-1 (TIROS-M) Nimbus-4

Met operational prototype Met research

01/23/10

1,456

04/08/70

1,240

99.9

NOAA-1 Landsat-1 NOAA-2 Nimbus-5

Met operations Land research Met operations Met research

12111/70 07 109/I2 10/15/12 12111/12

1,438 919 1,451 1,099

102 99.09 98.8 99.9

NOAA-3 SMS-1

Met operations Met operational prototype Met operations Land research Met operational prototype Sea surface topography research Met research

11/06/73 051 11/14

1,505 35.790

102 0.6

IDCS, THIR, IRIS, SIRS, SCR, FWS, IRLS, BVV APT, AVCS, SR, FPR RBV, MSS SR, VHRR, VTPR THIR. ESMR, ITPR, MWS, RDR, SCMR SR, VHRR, VTPR VISSR, SEM, DCDR

11/15/14 01122115 02/06/75

1,451 919 35,793

102 99.09 0.4

SR, VHRR, VTPR RBV, MSS VISSR, SEM, DCS

04/09/75

843

114.98

Radar altimeter

06/12/15

1,057

99.9

NOAA-4 Landsat-2 SMS-2 GEOS-3 Nimbus-6

102

APT, AVCS, SR, FPR

THIR, ESSMR, TWERLE, HIRS, SCAMS, ERB, TDRE (Continued)

TABLE11. (Conzinued) Orbital characteristics Satellite" NOAAJ GOES-1 GOES2 Landsat-3 HCMM GOES-3 GOES4

Primary applicationb Met operations Met operations Met operations Land research Land research Met operations Met operations

Launch date 07/29/76 10J 16/16 06/16/77 03/05/78 04/26/78 06/16/78 09/09/80

Altitude (km)

Inclination (deg)

1,511 35,788 35,900 919 600 35,900 35,900

102 0.8 0.5 99.09 100 0.5 0.5

Primary sensors' SR, VHRR, VTPR VISSR, DCS VISSR, DCS RBV, MSS HCMR VISSR, DCS VAS, SEM, DCS

a Satellite acronyms: ITOS, Improved TIROS Operational Satellite; NOAA, National Oceanic and Atmospheric Administration; SMS, Synchronous Meteorological Satellite;GEOS, Geodynamic Experimental Ocean Satellite;GOES, Geostationary Operational Environmental Satellite. As footnote b to Table I, but operational prototype satellites are NASA-sponsored systems built to NOAA requirements. Sensoracronyms: APT, Automatic picture Transmission; AVCS, Advanced Vidicon Camera System; BUV, Backscatter Ultraviolet Spectrometer; DCDR, Data Collection and Data Relay; DCS, Data Collection System; ERB, Earth Radiation Budget; ESMR, Electrically Scanning Microwave Radiometer; FPR, Flat Plate Radiometers; FWS, Filter Wedge Spectrometer; HIRS, High-resolution InfraRed Sounder; HCMR, Heat Capacity Mapping Radiometer; IDCS, Image Dissecter Camera System; IRIS, InfraRed Interferometer Spectrometer; IRLS, Interrogation, Recording, and Location System; ITPR, Infrared Temperature Profile Radiometer; MSS, MultiSpectral Scanner; MWS, Microwave Spectrometer; RBV, Return Beam Vidicon; RDR, Real-time Data Relay; SCAMS, Scanning Microwave Spectrometer; SCMR, Surface Composition Mapping Radiometer; SCR, Selective Chopper Radiometer; SIRS, Satellite InfraRed Spectrometer; SR, Scanning Radiometer; TDRE, Tracking and Data Relay Experiment; THIR, Temperature-Humidity Infrared Radiometer; TWERLE,Tropical Wind, Energy conversion,and Reference Level Experiment; VHRR, Very High Resolution Radiometer; VISSR, Visual and Infrared SpinScan Radiometer; VTPR, Vertical Temperature Profile Radiometer.

TABLE111. CHRONOLQGYAND APPLICATIONS OF THE THIRDGENERATION U.S. CIVIL ENVIRONMENTAL SATELLITES

Deactivation

Orbit Alt./Incl. (Wdeg)

10110178

800/108

Date Satellite Seasat

Launch 06/27/18

Sensor(s)" Altimeter

SMMR SASS VIRR SAR

4

TIROS-N

10/13/78

Nimbus-7

10/24/78

Landsat-4

07/16/82

02/17/81

8561102.3

AVHRR TOVS (three units: SSU, HIRS-2, and MSU), DSC, and SEM

955199.3

czcs

705.3198.2

SMMR LIMS, SAMS, SAM-1 1, SBV/TOMS, ERB, and THIR TM MSS

Landsat-5

03/01/84

7053198.2

TM, MSS

Marine application Sea surface topography, currents and circulation features, surface wind speed, significant wave height SST, surface wind speed, sea ice mapping, atmospheric H,O and precipitation Surface wind velocity Meteorological features, SST Wave spectra, sea ice dynamics, internal waves, current boundaries and eddy features SST, current boundaries, sea ice mapping Meteorological and weather operations Ocean color, color fronts, SST,chlorophyll concentration, diffuse attenuation coefficient. Same as Seasat Atmospheric research

Research for coastal mapping, shallow wave bathymetry, and coastal effluents Similar to TM but with less resolution and/or contrast (operational for land use) Duplicates Landsat-4 satellite

Acronyms: AVHRR, Advanced Very High Resolution Radiometer; CZCS, Coastal Zone Color Scanner; DCS, Data Collection System; ERB, Earth Radiation Budget (radiometer); HIRS/2, High-resolution InfraRed Sounder (second generation); LIMS, Limb Infrared Monitoring of the Stratosphere; MSS, MultiSpectral Scanner; MSU, Microwave Sounding Unit; SAMS, Stratospheric Aerosol Measurement Sensor; SAM-11, Stratospheric Aerosol Measurement (second generation); SAR, Synthetic Aperture Radar; SASS, Seasat-A Scatterometer System; SBV/TOMS, Solar and Backscatter ultraViolet/Total Ozone Mapping System; SEM, Space Environment Monitor; SMMR, Scanning Multichannel Microwave Radiometer; SSU, Stratospheric Sounding Unit; THIR, Temperature-Humidity Infrared Radiometer; TM, Thematic Mapper; TOVS, TIROS-N Operation Vertical Sounder (combination of SSU, HIRS-2, and MSU); VIRR, Visible and InfraRed Radiometer.

8

JOHN W.SHERMAN, I11

3. APPROACH AND ORGANIZATION With the exception of Chapters 2 and 3, the results and accomplishmentsof the three ocean-unique satellite systems are presented in this volume of Advances in Geophysics with regard to the observable rather than the specific sensor. An overview summary of the results of each satellite is contained in Chapter 2, followed by the nature of the interaction of microwave electromagnetic energy with the ocean surface in Chapter 3. (The needed infrared and optical-region interactions are discussed in the specific chapters on SST and ocean-color measurements.) Subsequently, the state of the art for observation of winds, waves, severe marine storm winds and waves, sea surface temperature, ocean color, cryosphere, precipitation, and living marine resources is reviewed in the remaining chapters. Supporting appendices are provided to give quick reference to’satellite details and data availability. Several general references are noted which complement and augment this volume of Advances in Geophysics. These references are listed below in chronological order. Science, Vol. 204, No. 4400, June 29, 1979. Contains eight papers on the initial assessment of Seasat performance. IEEE Journal of Oceanic Engineering, Vol. OE-5, No. 2, ISSN 03364-9059, April 1980. Provided a special issue on the Seasat sensors with emphasis on engineering aspects. Gower, J. F. R., ed., Oceanography from Space, COSPAR/SCOR/IUCRM Symposium (May 1980),Plenum Press, New York, 1981. Contains results from Seasat,Nimbus-7, and TIROSN. IEEE International Geoscience and Remote Sensing Symposium, June 8- 10,1981(IGARRS’ 81, IEEE Digest, IEEE Catalog No. 81CH1656-8). Reviews specific oceanic instruments in detail. Beal, R. C., DeLeonibus, P. S., and Katz, I., eds., Spaceborne Synthetic Aperture Radarfor Oceanography, The Johns Hopkins Oceanographic Studies, No. 7, The Johns Hopkins University Press, Baltimore, London, 1981. Provides a comprehensive summary of the Seasat radar system with detailed theoretical and experimental results. Bernstein, R. L., ed.,Journal of Geophysical Research, “Seasat Special Issue I: Geophysical Evaluation,” Vol. 87, No. C5, April 30, 1982. Provides an initial geophysical evaluation of Seasat, focused on the development and validation of algorithms for converting sensor signals to geophysical data. Kirwan, A. D., Ahrens, T. J., and Born, G. H., eds., Journal of Geophysical Research, “Seasat Special Issue 11: Scientific Results,” Vol. 88, No.C3, February 28, 1983. Devoted to the scientific results from the Seasat sensors and to the research achievements by many investigators in the field. Gordon, H. R. and Morel, A. Y.,“Remote Assessment of Ocean Color for Interpretation of Satellite Visible Imagery, A Review,” Lecture Notes on Coastal and Estuarine Studies, Springer-Verlag, New York, 1983.

1. INTRODUCTION

9

REFERENCES Barrick, D. E., and Swift, C. T. (1980). The Seasat microwave instruments in historical perspective. IEEE J. Ocean Eng. OE-S,I4-19. Ewing, G. C., ed. (1965). “Oceanography from Space.” Woods Hole Oceanographic Institution, Ref. No. 65-10. Fairbridge, R. W. (1966). “Encyclopedia of Oceanography.” Reinhold, New York. Kaula, W. M., ed. (1970). “The Terrestrial Environment: Solid Earth and Ocean Physics.” MIT NASA CR-1579. Legeckis, R., Pichel, W., and Nesterczuk, G. (1983). Equatorial long waves in geostationary satellite observations and in a multi-channel sea surface temperature analysis, Bull. Am. Meteorol. SOC.64 (2), 133-139. Saxena, N. K., ed. (1980). Interaction of marine geodesy and ocean dynamics (special issue of Marine Geodesy).Int. Ocean Suru.. Mapping, Sensing 3 (1-4). Skolnik, M. I. (1962). “Introduction to Radar Systems.” McGraw-Hill,New York. Stehling, K. R. (1953). Earth scanning techniques for a small orbital rocket vehicle. SpaceFlight Problems.” Astronaut. Congr.. 4th. Zurich. Stommel, H.W., von Arx, W. S., Parson, D., and Richardson, W. S. (1953). Rapid aerial survey of Gulf Stream with camera and radiation thermometer. Science 117 (3049), 639-640.

This Page Intentionally Left Blank

THE 1978 OCEANIC TRILOGY: SEASAT, NIMBUS-7, AND TIROS-N JOHNW. SHERMAN, I11 National Oceanic and Atmospheric Administralion National Environmental Satellite, Data, and Information Service Washingfon, D.C.

1. Introduction. . . . . . . . . . . . . . . . . 2. The Seasat Sensors and Results . . . . . . . . . 2.1. SurfaceInformation . . . . . . . . . . . . 2.2. Preliminary Results. . . . . . . . . . . . . 3. The Nimbus-7 Sensors and Results . . . . . . . . 3.1. Surface Information . . . . . . . . . . . . 3.2. Preliminary Results. . . . . . . . . . . . . 4. The TIROS-N Series and Results . . . . . . . . 5. S u m m a r y . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . .

. . . . . . . . .

11

. . . . . . . . . . 12 . . . . . . . . . 14

. . . . . . . . .

15

. . . . . . . . . . . . . . . . . . .

47

. . . . . . . . .

. . . . . . . . . . . . . . . . . . .

. . . . . . . . .

50 50 52 56 59

1. INTRODUCTION

The United States, through the National Aeronautics and Space Administration (NASA), Jet Propulsion Laboratory (JPL), launched Seasat, the first ocean-dedicated satellite, on June 26,1978, local time (June 27 at 02: 12 GMT, day 178)from the Air Force Western Test Range (AFWTR)at Vandenberg Air Force Base, California. This research spacecraft carried five sensors on board the 2290-kg platform, operating in a 108" inclination circular orbit 800 km above the earth. Seasat circled the earth 14 times daily, covering 95% of the global ocean area every 36 hr. Data transmitted from five sensors on board included information on sea surface winds, waves, and temperatures; sea ice features; storm surge; ocean topography; characteristics of severe storms at sea; and atmospheric water vapor and precipitation. On October 10,1978,at about 03:12 GMT (day 283), a catastrophic failure occurred during revolution (Rev) 1503. The failure was apparently in the satellite's prime power electrical subsystem and resulted in no signals being received from Seasat after 04:08 GMT. All further efforts to make contact with the spacecraft were unsuccessful. NASA's Goddard Space Flight Center (GSFC) launched on October 24, 1978, at 08: 14 GMT (day 297) the Nimbus-7 satellite, which began operation on October 29 and has been in operation since that time. This launch also occurred from AFWTR. The nominal sun-synchronous orbit parameters for 11 ADVANCES IN GEOPHYSICS, VOLUME 27 ISBN 0-12-018827-9

12

JOHN W.SHERMAN,111

the 933-kg Nimbus-7 platform are an inclination of 99.2", altitude of 955 km, period of 104.16 min, and ascending node time of 1152. Successive orbits are displaced westward by 26.04" at the equator. There are 13.83 orbits day-' with the fourteenth orbit 4.56" west of the corresponding orbit of the previous day. After 6 days the satellite again passes over a specific equatorial site, but the track is 1.3" west of the previous 6-day earlier overpass. The third generation of operational polar-orbiting satellites began with TIROS-N, a 715-kg satellite (excluding propellants and motor assembly case) launched from AFWTR at 11:23 GMT on October 13,1978, and deactivated on February 17, 1981. The TIROS-N satellite served as the prototype operational satellite used in the National Oceanic and Atmospheric Administration (NOAA) series of polar orbiters. NASA funded TIROS-N development and launch and NOAA subsequently sponsored the remaining satellites in this series, which began with NOAA-6, launched from AFWTR on June 27, 1979. TIROS-N operated at an altitude of 856 km, inclination of 98.9", 102min orbit (14.2 orbits day-'), and with an ascending crossing time of 1500 local time. NOAA-6 operates with essentially identical characteristics except its altitude is 833 km and has a 07:30 local descending equatorial crossing time. NOAA-7, launched June 23,1981, replaced TIROS-N in the afternoon orbit to maintain a two-polar satellite coverage system. The orbits of NOAA6 and NOAA-7 each repeat approximately every 10 days, although each satellite views the entire earth at least once per day. It is noteworthy to indicate that the TIROS-N predecessor, the Improved TIROS Operational Satellite (ITOS),operated as the second-generationseries of NOAA operational polar-orbiting spacecraft from October 1972 until March 1979. This series provided the NOAA-1 through -5 satellites and operated as a single-satellitepolar-orbiting system at an altitude of about 1475 km and an orbital period of 115 min. The reduced altitude of the TIROS-N series required the use of a two-satellite series to provide comparable daily coverage. The three 1978 satellite systems formed a virtual trilogy of oceanic monitoring capability. This chapter summarizes the results achieved from Seasat, Nimbus-7, and TIROS-N as a function of the sensors rather than the geophysical observables as used in the remaining chapters.

2. THESEASATSENSORS AND REsut-rs The five Seasat instruments included a radar altimeter, a Seasat-A Scatterometer System (SASS), a Synthetic Aperture Radar (SAR), a Visible and InfraRed Radiometer (VIRR), and a Scanning Multichannel Microwave

2. SEASAT, NIMBUS-7, AND TIROS-N

13

Radiometer (SMMR).' These instruments are discussed in summary form in Appendix A and in the chapters relevant to the results of the specific geophysical measurements. A short-pulse (3-nsec) radar altimeter operating at 13.5 GHz was the first instrument selected for Seasat. Because the sensor looked only at nadir (its swath width was between 1.8 and 12 km, depending on sea state), the orbit of Seasat was tailored to support the geodetic requirements of the altimeter. Precision of the altimeter measurement was expected to be 10-cm RMS over seas with a significant wave height HI,, from calm to about 20 m. Precise spacecraft tracking was needed for geodesy, currents, and storm-surge analyses. Processing of the altimeter pulse yielded an estimate of HI,, to +0.5 m or lo%,whichever was larger, over seas from calm to 20 m. The SASS was an active microwave instrument that illuminated the sea surface with four fan-shaped beams (two orthogonal beams, each 500 km wide on each side of the Seasat groundtrack). The amount of energy returned depended on the capillary waves and, by inference, provided an estimate of sea surface wind magnitude and direction. The transmitted frequency was 14.6 GHz with the returned energy shifted slightly because of Doppler effects. Doppler shift processing established a spatial resolution of about 50 km over a region from 200 to 700 km on either side of the spacecraft. As a goal, surface winds were to be determined to L- 2 m sec-' or 10%in magnitude, whichever was greater, and & 20"in direction. The SASS was expected to measure, with less precision, winds out to 950 km from the spacecraft. The range of winds expected to be measured was from 3 to 25 m sec-'. The VIRR was identical to the NOAA-series Scanning Radiometer (SR) sensor except for modification because of orbital altitude differences and a digital telemetry down-link tailored for the signals associated with ocean surface phenomena as well as clouds. VIRR, on Seasat primarily for feature identification,operated with both a visible channel (490 to 940 nm), providing information on cloud conditions (day only), and a thermal infrared channel (10.5 to 12.5 pm), providing surface and cloud temperatures. Temperature sensitivity was expected to be 0.5"C. The L-band (1.275 GHz) SAR looked to the starboard side of Seasat and was centered 20" off nadir with a swath width of 100 km. The length of the SAR image track was determined by the receive-station-view duration, with about 4000 km being the maximum. Spatial resolution of 25 m needed for wave analyses generated a very high data rate so that on-board recording was not used. Only data within view of Merritt Island (Florida), Goldstone (California),and Fairbanks (Alaska) within the United States, and Shoecove, Seasat also carried a laser retroreflector and tracking beacons for precise orbit determination, but these are not discussed here.

14

JOHN W. SHERMAN, 111

Newfoundland (Canada) and Oakhanger (England) receiving stations were collected from SAR. The goal was to measure waves and wave spectra to ocean wavelengthsof 50 m or greater, with additional objectives to identifysea ice features; detect possible icebergs; identify wave-land interfaces; observe coastal and current-induced wave refraction, internal waves, and current boundaries; and penetrate to the ocean surface through major storms such as hurricanes. The SMMR sensor operated at frequencies of 6.63, 10.69, 18, 21, and 37 GHz. Dual polarization was available at all frequencies. Spatial resolution ranged from about 100 km at 6.63 GHz to 22 km at 37 GHz. Three primary classes of data obtained from SMMR were sea surface temperature (SST),sea ice mapping, and surface winds, Liquid water and water vapor were measurable as well and were used to formulate path length and attenuation corrections for the altimeter and SASS, respectively. SST was expected to be + 2 K, an important first step to determining SST under cloudy conditions. The magnitude of surface winds was expected to be measured to f 2 m sec-' or lo%, whichever was greater, from 7 to about 40 to 50 m sec-'. The Seasat SMMR conically scanned the starboard side of the spacecraft, aft viewing, with a constant 42" angle from nadir. The scan angle was from about 0 to 50" from the aft-starboard side, resulting in a swath width of about 600 km. The 42.0"angle from nadir resulted in a 48.8"angle of incidence at the surface because of the earth's curvature. The Seasat SMMR coverage was chosen for maximum overlap with SASS. The SMMR on Seasat was identical to the SMMR on Nimbus-7 except for a slight change in scan rate to accommodate the difference in altitude of the two satellites. Hence, SMMR is not discussed in Section 3 on Nimbus-7. Data from the microwave sensors, excluding SAR, were recorded on board and telemetered to one of the previously noted SAR receiving-station locations. Hence, global coverage was available for four of the five Seasat instruments. 2.1. Surface Information

Two major sea surface information activities were jointly planned by NASA and NOAA in the United States and the multinational Joint Air-Sea Interaction (JASIN) experiment. The Gulf of Alaska Seasat Experiment (GOASEX) was specifically dedicated to the early validation of Seasat data. The JASIN program, conducted in the eastern Atlantic near Scotland (August-September 1978),focused on the marine boundary layer and air-sea energy transfer. This Atlantic data set was used for independent validation of Seasat but is not considered in this overview chapter.

2. SEASAT, NIMBUS-7,AND TIROS-N

15

The initial validation of Seasat used the GOASEX surface data set (Born et al., 1979). This activity was planned and conducted by NOAA, including the Pacific Marine Environmental Laboratory (PMEL), the National Earth Satellite Service (NESS),2 the Atlantic Oceanographic and Meteorological Laboratory (AOML), the Wave Propagation Laboratory (WPL), and the National Data Buoy Office (NDBO). The principal oceanic research facility deployed during GOASEX was NOAA's Class 1 Research Vessel Oceanographer. The Canadian weather ships Quadra and Vancouver, alternating at Ocean Weather Station PAPA, also obtained special data at satellite overpass times. Major support was provided by NASA (JPL, Johnson Space Center, Ames Research Center, and Langley Research Center) and the U.S. Navy (Naval Research Laboratory). The GOASEX operating area is shown in Fig. 1. Participating aircraft included the NASA Ames Research Center CV-990 equipped with an airborne version of the SMMR,the NASA Johnson Space Center NC-130B with the Seasat underflight scatterometer, the Naval Research Laboratory RP-3A equipped with meteorological and microwave radiometer instrumentation, and a Canadian CV-580A aircraft carrying the Environmental Research Institute of Michigan's multifrequency synthetic aperture radar system. A typical flight track is shown in Fig. 2. This experiment was also supported by nine NOAA data buoys moored in the Gulf of Alaska. Selected research vessels from the U.S. Geological Survey and the University of Alaska made special weather observations during satellite overpasses of their positions. A very comprehensive data set was collected which corresponded to some 60 satellite overpasses, including more than a dozen SAR passes. An intensive, coordinated study of this data set was conducted in January 1979 and is the basis for much of the initial Seasat validation (Weissman, 1980). Appendix B presents additional details on the Seasat validation program.

2.2. Preliminary Results 2.2.1. Altimeter. Studies with the Seasat radar altimeter have been divided into three classes of activities in this chapter: those concerned with highly accurate altitude measurements, those with wave heights, and those with surface wind speeds. A fourth study of altimeter measurements of ice-sheet heights is contained in Chapter 9. Aside from the discussion in this chapter, the altimetric application to geodesy and circulation are not considered in this volume of Advances in Geophysics. The schematic of the'altimeter and its measurement geometry are Now the National Environmental Satellite, Data, and Information Service.

-@- OCEAN STATION PAPA @ OATAEUOY

...1

ye-CANADA -----_ UNITED SITE B 8 SEPT

.

170

1

I60

I

150

I

140

9 SEPT

- 26 SEPT I

130

STATES

SEATTLE

1

120

FIG.1. Ship track for the NOAA Research Vessel Oceanographer for the Gulf of Alaska Seasat Experiment (GOASEX)August 28-September 26,1978 (J. Wilkerson, Experiment Coordinator; R. Reed,R. V. Oceanographer Chief Scientist; and P. Deleonibus, Senior Oceanographer; all are members of NOAA).

I 55-

55

50-

sa

UNITED STATES

65

I

145

140

135

I30

I 25

120

FIG.2. Data flight lines of the NASA CV-990A on September 16, 1978, for orbit 1163. Time 05:33-08:42 GMT (J. Blue, JPL, Aircraft Coordinator; E. Peterson, NASA, Mission Manager; and T. Wilheit, NASA, Chief Scientist).

18

JOHN W. SHERMAN, I11

illustrated in Fig. 3 (after Tapley et al., 1982). The design requirements were such that over a significant wave height interval between 1 to 20 m, the altimeter was to satisfy the following criteria (Townsend, 1980): 1. At an output rate of one height measurement data point per second, the noise level of these data points should be such that 68% of the points lie within 10 ern of the fitted mean. 2. At an output rate of one measurement per second, the altimeter should provide a measurement of the of the ocean surface beneath the spacecraft.

SEASAT

CORRECTIONS

ALTIMETER

1?iI t

f

ORB> l

I

I I I

ATMOSPHERIC CORRECTIONS

h

Ih'

I

- -* I

OCEAN

7

SURFACE GEOPHYSICAL CORRECTIONS

I

I f -

a

LASER SITE

OCEAN SURFACE TOPOGRAPHY

# ,"I #.'

k BOTTOM TOPOGRAPHY \

\

FIG.3. Schematic of Seasat altimeter data collection modeling and tracking system. (After Tapley et al., 1982.)

2. SEASAT, NIMBUS-7, AND TIROS-N

19

The absolute accuracy of this measurement should be at least f 10% of or 0.5 m, whichever is greater. 3. The altimeter should provide information concerning the measurement of the normalized radar backscatter coefficient (ao)of the ocean surface beneath the spacecraft,i.e., normal incidence. When subjected to appropriate ground processing, this information should result in the measurement of a 0’ to an absolute accuracy of at least k 1.0 dB. In addition to the measurement of altitude, and ooby the altimeter, the orbit was required to comparable accuracy. The altimeter operated from July 3 until October 10, 1978, in two basic orbital modes. Equatorial spacing of 165 km was acquired for a complete global data set during the period July 7 to August 17 and was designated as the baseline orbit. By September 5 the orbit was changed to a 3-day repeat cycle with a 900-km equatorial spacing and remained in this orbit until failure on October 10. This latter orbit, designated as. the “frozen” orbit, was used to evaluate altimeter performance in which eight repeating passes (within about 1 km) were acquired. The prime goal of the altimeter was to provide improved global maps of the mean sea surface topography. An 18-day set (July 28 to August 15, 1978) of altimeter data was merged with the precise ephemerides of Seasat to obtain two forms of contour maps of the height of the mean sea surface above a reference ellipsoid (see Fig. 3). The approach is discussed in detail (Marsh and Martin, 1982) in combination with the Seasat ephemerides analyses (Lerch et al., 1982). The results are shown in Fig. 4,designated as the SS4 mean sea surface topography. The SS4 model uses the Lerch et al. (1982) ephemerides, which has an RMS radial accuracy of about 70 cm and is corrected for ocean tides and sea state bias effects. The number of data points contained in Fig. 4 is 692,860. Study of Fig. 4 reveals the major maxima in the northeast and southeast Atlantic, the southwest Pacific, and the Indian Ocean. Also the major minima are seen over the Puerto Rican Trench, off the coast of Baja California, and just off the southern tip of India. Island chains are readily evident. Figure 5 is a color-enhanced version of this same data set. Removal of geoidal data from the altimeter-measured altitude provides residual sea height. This height can be directly related to ocean current features as shown in Fig. 6 (Cheney, 1982), in which the Gulf Stream west wall was passed over twice for the descending revolution 558. In general, to achieve these detailed results a number of environmental and measurement errors had to be assessed, including topographically trapped waves and currents; local currents; wind setup; atmospheric water vapor, pressure, and tropospheric refractioneffects;tidal measurement uncertainty; shelf topographic effect;and leveling errors.

FIG.4. SS4 mean sea surface topograph based upon Seasat altimeter data. (After Marsh and Martin, 1982.) Also see Fig. 5. ~

~~

FIG.5. This topographical relief map of the world’s ocean surface was produced from Seasat altimeter data by the Jet PropuIsion Laboratory of the California Institute of Technology. The image is dominated by features indicative of sea-floor spreading, including midocean ridges, trenches, and seamount chains. Steep, small-scale features appear white. More quantitative data are shown in Fig. 4. (Courtesy of Dr. Michael Parke, JPL.)

This Page Intentionally Left Blank

21

2. SEASAT, NIMBUS-7, AND TIROS-N

't

F

=

-2-

=

-3-

(7 w

3

GULF STREAM

u1 A

a 3 0

2 K

-4-

.,:

-5-

-6 -

-71

-81

39.00

SEASATREV 558 AUGUST 5,1978

'

' 37.00 285.64

'

' 35.00 284.47

'

I

33.00 283.36

'

' 31.00 282.29

'

'

29.00 281.26

1

"

27.m 280.27

' 25.00 279.31

LATITUDEONILONGITUDE "E

FIG.6. Seasat Rev 558 after geoid removal. The pass first intersected the Gulf Stream near Cape Hatteras and then recrossed the Gulf Stream near the Florida Straits. (After Cheney, 1982.)

Significant wave height and surface wind speed can be determined from analysis of the altimeter return pulse shape and the backscattering cross section, respectively. These mechanisms for deriving winds and waves from the nadir-view electromagnetic response to the ocean surface are discussed in Chapter 3, Interpretation of Instrument Observations. Two algorithms were used to process wave-height data: one predetermined on board using results from GEOS-3, and one using a surface-processed algorithm designated as the "Fedor" algorithm (Fedor and Brown, 1982). These results are considered in Chapter 5 on wave observations. Both algorithms provided excellent wave information. The Fedor results are shown in Fig. 7 where the altimeter data is compared to buoy data. At nadir the radar backscattering cross section oodecreases with increasing wind speed because winds are inferred by capillary wave scattering action. This nadir response is in contrast to the off-nadir behavior of with wind speed (see Chapters 3 and 4). Figure 8 compares the altimeter wind speed to buoy wind speed using the Brown algorithm (Fedor and Brown, 1982). Unlike the SASS, the altimeter does not measure wind direction. GO,

GO

2.2.2. Seasat-A Scatterometer System (SASS). The dual-polarization SASS, operating at a frequency of 14.599 GHz, provided a footprint on the surface of earth as shown in Fig. 9. An asymmetry in the actual footprint arose due to spacecraft motion and earth rotation. As noted earlier, maximum

22

JOHN W.SHERMAN, I11 6.0.

5,0

I

-

4.0

-

3.0

-

2.0

-

I

1

Total iV Points = 51 Mean Din. = 0.065 m RMS Din. = 0 . W m Regression Cwff. = 0.902 Y-Intercept = 0.123 m CorrelationCorff. = 0.985 RMS Diff. from Regr. Line = 0.288

/

1

'

//

m

I

E

F 6

3

I

3

v)

0

PAPA

+

41001 4Mo1 42003

x 0I

1 .o

I

I

2.0

3.0

SWH (Fedor Algorithm)

I 4.0

1 5.0

-

6.0

(m)

FIG.7. Scatter diagram comparing significant wave height (SWH), IfLI3,estimates from the NOAA National Data Buoy Office (NDBO)and Ocean Station PAPA with altimeter estimates using the Fedor algorithm and 51 observations. (After Fedor and Brown, 1982.)

sensitivity to surface winds occurred between about 200 and 700 km on either side of the spacecraft. Limited sensitivity existed at nadir (& 70km) and from 700 to 950 km (each side of the groundtrack). Several wind algorithms have been developed (Jones et al., 1982;Schroeder et al., 1982) requiring use of fore and aft antenna beams and dual polarization. This discussion does not compare the algorithms, only their geophysical results (Chapter 4 provides details). All appear to agree well in defining wind direction (subject to a 180"directional ambiguity)and to illustrate the need for individual spot reports for surface data. Figure 10, based on the City University of New York (CUNY) algorithm (Schroeder et al., 1982),illustrates this point and the nature of high-resolution wind fields derivable from SASS

23

2. SEASAT, NIMBUS-7, AND TIROS-N

14.0

--

F-

12.0

-

10.0

-

I

I

I

Total Points = 87 ~n D ~ I I=. 0.249 rn sec RMS Ditl = 1575 m sec-' Regression Coeff = 0.893 Y-Intercept = 0 806 m secCorrelation Cosfl. = 0.858

1 2

I

I

-' I

/

l

0

E

8.0

-

0

-

0

E m5

Q /

-

6.0-

0

41001 42001 42002 X 42003 0 44001

A

+

40-

-

9 44002

x 44003 2 44004

-

Y 46001

146002

*I 0 0

2.0

1 40

I 60

48005 46006

I

I

I

I

8.0

10.0

12 0

14.0

Seasat Wind (Brown Algorithm) ( m sec-

I )

-

FIG.8. Scatterdiagram comparingaltimeter(Brown algorithm) wind speed with NDBO wind measurements. (After Fedor and Brown, 1982.)

and the variations and gradients that may exist in surface winds about a specific point. In this particular data set the winds appeared to maintain about the same direction with a gradient of the wind from about 2 to 5 m sec-' over a distance of about 350 km. For SASS comparisons, surface winds have been corrected to the 19.5-m anemometer height. A more general comparison of SASS data to surface data is shown in Fig. 11 (Schroeder et al., 1982). The surface data have been derived from the JASIN experiment and the CWK model is an algorithm which combined several earlier models. The error statistics developed early in the SASS analysis are shown in Table I, and most importantly, indicate the need for controlled surface data such as buoys (Jones et al., 1979). Note that the error statistics compared only to the buoys are within SASS specifications.

SIDE VIEW

INSTRUMENT CHARACTERISTICS 14.59927 GHz 100 W PEAK RF POWER ELECTRONIC SCAN (15 DOPPLER CEI.LS) ORTHOGONAL MEAS. (AZIMUTH) 4 ANTENNAS DUAL POLARIZATION 0.5" x 25" ANTENNA BEAM

ANTENNA ILLUMINATION PATTERN

ANTENNA BEAM NUMBER

HIGH WINDS ON1 HIGH AND LOW WINDS-

CE ROUGHNESS MEASUREMENT SWATH SAT TRACK

FIG.9. Plan view geometry of the Seasat scatterometersystem.

2. SEASAT, NIMBUS-7, AND TIROS-N 50'1

I

I

I

-

49'1

y

48°t.l -

'

1309

Wind speed

47%

25

1

m sec-'

I

I

I

229OE

23O0E

240°E

FIG. 10. Scatterometer wind field (Rev 1298)in the vicinity of Weather Station PAPA (Canada) using the CUNY algorithm.

Seasat made a number of passes over hurricanes and typhoons, and Chapter 6 discusses the results for severe marine storms. Figure 12 shows a visible spectrum image from the NOAA Geostationary Operational Environmental Satellite (GOES) which was taken over Hurrican Fico just before a Seasat pass on July 20. The Seasat subtrack is the dotted line in the image. The SASS-derived surface wind fields were believed to be the first time that synoptic-scale oceanic wind velocities had been obtained in a hurricane. In the 450-km SASS swath width, the winds were seen to range from about 2 m sec-' in the doldrums south of Fico to more than 25 m sec-' near the hurricane's center. Again, atmospheric corrections were not made, nor was this SASS data set processed at its full 50-km spatial resolution. Winds were averaged over a 1 x 1" grid, and were probably underestimated due to the spatial averaging. Details of other specific storms are documented (Cane and

24

6l

Slope = 1.0439 Intercept = 0.1071 Correlation = 0.91 14

3

0'

6

3

9

12

15

18

21

24

27

30

GROUND TRUTH WIND SPEED (m sec")

*.

/

0

0

144-

3 $

10872

-

t

//

/

All polarization pairs CWK algorithm

/

L'

Slope = 0.9530 Intercept = 10.2468 Correlation = 0.9847

, ~ * l " ' " l l l " ' " ' ' " l l ' ' l ' ' l l ' ' 0 36 72 108 144 180 218 252 288 324

360

GROUND TRUTH WIND DIRECTION (DEG)

FIG.11. Scatterometer versus surface data collected during the Joint Air-Sea Interaction experiment (JASIN). (a) Wind-speed comparisons; (b) wind-direction comparison. (After Schroeder et a/., 1982.) 26

TABLE I.

STATISTICS FOR

(SASS-SwT

OBSERVATIONS) WIND SPEED AND W I N D

DIRECTION'

~~

Wind speed (m sec-')

Wind direction (deg)

Standard deviation of error

Mean errorb

Mean error

V+H

V

V+H

V

No. of samples

1.79

1.48

0.9

1.5

22, 14

2.33

V+ H

-

Standard deviation of error

V

V+H

V

0.36

16.2

18.6

I . N

Buoy Ship Known anemometer height Unknown anemometer height Beaufort

'From Jones et nl. (1 979). V, Vertical; H, horizontal.

3.12

2.44

9

- 1.39

3.0

2.69

24

3.34

22.92

2.11

2.66

30

7.6

16

17.9

28

JOHN W. SHERMAN, 111

FIG.12. NOAA GOES image of Hurricane Fico with scatterometer-derivedwinds superimposed for July 20, 1978.

2. SEASAT, NIMBUS-7, A N D TIROS-N

29

Cardone, 1981; Gonzalez et al., 1982)and a series of workshops conducted are summarized in Chapter 6. 2.2.3. Scanning Multichannel Microwave Radiometer ( S M M R ) . The Nimbus-7 and Seasat SMMR instruments, whose characteristics were described earlier, were identical in all respects except for slight differences in scan rates necessitated by the difference in the altitudes of the two spacecraft (955 km for Nimbus-7 and 800 km for Seasat). The SMMR performed well over open oceans (away from land contamination of the antenna sidelobes and radiofrequency interference) in the absence of rain contamination. SMMR was designed principally to measure SST and surface windspeed and to map sea ice. Additionally, SMMR provided atmospheric corrections to other instruments, for which the correctional data were quantified to provide quantitative data on atmospheric water content and precipitation. The SMMR provided the first microwave observations of SST, thus initiating the much needed observation of ocean surface temperature through clouds. The goal was to retrieve temperatures over 100-km areas to an absolute accuracy of +2"C. This goal was achieved for open-ocean measurements when sun glint and rain were avoided. Figure 13 (Lipes, 1980) summarizes the SST results from several surface sources, and Table I1 (Lipes, 1982)provides the statistical results. It is noted that the tropical Pacific results are ship injection temperature measurements and yield the poorest comparison. More detail on SST measurements by SMMR can be found in Chapter 7 and in Bernstein (1981). The SASS wind velocities were well calibrated, typically using surface data from near-shore locations. For SMMR open-ocean analysis of wind speed, the SASS data have been used for comparison (Lipes, 1982) as shown in Fig. 14, which also includes the statistics. The SMMR wind-speed goal of 2 m sec-' was achieved, assuming the reliability of SASS over the open ocean and subject to the same constraints on the measurement of SST (no sun glint or rain). Chapter 9 thoroughly reviews the performance of SMMR and its applications to sea ice mapping, dynamics, and edge observations. By comparison, the Seasat SMMR collected only a fraction of the data that the Nimbus-7 SMMR collected. The latter instrument has collected ice data on an alternate-day operation since it became operational shortly after launch in late October 1978. Several SMMR research products are being generated for the polar science community, in particular, sea ice concentrations and the mix of multiyear ice with first-year ice. Figure 15 (courtesy of P. Gloersen, NASA/GSFC) is an example of the multiyear ice fraction wherein the gray scale indicates the percentage of total ice cover which is multiyear sea ice. The total sea ice concentration during the same period for this region is an additional product

30

JOHN W. SHERMAN, 111

25

-

V

$ 20 -

15

-

SURFACE SST, OC

FIG.13. Seasat Scanning Multichannel Microwave Radiometer (SMMR) sea surface temperature comparison with surface observations. (After Lipes, 1980.) COMPARISONS WITH IMPROVED ALGORITHM' TABLE11. SEA SURFACE TEMPERATURE Comparison PAPA Buoy 46006 N W Pacific XBTs Rev 1223 Atlantic AH points with good surface truth Tropical Pacific All comparisons ~

~~~~~

li

From L i p s (1982).

Number and type of revolution

Number of points

20 Descending I Ascending

20

0.0

7

0.2

3 Descending, 4 ascending 1 Descending, 1 ascending

32 12

-0.3 0.6

0.9

14

0.4

0.8

85

0.1

0.8

16

0.4 0.1

1.3 0.9

4 Ascending

101

Bias

U

0.4 1.1

1.2

31

2. SEASAT, NIMBUS-7, AND TIROS-N

r-----N=250

BIAS = -0.6 m

sac-

u = 1.4 m sec-

0 0

5

10

15

20

'

1

25

SASS WIND, rn sac-'

FIG. 14. Open-ocean wind-scatter diagram comparing SMMR-derived winds with SASSderived winds in Rev 1135. (After Lipes, 1982.)

and, when multiplied by the multiyear ice fraction in Fig. 15, will provide the multiyear sea ice concentration (Cavalieri et al., 1981). The SMMR performance for atmospheric water content has been analyzed in terms of columnar water vapor and liquid water content and rain rate. Only columnar water vapor is illustrated here based on the frozen-orbit data sets which provided radiosonde observations (raobs) deployed from Funafuti, Majuro, Kwajalein, Wake Island, Truk, Ponape, and Ocean Station TANGO (Katsaros et al., 1981). The results are summarized in Table 111 (after Lipes, 1982) as a function of tropic and midlatitude sampling. The comparisons between the raobs and SMMR retrievals, which involved matchups to within 1O in latitude and longitude and no rain in the SMMR field of view, are shown in Fig. 16. In general, SMMR underestimated the water vapor in the tropics and slightly overestimated in the midlatitudes, but the deviation of SMMR

JOHN W. SHERMAN, I11

32

FIG.15. SMMR-derivedsea ice fraction(day 26-30,1979) wherein the gray scale indicates the percentage of total ice cover which is multiyear ice. (Courtesy of P. Gloersen, NASA/GSFC.)

TABLE 111. ~~~

~~

Data set (g cm- '1

PRECIPITABLE WATER COMPARISON' ~~

Bias (SMMR-raobs)

Standard deviation about bias

Mean

Standard deviation

5.0 5.0

0.8, 16%

0.8, 16%

-0.007, 0.14%

0.3, 6%

1.7 1.6

0.6,35% 0.6, 38%

-0.03,2%

0.16, 10%

~

Tropics, n = 30 Raobs SMMR Midlatitudes, n = 26 Raobs SMMR ~~~

~

~~

From Alishouse (1983).

-

2. SEASAT, NIMBUS-7, AND TIROS-N

33

raobs (g cm-* 1

FIG. 16. Comparison of SMMR columnar water-vapor retrievals with radiosonde observations (raobs) in the tropics and midlatitudes. (After Lipes, 1982.)

from the raobs is within the accepted error of the raobs themselves and the spatial variation of water vapor over 1" increments. 2.2.4. Visible and InfraRed Radiometer (VIRR). The VIRR was not designed as a primary sensor on Seasat, but rather as a supporting instrument to the microwave sensors (McClain and Marks, 1980). The VIRR provided more conventional visible reflectances and thermal infrared emissions from oceanic, coastal, and atmospheric features. These features aided interpretation of data from other sensors.

34

JOHN W. SHERMAN, 111

300-

-

-

-

-

*

295-

@

-

8

8

$

c

-> 02

TVIRR = 0.846TNOM+46

290-

r

= 0.84

R M S DIFF. = 1.7 K N = 139

-

-/ 285

285

I

I

1

)

1 290

'

1

1

I T

1 295

1 ~

1 ~

1

1 ~

1

300

1

I

1

1

*

FIG.17. NOAA VHRR temperature field estimatesversus Seasat VIRR temperatures. VHRR field for July 5-10,1978; VIRR temperatures for July 7, 1978. Region of data comparison is in Fig. 18. (After McClain and Marks, 1979.)

However, by using corrections to satellite brightness temperature for atmospheric water vapor, it was possible to compare (on a pixel-by-pixelbasis) the NOAA SSTs, as reported by buoys, to the VIRR-corrected temperatures. Figure 17 shows the results of this comparison. The linear regression correlation coefficient, r = 0.84, and the RMS difference of 1.7"C represented excellent agreement in view of uncertainty in the atmospheric correction and uncertainty and smoothness of the NOAA field. Figure 18 shows the VIRR thermal channel of the east coast of the United States acquired on July 7, 1978 (Rev 156, day 188). The gray scale was enhanced to improve contrast between warmer water (dark) and colder water (light). Clouds are also shown where warmer clouds (lower in altitude) were discriminated from the colder clouds (higher in altitude). These images permitted detailed definition of weather patterns through which microwave energy from other Seasat sensors penetrated.

305

FIG. 18. Seasat VIRR infrared image obtained July 7, 1978, used for day-night feature identification for other Seasat sensors. Approximate collection time 22:51 :OOGMT. (Courtesy of E. P. McClain and the JPL Image Processing Laboratory.)

36

JOHN W.SHERMAN, 111

a

Components

Image

Two-dimensional Transform

C

Image

Two-dimensional Transform

FIG.19. Diagrams of linear images and corresponding two-dimensional Fourier transforms. Note that the distribution in the two-dimensional Fourier transform is governed by the frequency and orientation of the spatial distribution of the images. Spacings of components in (b) and (d) are inversely proportional to spacings of lines in the images (a) and (c). The sizes of the dots indicate the amount of energy in the diffracted orders-smaller dots indicate smaller amounts of energy. (After Shuchman et al., 1979.)

2.2.5. Seasat Synthetic Aperture Radar ( S A R ) . The SAR was designed to image ocean wavelengths as short as 50 m. A principal goal was to derive wave spectra for ocean wavelengths between 50 and lo00 m. Other goals included studies of sea and lake ice, icebergs, internal waves, waves in hurricanes, oil spills, and other environmental features such as current boundaries and waverefraction effects. The methodology for deriving wave spectra using Fourier transform is shown schematically in Fig. 19 (after Shuchman et al., 1979). A portion of an SAR image from Hurricane Fico is shown in Fig. 20, which indicates the nature of a confused sea generated by hurricane-force winds. The area in Fig. 20 is about 5 x 5 km. The optical Fourier transform of Fig. 20 is shown in Fig. 21. The transform in Fig. 21 shows that while significant energy is oriented at angles about - 10" off the abscissa, some wave energy is available at nearly all angles. To illustrate the domain wave energy and wavelength, the spectrum in Fig. 21 is filtered to the principal angular component of waves. The result is shown in Fig. 22. The enhanced image of the dominant waves

2. SEASAT, NIMBUS-7, AND TIROS-N

37

FIG.20. An analog-processed Synthetic Aperture Radar (SAR) image of area 1 of Hurricane Fico covering about a 5 x 5 km segment. (Courtesyof W. Brown of JPL and D. Ross of NOAA.)

resulting from this filtering is shown in Fig. 23 wherein waves on the order of 160-m wavelength are most apparent, Details on the interaction and imaging of ocean waves by radar are contained in Chapter 5. A second major application of SAR was for high-resolution imagery of sea ice. Chapter 9 reviews the satellite observations of the polar regions. The untimely demise of Seasat prevented significant SAR temporal coverage through the winter season, as has been possible with the Nimbus-7 SMMR. Figure 24 is an SAR image of a portion of the Beaufort sea ice pack west of Banks Island, Canada (right portion of image). The region imaged covered an area about 30 x 120 km and was northeast of Alaska, some 800 km iqside the

38

JOHN W.SHERMAN, Ill

FIG.21. Optical Fourier transform of Fig. 20. Range dependence of mean and variance removed. Note that the optical transform produces two peaks for each dominant wavelength present. (Courtesy of W. Brown of JPLand D. Ross of NOAA.)

Arctic Circle. The image was acquired at very low visibility conditions (Rev 205, around 2:OO AM local time on July 22,1978) when visible imagery would have been difficult. The dark zone adjacent to Banks Island is an area of

shore-fast ice composed primarily of first-year sea ice, 1 to 2 m thick. Linear pressure ridges are seen within the shore-fast zone, and west (left) of this zone is a shore lead (open water). At the edge of the lead is a marginal ice zone composed of a mixture of open water and large and small rounded multiyear floes, typically 3 to 4 m thick. Some first-year ice is also present. Further west is the main polar pack, consisting of floes up to 20 km in extent, and

2. SEASAT, NIMBUS-7, A N D TIROS-N

39

FIG.22. Optical Fourier transform in Fig. 21 limited to the dominant waves located about the -10" angle with respect to the abscissa.

surrounded by new leads. The very bright areas within the floes indicate intensive surface roughness. Additional SAR imagery of sea ice is provided in Chapter 9. The SAR demonstrated a consistent ability to image other features including the surface manifestations of internal waves, current and eddy boundaries, bathymetry-related surface features, ships and ship wakes, and oil slicks (Fu and HoIt, 1982). Figure 25 shows one of many SAR images of internal waves observed in the Gulf of California3 (as obtained on Rev 1355,

'

Internal waves cause a concentration of surface natural oils and materials whjch modify surface tension that alters the small wave structure and hence the radar backscattered energy.

JOHN W. SHERMAN, I11

40

FIG.23. Dominant wave system of area 1 in Hurricane Fico with waves on the order of 160-m wavelength. (Courtesy of W. Brown of JPL and D. Ross of NOAA.)

September 29, 1978). The bathymetric chart, as prepared by Fu and Holt, shows the locations of eight major internal wave groups, labeled A-H. Strong tidal currents of the twice-monthly spring tides (6-mtides in this region) interacting with the bottom topography are assumed to generate these internal wave features. If the M 2 tidal cycles (period 12.42 hr) sequentially generated the E,D, and C internal wave groups, then the extrapolated group speed is 1.2 m sec- The maximum wavelength of the internal waves shown in all groups is around 2 km. The shear and strain effects on the ocean surface because of currents also alters the radar backscattering of electromagnetic energy. Two examples associated with the currents system illustrate this effect, an effect which had

'.

FIG.24. SAR image of the Beaufort Sea area just west of Banks Island, Canada. The area is about 30 x 120 km acquired on July 22, 1978, at about 2:OO AM local time. Banks Island, right corner, is bound by shore-fast ice (black) with open water (about 20-25 km in extent) westward and then pack ice and leads composing the left portion of the image. (Courtesy of W. Brown of JPL and W.Campbell of USGS.)

-

ILLUMINATION

DIRECTION

"1'

-O

25 km

FIG.25. Signatures of internal waves have been acquired by SAR in many coastal regions. This Scene of the Gulf of California, with the bathymetric chart, illustrates the complexity of internal waves believed to be generated by spring tides. This image was obtained September 29, 1978, on Rev 1355 (Fu and Holt, 1982).

2. SEASAT, NIMBUS-7, AND TIROS-N

43

FIG.26. Gulf Stream western and eastern (lower left) boundaries obtained by SAR on August 31,1978, Rev 931 (Fu and Holt, 1982).

been underestimated prior to Seasat. First, both the west and east boundaries of the Gulf Stream appear to have been detected in Fig. 26, acquired August 31,1978, on Rev 931. This image is about 100 km wide and 400 km long with the tip of Cape Hatteras, North Carolina, showing in the top left portion. The deflection of the western boundary of the Gulf Stream is generally believed to be caused by the submarine ridge off Charleston, South Carolina (Legeckis, 1979). The eastward wall of the Gulf Stream is believed to be in the bottom right portion of the image as the diagonal feature because this feature corresponds to satellite IR imagery location of the Gulf Stream during this time period. Second, eddies of varying size from that of the warm and cold rings of the Gulf Stream (Lichy et al., 1981)-rings of the order of 100 to 200 km down to eddies of the order of 5 to 10 km-were imaged by SAR. The latter type of eddy structure is shown in the vortices in Fig. 27, an image that is about 100 x 150 km in extent. The location is just off the Florida coast with the Grand Bahama Island showing in the upper portion of the figure. From the orientation at the vortices, it appears that they are generated by a southeastward current along the westernmost edge of the Grand Bahama Island (Fu and Holt, 1982). The southeasterly flow apparently weakens, leading to a compression of the distance between the eddies. Three ships and their wakes are visible, two having passed through fourth and sixth eddies, respectively, from the eddy point of origin, and one approximately 7 or 8 km south of the island and approximately 20 km from the eastern portion of the island. Surface stress changes induced by current interaction with bottom features were frequently detected by SAR in coastal regions. These slight changes altered the backscattering of microwave energy to be within the sensitivity of the Seasat SAR. To illustrate the nature of this sensitivity, a region of highly variable bottom features,from depths of less than a meter to around 15m and then to 1500 m, is found off the coast of Andros Island and is known as the Tongue Of The Ocean (TOTO). A Landsat-1 image (185 x 185km) using the green channel (MSS 4 is 500-600 nm) from January 24, 1973, is shown in

FIG.27. SAR-imaged small-scale eddy structures south of Grand Bahama Island. Of a number of SAR Scenescollected over this region, this is the only one which showed these cyclonic vortices (Fu and Holt, 1982).

2. SEASAT, NIMBUS-7, AND TIROS-N

45

Fig. 28a and illustrates the TOTO region and the pattern of sandbars that run along the southern edge of the tongue-like feature which gives rise to the name “TOTO.” A few of these bars are exposed during low tide (total tidal range for the Bahama region is only 0.6 to 1.0 m), but most remain under water. Depth between the sandbars is typically from 5 to 15 m and the ocean water in the entire area is very clear. The 100 x 200-km SAR image is shown in Fig. 28b to approximately the same scale size but with a slight difference in orientation because of differences in the Landsat and Seasat orbits. The sandbar regions and the tongue feature are essentially identical but the mechanism by which the Landsat and SAR images are created are fundamentally different. Landsat-imaged differences are due to variations in the green response because of sunlight

FIG.28a. The Landsat green-channel image of the Tongue Of The Ocean (TOTO) acquired January 24,1973),shows both the deep-water tongue (about 1500 m deep) and the shallow-water sandbars (1 to 15 m deep).

46

JOHN W. SHERMAN. 111

FIG.28b. SAR-acquired image of TOT0 more than 5 years later than the image seen in Fig. 28a. The bathymetric features are believed to be imaged by the coupling of currents to bottom features which in turn affect the surface wave structure at wavelengths of 20-30 cm.

2. SEASAT, NIMBUS-7, AND TIROS-N

47

penetration and reflectance from the ocean bottom. The optical depth is tens of meters in the green region. In contrast, the same “optical” depth or skin depth for microwave energy at the SAR frequency (1.275 GHz or 23.5-cm wavelength) is only around 8 mm, and the SAR image must of necessity be created by a surface manifestation of a subsurface. phenomenon. The similarity of the sandbars between the two images obtained more than 5 years apart indicates the stability of the bottom features in the TOT0 region. Because the SAR is sensitive to slight changes in surface tension the potential to detect oil sheen/spills on the surface was anticipated. Damping of capillary waves in the presence of surface winds reduces the energy backscattered from oil sheen relative to surrounding areas. This is shown in Fig. 29 in a scene that occurred on October 3, 1978, in the Caribbean Sea at about 82”W, 20”N (Rev 1404). The area is about 25 x 50 km in this image. The history of the apparent petroleum productlresidue is unknown but its presence persisted for at least three additional days when the streaks were again imaged (Fu and Holt, 1982). The two ships visible also indicate the nature of the Doppler processing involved in imaging by synthetic aperture radars. Because the wakes are relatively stationary compared to the ship, a moving vessel with a radial component of velocity with respect to the radar appears to be displaced from its wake (Jain, 1978). If the ship velocity component is toward the radar, the ship appears shifted in the direction the radar is moving; this is reversed for ships moving away from the radar. Accurate measurement of the displacement of the ship from its wake, coupled with knowledge of the radar processing characteristics, allows ship velocity to be determined. 3. THENIMBUS-? SENSORS AND RESULTS The Nimbus-7 spacecraft carries sensors designed for studies related to atmospheric sciences and pollution and to oceanology, including sea and lake ice. These sensors include Earth Radiation Budget (ERB) radiometer, Limb Infrared Monitoring of the Stratosphere (LIMS) sensor, Stratospheric Aerosol Measurement (SAM 11) sensor, Solar and Backscatter ultraViolet/Total Ozone Mapping System (SBV/TOMS), Temperature-Humidity Infrared Radiometer (THIR), Scanning Multichannel Microwave Radiometer (SMMR), and Coastal Zone Color Scanner (CZCS). Only the last two instruments collect data directly related to oceanic monitoring. Because the SMMR is identical to the radiometer flown on Seasat, it is not reviewed in this section. The focus is on the application of a high-spectral-resolution visible region sensor, the CZCS, to oceanic sciences (see Chapter 8).

48

JOHN W. SHERMAN, I11

FIG.29. The effects of both ships and oil on the ocean surface are shown in this SAR image of October 3,1978, Rev 1404, off the southerncoast of Florida. Thedirectionof illuminationis from bottom to top and the SAR flight direction is from right to left. (Courtesy B. Holt, JPL.)

2. SEASAT, NIMBUS-7, A N D TIROS-N

49

The CZCS began operation on October 29,1978,5 days after the launch of Nimbus-7, and has been in operation over U.S.coastal areas for the majority of the overpassingorbits since that time, and for many other parts of the world using on-board tape recorders. The CZCS is an image scanner which provides measurementsof the apparent radiance of the scene below the spacecraft in six coregistered spectral bands centered at 443, 520, 550, 670, and 750 nm, and 11.5 pm. Spectral bands of CZCS were chosen to correspond to strong absorption features of organic material in the ocean and to spectral regions where absorption is minimal. The 11.5-pm band provides registered, simultaneous measurements of equivalent blackbody temperature. CZCS scans across track with an instantaneous field of view of 0.05", or 825-m resolution near nadir at the surface, from a nominal altitude of 955 km. The active portion of the scan is 75" centered about the nadir trackline, corresponding to a swath width of 1469 km. Of this, the central 40" is utilized for most processing to reduce limb-brightening (path radiance change with scan angle) effects. The remainder of the image is used primarily for image location, where identifiable land masses are present in the scene, and for more qualitative analyses. Two minutes of along-track scanning are defined as an image for the purpose of data storage on computer-compatible tapes (CCTs). This corresponds to a surface displacementof 769.6 km. (Thus, the complete 2-min image is about 770 x 1470 km.) The information is slightly oversampled resulting in 9.7 x lo5pixels in each of the six bands for the central 40" portion of the image (or an area about 770 x 695 km). At 59" latitude and greater, successive orbits overlap (for the full 75" swath width). The scanner can be tilted k20" in 2" increments in the along-track direction to aid in reducing the effects of sun glint. The total power requirements of the spacecraft and the various sensors exceeded the available power provided. Because of this power budget, CZCS data are recorded or transmitted directly to the ground for an average total of 2 hr day-'. Two hours of data are equivalent to 32 x lo6km2, an area just under 10% of the entire global oceans and seas area. Stated in other terms, CZCS can collect data daily from an area about eight times the size of the Gulf of Mexico and Caribbean, or all global oceans and seas could be covered about three times a month. Data from the CZCS are processed into calibrated radiance for those bands sensing reflected sunlight and equivalent blackbody temperature for the infrared wavelength band. In addition, four derived products are produced from algorithms developed by the Nimbus Experiment Team (NET). They are K (the diffuse attenuation coefficient), chlorophyll pigment concentration, aerosol radiance (670 nm), and sea surface radiance (443nm). The algorithms are reviewed in Gordon et al. (1983), Clark (1981), and in Chapter 8 of this

50

JOHN W.SHERMAN, I11

book. The diffuse attenuation coefficient is dependent upon the total particulate matter concentration in the water, and the pigment concentration is principally an indication of chlorophyll a and its degration product, pheopigment a. All of these products, except SMMR data from Nimbus-7, are available through NOAA’s Satellite Data Services Division of NESDIS in both photographic and CCT format (see Appendix C). 3.1. Surface Information

Data were collected from a number of dedicated oceanic stations to permit quantitative analyses of the CZCS data set. The extent of these stations is shown in Fig. 30. Other experimentaldata sets were collected and analyzed by Scripps Institute of Oceanography, Bigelow Laboratories, University of Miami, University of Southern California, San Jose State, and Texas A & M. At the oceanic stations data typically collected were upwelled (nadir) spectral radiance, upwelled spectral irradiance, and downwelled spectral irradiance, all as a function of depth, using submersible spectroradiometers. Continuous monitoring of irradiance from the sun and sky was accomplished at the surface as a function of wavelength. Beam transmittance/volume attenuation coefficient provided a rapid means of assessing the vertical structure of the water column. Munsell color scale and Secchi disk completed the optical measurements made. The biological measurements included chlorophyll a observations by both fluorometer and acetone fixation, total suspended particulates (dry weight), and particulate size distributions (Coulter Counter). 3.2. Preliminary Results

Figures 31a-c show the radiance values in an image format at 443, 550, and 670 nm, respectively. For illustrative purposes of the CZCS algorithms, Wrigley (NASA/Ames)processed November 2,1978, data to derive the chlorophyll alpheopigment a ratio in the northeastern portion of the Gulf of Mexico. The CZCS algorithm for atmospheric correction is the key to successful application of this sensor. The two main assumptions made in this algorithm are that (1) Rayleigh scattering in the atmosphere is known, and (2) the reflection coefficient of the ocean surface at 670 nm is zero (total absorption). Using the first assumption, the Rayleigh scatter was subtracted from the radiance values shown in Figs. 31a-c. For the over-water portion of Fig. 31 with the Rayleigh term subtracted, the difference between the remaining radiance value at 670 nm is assumed to be because of aerosols. This residual radiance is then adjusted for aerosol wavelength dependency and

FIG.30. Location of oceanic stations for surface comparative data for Coastal Zone Color Scanner (CZCS)analyses. (Courtesy of D. Clark, NOAA.)

52

JOHN W.SHERMAN, 111

solar flux and ozone transmittance at the 443- and 550-nm wavelengths. The Rayleigh-corrected images, blue (a) and green (b), are then corrected for aerosol scattering to generate the images labeled (d) and (e) in Fig. 31. This is done on a pixel-by-pixel basis. Thus, images (d) and (e) are the equivalent of viewing the ocean surface with the atmosphere removed. The two atmospherically corrected images provide significantly improved contrast with regard to water features. The blue channel (d) emphasizes contrast in differing water masses based on pigments, and the green (e), which is relatively insensitive to chlorophyll alpheopigment a, emphasizes total suspended sediment. The variation in response is made possible only through the use of high-spectral-resolution (20 nm) channels of CZCS. The remaining two images in Fig. 31 (f and g) provide data on the total chlorophyll u/pheopigment a in the water column. Image (f) provides relative chlorophyll ulpheopigment a for high levels of concentration (the higher the concentration, the whiter). Image (g) is reversed to emphasize low levels of chlorophyll (the lower the concentration, the whiter). The methodology used to provide the chlorophyll u/pheopigment a concentrations is more fully discussed in Chapter 8. In essence, the data in (f) and (g)were derived from the corrected images in (d) and (e) using an equation of the form, Concentration = A(LA,/L&)B where Lis the corrected radiance term at wavelength I and A and B are constants. Analyses are also presented in Chapter 8 on the derived total suspended particulates from CZCS. 4. THETIROS-N SERIES AND RESULTS

The October 13, 1978, launch of TIROS-N began the third generation of civil satellites to be used operationally during most of the 1980s. TIROS-N was a NASA-sponsored satellite used as an operational prototype by NOAA. The follow-on satellites, NOAA-6, NOAA-7, and NOAA-8, are essentially identical spacecraft. The instrument payloads which have flown or are expected in future NOAA series include two versions of the Advanced Very High Resolution Radiometer (AVHRR/1 or 2), High resolution InfraRed Spectrometer (HIRS/2), Stratospheric Sounding Unit (SSU), Microwave Sounding Unit (MSU), Data Collection System4 (DCS), Solar and Backscattered UltraViolet/Total Ozone Mapping System (SBUV/TOMS), and the Earth Radiation Budget (ERB)radiometer. A Solar Environmental Monitor This system is furnished by the Centre National &Etudes Spatiales(CNES) of France and is designated as the “ARGOS Data Collection and Location System.”

FIG.31. Nimbus-7 CZCS imagery and derived products from orbit 130 in the northeastern portion of the Gulf of Mexico, November 2, 1978. (a) Blue band, 4.43 nm; (b) green band, 550 nm; (c) red band, 670 nm; (d)blue band, atmospheric correction using red-band data; (e) green band, atmospheric correction using red-band data; (f) chlorophyll analysis using 443/550algorithm: white indicates high chlorophyll concentration, dark indicates low chlorophyll concentration; (g) identical to (f ) but white indicates low chlorophyll concentration, dark indicates high chlorophyll concentration. (Courtesy of R.Wrigley, NASA.)

54

JOHN W.SHERMAN, 111 TABLE IV. SUMMARYOF TIROS-N AND NOAA-SERIES~ SENSORS

Satellite Instrument

TIROS-N

NOAA-6-7-C-D

NOAA-E/SARb

NOAA-F-G/SARb

~

AVHRR/l AVHRR/2 HIRS/2

ssu

MSU DCS SBUV ERBE

X

X

X X X

X X X X

X

X

NOAA-B failed to achieve orbit, thus NOAA-C became NOAA-7 and NOAA-E became NOAA-8. SAR, Search and rescue.

(SEM) completes the TIROS-N sensor complement. Table IV is a summary of the TIROS-N and NOAA-series instrumentation. The SSU, HIRS, and MSU instrument combination forms the TIROS-N Operational Vertical Sounder (TOVS) system and replaces the Vertical Temperature Profile Radiometer (VTPR) used on the NOAA-2 through -5 satellites (see Chapter 1). Also, the HIRS/2 is an adaption of the HIRS sensor flown on Nimbus-6. Further details on the TIROS-N series of satellites can be found in Yates (1981) and Schwalb (1982). The principal oceanic data derived from the TIROS-N satellite are from the AVHRR system. The other sensors provide indirect support to marine users through improved weather forecasts and storm warnings. The development of appropriate real-time algorithms has permitted the AVHRR to provide SST to an accuracy of 0.75"Cat resolutions as small as 1.1 km (Chapter 7 and McClain, 1981). The AVHRR characteristics delineated in Table V define the spectral coverage of the system. The change in Channel 1 between the prototype unit on TIROS-N and the NOAA series was done to eliminate overlap with Channel 2, thus providing improved areal extent of snow cover. A swath width of about 3000 km is achieved from the AVHRR with a 1.3 & 0.1 mr IFOV (1.1-km resolution near nadir) with all channels registered to 20.1 mr. The noise-equivalent temperature difference in Channels 3, 4, and 5 is designed to be at least 0.12 K for a 300 K scene. The equivalent to the APT used on earlier civil environmental satellites (Chapter 1) is derived from AVHRR and is continuously transmitted at a 4-km resolution for Channels 2 and 4. (For NOAA-7 this is 725 to 1100 nm and 10.3 pm to 11.3 pm, respectively.)

55

2. SEASAT, NIMBUS-7, A N D TIROS-N TABLEV. AVHRR SPECTRAL CHARACTERISTICS' _

_

_

_

~

~

~

~

AVHRRjl Channel no.

AVHRRJ2

TIROS-N

NOAA-A-D

NOAA-E-G

1

0.55-0.90

2 3 4 5

0.72-1.10 3.55-3.93 10.5-11.5 -

0.55-0.68 0.72-1.10 3.55-3.93 10.5-11.5 -

0.72- 1.10 3.55-3.93 10.3-1 1.3 11.5-12.5

0.58-0.68

Channel wavelengths in micrometers.

A multichannel sea surface temperature (MCSST) procedure using AVHRR data has been developed by NESDIS. Since becoming operational in November 1981, the technique has demonstrated superior results to the older Global Sea Surface Temperature Computation (GOSSTCOMP) procedure it replaced (1972-1981). Atmospheric water vapor is now corrected for on a pixel-by-pixel basis. Validations are being updated monthly using highquality drifting-buoy data scattered over the global oceans. Results from these drifter/MCSST matchups are shown in Fig. 32 (courtesy of Alan E. Strong, NOAA). Results have also been encouraging from comparisons of drifting-buoy SST measurements to the MCSST-analyzed field, produced on a weekly basis by NESDIS. Typical results of these comparisons are illustrated in Fig. 33, where eight buoys (denoted by x's with temperature) were reporting in February 1982

25

#

Matchups

Mean Bias

124

-0.o'C 0.6T

Scatter

0.6.C

RMSD

Night +-

-+

# 4

-

L

.

-5

Matchups 143 Mean Bias -0.4.C Scatter 0.6-c RMSD 0.8'C

0

5

10

15

20

25

30

MCSST ('C)

FIG.32. Scatter diagram for AVHRR-derived sea surface temperatures versus drifting-buoy surface temperature. (Courtesy A. Strong, NOAA.)

56

JOHN W. SHERMAN, 111

FIG.33. Multichannel sea surface temperature (MCSST) for the South Atlantic and southern Indian Ocean. (Courtesy W. Pichel, NOAA.)

from the South Atlantic and South Indian Ocean (Chapter 7). Only two buoys, one to the extreme west (32"W) and one to the extreme east (88"E), on this figure depart by 1" or more from the MCSST analysis. Two difficulties with the present AVHRR-derived SST measurements that the reader should understand in the routine use of these data are aerosol contamination and noise problems in the detectors. Such problems can arise in any such system and are cited here to illustrate the need to validate constantly the data. The aerosol contamination problem was highlighted dramatically by the April 1982 eruption of the Mexican volcano, El Chichon. Contamination of several degrees on the retrieved MCSST was typical during the summer/fall of 1982 from H,S04 aerosol attenuation between 10"N and 30"N latitude (El Chichon is at 17"24'N,93"W). Channel 3 AVHRR noise on NOAA-7 has diminished the number of MCSST retrievals beginning in late summer 1982. While the specificnoise problem has been identified and should be alleviated on future AVHRR systems, additional quality control must be exercised to maintain temperature accuracy and provide error limits. 5.

SUMMARY

The six new oceanic sensors outlined in this chapter all met or exceeded the original design specifications for geophysical data. Each has provided a unique view of the ocean surface or near-surface, and except for the altimeter

TABLE V1. GENERAL SUMMARY OF OCEAMC SENSQRRESULTS' Sensor type Multichannel radiometer Observable Altitude Geodesy Mean sea surface model Waves HI,, Wavelength Wave direction Internal Winds (surface) speed Direction Sea surface temperature Sea ice Location Type Edge

Altimeter

Scatterometer

Microwave

Infrared

Synthetic aperture radar

Ocean-color instrument

8 an (prec.) 70 cm (prec.)

10% or 0.5 m 12% 15"

Detected

2 m sec-'

1.3 m sec-

16" 1"C

Detected

Detected

N.Q. First year versus multiyear Detected

0.75"C

7km

0.5km

7km

0.5 km

(Continued)

TABUVI. (Continued)

Sensor type Multichannel radiometer Observable Ice sheets Height change Icebergs Ocean color Pigments Diffuse atten. coeff. Water mass detection Atmospheric water Columnar H 2 0 vapor Columnar H,O liquid Rain rate Circulation Current boundary Currents Eddies

Altimeter

Scatterometer

Microwave

Infrared

Synthetic aperture radar

(Very large)

N.Q.

Ocean-color instrument

1.6 m (prec.)

40% 40% N.Q. 10%or 0.2 g cn-’ N.Q. N.Q.

Detected Variability N.Q.

Detected

Detected

Detected

Warm eddies

Detected

Detected

a Unless otherwise noted, the values are the lu accuracies compared to in situ sources; where a percentage and a number are given, whichever is greater applies; N.Q. indicates not quantified; detected means the feature was observed but did not lend itself to further quantification.

2. SEASAT, NIMBUS-7, AND TIROS-N

59

and the AVHRR, each sensor has offered for the first time other methods for observing global oceans and ice dynamics. For the original design expectations quantitative measurements have been made. Table VI is a summary of results of these measurements. This table also includes observables which have not been quantified but still provide useful qualitative data on other ocean features. For example, the variability in ocean currents has been derived from the altimeter data, but surface quantification of total volume flow has not been done. Similarly, the SAR has detected numerous internal wave systems and permitted measurements on internal wave surface features, but quantification or limits have not been established. Other observables also have been noted in this manner. REFERENCES Alishouse, J. C. (1983). Total precipitable water and rainfall determinations from the Seasat scanning multichannel microwave radiometer. J. Geophys. Res. 88, 1929-1935. Bernstein, R. L. (1981). SMMR-derived sea surface temperature and temperature anomaly patterns in the mid-latitude Western Pacific. EOS 62,294. Born, G. H., Wilkerson, J. C., Sherman, J. W., and Lame, D. B. (1979). Seasat Gulf of Alaska workshop report, executive summary. Publ. No. 622- 101, Jet Propulsion Lab., Pasadena, Calif. Cane, M. A,, and Cardone, V. J. (1981). The potential impact of scatterometry on oceanography: A wave forecasting case. “Oceanography from Space” (J. F. R. Cower ed.), pp, 587-595. Plenum, New York. Cavalieri, D. J., Gloersen, P., and Campbell, W. J. (1981). Observation of sea ice properties with Vol. 1, pp. 69-78. the Nimbus-7 SMMR. IEEE Catalog No. 81CH1656-8, Cheney, R. E. (1982). Comparison data for Seasat altimetry in the Western North Atlantic. J. Geophys. Res. 87,3247-3253. Clark, D. K. (1981). Phytoplankton pigment algorithms for the Nimbus-7 CZCS. “Oceanography from Space” (J. F. R. Cower, ed.), pp. 227-237. Plenum, New York. Fedor, L. S., and Brown, G. S. (1982). Waveheight and wind speed measurements from Seasat radar altimeter. J. Geophys. Res. 87, 3254-3260. Fu, L., and Holt, B. (1982).Seasat viewsoceans and sea ice with synthetic aperture radar. Publ. No. 81-120, Jet Propulsion Lab., Pasadena, Calif. Gonzalez, F. I., Thompson, T. W., Brown, W. E., Jr., and Weissman, D. E. (1982). Seasat wind and wave observations of Northern Pacific Hurricane Iva, August 13,1978. J. Geophys. Res. 87, 3431-3438. Gordon, H. R., Clark, D. K., Brown, J. W., Brown, 0. T., Evans, R. H., and Broenkow, W. W. (1983). Phytoplankton pigment concentrations in the middle Atlantic Bight: Comparison of ship determinations and CZCS estimates. Appl. Opt. 22.20-36. Jain, A. (1978). Focusing effects in the synthetic aperture radar imaging of ocean waves. Appl. Phys. 15,323-333. Jones, W. L., Black, P. G., Boggs, D. M., Bracalente, E. M., Brown, R. A., Dome, G., Emst, J. A., Halberstam, J. M., Overland, J . E., Peteherych, S., Pierson, W. J., Wentz, F. J., Woiceshyn, P. M., and Wurtelle, M. G. (1979). Seasat scatterometer: Results of the Gulf of Alaska workshop. Science 204, 1413-1415.

60

JOHN W. SHERMAN, 111

Jones, W. L., Schroeder, L. C., Boggs, D. H., Bracalente, E. M., Brown, R. A., Dome, G. J., Pierson, W. J., and Wentz, F. J. (1982). The Seasat-A satellite scatterometer: The geophysical evaluation of remotely sensed wind vectors over the ocean. J. Geophys. Res. 87,3297-3317. Katsaros, K. B.,Taylor, P.K., Alishouse, J. C.,and Lipes, R. G.(1981). Quality of Seasat SMMR atmospheric water determinations. “Oceanography from Space” (J. F. R. Gower, ed.), pp. 691-706. Plenum, New York. Legeckis, R. V. (1979). Satellite observations of the influence of bottom topography on the seaward deflection of the Gulf Stream off Charleston, South Carolina. J . Geophys. Oceanogr. 9,403-497. Lerch, F. J., Marsh, J. G., Klosko, S. M., and Williamson, R. G. (1982). Gravity model improvement for Seasat. J. Geophys. Res. 87,3281-3296. Lichy,D. E., Mattie, M. G.,and Mancini, L. J.(1981). Trackingof a warm water ring. Spaceborne Synthetic Aperture Radar for Oceanography (R. C. Beal, P. S. Deleonibus, and I. Katz, eds.), pp. 171-182. Johns Hopkins Univ. Press, Baltimore. Lipes, R. G., ed. (1980). Seasat scanning multichannel microwave radiometer mini-workshop 111 report. Publ. No. 622-224, Jet Propulsion Lab., Pasadena, Calif. Lipes, R. G. (1982). Description of Seasat radiometer status and results. J. Geophys. Res. 87, 3385-3395. McClain, E. P. (198 1). Multiple atmospheric-window techniques for satellite-derived sea surface temperatures. “Oceanography from Space” (J. F. R.Gower, ed.), pp. 73-86. Plenum, New

York. McClain, E. P.,and Marks, R. A. (1979). Seasat visible and infrared radiometer. 204,1421-1424. Marsh, J. G., and Martin, T. V. (1982). The Seasat altimeter mean sea surface model. J. Geophys. Res. 87,3269-3280. Schroeder, L. C., Boggs, D. H., Dome, G., Halberstam, I. M., Jones, W. L., Pierson, W. J., and Wentz, F. J. (1982). The relationship between wind vector and normalized radar cross section used to derive SFasat-A satellite scatterometer winds. J. Geophys. Res. 87,3318-3336. Schwalb, A. (1982). Modified version of the TIROS-N/NOAA A-G satellite series (NOAA E-J)Advanced TIROS N (ATN). NOAA TM Memo. NESS 116, Washington, DC. Shuchman, R. A., Kasischke, E. S., Klooster, A., and Jackson, P. L. (1979). Seasat SAR coastal ocean wave analysis. Environmental Research Institute of Michigan Report 138600-2-F. Tapley, B. D., Born, G .H., and Parke, M. E. (1982). The Seasat Altimeter data and its assessment. J . Geophys. Res. 87,3179-3188. Townsend, W. F. (1980). An initial assessment of the performance achieved by the Seasat-1 radar altimeter. IEEE J. Oceanic. Eng. OE-5, 80-92. Weissman, D. E., Ed. (1980). I E E E J . Oceanic Eng. OE-5,71-76. Yates, H. (1981). The United States operational polar-orbiting satellite series, TIROS-N. Ado. Space Res. ( C O S P A R ) 1,57-71.

ANALYSIS AND INTERPRETATION OF ALTIMETER SEA ECHO J. LIPA DONALD E. BARRICK BELINDA Ocean Surface Research Boulder, Colorado

Ocean Surface Research Woorlside. California

1. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . 2. TheConvolutional Representation of the Signal and Its Use . . . . . . . . 2.1. Derivation of Convolutional Form . . . . . . . . . . . . . . 2.2. Recovery of Joint Probability Density from Seasat Data . . . . . . . . 3. Model Fits of Recovered Sea Surface Probability Density . . . . . . . . . 4. The Study of Altimetric Biases Using Models . . . . . . . . . . . . . 4.1. Echo Model with Gaussian Beam/Pulse Shapes and Gram-Charlier Surface Probability Density. . . . . . . . . . . . . . . . . . . . . . 4.2. Semiempirical Seasat Model, Neglecting Pointing Error . . . . . . . . 4.3. Tracker-Bias Study Using the Semiempirical Model . . . . . . . . . 4.4. Antenna Pointing-Error Effects-Model for Echo Plateau . . . . . . . 4.5. Rain Effects on Altimeter Echo. . . . . . . . . . . . . . . . . 5. Electromagnetic Bias. . . . . . . . . . . . . . . . . . . . . . . 6. A General, Improved Deconvolution Algorithm . . . . . . . . . . . . 7. Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . Appendix.. . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . .

.

61 62 62 65 68 73 73

. 75 . 78 . 79

.

83 86 93 96 97 99

1. INTRODUCTION

Short-pulse altimetry from space was first suggested in the mid-1960s in a study supported by NASA at Woods Hole, Massachusetts(Ewing, 1965). This study drew on the state of the art of airborne remote sensing as the basis for satellite techniques and applications. Microwave altimeters were proposed for the measurement of sea level, sea state, and tsunamis. Further development of specific sensors for oceanic physics evolved during what has become known as the “Wil1iamstown”study (held at Williams College 5 years after the Woods Hole gathering), Here a strong case was made for microwave satellite altimetry (Kaula, 1970) that provided impetus for the altimeters flown on Skylab and GEOS-3. In contrast with other microwave instruments (e.g., the scatterometer, radiometer, and synthetic aperture radar), the altimeter is supported by a mathematical model relating the echo to the sea surface interaction that is both noncontroversial and useful for designing algorithms to extract information. Since the backscatter seen by the altimeter in space is restricted to a fraction of a degree around the nadir position, the scattering mechanism is 61 ADVANCES IN GEOPHYSICS, VOLUME 27

Copyright @ 1985 by Academic Press, Inc. All rights of reproduction in any form reserved. ISBN 0-12-018827-9

62

DONALD E. BARRICK AND BELINDA J. LIPA

essentially optics-type reflection from thousands of specular points randomly distributed across the rough, moving sea surface. The mathematical model describing the altimetric echo waveform was derived by Barrick (1972a)using physical optics to represent the scatter from the rough surface. This model is a double integral, and can be written in a convolutional form. It shows that the sea surface response to a short pulse is a ramp starting at zero and rising sharply in time to a plateau, after which it falls off slowly. Sea surface mean position and wave-height information is contained in the ramp portion, called the leading edge. Wind-speed information is extracted from the backscatter signal intensity on the plateau. Models have been devised that are fitted to the leading edge directly to extract mean surface position and wave height, both on board the satellite [for Seasat, see MacArthur (1978) and Townsend (1980)], as well as on earth, post facto (Hayne, 1981). In this chapter is discussed the physics behind and use of Barrick’s model in information extraction and data interpretation. In particular, in Section 2 is presented the convolutional form of the model, demonstrating a much simpler but more illuminating method for its inversion than those used in the above references; it is based on deconvolution by straightforward fast Fourier transform (FFT)algorithms. In the subsequent section the recovered quantity is then interpreted, namely, the sea surface wave-height probability density function in terms of models that allow maximum-likelihood parameter extraction and uncertainty estimation. In Section 4 we develop and employ models to study various factors (both instrumental and near-surface effects) that bias or distort the altimeter echo, and use Seasat to demonstrate their application. In Section 5 we discuss the important and interesting phenomenon called electromagnetic bias, i.e., where the altimeter reckons the mean sea surface position to be, compared with its actual position (with all other errors/biases removed). Finally, in Section 6 a double-deconvolutional-based algorithm for altimeter echo analysis is discussed that can handle various antenna error and rain biases, is computationally efficient, and outputs parameter uncertainties along with the parameters themselves.

2. THECONVOLUTIONAL REPRESENTATION OF THE SIGNAL AND ITS USE 2.1. Derivation of Convolutional Form

Scatter from a Gaussian, random distribution of rough-surface specular points contained in a downward-propagating spherical altimeter pulse was derived by Barrick (1972a,b) using physical optics, and is repeated here. The result gives the average radar cross section for backscatter, polarized in the

63

3. ALTIMETER SEA ECHO

same sense as was transmitted, as a function of time t , while the altimeter receiver is responding to the interaction of its pulse with the rough sea or earth surface. For the coordinate geometry, see Fig. 1.

1

o ( t ) = 2 n ' a 2 1 ~ ( 0 ) 1 ' ~ ~ g ( ~ ) s e c -4m ~~ s i n(- ~tP [ jj ~( t ) d t

d4

(1)

where II/ is the angle at the antenna from nadir to a point ( on the ocean surface; 4 is the angle at earth center from the satellite to a point 5 on the ocean surface;&) is the two-way antenna gain pattern, normalized so that it is unity at its maximum (accounts for pointing error); P(x) is the effective pulse shape at the receiver output, normalized to unity at its maximum, versus spatial propagation distance x = c t / 2 ; a is the earth's radius; R(0) is the Fresnel reflection coefficient of sea surface at normal incidence; 6 is the angle between the local normal to the surface at [ and the satellite; and pj([) is the joint height-slope probability density function of the surface height i (positive upward) of the waves above a mean local surface, and wave slopes corresponding to specular angle 0. The above expression is thus far exact; the only approximations implied are those inherent in the specular-point explanation of scatter. For backscatter very near vertical, two decades of experimental data have shown the specular-point model to be totally adequate for the microwave radar echo. Operation from a satellite such as Seasat requires the use of a narrow-beam antenna, which restricts backscatter to a region very near nadir, i.e., backscatter near the normal or vertical to the mean surface. It is this condition that permits considerable simplification of Eq. (1) to obtain linearization and reduction to a convolutional form. We employ the parameters of Seasat to demonstrate this process. Although exaggerated in Fig. 1, theangles t+b, 4, and 8 are very small. The angle IC/ at Seasat's two-way antenna pattern half-power point is 1.6"/(2fi) 'Y 0.57". The last sampling gate at which data are taken

Satelliten

b Earth Center FIG.1. Coordinate geometry for satellite altimeter;( is height of surface point above the mean (spherical)earth/sea, and 0 is slope angle (from vertical) seen from altimeter to the mean sea.

64

-

DONALD E. BARRICK AND BELINDA J. LIPA

and preserved on Seasat (i.e., gate 60)restricts $ even further; at its altitude of 800 km, $ at the last gate is 0.3”. Therefore, small-angle trigonometric simplification of the exact law of cosines (see Fig. 1) relating the height ( of the roughness above the mean spherical earth of radius a to time t and satellite height H,i.e.,

(C

+

=(H

+ Ct/2)2 + (a + H ) 2 - 2(H + ct/2)(a + H)cos JI

(2)

yields (=-4 2

+ H’($2/2)

(3)

where time t measured at the receiver is taken to be zero when the radar cell center intercepts the mean spherical earth. We also employ the fact that << H < a (for Seasat and the sea, (c2) < 5 m; H = 800 km; a = 6370 km), and define an “extended” satellite height as H’ 3 H(l H / a ) . We also employ the facts that $ =(a/H)4 and 8 = ( a / H “ ) 4 , where we define a “reduced satellite height as H” = H/(1 + H/a). In obtaining the final convolutional form for Eq. (l), we change variables of integration from 4 to u = a242/2H”, and replace ct/2 by x. The units of the problem are now distance x traversed by the radar cell (measured downward from mean sea level). We also define a normalized radar cross section as as@) = ~ ( ~ X / C ) / ( ~ Z ~ H ” I Rand ( O new ) ~ ~ )antenna , gain factor as C(u) = g ( , / m ) , where $ = Making these substitutions and simplifications to Eq. (l),we obtain

+

JQ

LJ-a

J

The double-convolutional form of Eq.(4a) is written in concise mathematics as

0 P ( - x ) 0 G(-x)(U -x) (4b) where the symbol 0 denotes convolution. This is precisely the form used by Brown (1977), Walsh (1979), and Hayne (1981). Hayne refers to G(x)U(x)as the “flat-sea impulse response, including antenna pattern and off-nadir effects.” a,(x) = &(-X)

3. ALTIMETER SEA ECHO

65

Let us now examine Eq. (4).The altimeter measures a,(x), on the left side of the equation. P(x) is the pulse shape at the receiver output when the transmitted pulse is sent directly through the receiver; for Seasat, this is measured on board and transmitted to earth, and hence it is known. G(x)is the antenna pattern, and it too is known (when the antenna points toward nadir) from prelaunch calibration measurements. Therefore, all of the desired information about the surface statistics and position is contained in the function pj(x). We must therefore solve a double, linear integral equation to obtain this function. The measured quantity will have some random noise added to it. This seemingly formidable problem is made quite simple, however, by its convolutional nature, Such convolutional integral equations are most readily solved by Fourier transform procedures. When all of the factors appearing in Eq. (4)are Fourier transformed, the convolutional operator 0 appearing in Eq. (4b) becomes merely a multiplication sign in the other domain. This procedure is demonstrated in the next section.

2.2. Recovery of Joint Probability Density from Seasat Data As has been mentioned previously, the usual methods of extracting surface information from altimeter echo data have involved essentially the fitting of a model directly to the echo as a function of time. In this approach, one employs a model for the pulse shape, a model for the antenna gain, and a model for the surface probability density. These are substituted into Eq. (4a) and the integrations are performed,to obtain a model for the echo, a,(x). Since several undetermined parameters appear in the model functions under the integral (e.g., antenna pointing error, tracker error), this integral is usually not soluble in closed form (Hayne, 1981).' Then various methods have been used to obtain the best fit possible for the model a,(x) to the data by varying the parameters of the models under the integral (including least squares, estimates of echo leading-edge slope, etc.) We are suggesting that a more straightforward and illuminating alternative to these echo/model fits (and less time-consumingnumerically) is to solve for the desired functions in Eq. (4) directly by using the unique properties of convolutional integrals and their Fourier transforms. This approach also gives estimates of statistical uncertainties in the derived quantities as an important by-product. Separate models can then be fitted to the recovered functions(if desired),a procedure that is much less time consuming than trying to recover six parameters all at once by the methods mentioned in the preceding paragraph. In subsequent sections, this integral will be solved in closed form for certain models and assumptions in order to study various biases in altimeter operation.

66

DONALD E. BARRICK AND BELINDA J. LIPA

By restricting attention to the “leading-edge” portion of the echo, Lipa and Barrick (1981) obtained pj(x)by deconvolution. That method and the results are briefly reviewed here to facilitate understanding of the method and its proposed extension in subsequent sections. The double convolution of Eq. (4) reduces to a single convolution valid on the echo leading edge (where 1x1 and 1 1 are small)by differentiating Eq. (4a) with respect to x. Also assumed there is the fact that there is no antenna pointing error, in which case G(u) ‘v 1 on the leading edge. Then the expression for the leading-edge derivative or slope is given by ci(x) =

J’lk

~ (+ xt ) p j ( t ) d t = p(-x)

opj(-x)

(5)

Knowing the pulse shape P(x) from samples transmitted to earth during Seasat’s Internal Calibration Mode I, one would theoretically solve this equation for the surface probability density p j ( x )by taking the FFT of a:(x), dividing by the FFT of P(-x), and inverse Fourier transforming the quotient back to obtain pi( -x). Because additive noise is present along with a,(x),as well as statistical fluctuation in o,(x) itself (due to the random nature of sea echo), and because the instrument was subject to several observed malfunctions, the procedure was not that simple. Below is a summary of the steps used by Lipa and Barrick in the extraction of ~ ~ ( x ) ~ : 1. Each waveform (every 0.1 sec) is numerically renormalized. This is necessary because the Seasat automatic gain control (AGC) system somehow malfunctions for moderate-to-low sea states, causing approximately every third waveform to be too large (or sometimes too small) by as much as 50%. The renormalization procedure simply divides all gates by the average power level in the last 15 plateau gates (e.g., 45-60). 2. A predetermined number of waveforms are then averaged together, In our case we used both 60 waveforms (over 6 sec or -40-km groundtrack) and 240 waveforms (over 24 sec or 160-km groundtrack). 3. The average waveform is broken into its three distinctive regions: preleading-edge noise, the leading edge, and the plateau. This is very easily

-

* Seasat samples the region around x = t = 0 as a series of 60 time/range gates 3.125 nsec apart. If the tracker worked perfectly and no other biases were present, t = 0 would fall halfway between gates 30 and 31. One hundred power pulses corresponding to u(t)are averaged on the satellite for each gate (each pulse is 1 msec apart) and the 100-sample averages are therefore spaced 0.1 sex apart in time. Three additional “tracking gates” are sampled and transmitted on Seasat; labeled gates 61,62, and 63, these fall on the leading edge precisely between gates 29 and 30,30 and 3 I , 31 and 32. Because of this, these tracking gates are referred to here and elsewhere as gates 29.5,30.5, and 31.5.

67

3. ALTIMETER SEA ECHO 1

I

1-

0' 20

Plateau

I , I

1

25

30

35

I

I

I 40

RangeGate

FIG.2. Leading edges of the returned Seasat altimeter echoes for averages over the samples from orbit 280 shown in Fig. 3. Samples 1 and 4 represent the lowest sea states; sample 2, being closest to the hurricane eye, is the highest.

accomplished by noting changes in slope. (See Fig. 2 for typical, measured altimeter echoes to locate the regions being discussed.) 4. The pre-leading-edge average noise level No is determined and subtracted from the remainder of the waveform. [The pre-leading-edge noise is one of the six parameters determined also by Hayne (1981).] 5. Each waveform gate amplitude is multiplied by an appropriate gain bias correction. For gates 1-60, these were determined and supplied by NASAWallops; for tracking gates 61-63, we estimated these corrections ourselves. 6. The derivative of the leading edge, CT;(X), is then taken using a three-point numerical difference. 7. When deconvolution is used, the FFT of oi(x) is taken; the result is then divided by the FFT of the pulse shape, and the inverse FFT is taken again to yield pj([), the joint height/zero-slope probability density function. The actual pulse shape determined from the Seasat Internal Calibration Mode I is used rather than a model for the pulse. Because this pulse is so narrow, and therefore measured at a half-gate sampling rate (i.e., every 3.125/2 nsec), and also because the important middle of the leading edge is sampled at this same higher rate, we use a uniform digitization interval of 3.125/2 nsec for the entire leading edge. Hence, waveform regions not sampled at this rate are interpolated to give values at these points. The maximum FFT length required, therefore, to cover the leading edge even in high seas was N = 64 points. 8. As a check, and to demonstrate an alternative technique, standard integral inversion was employed on Eq. ( 5 ) to give p j ( [ ) . This method also

68

DONALD E. BARRICK AND BELINDA J. LIPA

gives statistical uncertainties in a standard fashion. Theoretically the two techniques give equivalent results. Practically, however, integral inversion (in matrix form) is lengthier timewise, requiring N 2 steps compared to N log,(N) steps for deconvolution. (This assumes the inverse matrix for the pulse shape P ( x ) has been obtained and stored as a two-dimensional array earlier.) Time differences between the two methods become significant only for N considerably bigger than 32. 9. Standard covariance techniques (Brandt, 1976) are then used to estimate statistical uncertainties in p j ( c i ) at each height point ci. Here, the covariance matrix of the actual observed echo, oS(xi)at consecutive points x i , is employed rather than theoretical models for these covariances. Lipa and Barrick (1981) found that-for unexplained reasons- the Seasat echo data had a normalized variance that varied with position on the leading edge, and that echo data between consecutive range gates are correlated. Neither of these findings agree with idealized echo theory; therefore, the actual data statistics were used in uncertainty estimates.

-

-

Results for four 24-sec samples during a Hurricane Fico overpass (orbit 280; see Fig. 3 for locations) are shown in Fig. 4. Significant wave heights at the

four locations are approximately 2.5, 7, 4.4,and 2.6 m. Figure 2 shows the Seasat waveform leading-edge averages for the four times. In Fig. 4, two curves are shown in each case: one obtained from deconvolution and the other from matrix inversion. In most cases the two curves are so close as to be indistinguishable. This consistency check is one measure of the accuracy of these techniques (no “ground truth” was available beneath the hurricane). Error bars are also shown, representing f4 standard deviations. The uncertainties are lower near the wavecrests than toward the wavetroughs, a result of the unexpected variation of normalized echo variance with position on the leading edge. In the next section we will show how this probability density retrieved from the echo data can be interpreted in terms of ocean surface parameters, both with and without the use of models. 3. MODELFITSOF RECOVERED SEASURFACE PROBABILITY DENSITY

As defined after Eq.(l),the probability density function pi(() arising from specular-point scattering theory that appears in Eqs. (1) and (4), and that was derived from Seasat data in the preceding section,is a joint sea surface heightslope density. Define p(c, [,, (), as the joint probability density function between the random sea surface wave height c(x, y) and wave slopes 5, = a[(x, y)/& and c, = dc(x, y)/dy, where the x and y lie in the horizontal

69

3. ALTIMETER SEA ECHO East Lonaitude

225

230

235

FIG.3. Location of the four data samples used from Seasat orbit 280, over Hurricane Fico, whose center (the dot) is about 100 km from the altimeter nadir track. Times are as follows: sample 1,1412:OO-1412:24 GMT; sample 2,1414:42-141S:M GMT; sample 3, 1415:30-141S:54 GMT; sample 4,1417:30-141754 GMT. Each sample shown is 24 sec long, covering 160 km of ground path.

plane tangent to the spherical earth at the nadir point. Used in relationships for specular-point scatter (Barrick, 1968, 1972a), the slopes in this expression are those required for facets on the waves that can reflect specularly,i.e., whose normals point in the backscatter direction. Since the satellite antenna beamwidth and range gating restrict the slope angle 8 [see Fig. 1 and the discussion after Eq. (l)] to be very small, (, and cy are effectively zero in p ( [ , c,, c,) for satellite altimeters; therefore, the axis directions x, y in the horizontal plane are arbitrary. Hence the required expression in Eqs. (1) and (4), and which we derived above, is pi(() = p(c,O, 0). To lowest order, the sea surface height [ and slopes [,, Ly are zero-mean, Gaussian, uncorrelated random variables. Higher order effects, including correlations between heights and slopes as well as skewnesses (e.g., nonzero where - .) denotes infinite wave-height skewness proportional to ensemble average) are definitely noticeable, indicating that the sea surface heights and slopes are not strictly Gaussian. The non-Gaussian nature of these quantities can be explained by higher order nonlinearities in the hydrodynamic boundary conditions for gravity waves ( Longuet-Higgins, 1963). Because of the prospect of extracting additional information about sea state and wave processes from the altimetric echo, it would seem desirable in

(c3),

(a

70

DONALD E. BARRICK AND BELINDA J. LIPA

Height (m)

FIG.4. Surface height-slope probability densities versus surface height for the data samples (1-4) of Figs. 2 and 3, obtained both by matrix inversion and Fourier deconvolution. Averaged over 24 sec, the echo yielded probability density error bars of f4a shown above, for the curve obtained from matrix inversion.

future examination of altimeter algorithms to avoid reducing p(C, 0,O)to the simple Gaussian height probability (no correlation between height and slopes) with only one parameter (mean-square wave height h2 E (c’)). We examine here the fitting of a Gram-Charlier expansion of p ( ( , 0,O) to the quantity pi(&) measured in the preceding section. This model takes the following form: FjN(C)

= (1/&h){l

+ A , c ( c / ~ ) ~+I A , ( C / ~ ) ) ~ X P ( - C ~ / ~ ~(6)~ )

which contains three unknown parameters, to be determined by fitting Eq. (6) to the functions extracted from Seasat data and shown in Fig. 4. These parameters are h, A l , and A,, As mentioned above, h is the RMS wave height. A, = A1/6, where 1, is the height skewness defined as I, = (C3>/h3. For

3. ALTIMETER SEA ECHO

71

ocean waves, the fact that this quantity is nonzero and positive is explained by the pointiness near the crests and flatness near the troughs. A2h includes any shifts of the waveform from its “perfect” location (with gate 30.5 centered halfway up the echo leading edge), normally due to the on-board tracker bias. The quantity A,h would normally be included in the exponential [i.e., instead of we would have (5 - A2h)’3, but since Seasat error A,h is known to be small in terms of RMS wave height h, this quantity is brought outside by a Taylor series expansion about zero bias, retaining only the first and second terms in A2h. The resulting model, Eq. (6), is therefore linear in two unknown parameters, A , and A,, while it is nonlinear only in h. This fact will make leastsquares estimation of these three quantities very easy. Finally, we “normalize” the data, pjN(li), such that the area under it is unity [i.e., p j N ( l i )= p j ( c i ) / Z i p j ( l i ) ] .We then do a least-squares fit of measured data p j N ( l i )to by minimizing E i [ p j N ( l i )- j j j N ( l i ) 1 2 with respect to h, A , , the model pjN(li) and A,. Because the model Eq. (6) is linear in A , and A,, but nonlinear in h, Lipa and Barrick (1981) show that this leads to a simple one-dimensional grid search in h, requiring approximately N 2 steps to find the minimum, with a final interpolation between the three lowest points h j - , , h j , and h j + , , to give the RMS wave-height estimate h: There is another obvious way of determining A , that does not involve use of a model: direct calculation of the first moment from the retrieved probability density, i.e.,

cz

J --oo

I J-m

This would be identically equal to A,h if the data fit the model of Eq. ( 6 ) perfectly. Theoretically, one can also determine hZ in this way from the second moment and A , or 1, from the third moment. There is a problem with higher moments, however. The numerical integration to obtain the moment runs theoretically from - co to co;practically, one must truncate at some point. Because of noise and statistical fluctuation in the original data, the tails of the retrieved probability p j N ( ( )never become identically zero, but fluctuate about a low but constant “noise” level. Multiplication of these tails by c” enhances this noise for larger values of n. Therefore, one must truncate the numerical integration at some upper limit to keep this enhanced “tail noise” low. We have found, however, that for larger values of n (e.g., for moments equal to or greater than the second, where n 2 2), the result then becomes quite sensitive to the limit chosen. Hence, our experience with the Seasat data has shown that moments higher than the first cannot be computed stably, and the model-fitting procedure described above must be applied. In Table I we present values of mean sea-level difference (from gate 30.5) f, using both the first-moment and model-fit techniques (along with standard

+

72

DONALD E. BARRICK AND BELINDA J. LIPA TABLEI. PARAMETERS OF HEIGHT-SLOPE PROBABILITY DENSITIES FOR SEASAT ORBIT280 Mean sea level difference (an)

Time (GMT) (hr:min:secl

First moment

Model fit

RMS wave height (cm)

Height skewness parameter

Csec averages

14: 12:00-14:12:06 14:12:06-14:12:12 14:12:12-14:12:18 14: 12:18-14: 12:24

10.4 f 1 10.6 f 1 11.4 f 1 11.4 & 1

11.0 f 2 11.1 f 2 11.8 f 2 11.9 f 2

56.3 f 2 59.5 f 2 59.5 f 2 60.5 f 2

0.22 f 0.1 0.29 f 0.1 0.25 f 0.1 0.30 f 0.1

14:14:42-14:14:48 14:14:48-14:14:54 14: 1454-14: 15:OO 14: 15:00-14:15:06

35.4 f 7 37.4 f 6 38.2 f 6 35.9f 5

51f8 47 f 9 39 f 8 36 f 8

166 f 5 177 f 5 169 f 4 164 f 4

0.23 f 0.2 0.32 f 0.2 0.08 f 0.2 0.15 f 0.1

14:15:30-14:15:36 14:15:36-14:15:42 14:15:42-14:15:48 14:15:48-14:15:54

16.4 f 3 18.3 f 3’ 14.7 f 3 18.2 f 3

17 f 5 19 f 4 16 f 5 1854

112 f 3 110 f 3 110 f 3 100f3

0.03.* 0.2 0.13 f 0.2 0.01 f 0.2 0.21 f 0.2

14: 17:30-14: 17:36 14: 17:36-14: 17~42 14:17~42-14:17:48 14:17:48-1417:54

10.5 f 2 9.4 f 2 11.7 f 2 11.5 f 2

12 f 3 11 f 2 12 f 2 1352

65.5 f 2 64.5 f 2 64.5 f 2 62.4 f 2

0.26 f 0.1 0.20 f 0.1 0.20 f 0.1 0.45 f 0.1

Wsec averages 14:12:00-14:12:24 14:14:42-14:15:06 14:15~30-14:1554 14:17:30-14:17:54

10.8 f 0.4 41.6 f 2 17.0 f 0.7 11.1 f 0.4

11.4f 0.5 47.2 f 2 17.0 f 1 11.3 f 0.6

59.7 f 0.4 167 f 1 108 f 0.8 64.2 f 0.5

0.27 f 0.03 0.27 f 0.04 0.10 f 0.04 0.29 f 0.03

deviations or errors in these quantities); positive numbers mean the actual shift from gate 30.5 is upward, The third and fourth columns are RMS wave-height 6 and height skewness A,, retrieved using the model-fit method. In the top section of this table, each of these is done over 60 total wave forms, comprising 6 sec (-40-km path distance). The bottom section is for a 24-sec average. Table I1 shows comparisons of RMS wave height 6 from our method for Hurricane Fico (24-sec averages), compared with the on-board wave-height estimator, as well as algorithms of Fedor and Hayne. (Hayne’s data were available for only two of the four periods we analyzed.) These results are taken from Lipa and Barrick (1981). It is possible to linearize Eq. (6) completely by expanding about an initial estimate of wave height ho (obtained either from the on-board wave-height

73

3. ALTIMETER SEA ECHO

TABLE 11. WAVE-HEIGHT COMPARISONS Time period 1412:00-14:12:24 14: 14:42-14 15~06 14: 15130-14:1554 14: 17:30-14: 1754

Present analysis (cm)

Fedor

(4

Hayne

(4

59.7 167 108 64.2

58.8 188 117 64.1

128 74 -

On board (cm)

-

60.5 206 116 63.7

estimator or from the leading-edge slope). This leads to an iterative search for the true value of h. However, we have found no savings in computer time over the one-dimensional grid search. In addition if ho is a poor estimate, the iterative method may converge to an incorrect value. Hence we do not recommend this method.

4.

THE

STUDY OF ALTIMETRIC BIASES USING MODELS

Thus far, in our convolutional representation for the altimeter echo waveform Eq.(4) we avoided using models for any of the three functions in the integrals that produce the echo: the antenna pattern function, the pulse-shape function, and the surface probability density function. Since the first two should be known from calibration tests of the instruments, the third is obtainable exactly using deconvolution. This method was then tested on Seasat data and the actual probability density was in fact accurately measured. Only in the last section, then, did we fit a model to the surfaceheight probability density in order to relate its most important descriptors (RMS wave height and wave-height skewness) to the same parameters obtained by other investigators. There are situations or reasons in which models for all three functions are illuminating. These have to do with how various departures of the system, propagation medium, or scattering process from the ideal affect the echo. Said another way, one can insert biases into the models one at a time and see how the system performs and/or how candidate algorithms will misinterpret the output. 4.1. Echo Model with Gaussian BeamlPulse Shapes and Gram-Charlier Surface Probability Density

Here we assume a Gaussian shape for the compressed altimeter pulse P(x), with z being the half-power width of the pulse. Likewise, we assume a Gaussian shape for the antenna beam near its boresight (i.e., the last range gate

74

DONALD E. BARRICK AND BELINDA J. LIPA

taken on Seasat subtends 0.3"from boresight, while the two-way half-power antenna beamwidth extends 0.57" from boresight). At this time we assume the antenna points directly at nadir; pointing-error biases and models will be examined later. The total two-way half-power antenna beamwidth is $, (1.13" for Seasat), from which we define +b = I,b,,/,,/KiiZ and u b = H'I,bi/2. Finally, we define a joint height-slope probability density after the fashion of Eq.(6) that includes height skewness as P i ( < ) = P(<,O,O)

- (271)312h~,~y 1 ,/-

{l

+ %[(:)'-3(;)]]e~p(-<~/2h~)

(7)

where h is the RMS wave height (related to significant wave height as H1/32: 4h), s, and sy are the RMS wave slopes along any two orthogonal axes tangent to the mean sphere, pxy is the correlation coefficient of the wave slopes along these axes, and 2, is the wave-height skewness coefficient defined after Eq. (6). Upon substitution of these models into Eq. (4a), the resulting integration can be done in closed form to give

where 0, is the average backscattering cross section per unit area at normal incidence, from which the nadir wind speed is derived (Brown, 1979; Fedor and Brown, 1982); specular-point theory gives the following form:

We also define the following terms at= c t / 4 4 % - 5

h = (C2)

,/-

= R M S wave height

= (ct,/2)/$

0,

=

t+,

= H ' ~ t / 2-N (Ct,/2)/2

[t,

as defined in Barrick (1972a)l

[t,

as defined in Barrick (1972a)l

and @(y) is the error function of argument y. Implicit in the above derivation are Seasat altimeter parameters, such that z = 3.074 nsec (from Seasat internal calibration mode), t, = 832 nsec, #b = 3120 cm. For h = 0, t , = 1.846 nsec and ap = 19.58 cm; while for

3. ALTIMETER SEA ECHO

75

h = 200 cm (i.e., significant wave height is 8 m, representing an extremely high sea state), t , = 9.473 nsec, and a, = 201 cm. Hence, all terms of order a,/w, and higher have been neglected in deriving Eq. (E), since ap/ub is less than 0.03 at even the highest sea states. The constants in Eq. (8) that determine the model altimeter echo waveform have meaningful physical interpretations. a, defines the spatial width of the effective receiver pulse. The quantity ap = defines the amount (in space) to which the pulse is stretched by scattering from the waves of RMS height h; therefore, this quantity determines the shape of the leading edge. Finally, the quantity ub, which depends on the antenna beamwidth, is the distance downward the altimeter pulse travels as it expands over the spherical earth until the finite beamwidth attenuates the energy. Hence, t, and uH determine the shape of the waveform in the plateau region, as seen from the factor e-'/"b. Note at this point that if we take the spatial derivative of Eq. (8) and restrict ourselves to the leading edge (so that x << ub), we obtain

d G :

where if RMS wave height h significantly exceeds pulse width of, we have a, = h; therefore Eq. (10) is identical in form to Eq. (7) except that distance is now turned around [i.e., wave height in Eq. (7) was upward, whereas x is in the direction of pulse delay time, namely downward]. Therefore, for higher wave heights, the shape of the leading-edge derivative is identically the shape of the Gaussian height density function with the Gram-Charlier correction added to include height skewness. O n the other hand, when pulse stretching is small ( h < a,) the skewness of wave heights has negligible effect on the altimeter waveform. The simple, direct relationship between the leading-edge derivative, given by Eq. (lo), and wave statistics is attractive, and forms the basis for Fedor's algorithms for extracting wave height (Fedor and Barrick, 1978; Fedor and Brown, 1982).

4.2. Semiempirical Seasat Model, Neglecting Pointing Error In the preceding section we presented an exact solution for a short-pulse altimeter waveform based on assumed models for the various convolutional factors' constants, as observed in the waveform. Thus system gain, aa, and AGC factors are all lumped as one. When this is done, the empirically derived constants truly appear to be independent of sea state, as will be shown subsequently. Therefore, the AGC and height tracker indeed accomplish their functions quite well.

76

DONALD E.BARRICK AND BELINDA J. LIPA

The observed Seasat mean waveform is therefore aS(2x/c)= No

+K

(:[

- 1

I)&(

+ Q,

- e-x'ub

If the theory that produced the model Eq. (8)that we adapted to Eq. (1 1) is valid, we should be able to use the nominal constants for opand #,for Seasatas measured by the instrument designers and internal calibration mode; this we will do. The remaining constants, No and K, we determine from the analysis of several data sets over the widely varying sea conditions of orbit 280 (the pass over Hurricane Fico analyzed previously). Doing this, we find No N 5.4 and K = 92, in terms of the units transmitted by the satellite. Because of the AGC and other constants constituting K, we can write K = Ano, where as before, Q, is the normalized nadir backscattering cross section; A is proportional to( 1) AGC gain, (2) a multiplicative factor due to antenna pointing error (to be discussed subsequently), and (3) path attenuation, due in part to rain (to be discussed subsequently). Since the data show that K remains constant, decreases in 0: for higher winds and seas are compensated by increases in AGC gain A. Therefore, measurement of A gives an uncorrected estimate of a,. The nominal relationship between gate number N (1 IN I60 for Seasat) and x appearing in Eq. (11) (in the absence of any height biases) is therefore x = XN = ( N - 30.5)Ax

(12)

where Ax = 46.875 cm based on the sampling time gate At = 3.125 nsec. To verify the general validity of the empirical model Eq. (11) for the four constants, No, K, a,,, and L(b (the first two empirically measured and the second two determined from hardware calibration), we plot the model values and measured values in Fig. 5. These two curves represent the lowest and highest sea states in the orbit-280 pass near the hurricane. Significant wave heights for these cases are 2 and 7m; each sample is an average ovEr 24 sec, consistingof 240 waveforms. The model was plotted using values of h and XI, and bias [ measured by deconvolution from the data, as described previously and shown at the bottom of Table I. The fit shown in Fig. 5 is reasonably good; the major area of difference between model and data is the plateau, where the actual Seasat echo falls off at a slightly slower rate with time than does the model. The model up to now ignores a number of degradations to both the altimeter return and the system itself. Any or all of these degradations in some combination will cause the measured plateau to droop

-

;

Ol;l

lb

1;

20

5;

do

35

4'0

4'5

$0

45

Range Gate Number

l

75 -

L

: n 0)

50-

c

!.-i

c

a

25 -

N.. 0

6

---

- - - a * -

2

----*-

I

I

I

I

1

1

1

I

I

I

I

5

10

15

20

25

30

35

40

45

50

55

Range Gate Number

FIG.5. Data points from Seasat orbit 280 2 4 s echo average at sample 1 and sample 2 and fit of model,Eq. (1 1). Parameter values used in model for h, A,, and height bias are those derived and listed at bottom of Table I, with nominal design values for pulse and beamwidthsgiven for Seasat.

78

DONALD E. BARRICK AND BELINDA J. LIPA

less than the model representing perfect operation. These degradations include the followingeffects. (1) The actual antenna beamwidth in space could be slightly greater than the nominal value obtained from prelaunch analyses. (2) Very small off-nadir pointing of the antenna boresight will decrease the droop in the plateau. An average pointing error of only 0.3" can explain the observed droop differences, which is not unreasonable to expect. (3) Rain partially filling the altimeter footprint can cause less droop. Both of the latter effects are examined quantitatively in subsequent sections using models for the altimeter return. 4.3. Tracker-Bias Study Using the Semiempirical Model

As mentioned previously, if the Seasat height tracker and AGC circuitry worked perfectly, gate 30.5 would always be centered halfway up the echo waveform leading edge, at the point t = 0 and x = 0. Therefore, gate 30.5 would itself be an accurate measure of the distance between the satellite and the electromagnetic mean sea surface. Our results (Lipa and Barrick, 1981)obtained by deconvolution and presented in Table I-show that this is not the case. Others have also found similar differences (Hayne and Hancock, 1982). There is definitely a bias, or difference, between gate 30.5 and the electromagnetic mean surface. Whether deconvolution is employed (as done here), or a model is fitted directly to the actual waveform [as done by Hayne (1981)], the same bias should appear. Either technique, therefore, will be able to measure and remove this bias. The bias is such that the true electromagnetic mean surface is higher (upward) than gate 30.5. In other words, gate 30.5 is too low, i.e., toward the troughs. Furthermore, this effect is definitely related to sea state, as seen in Table I. It might be suspected that it is also related to wave-height skewness, but since this dependence would be weak, many data sets with accurate independent measurements of skewness would be required to establish this empirically. To understand this important effect, we have simulated the Seasat tradker/AGC theoreticallyto study these dependences in greater detail. If our tracker simulation is correct, it should produce results that agree with observations. Such a simulation could then be used ( 1 ) to analyze parameter dependences, (2) to establish variations in tracker constants, and (3) for decisions and/or design criteria for future altimeter systems. The works of MacArthur (1978) and Townsend (1980) detail a simple tracker principle: a feedback system positions gate 30.5 laterally until its amplitude is equal to the average amplitude of all 60 waveform gates. Since gate 30.5 corresponds to x = 0 in our altimeter model waveform Eq. (1 1), we

79

3. ALTIMETER SEA ECHO

can express this balance condition mathematically as 1 60 Goo,( - f) = - o,(Ax(i - 30.5) - C ) 531=1 where f represents the difference or error (measured upward) in centimeters from gate 30.5. If the tracker performs perfectly, twill be zero. The number 1/53 was chosen by MacArthur (1978) and fixed in the hardware rather than 1/60 in the averaging process to account for the expected waveform droop in the plateau region. We have performed independent numerical checks on whether 53 is the optimal number by using our model waveform Eq. (1 l), and found it indeed to be the closest integer to the true factor. The factor Go accounts for any amplitude bias on gate 30.5. Ideally it should be unity, but we have found from carefully examining many data sets that Go N 0.9614, i.e., gate 30.5 is too high and hence the balance is such that it rides toward the troughs. The balance condition, Eq. (13), is solved numerically, employing the model Eq. (11) with the nominal Seasat constants given there, and Go above; a Newton root finder solves for height bias r a s a function of RMS wave height h and wave-height skewness ,Il. This height-tracker difference is given in Table 111; values in parentheses present as a percentage of RMS wave height h. As can be seen, the relation between height bias and wave height is not linear. Rather, the percentage bias increases with both increasing wave height and height skewness. To compare these percentages with observations from Lipa and Barrick (1981), which are repeated in Table I, we employ the same wave-height and skewness parameters as extracted there, and display the results in Table IV. Table IV shows that observations have a slightly greater bias than do the simulations. The numbers can be made to agree exactly by varying the multiplicative constant Go in Eq. (13), to reflect additional unknown gain factors in the circuitry that may have changed after launch. In fact, forcing agreement between simulated and observed values might be used to estimate this gain. Both simulations and observations show greater percentage errors for increasing wave height and skewness. Therefore the general behavior of the simulator algorithm appears correct.

-

t

t

4.4 Antenna Pointing-Error Effects- Model for Echo Plateau

Brown (1977) derived general expressions for the altimeter echo waveform that included the effects of antenna off-nadir pointing errors. We have independently reconfirmed those results. For the Gaussian representation of

TABLE 111.

SIMULATED VALUES OF MEAN HEIGHT ~ R R E C T I O NDUE TO TRACKER PERFORMANCE AS A OF RMS WAVE HEIGHT (h) AND WAVE-HEIGHT SKEWNESS (11)

FUNCTION

h

(cm)

0

0.05

0.10

0.15

0.20

0.25

0.30

0 25 50 75 100 125 150 175 200 225 250 275 300

1.92 3.17 (12.7)” 5.51 (11.0) 8.19 (10.9) 11.12 (11.1) 14.29 (11.4) 17.73 (11.8) 21.46 (12.3) 25.52 (12.8) 29.96 (13.3) 34.82 (13.9) 40.17 (14.6) 46.09 (15.4)

1.92 3.30 (13.2) 5.88 (1 1.8) 8.82 (11.8) 12.01 (12.0) 15.45 (12.4) 19.17 (128) 23.20 (13.3) 27.58 (13.8) 3236 (14.4) 37.59 (15.0) 43.33 (15.8) 49.66 (16.6)

1.92 3.44 (13.8) 6.26 (12.5) 9.44 (12.6) 12.89 (12.9) 16.60 (13.3) 20.60 (13.7) 24.93 (14.2) 29.63 (14.8) 34.74 (15.4) 40.33 (16.1) 46.46 (16.9) 53.21 (17.7)

192 3.57 (14.3) 6.64 (13.3) 10.07 (13.4) 13.77 (13.8) 17.74 (14.2) 2203 (14.7) 26.65 (15.2) 31.66 (15.8) 37.11 (16.5) 43.05 (17.2) 49.56 (18.0) 56.71 (18.9)

1.92 3.70 (14.8) 7.01 (14.0) 10.69 (14.3) 14.64 (14.6) 18.88 (15.1) 23.44 (15.6) 28.36 (16.2) 33.68 (16.8) 39.45 (17.5) 45.74 (18.3) 52.63 (19.1) 60.18 (20.1)

1.92 3.83 (15.3) 7.38 (14.8) 11.30 (15.1) 15.50 (15.5) 20.01 (16.0) 24.84 (16.6) 30.04 (17.2) 35.67 (17.8) 41.77 (18.6) 48.41 (19.4) 55.66 (20.2) 63.61 (21.0)

1.92 3.96 (15.8) 7.75 (15.5) 11.91 (15.9) 16.36 (16.4) 21.12 (16.9) 26.23 (17.5) 31.72 (18.1) 37.65 (18.8) 44.07 (19.6) 51.05 (20.4) 58.66 (21.3) 66.99 (22.3)

Values in parentheses are as a percentage of R M S wave height h.

81

3. ALTIMETER SEA ECHO

TABLE Iv. COMPARISON OF OBSERVED TRACKER HEIGHT BIASES (AS A PERCENTAGE OF SIGNIFICANT WAVEHEIGHT) WITH THOSE OBTAINED FROM SIMULATIONS USING

MODELS AND EQ. ( 13)” Sample no. 1 2 3 4

RMS wave height

(4 59.7 167 108 64.2

Observations (%) Simulations First moment

Model fit

(%I

18.1 24.9 15.7 17.3

19.1 28.3 15.7 17.6

15.3 17.5 13.0 15.7

a Nominal Seasat constants were employed in the models, along with retrieved sea surface parameters for the four samples of orbit 280 over Hurricane Fico.

the antenna beam pattern very near boresight, we previously used G(u) = 1, which is valid (1) when no pointing error is present, and (2) on the echo leading edge only. A general expression for G(u)that includes angular pointing error p from nadir is obtained by integrating out the azimuthal dependence of the actual antenna pattern around a circular range cell on the surface to obtain V(u)G(u)= e - a z / ~ b - Y I U b l g [ 2 ( P / ~ b ) ~for ] u >0

(14)

where l,(z) is the zero-order modified Bessel function of the first kind with argument 2. Here, we will employ the Gaussian pulse-shape model and Gaussian height probability density (neglecting skewness) in the general echo-waveform double integral, Eq. (4a), to study specifically and separately the effect of pointing error p. Therefore, substituting Eq. (14) into Eq. (4a), along with Gaussian models for the other two functions,and integrating out <,we obtain

(15)

where all of the parameters appearing here have been defined following Eqs. (1) and (8). Unfortunately, Eq. (15) is not integrable in closed form. Therefore, we have integrated it numerically, normalizing the expression by dividing by (211)3/2H”aob, (such that the plateau becomes unity when p + 0 and J / b + m). Results for RMS wave height, h = 100 cm, are shown in Fig. 6 for values of antenna pointing error between 0 and lo, in steps of 0.25’. This is done for the Seasat system, with 60 waveform gates spaced 3.125 nsec (46.875 cm) apart in time (space). In addition, nominal Seasat values for t , and z used previously are employed here; a value for zbthat is larger than Seasat’s by f l was used

82

DONALD E. BARRICK AND BELINDA J. LIPA 1.00

,

I

I

I

I

I

I

I

1

I

1

I

I

Range Gate Number

FIG.6. Model Seasat altimeterecho using Eq.(15), showing effect of antenna pointing error p (in degrees) away from nadir. Nominal Seasat altimeter constants have been used, along with RMS ocean wave height h = 100 cm.

for this study. (It is highly unlikely that pointing errors exceeding 1” could occur without track being lost.) Two effects are evident. As pointing error increases, the overall echo level decreases. This results primarily from the exp( -f12/$t)factor multiplying the integral. The obvious impact of this error will be to give a false value for 6,as the AGC compensates for this drop. The second effect is the change of the normal plateau “droop” to a “rise.” This occurs because of the I, factor in the integrand, and is explained physically by the fact the largest power level no longer intercepts the earth at nadir (i.e., zero time), but later as the beam points off nadir. Both of these effects, in addition to producing false values of Q,, will give erroneous mean surface-height values because of combined AGC and height-tracker responses. Fedor has shown that this height error can be several tens of centimeters for a pointing error of only a degree. Therefore, when tilt occurs, it must be identified and taken into account if the parameters obtained from the altimeter are to be meaningful. A simple way to do this follows. From Fig. 6, it is apparent that-for the 60 waveform gates retained by the Seasat altimeter-the plateau region is very nearly linear. Furthermore, the inclusion of pointing error does not make this region less linear; it merely changes the slope of the plateau. This has led us to derive a very simple, closed-form expression for the plateau. Interpretation of the plateau in terms of this expression can then be used to estimate pointing-angle

3. ALTIMETER SEA ECHO

83

error by measuring the slope. Then with the straightforward double FFT deconvolution process discussed subsequently, all pointing errors can be easily and quickly removed. We define the plateau as the region for which x > 20,. The integral in Eq. (16) can be solved by the saddle-point method: it is determined by the shape of exp[ -(u - ~ ) ~ / 2 afor ; ] u z x. Since x is significantly greater than zero in the plateau, the lower integration limit may be taken to be - 03. Then we do a saddle-point expansion of the exponential argument and integrate, approximating I , by its value at the saddle. Then, normalizing by ( ~ ~ C ) ~ / ~ H ”we C Tobtain ,(T~,

Since the plateau for Seasat has t << t, (e.g., t = 89 nsec for the final gate, gate

60,while t , N 425 nsec), we can express the exponential in 2 t / t , by its first two terms. Furthermore, the modified Bessel function argument is also small in the plateau as long as pointing error pis less than, say, 3$b; hence it also can be represented by the first two terms of its series expansion. We then obtain the following linear form for the Seasat plateau: l (2t/t,)(1 - p Z / $ : ) ] o,,(t) = e - p z ’ ~ i [ -

(17)

It can be seen from Eq.(17) that the slope of the plateau increases with p/$b, coming positive when p / $ b > 1 . A very straightforward method of measuring pointing error is obvious from Eq. (17). One simply isolates the plateau region (there are always at least 20 gates that define the plateau) and fits a linear regression line to it. The slope of this line is then a direct measure of pointingangle error p. This method avoids fitting the complicated nonlinear Bessel function convolution to the leading edge (Hayne, 1981).

4.5. Rain Eflects on Altimeter Echo Anomalously high winds obtained from unusually low values of CT on the altimeter echo plateau when Seasat passed over storms has led to examination of rain effects on the echo. A uniform rainfall over the entire altimeter footprint (which has a maximum diameter of 10 km corresponding to the last range gate on Seasat)can in principle produce at least three physical effects: (1) attenuation of the signal as it passes through the rain, (2) change in signal phase-path distance due to the slight modification of the refractive index of the rain region, and (3) direct backscatter from the raindrops themselves that fill regions of the altimeter range cells above the sea surface. The first effect will reduce the absolute level of the entire altimeter echo strength, includingthe plateau. If ignored, a lower value of CT, will be deduced, and hence an erroneously high value of wind speed. In Chapter 10 of this

84

DONALD E. BARRICK AND BELINDA J. LIPA

volume, Fedor and Griffith show how this attenuation can be estimated and removed using data from the Seasat Visible and InfraRed Radiometer (VIRR). The second effect-if uncorrected-produces an error in altimeter-derived mean sea level; the VIRR rain estimates are also used subsequently to remove this height bias. When the rainfall is uniform across the cell, however, these two effects do not change the shape of the altimeter echo, only its amplitude and time position. The third effect,namely the rain echo itself, can be shown to be sufficiently small as to be negligible for the short-pulse altimeters flown on Seasat and GEOS; within the cell subtended by Seasat, for example, rain echo is four orders of magnitude lower than sea surface echo, even in a heavy rainfall. Hence, direct raindrop echoes cannot compete with sea echo.3 Examination of rainstorm geometries and their statistics reveals, however, that rain often will not uniformly fill a horizontal cell equivalent to that seen by Seasat (at gate 60, the footprint diameter is -9 km). The more intense the rainfall, the smaller the rain cell on the average (Walsh, 1981). Hence, using models, we examine here the question of whether a typical-size rain cell at an arbitrary location with respect to satellite nadir (either fully or partially filling the altimeter footprint) will produce distortion of the echo because of increased attenuation within the rain region. Referring to Fig. 7, we assume a circularly cylindrical rain cell of height H,,half-power diameter d, and displacement xo from the satellite nadir. Furthermore, for ease of calculation we assume that the rain density-and hence attenuation-falls off in a Gaussian fashion from the ceI1 center at xo. Neglecting height skewness here, we then arrive at the following expression for the (normalized) sea surface radar echo as modified by rain attenuation:

where

u; =

ub

1

+2 U b H / 9

and r =d / 2 m

’

Goldhirsh and Walsh (1982) propose a modification to the Seasat design that would purposely measure rain echo above the sea with a future altimeter; however, the rain cell is considerablylarger so as to increase the total rain echo.

85

3. ALTIMETER SEA ECHO Satellite Altimeter I

Rain

\

\

\

I

-9km FIG.7. Geometry for rainstorm model used, which partially fills the Seasat altimeterfootprint; the maximum footprint diameter for Seasat is -9 km.

Here, k, is the one-way signal attenuation rate at 13.5 GHz (in decibels per kilometer) due to rain and H,is the height of the rain cell. The first term of Eq. (18) is the idealized altimeter sea echo in the absence of rain; the second term therefore represents the correction accounting for rain attenuation. We employ the above model to calculate numerically altimeter echo curves, assuming an RMS wave height of 100 cm. The echo is plotted as a function of Seasat range gate, with the same altimeter model parameters used for Fig. 6. For the rain;we use the Marshall-Palmer relationship for k , = aRb (dB km-I), where a = 2.038 x b = 1.023, and R is rainfall rate in mm hr-' (Goldhirsh and Walsh, 1982). Walsh (1981) employs rainstorm statistics reported by others to establish an inverse relationship between rainfall rate R and storm cell size d; for example, at R = 5 mm hr-' (relatively light rain), the average cell diameter is d = 36 km; for R = 10 mm hr-*, d = 23 km; for R = 20 mm hr-' (heavy storm), d = 13 km. Using these values, we plot normalized altimeter echoes in Fig. 8 with Seasat values for U b (corresponding to t, = 650 nsec) and H (800 km), with RMS wave height h = 100 cm. We take H,,the effective height of the rain cell column, to be .5 km. The curves of Fig. 8 show interesting effects and explain some of the strange echo shapes seen by Seasat when it occasionally passed over identified rain

86

DONALD E. BARRICK AND BELINDA J. LIPA

cells. As expected, the severest attenuation and plateau distortion occur when xo = 0, i.e., when the storm is centered directly beneath the satellite. For light rainfall (Fig. 8a), the attenuation is small; in addition there is almost no distortion (change in slope) to the plateau. The latter is true because with the cell half-power width of 36 km, the 9-km Seasat footprint (out to gate 60) is essentially uniformly filled with rain, no matter where the storm center is located. On the other hand, when rain is heavy (Fig. 8c), plateau distortion is severe when xo = 0 (i.e., the storm is centered on nadir) because the storm cell width of only 13 km causes an added echo-signal taper across the 10-km footprint. With the rain being severe at nadir, the echo is attenuated greatest on the leading edge, while the echo attenuation becomes less near gate 60 where the annular altimeter cell is out at 9 km. Even in moderate or light rain, the echo distortion will produce three errors or biases: (1) n oon the plateau will be lower, and hence wind speed derived therefrom will be overestimated; and (2) mean sea surface height will be in error by as much as tens of centimeters, depending on how the on-board or post facto algorithms respond to the distorted waveforms. Significant wave height will not be appreciably biased by the presence of rain, however. Although further study and analysis of these and other rain models could be attempted [for example, we could derive a simplified, closed-form expression from Eq. (18) for the line describing the plateau, as we did in Eqs. (16)and (17) for pointing-error effects], such efforts appear rather pointless. This is because the rain models above (and other models) contain too many parameters (unlike pointing error, which has only one parameter). Hence, a confident, unique determination of these parameters and separation from other possible effects (e.g., actual change in om pointing error) is impossible without additional outside information. Therefore, there is considerable support for both (1) an altimeter modification that would create a special “rain cell” at some distance above the surface to identify rain and parameterize it as much as possible (Goldhirsh and Walsh, 1982);and (2) using other instruments such as infrared and/or microwave radiometers to identify and quantify rain effects (Fedor and Griffith, Chapter 10, this volume).

-

5. ELECTROMAGNETIC BIAS

Over a decade ago, very short-pulse altimetric measurements of the ocean surface from a tower (Yaplee et al., 1971) uncovered a very interesting phenomenon. When the altimeter beam is so narrow that it essentially profiles the longer waves in height (like a laser), the centroid of the surface echo power is lower in position than the mean sea level as determined by the time-gated echo returns. Said another way, ocean waves are stronger reflectors near their troughs than near their crests. This is not surprising, for the specular-point

1

1'0°

0.75

B 0

a iO.50 m

E

2

0.25

@O

5

10

15

20

25 30 35 40 Range Gate Number

45

50

55

60

20

25 30 35 40 Range Gale Number

45

50

55

60

R = lOmm hr''

0 75

B

b

E b

0 25

'0

l b 5

10

15

1.00

I

0.75 -

t

R

= 20mm h r - ' d = 13km

n. U

-

-,950

E B

0.25

-

@O

5

10

15

20

25 30 35 40 Range Gate Number

45

50

55

60

FIG.8. Model Seasat altimeter echo using Eq. (18), showing effect of rain in distorting the waveform. Here, xo is the distance of the rain cell center from nadir, d is the cell diameter [which is related to rainfall rate R by Walsh (198l)l. Nominal Seasat parameter values are used in the model, along with an RMS wave height h = 100 cm. The three plots represent (a) light, (b) medium, and (c) heavy rainfall.

88

DONALD E. BARRICK AND BELINDA J. LIPA

result for the average backscatter cross section (per unit area) of a rough surface at normal incidence-as given in Eq. (9)-is inversely proportional to surfaceslopes. Since it is readily observed that ocean waves are pointier at the crests, this inverse relationship between slopes and echo strength confirms that the crest regions should be poorer reflectors than the trough regions. Water waves exhibit this unsymmetrical appearance about the vertical because of the slightly nonlinear hydrodynamic boundary conditions at the air-water interface. For a typical satellite altimeter (such as Seasat or GEOS), this means that the recovered “mean” sea level extracted from the echo will not coincide with the true mean sea level at that point (the latter defined as the surface within the footprint if all waves came to rest). This altimetric shift downward from the desired, true position has been termed “electromagnetic” (EM) bias. Unlike some of the biases considered in the preceding section, EM bias cannot be easily removed from the apparent height measurement. Although the nonlinear boundary conditions at the sea surface are known exactly, the mathematical methods for solving them have heretofore been too intractable to permit an adequate, quantitative, theoretical investigation of this bias. Therefore, a number of experimental investigations have been undertaken from aircraft over the past years to quantify this effect empirically (Walsh et al., 1983; Choy et al., 1983). We examine here the theoretical expression-and source-for EM bias in the altimeter echo. Although we have not yet been able to solve the theoretical expression exactly, we obtain bounds for this bias using models in the expression. We interpret these EM bias bounds and compare them with the experimentally observed values cited above. Longuet-Higgins (1963) showed that a generalization of the GramCharlier extension of the Gaussian joint probability density is adequate when the random variables appearing therein are weakly correlated. This generalization can be applied to the joint height-slope probability density function p j ( ( ) = p(C, c,, C,) at C, = C, = 0, that appears in the altimeter echo of Eqs. (1) and (4), by the simple addition of another term to Eq. (7),giving

x exp( - C2/2h2) (19) where for a two-dimensionally rough surface, 1, is defined in terms of moments as

with the moments plnn defined as p,,,,,, = ([‘{FC;).

3. ALTIMETER SEA ECHO

89

For example, the second moments take the following familiar forms:

= s:

P200

= (C2> = h2

Po20

Po02

= (C;)

Poll = < 5 x L y ) = SxSyPxy

= s,”

=

where we have taken our local coordinate system such that all first moments are zero, i.e.,

If we restrict ourselves to a one-dimensional (collinear) sea with waves propagating in the x direction, the expression Eq. (19)for the Gram-Charlier model is modified by changing the constant before the braces to 1/2xhs,, and we have 2, = ~ 1 2 0 / ( ~ ~ ~ ~=~( c0c 2; ) 0/ ( )h s ; ) , a result derived by LonguetHiggins (1963)and Jackson (1979). We see in this much simpler, collinear form for A, that it is directly proportional to ((C;), the correlation between height and the square of the slope. This quantity is obviously nonzero and positive for a surface like the sea that is pointier near the crests (5 > 0),where the meansquare slopes will be greater. Therefore, the term -(A2/2)(C/h) appearing in Eq. (19) represents a bias downward by an amount

in terms of RMS or signifidant wave height. It has been observed in many of the previously cited experimental investigations that EM bias is definitely proportional to wave height, and those investigators present their results as a percentage of significant wave height H1,3 . To pursue the theoretical expressions and their interpretation a bit further, we employ perturbation theory to find expressions for the third moments required in A,, as done in Longuet-Higgins(1963),Weber and Barrick (1977), and Barrick and Weber (1977): c”

cc

90

DONALD E. BARRICK AND BELINDA J. LIPA

PP

PP

where S(ki) is the wave-height directional spectrum at wavenumber & = ki$ + kiyy^. The “coupling coefficient” A is obtained from the perturbational solution to the nonlinear surface boundary conditions for gravity waves, and correcting a factor of 2 in the Longuet-Higgins(1963)and Jackson (1979) expressions, is given by Weber and Barrick (1977):

where

and g is the acceleration of gravity. Second moments needed are easier:

As long as one is willing to specify the form of a model for the wave-height directional spectrum S(E), the preceding expressions show that in principle the integrals of Eqs. (22) and (25) should be solvable at least numerically and

3. ALTIMETER SEA ECHO

91

should produce results for EM bias, as represented by I,. Obviously the factor A, depends in a very complicated way on the nature of the directional spectrum and its parameters, and hence EM bias would be expected to be a function of more variables than merely significant wave height. Jackson (1979) solved for A , and A2 in closed form for the simpler case of collinear wave fields (5, = 0), but obtained values for EM bias that were considerably larger than those observed experimentally. This is now known to be due to the breakdown of the perturbation theory basis for Eq. (22) at large wavenumbers, k , and k,. This breakdown manifestsitself in the fact that A given in Eq. (23) “saturates,” i.e., no longer continues to increase in proportion to k , and/or k , beyond a certain point. This saturation effect is only important in Eqs. (22b)-(22d) because the overall integral is quite sensitive to short waves, i.e., large k , and k,. It would be ideal to have an expression for A that accounts for this “saturation effect” but such an expression has not yet been calculated. The point at which perturbation theory breaks down and A saturates occurs when the perturbational parameters k,h and k2h are near unity. Since A no longer increases beyond that point, an approximation that can give bounds to EM bias is obtained here by truncating the integrals in Eqs. (22b)-(22d) at upper limits defined by kl,, kzU = f / h , where f is near unity. We employ a JONSWAP (Joint North Sea Wave Project) model for the wave spectrum S(k) (Hasselmann et al., 1973,1976,1980),which is presently believed to provide the best parametric representation of waves versus wind, fetch, and duration; empirically “tuned,” this model incorporates features predicted by nonlinear wave-wave energy transfer. It is adapted from the above references and transformed to wavenumber variables in the appendix to this chapter. Whereas the third moments defined in Eqs. (22b)-(22d) have their upper limits defined as described above to approximate the saturation effect, the integrals for the second moments defined in Eq. (25) are truncated in another way. Again, only Eqs. (25b)-(25d) are sensitive to the choice for the upper limit because the appearance of wavenumber squared in the integrand enhances the effect of the smaller scale roughness (at larger wavenumbers) in contributing to the slopes. Truncation at some point is necessary, for if we assume the usual equilibrium-range spectral behavior (i.e., f - 5 frequency dependence) used in Phillips, Pierson-Moskowitz, and JONSWAP models, these slope integrals diverge as the upper limit goes to infinity. On physical grounds, therefore, we take the upper limit to be k, N k0/20, where k , is the radar wavenumber. For sea surface gravity waves, it is approximately at this point beyond which (in wavenumber) specular points no longer contribute to the scattering process. For the f - 5 equilibrium-range wave spectral behavior, however, this exact upper-limit value is not that critical; for example, the use of ko/10 would have produced a change in the second-moment (mean-square slope) integrals of

92

-

DONALD E. BARRICK AND BELINDA J. LIPA

14%,or 0.5 dB. We therefore will employ k , = k,/20 N 30 m-l as an upper limit in ensuing examples, corresponding to the Seasat altimeter operating frequency of 13.9 GHz. Using the JONSWAP wind-wave spectral model adapted in the appendix to this chapter and the upper limits defined for Eqs. (22) and (25), we numerically evalute these integrals, understanding that they are only approximations. (More exact evaluations await the derivation of a correct theory explaining saturation.) If we evaluate I , defined in Eq. (20), then EM bias given by Eq. (21)in terms of Az bears an explicit direct proportionality to R M S or significant wave height. We find, however, that %z evaluated in the above approximate manner still retains a weak, inverse power-law dependence on significant wave height, independent of the JONSWAP spectral development parameter v (the latter is taken to be -0.14 for fully developed seas, increasing to possibly 0.3 for newly arising or fetch-limited seas). A regression, power-law fit to the numerical results for f = 1 gives

A2 I+I 0.25H;$20 (26) Individual scatter in the theoretical model results for Az from the above bestfit model does not exceed k0.06 when significant wave height is greater than 0.2 m. A change of &50% in the upper integration limit on third moments-as represented by the factor f-results in corresponding changes of & 20% in Az. Recent experimental determinations of EM bias have been undertaken using a contouring radar from an aircraft at 36 GHz (Walsh et al., 1983), and using a microwave altimeter from an aircraft at 10 GHz (Choy et al., 1983). Walsh’s results at the higher frequency show an EM bias of - 1.1% of significantwave height, with a scatter between -0.5 and -2.0%;no obvious trend of EM bias (expressed as a percentage of significant wave +eight) versus significant wave height is seen from Walsh’s data. Choy, on the other hand, finds an EM bias of -3.5% of significant wave height.at the lower radar frequency, with a scatter between -2.0 and -5.0%. The significant difference versus radar frequency is surprising and as yet unexplained quantitatively. Using the crudely estimated theoretical expression for A, of Eq. (26) in Eq. (21), we find at 13.9 GHz that EM bias varies between -3.0 and -2.0% for significant wave heights between 1.0 and 5.0 m, respectively. With k20% uncertainty (at least) because of the upper-limit approximation, our values fall between those of Walsh and Choy. The scatter in the measurements, as well as the approximations employed in the theoretical predictions, preclude serious attempts at more detailed quantitative comparisons. The reason for the considerable experimental investigations of EM biasand also more exact theoretical studies-is to understand this effect by noting

3. ALTIMETER SEA ECHO

93

which surface parameters (in addition to significant wave height) cause variation in ,I2. The above experimental studies attempt to correlate E M bias to such things as surface wind speed, wave-height skewness, dominant wave period, and wave-height kurtosis. The correlations obtained by the two investigators appear in some cases to be at odds with each other. The number of data points employed in the analyses is too meager to pinpoint the source of these correlation differences. Needless to say, additional studies (both theoretical and experimental)will be required to quantize the dependences of EM bias more accurately. In summary, both the experimental and theoretical results show that-at Seasat operating frequencies-EM bias can be - 15 cm at 5 m significant wave height, with actual variations about this mean value between - 10 and -25 cm (at the same wave height). Such a bias, and particularly its uncertainty, lies well beyond the desired accuracy limits for satellite altimetric sea surface height measurement applications. The present inability to remove it from the data (in contrast, for example, with tracker bias) further encourages attempts to correlate EM bias to other sea surface parameters that can be measured by independent satellite techniques, so that this source of considerable error can be eliminated and/or reduced. 6. A GENERAL, IMPROVED DECONVOLUTION ALGORITHM

Lipa and Barrick (1981) developed and demonstrated a simple, efficient algorithm that can be applied to the echo leading edge involving a single deconvolution: we summarized that investigation previously. That method will not work for the plateau region, however. More important, when antenna pointing-error effects are present, that method will fail for both the plateau and leading edge. Therefore, we introduce here a general double-deconvolutional algorithm that can be used when pointing error contaminates the echo. The algorithm proposed is efficient,in that it does not attempt to determine six parameters all at once from a least-squares model fit to the entire echo waveform. It identifies and analyzes separately the three easily recognizable portions of the sea echo: (1) the pre-leading-edge noise level No, (2) the leading edge, and (3) the plateau. From the plateau, we do a linear regression fit based on Eq. (17) to determine the pointing error 8, if any. Then we know two of the three quantities occurring in the double convolution on the right side of Eq. (4): (1) the pulse shape P(u), obtained from the internal calibration mode, and (2) the antenna gain factor G(u)U(u), with G(u) given by Eq. (14) when pointing error is present. The unknown but desired quantity is the third factor in the double convolution: the surface height-slope probability density pj(().

94

DONALD E. BARRICK AND BELINDA J. LIPA

The latter is obtained by divisions of the Fourier transforms of known quantities. Define F(P(u))= Q(q) =

P(u)e-iq"du

(27)

with ~(0Ax)) Ss(q)

F(G(u)U(u)) H ( v )

F(pj(C>) qj(q)

(28)

being Fourier transforms of the other quantities defined similarly. Then, because of Eq. (4),the Fourier transform of the desired quantity, qj(q), is given by qj(q) = Ss(-q)/Q(qW(q)

(29)

Then the desired quantity is obtained by an inverse FFT, i.e.,

1

m

p i ( [ ) = F-'(qj(q)) = (1/2a)

qj(q)eic"q

(30)

-m

It is important to note that it is necessary to actually do FFTs only twice: once on a&) and once again on qj(q). The Fourier transform of P(u), namely Q(q), is done once for the pulse obtained from the calibrate mode and stored as a table. (The pulse shape does not appear to change over many orbits.) The Fourier transform of the antenna gain factor is known in closed form. Substituting Eq. (14) into Eq. (27),we find from integral tables that

As a check on Eq. (31), note that as ub+ 00 (i.e., the antenna becomes omnidirectional in its gain pattern), we obtain H ( q ) -+ l / i q , which is the Fourier transform for the unit step function. This is as it should be, as one sees from Eq. (14), where the unit step is all that is left in this limit. In other words, if the beamwidth is large enough, pointing error obviously does not matter. Therefore, a general, double-deconvolutional algorithm incorporating these effects and producing real-time uncertainties in desired output parameters is summarized here, based on the Seasat altimeter: 1. Renormalize each waveform (every 0.1 sec) to correct occasional high and low waveforms caused by AGC malfunction. This is done by dividing each waveform by the average energy in gates 45-60. 2. Average a desired, predetermined number of waveforms together. 3. Multiply all range-gate amplitudes by predetermined gain bias corrections.

3. ALTIMETER SEA ECHO

95

4. Identify and separate the three characteristic portions of the waveform: pre-leading-edge noise, the leading edge, and the plateau. Store the gate positions representing the beginnings and ends of these three segments. This separation is easily done by noting the maximum slope for the three middle gates, and using a simple predetermined criterion for these gate positions based on this slope. 5. Fit a straight, horizontal line to the pre-leading-edge portion. Determine the mean and standard deviation of this constant. 6. Subtract the constant determined in (5) from each gate amplitude constituting the remaining two waveform regions. 7. Fit a linear regression line to the plateau segment. Determine from Eq. (17) the amplitude constant for the plateau, the pointing-angle error fl from the slope, and the standard deviations in these quantities. 8. Use the constant and /3 to do a first correction to the o0 value obtained from the AGC gain. Rain corrections, if applicable and desired, can then be applied to no. 9. Deconvolve the leading-edge waveform segment using Eqs. (27)-(31) to obtain the joint height-slope probability density for the sea. Lookup tables of the Fourier transform of the pulse (obtained from the internal calibration mode) are divided into the Fourier transform of the leading edge. Likewise, the Fourier transform of the antenna beam factor including the tilt /3 is simply calculated from Eq. (31) and divided into the leading-edge Fourier transform also. Then the inverse FFT of this quantity [i.e., Eq. (3011 gives the desired probability density. 10. Renormalize the probability density so that the area under it is unity; this is simply done by dividing the probability density at each gate position by the sum over all gate positions. 11. Using matrix covariance techniques employed in Lipa and Barrick (1981),determine uncertainties in the probability density at each gate position. 12. Fit the three-parameter model of Eq. (6) (linear in two parameters: height skewness and height bias) to the recovered probability density. Do a one-dimensional grid search to determine wave height. These techniques are tested and described in Section 3. 13. Use linear error propagation theory and covariance matrix methods (as done in Lipa and Barrick, 1981) to determine the statistical uncertainties in waveheight, skewness, and height bias. 14. Determine nadir wind speed from co using the best, empirically supported model function available at the time. This method has the following advantages over versions that attempt to fit a model with six parameters to the entire leading edge by least-squares methods (Hayne, 1981):

96

DONALD E. BARRICK AND BELINDA J. LIPA

1. It provides the completejoint height probability density function for the sea surface, rather than a two-parameter description of it. This additional information will prove useful for future research and applications involving ocean surface processes. If the same two parameters are desired, they have been shown in Lipa and Barrick (1981) to be easily obtainable from this probability density function. 2. Because of the natural segmentation of the waveform into its three constituent regions, our method is much more efficient in terms of computer operations and time. 3. The method here, not involving multiparameter grid searches and/or matrix inversions, is’stable. If one attempts to get around a time-consuming, multidimensional grid search, the usual procedures are (a) to linearize the model about initial guesses for the several parameters and then solve the leastsquares problem by matrix inversion; (b) to “home in” on the minimum in an iterative fashion, starting with an initial guess and using a variation of a rootfinding scheme. One hopes these will converge to the solution. When noise is present, however, a poor initial guess can cause either no convergence, or worse yet, convergence to parameters that represent the least-squares solution for a local minimum rather than the global minimum. The present algorithm involves no initial guess or repeated iterations, and hence cannot be unstable. 4. The present approach uses standard matrix covariance and linear error propagation techniques to output statistical uncertainties (or confidence limits)for all derived quantities continuously; no other existing algorithm does this.

7. CONCLUSIONS Much is ,known about the interaction of the altimeter pulse with the ocean surface. Solutions based on specular-point scatter theory show that the echo waveform is a double convolution. This special form of a double-integral equation has been inverted-or deconvolved-using FFT methods, yielding the surface wave-height and slope probability density in a very efficient algorithm. A straightforward least-squares model fit to this probability density (nonlinear in only one parameter) then yields wave height, mean surface position, and wave-height skewness. Uncertainties in these three parameters are routinely provided by the algorithm. Tested with Seasat data for a pass over Hurricane Fico (orbit 280), these uncertainties for a 160-km section of path data are 0.7, 4, and 15% (RMS)of the mean values, respectively. Models for the sea surface probability density are used to study and interpret other biases quantitatively. For example, the Seasat height tracker

3. ALTIMETER SEA ECHO

97

outputs a mean height position that is biased downward by an amount between 3.5 and 6.5% of significant wave height. This bias is sea-state dependent, but can be removed by an algorithm that fits a model to the inverted echo waveform. Both antenna beam pointing error and moderate rain within the radar cell can distort the echo severely, and if uncorrected will produce values for height and wind speed (deduced from the plateau backscatter cross section, a,) that are grossly in error. Pointing error is easily removed when it occurs by a double-deconvolutional algorithm described here; its magnitude is first measured by either (1) using an independent attitude sensor or (2) measuring the slope of the echo plateau. Rain distortion is not easily removed. The best hope appears to be to identify those situations when rain is present (either from independent measurements or from the distorted altimeter signal itself) and throw out those samples whose distortion exceeds a certain amount. Electromagnetic bias is a height error not easily removed. Although it varies with sea state, it is seen to depend significantly on other factors also. Quantitative estimates of these dependences from both theoretical and experimental investigations are as yet incomplete. Since altimeter-measured surface heights can be in error by as much as 15-25 cm because of EM bias, further investigations are necessary if accurate sea surface topography is to be realized from future altimeters. We present a brief outline of an efficient, alternative algorithm for the altimeter echo, different from those presently being employed. It incorporates most of the techniques studied in this manuscript for interpreting biases and the echo waveform statis‘tics. Furthermore, it can provide uncertainties in all of the extracted parameters-along with the parameters-so that the user can decide how or whether to apply each geophysical data record. APPENDIX A definitive series of experiments done over a decade ago resulted in the synthesis of a new model-called the JONSWAP spectrum-for the waveheight directional spectrum that supports the theoretical concepts of nonlinear energy transfer to the longer ocean waves developed by Hasselmann in the early 1960s. The first studies (Hasselmann et al., 1973, 1976)developed a parametric representation for the nondirectional temporal wave-height spectrum. The term “parametric” refers to the fact that this spectrum has the same shape regardless of the physical conditions producing it. For the user, only two parameters are needed to produce the final, absolute spectrum: the wind velocity ii = (u,0,) and the development factor v. The latter is shown experimentally to be a function of fetch (i.e., the distance over which the wind

98

DONALD E. BARRICK AND BELINDA J. LIPA

has blown at a more or less constant velocity),and is known to depend on the duration (i.e., the time during which the wind has blown at a constant velocity). For “fully developed” seas (where the fetch and duration are very large), v z 0.14, whereas it increases to 0.25-0.4 for seas that are less than fully developed. Recently the nondirectional spectrum has been extended (Hasselmannet al., 1980), based on the same measurements, to include a directional factor, resulting in a complete model for the wave-height directional spectrum. The directional factor is parametric also, being a function of the same two parameters as the nondirectional factor. The spectrum peaks azimuthally, of course, along the wind direction 6,; it is narrowest in angular spread at frequencies or wavenumbers near the most energetic waves, and tends to isotropic in angle toward the shorter waves. Here, we put both the nondirectional and directional factors together, and convert to wavenumber k [k = (k, O)] rather than wave frequency f. The resulting model is then defined as S A ~= ) W, 6 ) = f ( k ) g ( k ,e)

(All

normalized such that

j:

h Z = c ( =~ ~k dk~

:j

do $(k, 6) = -z

f ( k ) k dk

where

j;Kg(k,6)d6= 1 The nondirectional spectral factor is given by

where rJ={

cra = 0.07

c,,= 0.09

for k I k , for k 2 k ,

y = 3.3 k,

= ( 2 ~ v ) ~ g / u=’ position of

spectrum maximum

a =0 . 0 3 2 5 ~ ~ ‘ ~

h Z N (u4/g2)(5.2 x 10-6v-10/3)= mean-square wave height u = wind speed (m sec- ’)

g

= 9.806 m sec-’ = gravitational constant

(A21

3. ALTIMETER SEA ECHO

99

This factor peaks at wavenumber k,, and has the same equilibrium-range k-4 dependence as the Phillips and Pierson-Moskowitz models (corresponding to an f-’frequency dependence). The JONSWAP spectrum has a sharper peak than previous models, as represented by the second term in the exponential argument. By setting that second term to zero and letting a = 0.0081, one recovers the Pierson-Moskowitz (1964) model for fully developed seas that has been accepted over the previous decade. The directional factor is given by

s(k@

= C~/A(s)lIcosC(~ - ~,)/21IS

(A5)

where

s = 20(k/k,)”

for k > l.lk,,,

and = -2.33 - 1 . 4 5 ( 2 ~-~ 1.17)

while 2

s = 14(&)

for k c l.lk,,,

REFERENCES Barrick, D.E. (1968). Rough surface scattering based on the specular point theory, IEEE Trans. Antennas Propag. AP-16,449-454. Barrick, D.E. (1972a). Remote sensing of sea state by radar. In “Remote Sensing of the Troposphere” (V.E. Derr, ed.), Ch. 12. U. S. Govt. Printing Office, Washington, D.C. Barrick, D. E. (1972b). Determination of mean surface position and sea state from the radar return of a short-pulse satellite altimeter. In “Sea Surface Topography from Space” (J. R. Apel, ed.), Vol. I, Ch. 16. U.S.Govt. Printing Office, Washington, D.C. Barrick, D. E., and Weber, B.L. (1977). On the nonlinear theory for gravity waves on the ocean’s surface. Part 11: Interpretation and applications. J. Phys. Oceanogr. 7 , 11-21. Brandt, S.(1976). “Statistical and Computational Methods in Data Analysis.” North- Holland, Publ., Amsterdam. Brown, G. S. (1977). The average impulse response of a rough surface and its applications. IEEE Trans. Antennas Propag. AP-25,67-74. Brown, G. S. (1979). Estimation of surface wind speeds using satellite-borne radar measurements at normal incidence. J . Geophys. Res. 84(B8),3974-3978. Choy, L. W., Hammond, D. L., and Uliana, E. A. (1984). Electromagnetic bias of 10 GHz radar altimetric measurements of MSL. J . Mar. Geod. 8,296-312. Ewing, G. C., ed. (1965). Oceanography from space. Woods Hole Oceanographic Institute, Ref. NO. 65-10.

100

DONALD E. BARRICK AND BELINDA J. LIPA

Fedor, L. S.,and Barrick, D. E. (1978). Measurement of ocean wave heights with a satellite radar altimeter, EOS 59,843-847. Fedor, L. S., and Brown, G. S. (1982). Waveheight and windspeed measurements from the SEASAT radar altimeter. J. Geophys. Res. 87(C5), 3254-3260. Goldhirsh, J., and Walsh, E. J. (1982). Rain measurements from space using a modified SEASAT type radar altimeter. I E E E Trans. Antennas Propag. AP-30,726-733. Hasselmann, K., Barnett, T. P., Bouws, E., Carlson, H., Cartwright, D. E., Enke, K., Ewing, J. A., Gienapp, H., Hasselmann, D. E., Kruseman, P., Meerburg, A., Muller, P., Olbers, D. J,, Richter, K., Sell, W. and Walden, W. (1973). Measurements of wind-wave growth and swell decay during the Joint North Sea Wave Project. Deutsches Hydrographisches Institut, Hamburg. Hasselmann, K., Ross, D. B., Muller, P., and Sell,W. (1976). A parametric wave prediction model. J. Phys. Oceanogr. 6,200-228. Hasselmann, D. E., Dunckel, M., and Ewing, J. A. (1980). Directional wave spectra observed during JONSWAP 1973. J . Phys. Oceanogr. 10.1264-1280. Hayne, G. S. (1981). Radar altimeter waveform modeled parameter recovery. NASA Tech. Memo. No. 73294. NASA Wallops Flight Center. Hayne, G. S.,and Hancock, D. W., 111 (1982). Sea-state-related altitude errors in the SEASAT radar altimeter. J . Geophys. Res. 87(C5), 3227-3231. Jackson, F. C. (1979). The reflection of impulses from a nonlinear random sea. J. Geophys. Res. 84,4939-4943. Kaula, W. M. (1970). ‘“The Terrestrial Environment: Solid-Earth and Ocean Physics” (NASA Contractor Rep. No. 1579). MIT Press, Cambridge, Massachusetts. Lipa, B. J., and Barrick, D. E. (1981). Ocean surface height-slope probability density function from SEASAT altimeter echo. J . Geophys. Res. 86(Cll), 10921-10930. Longuet-Higgins, M. S. (1963). The effect of non-linearities on statistical distributions in the theory of sea waves. J. Fluid Mech. 17,459-480. MacArthur, J. L.(1978). SEASAT, a radar altimeter design description. Johns Hopkins Unio. Appl. Phys. Lab. Rep. SDO-5232. Pierson, W. J., and Moskowitz, L. (1964). A proposed spectral form for fully developed wind seas based on the similarity theory S.A. Kitaigorodsky. J . Geophys. Res. 69(24). Townsend, W. F. (1980). An initial assessment of the performance achieved by the SEASAT-1 radar altimeter, IEEE J . Oceanic Eng. OE-5,80-92. Walsh, E. J. (1979). Extraction of ocean waveheight and dominant wavelength from GEOS-3 data. J . Geophys. Res. 84,4003-4010. Walsh, E. J. (1981). Altimeter rain detection. NASA Tech. Memo 73291, NASA Wallops Flight Center. Walsh, E. J. Hancock, D. W., 111, Hines, D. W., and Kenney, E. J. (1984). Electromagnetic bias of 36 GHz radar altimetric measurements of MSL. J. Mar. Geod. 8,265-296. Weber, B. L., and Barrick, D. E. (1977),On the nonlinear theory for gravity waves on the ocean’s surface. Part I: Derivations. J . Phys. Oceanogr.7,3-10. Yaplee, B. S., Shapiro, S.,Hammond, D. L., Au, B. D., and Uliana, E. A. (1971). Nanosecond radar observations of the Ocean surface from a stable platform. IEEE Trans. Geosci. Electron. GE-9,170-174.

CHAFIZII4

OCEANIC SURFACE WINDS DUNCAN B. Ross

VINCENT J. CARDONE

National Oceanic and Atmospheric Administration Allanric Oceanographicand Meteorological Laboratory Miami, Florida

Oceanweather, Inc. White Plains, New York

RONALDD. MCPHERSON

JAMES OVERLAND National Oceanic and Atmospheric Administration Pacific Marine Environmental Laboratories Seattle. Washington

National Oceanic and Atmospheric Administration National Meteorological Center Washington, D.C.

WILLARD J. PIERSON, JR.

TSANN-WANG Yu

CUNY Institute of Marine and Atmospheric Sciences The City College of New York New York. New York

National Oceanic and Atmospheric Administration National Meteorological Center Washington, D.C.

1. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . 101 2. Mechanism for Measurement of Surface Wind Speed Using Microwave Systems . . 103 3. The Marine Surface Boundary Layer . . . . . . . . . . . . . . . . . 105 3.1. The Surface Roughness and Drag Coefficient . . . . . . . . . . . . . 107 3.2. Stability Effects . . . . . . . . . . . . . . . . . . . . . . . 108 4. Development of a Model Function Relating SASS Data to Surface Wind Speed . . 109 4.1. The Comparison Data. . . . . . . . . . . . . . . . . . . . . 110 4.2. The Time-Averaging Problem . . . . . . . . . . . . . . . . . . 116 4.3. Definition of Surface Winds. . . . . . . . . . . . . . . . . . . 117 4.4. The Synoptic Scale . . . . . . . . . . . . . . . . . . . . . . 120 5. Global Data Assimilation Experiments . . . . . . . . . . . . . . . . 121 5.1. Assimilation System . . . . . . . . . . . . . . . . . . . . . 122 5.2. Preprocessing of the SASS Wind Data . . . . . . . . . . . . . . . 127 5.3. Assimilation Experiments. . . . . . . . . . . . . . . . . . . . 128 132 5.4. Forecast Experiments . . . . . . . . . . . . . . . . . . . . . 5.5. Summary . . . . . . . . . . . . . . . . . . . . . . . . . 135 6. Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . 137 References . . . . . . . . . . . . . . . . . . . . . . . . . . 138

1. INTRODUCTION For the past century, man has resorted primarily to transient ship reports to obtain information on the global characteristics of the actual surface wind field. These reports have proved invaluable in developing a better understanding of the global ocean circulation. In recent years important new sources of in situ data have been instrumented buoys, both free floating and 101 ADVANCES IN GEOPHYSICS, VOLUME 21

Copyright 0 1985 by Academic Press, Inc. All rights of reproduction in any form n ~ e ~ e d . ISBN 0-1 2-018827-9

102

DUNCAN B. ROSS ET AL.

moored. The introduction of the higher quality data sources has not, however, materially effected the accuracy of global measurements due to the preponderance of ship reports which make up the data set. The Seasat experiment, however, has brought the space age to oceanography and demonstrated the tremendous potential of a satellite system to observe the global wind field frequently to scales as small as 50 km. The Seasat-A Scatterometer System (SASS) is an outgrowth of the Skylab program (Ross and Jones, 1978) and was designed to provide the required global measurements of surface wind speed and direction. Jones et al. (1978) and Schroeder et a!. (1982) describe in detail the instrument, the geophysical algorithm, and the results of two major experiments which provided validation data sets. Basically, radar energy backscattered from the ocean is a function of the wind speed and direction through the ability of the wind to roughen the scattering surface. The algorithm required to extract a valid wind speed is empirically based and also subject to additional constraints such as the effect of atmospheric stability on the ability of the wind to roughen the ocean surface. The Seasat-A Scanning Multifrequency Microwave Radiometer (SMMR) also provides an estimate of the surface wind through changes in the emissivity of the air/water interface due to the presence of foam and to the change in reflectivity of the surface due to roughening by the local wind [see Lipes et al. (1979) for a description of the instrument]. The SMMR observed upwelling microwave energy TBat five frequencies and at both horizontal and vertical polarization (6.6, 10.7, 18.0, 21.0, and 37.6 Hz). The higher frequencies are predominately influenced by atmospheric effects while the lower frequencies (6.6 and 10.7) are sensitive primarily to see surface temperature and surface roughness. By exploiting these variable sensitivities, atmospheric water vapor (clouds) and liquid water (rain) may be separated from ocean parameters. The Seasat-A altimeter elsewhere described in this volume also provides an estimate of surface wind speed. The altimeter measures the radar backscatter go at nadir incidence angle which is inversely proportional to surface roughness. Although containing no directional information, the altimetermeasured wind speed seems to be at least as accurate as SASS (Fedor and Brown, 1982). In this chapter we present a brief review of radar backscatter which provides a foundation to the satellite approach. We then consider the vicissitudes of the marine surface wind field and how it is affected by varying degrees of surface roughness and thermodynamic influences. Some history is provided regarding the development of algorithms and an evaluation is described of the effect on a numerical atmospheric circulation model of inclusion of satellite data in the standard data base.

4. OCEANIC SURFACE WINDS

103

2. MECHANISM FOR MEASUREMENT OF SURFACE WINDSPEEDUSING SYSTEMS MICROWAVE

Electromagnetic waves incident on the ocean surface at angles away from nadir are backscattered by resonant Bragg waves whose wavenumber K is 2 p sin 6, where p = 2n/A is the wavenumber of the incident wave of length 1,and 8 is the angle of incidence with respect to the vertical. Furthermore, the magnitude of the backscattered signal is proportional to the normalized radar cross section 0’’which is proportional to the amplitude of the resonant roughness elements. This resonant scattering was confirmed by Crombie (1955) as the dominant mechanism responsible by radar backscatter. Wright (1968) developed a composite theory which explains the effect of “tilting” of the roughness elements by long gravity waves. The net effect of tilt is to increase the backscatter for a given depression angle and is most pronounced for horizontal polarization. The wind-speed dependence of the amplitude of the Bragg waves was poorly understood at the time, however, and was not included in the theory. Pierson and Stacy (1973) were the first to offer a model of .the coupling between surface wind speed and the energy density of the short-wave structure. The model was later abandoned as an unnecessary step if one desires only wind speed from a measurement of radar 0’. Subsequent efforts, therefore, concentrated on the anisotropic nature of the radar return and its dependence on wind speed. The high radar signature of breaking waves when observed at low grazing angles had been known for many years, although the importance was not appreciated until high-resolution radar imagery of rough sea conditions became available [see Zhilko and Zagorodnikov (1975) and Alpers et al. (1981)l. The role of breaking waves, however, is not treated explicitly in existing geophysical algorithms. Rather, a model of the form oo = f ( 8 ,,y, V), where 8 is the incidence angle, x is the azimuth angle relative to the wind direction, and U is the 19-m wind speed (Schroeder et a!., 1982), is empirically tuned. Valenzuela et al. (1971) used the above Bragg theory to calculate the behavior of the high-frequency end of the wave spectrum and found good agreement with published data. Furthermore, their data also confirmed a dependency of the wave spectrum on wind speed for the higher wavenumbers. A difficulty with the results of Valenzuela was the apparent saturation or decreasing sensitivity, at rather low wind speeds of about 10-12 m sec-’. The experimental data of Valenzuela were for wavelengths of 3 cm (X band) and longer, Moore and Pierson (1971) suggested use of a higher radar frequency (Ku band) based upon their experimental work. Pierson and Stacy (1973) then proposed the high-frequency end of the wave spectrum to be wind-speed

104

DUNCAN B. ROSS ET AL.

dependent according to the region of the spectrum and the applicable restoring force (gravity or surface tension). Mitsuyasu and Honda (1974) presented data which confirmed a wind-speed dependency of the HF wave spectrum and the possibility of global wind speed and direction measurements from satellites was secure. It is clear that the small-scale roughness is responsible for the backscatter signal needed to extract a surface wind speed and direction by microwave techniques. It is not clear, however, exactly how the Ocean wave field responds to the surface wind speed. Snyder et al. (1981) and Dobson and Elliot (1978) studied the process of energy exchange between the wind and the wave field. They found the process to be proportional to the relative speed of the wind and the phase velocity of the waves. In their study, the process was referenced to the wind speed U as measured at an altitude of 5 m. Other studies use the wind friction velocity U, as a reference, where U t = C , U 2 and the drag coefficient C,depends upon the altitude at which the wind is measured. The relationship between the wind speed at some altitude and the actual momentum transfer is an area of active research and will be discussed in detail in Section 3. One mechanism which can significantlymodify o0for a given wind speed is the presence of ocean currents, The wind at any height U must be determined in a coordinate system moving with a current component, if present. Thus, U = U' - Us when U is the actual wind speed relative to a fixed coordinate system, U' is the wind observed by a ship, buoy, or satellite, and Us is the current vector in the direction of the wind. A complicating factor often associated with current boundaries is an abrupt change in sea surface temperature and hence stability of the atmosphere. Liu and Ross (1980) show the importance of atmospheric stability in wave growth. When the wind crosses a current boundary and encounters the opposing flow of a warmer sea surface,extremely dramatic roughness changes can occur (cf. Ross, 1981; Ross and Cardone, 1974). Stability and sea surface temperature effects are further examined by Liu and Timothy (1984) using SASS data and a large collection of ship reports. Surprisingly, he finds little stability dependence although the number of stable cases examined was very small and the wind speeds examined were moderate. We have briefly reviewed the mechanism involved in the backscatter of electromagnetic waves from the ocean surface that can be exploited by satellite to yield an estimate of the surface wind speed. The Seasat altimeter has been proved susceptible to rain attenuation, but useful, by Fedor and Brown (1982), Wentz et al. (1981), Mognard et al. (1981), Ross et al. (1981), and many others to observe surface winds on a global basis, including tropical hurricanes and winter cyclonesin the southern oceans. The Seasat SASS has produced global

4. OCEANIC SURFACE WINDS

105

wind speed and direction measurements in all types of weather situations. After correction for attenuation by clouds and light rain the SASS has proved capable of speed accuracies better than + 2 m sec-' and direction accuracies better than _+ 10"(Businger et al., 1980). The Seasat SAR has proved sensitive to local wind variations on scales of meters and has provided new insight into the submesoscale variability which can occur in squall lines (Ross, 1981; Gonzales et al., 1982). 3. THEMARINESURFACE BOUNDARYLAYER

Many concepts developed to describe flow over solid boundaries have been successfullyextended to the marine boundary layer. The sea surface, however, is basically different from a solid boundary in that the fluid interface cannot support a surface stress discontinuity. Moreover, the interface is characterized by traveling waves whose momentum is derived typically from the air flow above the surface. Nevertheless, the vast increase in our understanding of the processes governing wave generation and dissipation have provided a framework for application of many classical boundary layer concepts to the layer of air in contact with the sea. Consider first the flow over a horizontal plane surface with horizontal wind components ui(i,j = 1,2),and the vertical wind being w. The x axis is in the direction of the mean wind and the z axis directed opposite to the direction of gravity. The x2 axis is directed so that a right-handed coordinate system results. The horizontal components of the momentum equation can be written au, -+at

auiuJ ax,

au,w +-=

I ap

az

P axj

+ VV%,

where p is pressure, v is the kinematic viscosity, w is the vertical motion, and the continuity equation for a constant density fluid aui/ax,

+ aw/az = o

has been employed. In turbulent flows, the velocity and pressure can be decomposed into an average value and an instantaneous fluctuation as ui = ui

+ u;

w=W+w' p=P+p'

106

DUNCAN B. ROSS ET AL.

Substitution of Eq. (2) into Eq. (l), and averaging, yields

aq -+at

auluj auiq +-= ax,

a 1 ap --+ VVWi + -(-W) a2 p axi

ax,

(3)

Imposing the restrictions that the mean flow is steady and that all averaged quantities are functions of z only reduces Eq. (3) to

a

-ppuiw

az

- pv(dqp2) = 0

(4)

The quantity - pmrepresents the horizontal components of the Reynolds stress and implies that the total stress z =

+

- p u i ~ pv(aUi/az)

(5)

is constant with height. The assumptions leading to Eq. ( 5 ) are generally valid for a neutral boundary layer over land to heights of 20-200 m for typical stresses of 1-10 dynes cm-2 (Lumley and Panofsky, 1964). Except very near the surface, where viscous stress may be appreciable, the surface stress over land is supported entirely by the turbulent Reynolds stress, and a velocity scale

u:

= z/p

may be defined which alone characterizes the flow in the “constant stress” portion of a neutral boundary layer. The above analysis does not hold strictly for flow over waves since the air flow contains velocity fluctuations V, in fixed phase with respect to the water surface elevation. These fluctuations are more organized than turbulence and are associated with the wave-generation mechanisms of the type described originally by Miles (1957). The mean stress balance over the sea is then

(a/az)Vw + il,w + v(aQ/az)

=o

(6)

The total stress, which remains approximately constant with height in the surface layer, now includes a wave-induced Reynolds stress z, = -puiw

(7)

which is equivalent nearly to the mean flux of momentum to surface waves arising from pressure variations on the wave surface induced by the air flow (Phillips, 1977). The structure of the wind profile in the marine surface layer will depart from that characteristic of a land boundary layer if a large fraction of the stress is supported by momentum flux to the dominant waves on the sea surface. However, most recent estimates, derived in part from direct measurement of

4. OCEANIC SURFACE WINDS

107

pressure above waves (excluding the high-frequency ripples) suggest conservatively a value of about 0.2 for the ratio z,/o (Phillips, 1977). The friction velocity therefore characterizes the wind profile in the marine surface layer (except in a possible laminar sublayer in contact with the surface)and the wind speed in a neutral atmosphere is logarithmic a u p z = u,/kz

(8)

where k is the Karman constant (0.38-0.42). 3.I . The Surface Roughness and Drag Coeficient

The variation of wind speed with height in the surfacelayer is obtained from Eq. (8): U*/klog(z/zo) (9) where zo is the roughness parameter. The drag coefficient is defined through uz

=

?

= pc,u;

(10)

The relationship between C, and zo is therefore

c, = k2/(logz/z0)2

(11)

The variation of C, with sea state and/or wind speed has been debated for decades. By analogy, to flow over a smooth, flat plate it may be expected that for the case of light winds over a calm sea, the stress at the sea surface is supported by viscosity and the flow may be characterized as aerodynamically smooth. For such flow, z, and hence C, decrease with increasing wind speed (e.g., Kraus, 1967). With increasing roughness of the sea surface associated with increased amplitude of high-frequency gravity waves, an increasing proportion of the stress becomes attributable to form drag and momentum flux to the short waves and the flow may be expected to become aerodynamically rough. Charnock (1955) predicted that in fully rough flow z, should be proportional to U : : zo = a,Uig-' (12) It can be shown from Eqs. (9)-(11) (e.g., Wu, 1969) that for rough flows C, depends on the Froude number, U,/(gz)l'z, though most investigators have adopted a standard reference height of 10 m and simply relate C, to the 10-m wind speed. Open ocean determinations of C,, based upon direct and accurate measurementsof the Reynolds flux over a wide range of wind speedshave only recently been reported (Smith, 1980; Large and Pond, 1981). In both studies,

108

DUNCAN B. ROSS ET AL.

C,, was found to increase with wind speed above wind speeds of about 10 m sec-’ to the upper extent of the measurements (about 25 m sec-’). In light winds the variation with wind speed is not clear, and Large and Pond (1981) suggest a constant of 1.14 x for Clo in wind speeds less than 10 m sec-’ for neutral stability. At wind speeds greater than 10 m sec-’, Eq. (12) with a value of a, of 0.01 provides a reasonable fit to the data (Smith, 1980). The influenceof larger waves on Clo is not well understood. Sea state effects are implicit in the finding of Large and Pond that Ci0is larger on falling winds and smaller on rising winds than the steady state value, Smith, however, reported no discernible dependence of C,, on fetch in their data set. 3.2. Stability Efects

The similarity theory of Monin and Obukov has been shown to provide a reasonable framework for the description of the mean wind profile and turbulence properties of the marine surface layer in nonneutral stratification. According to this theory, the nondimensional wind shear 4, defined as

9,

= (kz/u&au/az)

(13)

is a function of the dimensionless ratio z / L where

H is the heat flux (positive from sea to air) and C, is the specific heat of air at constant pressure. A number of functions have been shown to provide a reasonable description of the variation of 9, with z / L . Cardone (1969) used the KEYPS function (Lumley and Panofsky, 1964) to describe unstable conditions and linear dependence for stable conditions. Most investigators have adopted the Businger-Dyer forms (Dyer, 1974): 0 < ZJL < 0.2 (1 - 1 6 ~ / L ) - ” ~ -1.0 < z/L < 0

(15)

The wind profile may be integrated (Paulson, 1970) to yield

uz = U*/kClog(z/zo) - Cpm(z/L)I

(16)

where Cp,(z/L) = - 5 z / L

0 < z / L < 0.2 (stable)

+ 4 / 2 1 + lnC(1 + x2)/2] tan-’ x + n/2 - 1.0 < z / L < O(unstab1e)

= 21n[(1 = -2

x = (1 - 1 6 ~ / L ) ” ~

(17)

4. OCEANIC SURFACE WINDS

109

Measurements of the drag coefficient at sea when stratified according to the stability (Deleonibus, 1971; Large and Pond, 1981; Smith, 1980) are not inconsistent with the dependence expected from Eq. (16), namely c z = k*/Clogz/z, -

4,(Z/~)lZ

(18) which implies that for a given anemometer-level wind speed, the surface stress will be higher for unstable conditions (positve heat flux, air colder than water) than for stable conditions (negative heat flux, air warmer than water). Several workers (e.g., Cardone, 1969)have used the above theory to develop iterative schemes for the calculation of the wind stress from routinely available marine surface measurements. The latter consist of a wind measurement at anemometer level [typically 10-50 m in elevation, though Ross and Cardone (1974) report surface-layer wind measurements derived from low-flying ( I 150 m) aircraft], and measurements of air temperature and humidity, and sea temperature. Cardone (1969) introduced the concept of the effective anemometer-level wind, which has been used extensively in the interpretation of remotely sensed winds. The preferred reference height is 19.5 m. The effective 19.5-m wind is simply the wind speed at 19.5-m height that would pertain in a neutral atmosphere for the same surface stress of a given stratification. Therefore, for unstable (stable) stratification, the effective wind would be greater (less) than the measured wind.

4. DEVELOPMENT. OF A MODELFUNCTION RELATING SASS DATATO SURFACEWINDSPEED Vector winds measured by a remote-sensing technique must be compared to vector winds measured by conventional means for two reasons: first, to develop the model function used to convert backscatter measurementsto wind speeds and directions (plus aliases); and second, once a model function is selected, to find out how good the winds computed from the backscatter are. If the winds measured by conventional means are in error, then the model function derived from them may be in error. The differences between the radar winds and the meteorological winds will be caused in part by the errors in the conventional measurements. As an extreme example, suppose that the winds measured by conventional means have RMS errors at all wind speeds of k3 m sec- in speed and k 20" in direction due to the combined effects of poor calibration, poor anemometer exposure, uncorrected variations in anemometer height, failure to correct for ship speed and direction properly, and an unrepresentative averaging time of 2 min. Wind speeds and directions obtained from the SASS at nearly the

110

DUNCAN B. ROSS ET AL.

same time and nearly the same place as conventionally measured wind speeds and directions would then differ in an RMS sense by & 3 m sec-’ and k20” from the conventional data. Those who do research in remote sensing frequently use the term “ground truth” for the measurements made by conventional means when they are compared to corresponding remotely sensed quantities. For measurements of the wind by conventional means, there is no “truth; there are only comparison data. These comparison data are of varying quality and accuracy, and, depending upon the source of the data, the RMS errors of the measurements vary. 4.1. The Comparison Data

A major effort of the Seasat program was to gather sets of high-quality comparison data to permit the assessment of the accuracy of the vector winds computed from the SASS backscatter measurements using a model function derived from pre-Seasat airborne measurements. The conventional measurements of wind speed and direction can be roughly ranked in order of increasing quality (or decreasing RMS variability). The poorest are ship reports based on Beaufort estimates. These are followed by reports from transient ships that have anemometers at known heights, analyzed fields based upon a mix of ship reports, and adjusted geostrophic winds. Better data are obtained from the National Data operational buoys, weather ships, and oceanographic ships making special observations. The highest quality data availablefor comparison to Seasat measurements were from the the Joint AirSea Interaction (JASIN) experiment obtained by a closely spaced network of cross-calibrated experimental buoys and ships in the North Atlantic. There were two major programs conducted to gather conventional meteorological comparison wind data during Seasat. One was the NOAA Gulf of Alaska experiment, which obtained operational data buoy reports every hour and also special observations from weather ship PAPA and an oceanographic research ship, the Oceanographer, as described by Wilkerson and McNutt (1979). The other took advantage of the JASIN experiment to ensure that Seasat obtained SASS measurementsin modes three and four over the area of concentrated wind measurements during that program. The primary objective of the measurement of winds at sea should be to obtain data that will define the synoptic-scale wind field in the planetary boundary layer. Each conventional measurement is meant to represent the movement of the air over an area of many thousands of square kilometers within a surface layer which varies from 500 m to 1 km. Even the most casual inspection of a recording of wind speed and direction by a ship at sea shows

111

4. OCEANIC SURFACE WINDS

that a 2-min average of the wind speed and direction is usually not representative of the longer term synoptic scale. A 20-min average would clearly be superior, but an even longer time average of the wind might often be better. Pierson (1983) has given results that indicate that a 1-hr average might be good for most synoptic-scale conditions and that the best averaging time will depend on the location of the measurement and the synoptic-scale pat tern. For comparison of Seasat SASS and SMMR winds with meteorological winds during the lifetime of Seasat, conventionally measured winds were obtained from analyzed fields weighted by ship reports, by data buoys each hour on the hour, and by PAPA and the Oceanographer at the time of the Seasat pass for the GOASEX program. The JASIN program provided the highest quality data set from the standpoint of consistency, but Weller at al. (1983) report a positive bias of about 10% in the buoy used as a standard. For the SASS, paired cells of backscatter measurements that usually did not fall one on top of the other were used to recover the winds from the backscatter data by means of the model function. The cells did not occur in the scanning pattern at locations that would contain the conventional measuring platform. Figures 1-6 are scatter plots of the intercomparison of Seasat data to windspeed and, in the case of SMMR, wind-friction velocity during the GOASEX experiment. The error statistics are tabulated in Tables I and 11. For the data buoys, the measurements by the buoy did not occur at the center position of the Seasat measurement or at the same time that the Seasat measurements were made. The PAPA and Oceanographer measurements also did not occur at the center position, although they were taken at approximately the same time. Because of the dense data network of JASIN, the satellite cells were quite a bit closer in both space and time to the various buoy and ship measurements that were obtained. Under such variable conditions, the diverse averaging times cause the winds to differ by various amounts in both speed and direction from a more stable longer average. For example, for two platforms, say 50 km apart, it would not TABLE 1. COMPARISON STATISTICS FOR GOASEX

(SASS-SUR)

Polarization

No. obs.

H

V

392 608

Wind speed (m see-') 0.2 1.7 0.2 1.8

H V

392 608

Wind direction (deg) 0.7 12.4 -0.4 11.7

RMS

Intercept

Slope

Correlation

-0.2

1.00 0.99

0.91 0.92

0.96 0.98

0.98 0.99

0.0

8.8 4.4

112

DUNCAN B. ROSS ET AL.

J 30 GROUND-TRUTH WIND SPEED (m see-' )

FIG.1. Wind speed from SASS versus surface observation for the GOASEX experiment. (From Jones et al., 1981.)

be expected that winds measured for only a few minutes at each location would be the same. Yet, they could both have been measured with quite small errors. There are consequently three different effects that cause the winds measured by the satellite, referred to some particular location on the sea surface, to differ from winds measured by buoys and ships at some other nearby sea surface location. These effects are (1) actual errors in the measurements of the winds by the satellite, (2) actual errors in the measurement of the winds by the anemometers, and (3) real differences between the two wind measurements that result from differing averaging times, the finite areas satnpled by the radar,

113

4. OCEANIC SURFACE WINDS 360

/*

288

..

* *

M = 0.9420

B = 13.4002

/. -' J

36c'

R

+ '

= 0.9909

**/

-9

0 6 ' 1

1

'

1

I

I

1

I

l

l

I

~

~

1

~

~

~

1~

lack of homogeneity within the footprint, and the lack of space-time coincidence of the two measurements. It is not an easy problem to separate the various contributions to the different winds from the actual errors of the satellite and anemometer measurements and to make these errors quantitative. It is also not an easy problem to find how the amount by which nearby measurements of the wind differ simply because they do differ and not because of an error in the measurement of either wind. A brief review, with a somewhat different emphasis, of the result of Pierson (1983) follows.

J

"

2!

/. ..... ..... . ..... ........ . ..:. ........ . . . . .....

/

/

a*.-

SASS WIND SPEED (m sec-I)

FIG.3. Wind speed from SASS versus surface comparison data during the JASIN experiment using the GOASEX tuned algorithm. (From Businger et al., 1980.)

I

0

R

I

I

1

144 216 SASS WINO DIRECTION, DEGREES

288

I

360

FIG.4. Wind direction versus surface comparison data for vertical polarization using JASIN data. (From Businger et al., 1980.)

115

4. OCEANIC SURFACE WINDS

u a

0.12

-

0.10

-

0.08

-

0.06

-

E>

z5

t y p i c a l error bar for points

v1

0.04 -

0 w

3

0.02

U X

-

. ..... ... .. n .we..

-

0.00

0.12

.

0

.

Do..

0 .

H.

.OD.*

0

I 40

20

-0.02

. .

Y.W.. YI

0..

0 0

I

I 60

I 100

80

1 120

i

4-

t y p l c a l error bar for points

1

m 0.08

. a.

0.06 a oo

0

ma

a 0

a

m

a

e

a

a a

a

.

O 0.02 * O I

0.00,

0 .

a

01

20

I

I

I

I

I

40

60

80

100

120

116

DUNCAN B. ROSS ET AL.

TABLE 11. WIND-SPEED ~ M P A R I S O N STATISTICS FOR JASIN" Incidence angle bin (deg) 20-25

25-30

30-35

No. obs. (SASS-SUR) RMS

22 -0.7 2.1

35 -0.4 2.0

H polarization 29 21 -0.2 0.0 1.4 1.3

No. obs. (SASS-SUR)

22 -0.7 2.0

21 -0.7 1.6

V polarization 31 20 -0.2 0.8 1.4 1.8

RMS a

35-40

40-45

45-50

50-55

23 2.3 2.5

80

27 2.6 2.7

42

86

0.1

0.3

1.2

1.o

59 1.3 2.0

49 0.6 1.5

14 1.1 1.5

1.5 2.0

55-60

Values expressed as meters per second.

4.2. The Time-Averaging Problem

Averages of the wind speed and direction for different averaging times can produce different averages. A running boxcar average of a function of time can be thought of as a filter that removes the higher frequencies from a time history. It acts on the variance spectrum of the time history as a filter of the form [sin(~rfT/nfT)]~,where T is the averaging time. Figure 7 shows a spectrum of the wind speed computed from a 5-day, 16-hr, 32-min sample of consecutive 1-min averages. It is plotted as nS(n) versus log n so as to be area preserving. The spectrum extends to 30 cycles per hour, which is the Nyquist frequency. Beyond that value, the spectrurp is based on data sampled five times per second. There is a relatively flat part over the range from 2 hr or so, to 2 min, which is called the mesoscale valley. The time history can be thought of as a series of points 1 min apart for the wind. If a running 20-min average of this time history were made, and if the spectrum of the resulting series of points, still 1 rnin apart, were computed, the spectrum would be close to zero to the right of the arrow marked 20 min. A running 60-min average would produce a spectrum close to zero to the right of the 1-hr arrow. If the original 1-min values were plotted, as well as the 20-min values (or perhaps 19 or 21) as well as the 60-min values for each minute for the original time history, there would be three curves, each different. The variance of the point-by-point differences between the 20-min values and the 1-min values would be given by the area under the spectrum from 20 to 2 min and by similar computations for 60 and 2 min and 60 and 20 min. If there had been another anemometer, say 20 km away, making the same measurements as a function of time, and if the data were processed in the same way, the spectrum would be very similar to the one in Fig. 7. The 1- or 2-hr

4. OCEANIC SURFACE WINDS

117

2 3 hr

l o g , o f (cycles hr")

FIG.7. Compositespectrum of the wind for an average wind speed of 6.6m sec-'. One-minute averages over a period of 5 days, 16 hr, 5 and 32 min were used for the low frequency of the spectrum. The HF region was obtained by averaging spectra with a maximum sample rate S (Hz). (Courtesy of Dr. Mark Dovelan.)

running averages of the winds would be similar, but the minute-by-minute details would be different. The 20-min averages, even at the same time, would not be equal. For this example the variance of the differencesbetween a 1-hr average and a 1-min average would be given approximately by the area under the spectrum between zero on the log scale and the end of the plotted points. A 20-min average versus a 1-hr average would involve the area between the two arrows. The variability of the mesoscale spectrum has been modeled as a function of the synoptic-scale wind speed and atmospheric stability so as to try to account for the effect of time- and space-varying averaging times. The results that follow are a simplified discussion of this model. 4.3. Definition of Surface Winds

Consider a vector wind chosen as the closest of the SASS solutions to a nearby vector wind measured by an anemometer. The wind measured by the SASS will be designated as V, (for radar) and the meteorologically measured

118

DUNCAN B. ROSS ET AL.

wind will be designated as V, (for meteorological). For a set of such vector measurements, the root-mean-square difference (RMSD) will be defined using the squares of’the magnitudes of the vector differences as found in Eq. (19). The differenceis partially the result of various errors and partially the result of actual differences.

Though simply written, Eq. (19) is presently too difficult to treat. AIternatively, the vector winds can be expressed in polar coordinates as VR, XR and k,ZM (20) The root-mean-square difference between the magnitudes of the two winds can be found as

A similar equation for direction is

For the area surrounding the two measurements, there is the concept of a correctly measured synoptic-scalevector wind that, if used for an initial value update in a numerical weather prediction model, would be the best possible and most representative value to use. An anemometer-averaged wind for 1 hr might come very close to being this best possible wind. Let this wind be V, (for synoptic), which can be resolved into a speed V, and a direction. For this discussion, only the variability of the magnitude of the vector wind will be treated. It will be assumed that the various quantities that are involved are more or less normally distributed random variables so that for illustrative purposes they can be modeled by Monte Carlo simulations as the standard deviations of various terms times numbers picked at random from a normal population with a zero mean and a unit variance. The wind speed measured by the anemometer will differ from the synopticscale wind speed that would have been the desired measurement because of a too brief averagingtime. This is an effect of mesoscale turbulence in the winds and will be designated by SDMA so that V, = V,

+ t,(SDMA)

(23) where SDMA is a function of the averagingtime and the speed of the synopticscale wind. This measurement could be obtained by a perfectly calibrated anemometer. There would be no real error in it.

119

4. OCEANIC SURFACE WINDS

The anemometer could be incorrectly exposed or incorrectly calibrated and there could have been an actual error in the measurement so that the meteorological wind becomes V, = V,

+ t,(SDMA) + t,(SDME)

(24)

where SDME is the actual error of the anemometer. The area-averaged winds over the two cells scanned by the radar are different from the wind in Eq. (23) because the equivalent averaging time, using Taylor’s hypothesis, is different; the area of the ocean sampled is not at the anemometer location and the mesoscale turbulence over the area is different. The magnitude of the wind measured by the radar would be V,

=

V, + t,(SDMR)

(25)

where SDMR represents the effect of mesoscale turbulence in causing the wind that the radar measured to be actually different from the wind that would represent the synoptic scale. The value of SDMR is wind-speed dependent and a function of the size and orientation, relative to the wind direction, of the radar cells. These are two identifiable sources of error for the backscatter measurements. These are discussed at length in other reports, especially those associated with the design stages of the SASS on Seasat. The radar wind then becomes V, = V,

+ t,(SDMR) + t4(SDRE)

(26)

where SDRE is the standard deviation of the radar error. It depends in a complex way on wind speed, wind direction, and incidence angle. When Eq. (21) is evaluated by means of Eqs. (24) and (26), the result, for a sample of comparisons of radar and meteorological winds, is RMSD,

= [(l/m)(K

+ t,(SDMR) + t,(SDRE)

- t,(SDMA)

- t2(SDME))2]”2

- Vs (27)

The mesoscale effects enter unless the wind is measured at exactly the same time as the radar measurement over an area exactly centered on the anemometer (and even then there is a mismatch). All of the difference terms are functions of a large number of different effects such as for one term or another, wind speed, air-sea temperature difference, anemometer averaging times, the SASS cell sizes and the incidence angles. Equation (21)can be evaluated simply from pairs of measurements. Results for the JASIN experiment are given in tabular form by Schroeder et al. (1982), stratified by wind-speed ranges and incidence-angle ranges.

120

DUNCAN B. ROSS ET AL.

If tl through t4 are randomly varying and independent, Eq. (27) reduces to RMSD,

+ [(l/m)((SDMR)2 + (SDRE)’ + (SDMA)2 + (SDME)2)]’’2

(28)

For JASIN vertical polarization measurements, RMSD2 was equal to 1.23 m sec-’, and for horizontal polarization it was equal to 1.39 m sec-*. The study by Pierson (1983) attempted to evaluate these four terms. It shows, if the model used is anywhere near correct, that SDMR and SDMA make very important contributions to the sum, which are increasingly important with increasing wind speed. The actual radar error for the actual wind over the cell (or cells) actually sampled by the radar is the t4 term in Eq. (26). Theoretical calculations and Monte Carlo simulations both indicate that it is very small. The values of 1.23 and 1.39 m sec-’ may be a very large upper bound on the actual accuracy of the SASS. Similar results can be obtained for the wind direction xR, and the radar measurement can be described by XR

= xs

+ t,(SDMR*) + t,(SDRE*)

(29)

4.4. The Synoptic Scale The surface measurements by Seasat SASS were extremely dense, and 10 or 20 of them can be averaged (perferably vectorally) to obtain a synoptic-scale wind at a grid point of a numerical model. The result would be 1

V = -C(& m

+ t,i(SDMR) + t,i(SDRE))

and

where m is the number of observations. In such a representation, the values of t j i and tSi could be weakly correlated because of the larger wavelengths in the mesoscale turbulence. If this complication is ignored, the average value of the wind speed and direction is the synoptic-scale wind speed and direction. The variances are Var(v- V,) = (l/m)((SDMR)2 + (SDRE)’)

(32)

and Var

(x- xs)= (l/m)((SDMR*)’ + (SDRE*)’)

(33) For 16 winds clustered around a grid point, conservatively the values of 1.23 and 1.39 would reduce to 0.31 and 0.35. Values such as these for defining the

4. OCEANIC SURFACE WINDS

121

synoptic-scale wind in the planetary boundary layer should make an important contribution to the initial value specification of a numerical weather-forecasting model if assimilated properly in a model that takes full advantage of the high quality of the data. 5. GLOBAL DATA ASSIMrLATIOW EXPERIMENTS Recently, Cane et al. (1981) discussed sensitivity and potential ,impact of marine surface winds on numerical weather prediction. Their study is based on a set of simulated ocean surface winds and used the general circulation model of Goddard Laboratory for Atmospheric Sciences. The purpose of this section is to discuss results of a global data assimilation experiment using 2 days (July 16, OOZ-July 18, OOZ, 1978) of real global scatterometer winds from Seasat-A. These experiments were designed to give a preliminary assessment of the impact of SASS winds on the operational numerical weather analysis and forecasting system used at the National Meteorological Center (NMC). This material is substantially the same as that of Yu and McPherson (1984).

The scatterometer on board Seasat-A provided fine-resolutionwind vectors (about 100-km spatial resolution) over the world's oceans for approximately 3 months in 1978. These winds have directional ambiguities, as previously discussed, with each SASS wind containing up to four directions. Only one is the correct wind direction; the rest are so-called aliases. It is necessary to remove these aliases before the SASS wind data can be usable. For this purpose, Yu and McPherson (1979) have designed an objective alias-removal scheme that uses conventional data and forecasts to help determine the correct wind direction. Then, the dealiased SASS winds are subjected to manual editing to correct those winds which still do not appear consistent with other information. A brief description on the alias-removal scheme is given in Section 5.2. After this preprocessing, the SASS wind data were used in two ways. First, the SASS winds were introduced into the surface-pressure analyses as demonstrated by Yu and McPherson (1979). Second, and perhaps more important, the SASS winds were used directly in the lower-tropospheric wind analysis. This was accomplished through the statistical-interpolationanalysis scheme incorporated in NMC's Global Data Assimilation System, used in this study. To assess the impact of SASS wind data, two parallel experiments were run, one with SASS winds (SASS mode), the other without (NOSASS mode). For the SASS mode, global SASS wind data were assimilated at 6-hr intervals, together with other data sources conventionally available at NMC. For the

122

DUNCAN B. ROSS E T AL.

NOSASS mode, an identical assimilation procedure was conducted, except that SASS data were excluded. Results of these two experiments after 48 hr of assimilation are discussed in Section 5.3 At the end of the assimilation period, a 72-hr forecast was made from each of the SASS and NOSASS analyses. Results of the forecast comparison are discussed in Section 5.4. 5.1. Assimilation System

It has been learned through considerable experience that the impact of a given type of data on a numerical weather-prediction system cannot be judged without considering the characteristics of the assimilation system itself (Tracton et al., 1981). That is, some assimilation systems are more amenable to improvement by the addition of the data being tested than are others. We therefore think it important to describe the structure and behavior of the system used in the experiments reported here, especially noting those aspects of particular relevance to the assimilation of the SASS winds. The assimilation system is the NMC Global Data Assimilation System in the configuration used operationally in January 1981. It is essentially the system described by McPherson et al. (1979), based on a statisicalinterpolation objective-analysis method developed by Bergman (1979)following the work of Gandin (1963), Schlatter (1975), and Rutherford (1976). First implemented in NMC operations in September 1978 (after the 2-day period of Seasat data used in this experiment), it has since undergone a series of evolutionary changes; the most important of these are described by Kistler and Parrish (1 982). In the configuration used in the experiments described here, the system consists of a primitive-equation prediction model (Sela, 1980)that is updated each 6 hr using statistical interpolation of available data. The prediction model uses spectral representation in terms of spherical harmonic functions to calculate quantities involving derivatives, but physical processes such as precipitation are parameterized on a network of grid points. Ordinary finite difference approximations are used in the vertical. Horizontal resolution in terms of spectral representation is 30 waves with rhomboidal truncation; the physical grid has 96 equally spaced points around latitude circles, and there are 76 points between poles. These are not quite equally spaced but constitute the so-called “Gaussian” grid, with points at the modes of the spherical barmonic functions. On average, the spacing between points meridionally is about 3.75“ latitude. Twelve layers are used to represent the vertical structure of the atmosphere, and the vertical coordinate in any column of air is pressure, normalized by the

4. OCEANIC SURFACE WINDS

123

pressure at the base of the column; i.e., the surface pressure. This sigma coordinate,' first introduced by Phillips (1957), is commonly used in numerical weather prediction and has the advantage of following the contours of the earth's surface. This is of no special benefit in the present experiment, of course, since the SASS winds are introduced only over the oceans. The vertical structure of the model is depicted in Fig. 8. Note especially the finer structure in the lower troposphere. The prediction model includes parameterization of some physical processes, such as precipitation, surface friction, and sensible and latent heat exchange; it does not attempt to parameterize others, such as radiative

-

SPECTRAL 12 LAYER (

12bar) t

0

50

0

50

@

50

@

50

50

DO

00 50

30 DO

0 0 .............. @ 0

50 !50 75

125

00 00

00 M)

DO 00

@.............J5.0 ................................................. 0 150 .................. 125 ................................. 73

I

FIG.8. Vertical structure of the model. From the Greek symbol almost universally used in the coordinate definition: c = P/P,,,.

124

DUNCAN B. ROSS ET AL.

processes. At the time this experiment was conducted, climatological sea surface temperature fields, rather than current analyzed fields, specified the lower thermal boundary conditions over oceanic areas. Each 6 hr, the forward integration of the prediction model is interrupted, and timely observations are used to correct or update the model’s representation of the atmosphere, where this is necessary. Data are stratified into 6-hr time blocks, and are treated as synoptic at the central time of the block; thus, the data base available for each update may contain data as much as 3 hr off time. For small-scale local applications, such an approximation would not be reasonable, but for the global, large-scale application addressed here, the error introduced is not as serious. The updating is done by first determining the error in the predicted fields of the model’s history variables (eastward and northward wind components, temperature, surface pressure, and specifichumidity) at the locations of each observation by subtracting the interpolated, predicted value from the observed value. These differences are then interpolated back to the physical grid points of the prediction model, forming corrections which are then added to the original prediction fields. The interpolation of the observed-minuspredicted differences from the locations of the data to the model grid points is accomplished by the statistical procedure frequently referred to as “optimum interpolation.” This is a method which minimizes, in principal, the meansquare error of interpolation. In practice, approximations necessitated by operational considerations result in some deviation from strict statistical optimality; hence, we use the term “statistical interpolation” to describe the procedure. Statistical interpolation has been adopted in operational numerical weather-prediction centers in several countries. One of the principal motivations for this popularity is that the statistical method provides a systematic framework for blending together observations from different sources, and importantly, with different error characteristics. Given the wide variety of data sources in today’s global data base, this capability is essential. Table 111 presents a summary of the RMS observational errors associated with various types of data used in this experiment. All else being equal, the weight assigned each observation is inversely proportional to its assumed error level: the greater the error, the less weight accorded in the analysis. The error of the SASS wind is assumed to be 5 m sec-’, about the same as low-level, cloud-motion winds and surface ship wind observations,the other sources of wind observations in the lower marine troposphere. This is higher than errors obtained by direct comparison with ship and buoy data in the GOASEX and JASIN programs and was chosen in part because of a suspected greater uncertainty in wind direction and speed in the gridded global data set. In any event, the assigned error levels mean that

125

4. OCEANIC SURFACE WINDS

TABLE 111. ROOT-MEAN-SQUARE OBSERVATIONAL ERRORS OF TEMPERATURES AND WINDSUSEDIN THE GLOBAL DATAASSIMILATION EXPERIMENTS Data types

Temperatures ("C)

Winds (m sec-')

Radiosonde Aircraft U.S. cloud-tracked winds Low level High level Japanese cloud-tracked winds Low level High level Surface ships Scatterometerwinds ASDAR (AIRCAR) Upper air bogus

0.8

1.5 2.0

1.o

4.2 1.5

6.I 13.0 5.0 5.0

2.0 1.o

the SASS data compete with the other two data types on an approximately equal footing. The fact that the statistical interpolation is done to the grid points of the prediction model's physical grid, including the midpoints of the 12 vertical levels, requires that a certain sequence of events be followed in the updating process. Because the vertical coordinate c i s a function of an updated variable (surface pressure), that variable must be updated first. The predicted surface pressure is updated by a two-dimensional version of the statisical-interpolationprocedure, using observed station pressures from land stations, ships, and buoys. Wind observations from ships and buoys are also allowed to influence the surface-pressure update poleward of about 15" latitude through a geostrophic relationship between the wind and the gradient of pressure. SASS winds (in the SASS mode) were also used in this way in the experiment reported here. Once the surface pressure has been corrected, the vertical coordinate is adjusted, and the updates of the wind, temperature, and humidity in each of the 12 layers begin. Here, three-dimensional statistical interpolation is used: that is, an observation at a given pressure may not only influence the update in its own layer, but also layers above and below. In particular, the SASS winds typically influence the lowest two layers significantly, and the next two to a much smaller degree. Figure 9 depicts the influence of a single wind observation at 1OOOmbaron the analysis at higher levels. The illustration may be interpreted as stating that a 10-m sec- change in the u component at 1000 mbar from a single observation implies a change of 6 m sec-' at 700 mbar,

'

126

DUNCAN B. ROSS ET AL.

100

i50 ,200 250

P

,300 (mbar)

400 500 -700

450

0 0.2

0.4 0.6 0.8

~1000 1.0

CORRELATION FIG.9. Modeled correlation function for forecast wind errors as a function of pressure.

about 2.5 m sec-' at 500 mbar, and very small changes at higher levels. This influence function is a part of the three-dimensional statistical interpolation representing the covariance of a prediction error in wind at one level with that at another level. For details, the reader is encouraged to consult Bergman ( 1979).

Thus, the SASS wind data are used in two ways. First, they are used to influence the surface-pressure update. This is likely most important in connection with remote temperature sounding data, which require an accurate reference level for maximum usefulness. Second, the SASS winds directly affect the wind update in the lower troposphere, through a moderately deep layer. Here is probably the greatest potential for beneficial impact. Following the completion of the statistical-interpolation part of the update, the corrected fields are adjusted by a nonlinear normal mode initialization procedure (Ballish, 1980) based on a method suggested by Machenhauer (1977). Initialization modified both mass and motion fields to resolve any imbalances arising from the updating process. The initialized fields serve as initial conditions for the next 6-hr forecast of the spectral prediction model.

4. OCEANIC SURFACE WINDS

127

5.2. Preprocessing of the SASS Wind Data In this section,we discuss the alias-removal scheme used to obtain the most nearly correct direction from the SASS data. The scheme is an iterative method consisting of five passes: 1. First pass. This step resolves the directional ambiguity in areas of uniform wind direction. Abundant examples of uniform wind fields are observed over the trade-wind regimes in the tropics. Here we define uniform winds as having directions in the same quadrant within a specified area. During this pass, the scheme locates the areas of uniform winds. Then, NMC's operational 1000-mbar wind analysis, which has been derived from a forecast plus conventional data, is used to select correct wind directions over a 5" x 5" longitude-latitude grid box. Vectors thus resolved are then separated from the remaining ambiguous winds. 2. Second pass. The SASS wind data are searched for unambiguous winds. If the unambiguous-wind distribution from the SASS data contains at least three vectors in the four quadrants of the 5" x 5" longtitude-latitude box, these winds are used to resolve ambiguous vectors in the box. These resolved winds are then used for the next pass. It turns out that this was a superfluous step; no unambiguous winds were found in the 2-day data set. 3. nird pass. Statistical interpolation of the winds resolved in the previous passes is used to select the most probable direction to the location of each twofold (i.e., one report contains two pairs of wind speeds and wind directions) ambiguous wind. Vectors thus resolved are added to the data base for the next pass. 4. Fourth puss. Same as the third pass except that threefold ambiguous winds are resolved. 5 . Fifth pass. Same as the fourth pass except that fourfold ambiguous winds are resolved.

Before processing actual SASS wind data, the alias-removal scheme was tested on a set of simulated data generated by the Langley Research Center (Jones et a!., 1978). The advantage of using the simulated case is that we can compare the final dealiased wind field with the starting wind field; thereby we may evaluate the performance of our alias-removal scheme. In general, it suffices to say that from the test experiment we found that the dealiased wind field agreed well with the surface-pressure pattern except over the areas where the pressure gradient was large. Within these areas, the resolved wind directions frequently did not agree well with the surface-pressure pattern; these constituted about 10 to 15% of total winds.

128

DUNCAN B. ROSS ET AL.

To address these remaining ambiguous winds, manual editing was performed on the residual 10- 15%. The information on which the manual editing was based consisted of satellite imagery, low-level, cloud-tracked winds from satellites, and the conventional surface-pressure analyses. Therefore, it is likely that over the areas where, for example, satellite low-level, cloud-tracked winds are available, the SASS wind data will tend to be redundant, and their impact on the analysis Iess evident. This will be discussed further in Section 5.3. 5.3. Assimilation Experiments In the lower atmosphere at 1000mbar, after 48 hr of data assimilation,large differencesbetween SASS and NOSASS wind and height analyses are found in the Southern Hemisphere. Differences in height analyses exceeding 100 m are seen over the Southern Hemispheric oceans (see Fig. 10). For example, height differences as large as 140 m are seen over the Indian Ocean near 260"W and Longitudes (WEST)

0 20

40 I

I c

u)

Q

0

a

.= c

0

20

60

FIG.10. (a) The 1000-mbar height differences and (b) vector-wind differences between SASS and NOSASS in the Southern Hemisphere after 48-hr assimilation valid at OOZ, July 18, 1978. The height unit is in meters and vector winds are represented by a full bar (10 m sec-') and a half bar ( 5 m sec-I). The map covers the area from 350"W to 235"Wand 0" to 60%

4. OCEANIC SURFACE WINDS

129

40"s (Fig.lOa), and over the west coast of South America near 80"Wand 45"s (Fig. 1la). Over those areas where analyzed height differences are large, there are correspondingly large differences in vector-wind analyses exceeding 20 m sec-' (see Figs. 10b and 1lb). Moreover, these differences are well organized and consistent with the height analysis differences. In the higher atmosphere at the 250-mbar level, large differences between SASS and NOSASS wind and height analyses are found as well in the Southern Hemisphere (see Figs. 12 and 13). This demonstrates that the influence of the scatterometer wind data has spread through a deep layer of the atmosphere as a result of the data assimilation process. Although some of the features which appear at the 1000-mbar level are well preserved at the 250mbar level, most of the features become less organized in the higher troposphere. Note also that percentage of height and wind differences in the higher troposphere are not as large as those near the surface. In the Northern Hemisphere,differences in geopotential and wind analyses between SASS and NOSASS experiments are rather small; differences in Longitudes ( W E S T )

20

40

60

FIG.11. Same as Fig. 10except showing the area from 11YW to 15"W.

130

DUNCAN B. ROSS ET AL. Longitudes

(WEST)

0

20

-

40

x

I3 0

- 60

r n

FIG.12. Same as Fig. 10 except for 250-mbar height (a) and vector-wind differences between SASS and NOSASS.

geopotential are in general less than 20 m and in wind speed less than 5 m sec- near the surface of the earth as well as in the higher atmosphere. This is due to the fact that it is summer in the Northern Hemisphere and lower winds prevail and, also, there are abundant other sources of data such as ship reports, buoy reports, and low-level, cloud-tracked winds (see Table IV for a comparison of the conventional NMC data sources between the Northern and Southern Hemispheres at a synoptic time). As a consequence, the impact of SASS winds on global-scale atmospheric analyses is not so great as in the Southern Hemisphere, where conventional data are sparse and a winter season prevails. It should also be pointed out that over the areas where low-level, cioudtracked winds from satellites are available, the impact of SASS winds on the analysis of wind and height becomes less significant due to overlapping observations. This is illustrated in Fig. 14. Note that in the areas bounded by 115"W,70"W,WS, and 30"s there are abundant low-level, cloud-tracked winds and the differences between SASS and NOSASS wind and height analyses were very small (see Fig. 11). As mentioned earlier, this is due to the

'

131

4. OCEANIC SURFACE WINDS Longitudes

(WEST)

0

20

40 I

I I-

3

0

- 60. v)

40

60

FIG.13. Same as Fig. 10 except showing the area from 115"W to 1S"W.

TABLE Iv. NUMBER OF DATAREPORTS USED IN WIND ANALYSES OF ASSIMILATION EXPERIMENTS'

THE GLOBAL DATA

~~~~~

Data types Radiosonde Aircraft Satellite cloudtracked winds Surface land reports Surface ship and buoy reports Scatterometer wind

Level (mbar)

Northern Hemisphere

Southern Hemisphere

Tropics (20"N-20°S)

850

250 lo00

66 1 256 137

133 16 156

-b -b

lo00

3221

195

1267

lo00

406

23

252

lo00

353

716

652

For 122, July 17,1978. Data counts already included the tropical region in each hemisphere.

-b

132

DUNCAN B. ROSS ET AL.

404

2dN

0 '

?dS

46s FIG.14. Analysis at 850mbar for OOZ, July 18,1978. The plotted wind vectors are thelow-level, cloud-tracked winds from satellites.

fact that the SASS wind data must be manually edited based on information from satellite imageries and cloud-tracked winds. Therefore, SASS wind data thus edited contain essentially the same information as cloud-tracked winds. 5.4. Forecast Experiments

The height and vector-wind 72-hr forecast differencesvalid at OOZ, July 21, 1978, from the SASS and NOSASS analyses are shown for the Southern Hemisphere at the 1000- (Figs. 15 and 16) and 250-mbar (Figs. 17 and 18) levels. We note that large differences in the initial analyses at 1000 mbar lead to large differences in the forecasts in the Southern Hemisphere (Fig. 15). These forecast differences are well distributedin the vertical, as can be seen from Figs. 17 and 18 for a higher level (250mbar) of the atmosphere. Note that a large vector-wind difference of about 30-35 m sec-l in magnitude was observed over the Indian Ocean at 250 mbar. A subjective comparison with satellite cloud imagery and streamline analyses suggests that, in general, the forecast from the analysis with SASS winds are an improvement over that from the analysis without SASS winds.

Longitudes (WEST)

340

320

300

280

260

240

FIG.15. (a) The 1000-mbar height differences and (b) vector-wind differences between SASS and NOSASS in the Southern Hemisphere after 72 hr of forecast valid at OOZ, July 21,1978. The height unit is meters, vector winds are represented by a full bar (10 m sec-') and a half bar (5 m o",and 60"s. sec-I). The map covers the area bounded by 350"W,235"W, Longitudes ( W E S T )

FIG.16. Same as Fig. 15 except showing the area between 115"Wand 15"W.

FIG. 17. Same as Fig. 15 except for 250-mbar height differences (a) and vector-wind differences (b) between SASS and NOSASS.

4. OCEANIC SURFACE WINDS

135

In contrast to the initial smaller differences shown in Figs. 12 and 13 at 250 mbar, 72-hr forecast differencesare very large at 250 mbar in vector winds (see Figs. 17 and 18). Moreover, several distinctly well-organized features which are present at 250 mbar are not as clearly depicted near the surface at 1000 mbar. In the Northern Hemisphere, 72-hr forecast differences in heights and winds between SASS and NOASS are very small. To objectively assess the impact of SASS winds on forecasts, we computed root-mean-square absolute vector-wind error for six radiosonde networks in the Southern Hemisphere. These radiosonde networks include (1) Southern Hemisphere, 31 stations; (2) South Africa, 20 stations; (3) South America, 30 stations;(4) Australia, 24 stations; (5) South Pacific, 22 stations; and (6)isolated stations in the Southern Hemisphere. Table V shows the calculated RMS absolute vector-wind errors for four radiosonde networks. From Table V we can see that in general, from 1000 up to 100 mbar, the 24- and 48-hr forecasts with SASS winds in the initial analysis are better than the forecasts generated from the initial analysis without SASS winds. However, for the 72-hr forecast, we notice that forecasts for the SASS case are slightly worse than those for the NOSASS case in the lower atmosphere (ie.,. 850 and 500 mbar), but slightly better at higher levels (ie., 250 and 100 mbar). Based on these statistics, one may conclude in general that forecasts are slighlty better for the SASS case than for the NOSASS case. 5.5. Summary

In the Southern Hemisphere, differences in wind and height analyses between SASS and NQSASS are quite large and well organized after 48 hr of assimilation. Height differences are as large as 140 m and wind differences as large as 15-20 m sec-'. These differences are not only in the lower atmosphere where SASS winds are injected (e.g., 1000 mbar), but also in the higher troposphere (e.g., 250 mbar), although the percentage of changes in the higher troposphere is smaller than that near the surface. These differences in the Southern Hemisphere in the initial analysis of height and wind fields between SASS and NOSASS in turn lead to significant differences in forecasts of wind and height fields through 72 hr. In the Northern Hemisphere, the differences in wind and height analyses between SASS and NOSASS are much smaller. This is due to the fact that in the Northern Hemisphere summer prevails with a relatively low average wind speed and there are many other sources of meteorological data to compete with SASS wind data. The fact that there is little difference between SASS and NOSASS initial analyses in the Northern Hemisphere suggests that SASS

136

DUNCAN B. ROSS ET AL. TABLE V. RMS ABSOLUTE VECTOR-WIND ERROR'

Forecast

Pressure level(mbar)

SASS

NOSASS

Southern Hemisphere, 31 stations 24 hr

48 hr

72 hr

850 500 250 100 850 500 250 100 850 500 250

100

9.9 10.5 18.6 14.1 9.3 9.0 16.0 12.0 8.3 13.7 18.8 9.7

10.0 10.7 19.1 14.4 10.2 9.8 18.0 11.6 8.0 12.5 16.3 10.1

Australia, 24 stations 24 hr

48 hr

72 hr

a

850 500 250 100 850 500 250 100 850 500 250 100

7.8 8.0 17.3 11.0 6.5 8.8 12.0 9.3 9.2 12.7 19.1 11.6

7.9 8.7 17.4 11.1 7.2 9.7 13.2 10.7 9.4 10.7 16.2 14.0

SASS

NOSASS

South Africa, 20 stations 5.4 6.1 15.6 17.0 6.4 5.3 16.6 12.5 7.9 8.8 16.7 7.4

6.6 6.9 16.2 17.8 8.3 5.4 17.1 11.8 6.5 7.5 15.8 8.8

South Pacific, 22 stations 6.7 7.9 14.4 10.7 6.4 8.7 11.3

9.0 7.9 9.5 10.4 8.o

7.2 8.7 15.0 10.8 6.6 9.0 12.4 9.6 7.7 9.4 10.8 8.2

SASS

NOSASS

South America, 30 stations 11.2 13.5 17.2 9.0 7.5 12.1 16.5 7.6 6.6 18.6 20.6 9.0

10.8 12.8 17.8 8.8 7.3 11.8 18.2 6.6 7.1 15.4 21.4 9.2

Southern Hemisphere, isolated island stations 6.3 9.4 11.5 5.2 7.4 13.0 8.7 3.4

9.2 7.5 12.9 -

10.4 10.6 12.5 9.5 12.5 15.2 14.4 5.7 8.2 9.4 7.3 -

Values expressed as meters per second.

winds are at least as useful as ship winds, buoy reports, and low-level, cloudtracked winds using the 5-m sec-' error assumption. Decreasing this tolerance should lead to increased differences. This also suggests that we may have confidence in the adequacy of the SASS winds for contributing to the atmospheric initial states in the Southern Hemisphere. Subjective evaluations by comparing the forecasts with satellite imagery and low-level, cloud-tracked winds indicate that with SASS winds, the lowpressure centers are more intense. Some smaller features appear in the

4. OCEANIC SURFACE WINDS

137

forecast generated from the SASS analysis, which though not clearly verified by large-scale analyses, are present in the cloud imagery as well. This suggests that SASS winds provide information on subsynoptic-scalefeatures. Objective evaluations by comparing the 24- and 48-hr forecasts with the radiosonde network in the Southern Hemisphere indicate that short-range forecasts generated with SASS winds are slightly better than the forecasts generated without SASS as initial analyses. This conclusion is not true, however, for the 72-hr forecast comparison. This assessment, based on only one case, must be labeled preliminary. A longer assimilation sequence and more forecasts are clearly necessary. 6 . CONCLUSIONS

The Seasat experience has proved the potential of a space platform to obtain global measurements of the surface wind speed and direction to an accuracy of better than k2.0 m sec-' and +20" in direction. While the comparison data sets have shown the magnitude of the error to be closer to or even less than 1 m sec-l, the data were predominantly of low-to-moderate winds under neutral-to-unstable atmospheric conditions. However, in many applications such as global ocean circulation studies, such low-to-moderate winds are the major driving force. Thus, a potential limitation with respect to extreme winds or highly stable atmospheres is relatively unimportant. Global atmospheric-modeling studies of the impact of Seasat winds have been limited in many respects by the models themselves in that they are not geared to assimilation of highly accurate and closely spaced global data sets. Furthermore, verification of model forecasts is limited by the relatively few essentially oceanic sites required to collect data sets uncontaminated by land proximity. In preparation for the launch of new satellite programs, the Seasat experience suggests that considerable emphasis should be placed on the development of global atmospheric circulation models which have the grid spacing and nonsynoptic assimilation routines appropriate for such highquality global data sets. Additional work is needed in verificationof the data under extreme situations such as in winter storms contaning high winds and moderate-to-heavy rainfall. The effect of a stable atmosphere needs to be examined further in view of the lack of sensitivity in the results of Liu and Timothy (1984),in conflict with the results of Liu and Ross (1980), who found that application of a boundary layer model to adjust the winds to a neutral or unstable equivalent nevertheless underestimated the strong effect of stability in reducing the growth rate of wind-generated waves.

138

DUNCAN B. ROSS ET AL.

REFERENCES Alpers, W. R.,Ross, D. B., and Rufenach, C. L. (1981). On the detectability of ocean surface waves by real and synthetic aperture radar. J. Geophys. Res. 86,6481-6498. Ballish, B. (1980). Initialization, theory and application to the NMC spectral model. Ph. D. dissertation, University of Maryland. Barrick, D. E. (1968). Rough surface scattering based on the specular point theory. IEEE Trans. Antennas Propag. AP-16,449. Bergman, K.(1979). Multivariate analysis of temperatures and winds using optimum interpolation. Mon. Weather Rev. 107,1423-1444. Businger, J., Stewart, R. H., Guymer, T., Lame, D. B., and Born, G. H. (1980). SEASAT-JASIN Workshop Report, Vol. 1. Publ. 80-62, Jet Propulsion Lab., Pasadena, CA. Cane, M. A., Cardone, V.J., Halem, M., and Halberstam, 1. (1981). On the sensitivity of numerical weather prediction to remotely sensed marine surface wind data: A simulation study. J. Geophys. Res. 86,8093-8106. Cardone, V. J. (1969). Specification of the wind distribution in the marine boundary layer for wave forecasting. Geophys. Sci. Lab. N.Y. Univ. (NTIS AD 702-490), Rep. TR 69-1. Charnock, H. (1955). Wind stress on a water surface. Q. J . R. Meteorol. SOC.81,639. Crombie, D. D. (1955). Dopper spectrum of sea echo at 13.56 mjs. Nature (London 195,681-682. Davidson, K. L., Schacher, G. E., Fairall, C. W., and Jarrell, J. D. (1981). Observational results pertaining to scatterometer interpretation. In “Oceanography From Space,” pp. 597-606. Plenum, New York. DeLeonibus, P. S. (1971). Momentum flux and wave spectrum observations from an ocean tower. J. Geophys. Res. 76,6506-6527. Dobson, F. W., and Elliott, J. A. (1978). Wave pressure correlation measurements over growing sea waves with a wave follower and fixed height pressure sensing. In “Turbulent Fluxes through the Sea Surface, Wave Dynamics and Prediction”(A. Farr and K. Hasselman, (eds.). Plenum, New York. Dyer, A. J. (1974). A review of flux-profile relationships. Bound. Layer Meteorol. 7 , 363-372. Fedor, L. S., and Brown, G. S. (1982). Waveheight and wind speed measurements from the Seasat radar altimeters. J . Geophys. Res. 87, 3254-3260. Gandin, L. S. (1963). Objective analysis of meteorological fields. Gidrometeor. Izdat. (Leningrad). Israel Program for ScientificTranslations, Jerusalem, 1965. Gonzalez, F. I., Thompson, J. W., Brown,W. E., Jr., and Weissman, D. E. (1982). SEASAT wind and wave observations of northeast Pacific Hurricane Iva, August 13,1978. J. Geophys. Res. 87,3431-3438.

Jones, W. L., Wentz, F. J., and Schroeder, L. C. (1978). Algorithm for inferring wind stress from Seasat-A. J. Space Rockets 15,368-374. Jones, W. L., Black, P. G., Bogs, D. M., Bracellente, E. M., Brown, R. A., Dome, G., Ernst, J. A., Halberstam, I. M., Overland, J. E., Peteherych, S., Pierson, W. J., Wentz, F. J., Waiceshyn, P. M., and Wurtele, M. G. (1979). Seasat-A scatterometer: Results of the Gulf of Alaska workshop. Science 204, 1413-1415. Jones, W. L.,Wentz. F. J., and Schroeder, L. C. (1981). Microwave scatterometer measurements of oceanic wind vector. in “Oceanography from Space,” pp. 553-563. Plenum, New York. Kistler, R. E., and Parrish, D. F. (1982). Evolution of the NMC‘s data assimilation system: September 1978-January 1982. In preparation. Kraus, E. B. (1967). The aerodynamic roughness of the sea surface. J. Atmos. Sci. 23,443-445. Large, W. G., and Pound, S. (1981). Open ocean momentum flux measurements in moderate to strong winds. J. Phys. Oceanogr. 11,324-336.

4. OCEANIC SURFACE WINDS

139

Lipes, R. G., Berstein, R. L., Cardone, V.J., Katsaros, K. B., Njoku, E. G., Riley, A. L., ROSS,D. B., Swift, C. T.,and Wentz, F. J. (1979). Seasat scanning multichannel microwave radiometer: Results of the Gulf of Alaska Workshop. Science 204,1415-1417. Liu, P. C., and Ross, D. B. (1980). Airborne measurements of wave growth for stable and unstable atmospheres in Lake Michigan. J. Phys. Oceanogr. 10, 1842-1853. Liu, P. C., and Timothy, W. (1984). The effects of the variations in sea surface temperature and atmospheric stability in the estimation of average windpseed by Seasat-SASS. J. Phys. Oceanogr. 14(2), 392-401. Lumley, J. L., and Panofsky, H. A. (1964). “The Structure of Atmospheric Turbulence.” Wiley (Interscience), New York. Machenhauer, B. (1977). On the dynamics of gravity oscillations in a shallow water model with application to normal mode initialization. Beitr. Phys. Atmos. 50,253-271. McPherson, R. D., Bergman, K. H., Kistler, R. E., Rasch, G. E., and Gordon, D. S. (1979). The NMC operational global data assimilation system. Mon. Weather Rev. 107, 1445-1461. Miles, J. W. (1957). On ihe generation of surface waves by shear Boys. J. Fluid Mech. 3,185-204. Mognard, N. M., Campbell, W. J., Cheney, R. E., Marsh, J. G., and Ross, D. B.(1981). Southern ocean waves and winds derived from SEASAT altimeter measurements. Proc. IVCRMIAMS Wave Dyn. Symp., Miami. Moore, R. K., and Pierson, W. J. (1971). Worldwide oceanic wind and wave predictions using a satellite radar-radiometer. J . Hydron. 5(2), 52-60. Paulson, C. A. (1970). Themathematical representation of wind speed and temperature profiles in the unstable atmospheric surface layer. J. Appl. Meteorol. 9,857-861. Phillips, N. A. (1957). A coordinate system having some special advantage for numerical forecasting. J. Meteor. 14,184-185. Phillips, 0.M. (1977). “The Dynamics of the Upper Ocean,” 2nd Ed. Cambridge Univ. Press, London. Pierson, W. J. (1983). The measurement of the synoptic scale wind and the ocean. J. Geophys. Res. 88, 1683-1708; also to be published as a LARC. Pierson, W. J., and Stacy, R. A. (1973). The elevation, slope, and curvature of a wind roughened sea surface. NASA Contr. Rep. 2247. NASA Langley Research Center, Va. Pierson, W. J., Peteherych, S., and Wilkerson, J. C. (1980). The winds of the comparison data set for the Seasat Gulf of Alaska Experiment. IEEE J. Ocean Engr. OE-5 (2). Ross, D. B. (1981). The windspeed dependancy of ocean microwave backscatter. In “Spaceborne Synthetic Aperture Radar for Oceanography” (R. C. Beal, P. S. DeLeonibus, and I. Katz, (eds.), pp. 75-86. J o l p Hopkins Univ. Press, Baltimore, MD. Ross, D. B., and Cardone, V. J. (1974). Observations of oceanic whitecaps and their relation to remote measurements of surface windspeed. J. Geophys. Res. 79,444-452. Ross, D. B., Lawson, L. M., and McLeish, W. (1981). Comparison of Hurricane Fico winds and waves from numerical models with observations from SEASAT-A. Proc. IUCRMIAMS Wave Dyn. Symp. Miami. Rutherford, I. D. (1976). An operational three-dimensional multivariate statistical objective analysis scheme. GARP Rep. No. 11, pp. 98-121. Proc. JOC Study Group Go@ FourDimensional Data Assimilation, Paris. Schlatter, T. W. (1975). Some experiments with a multivariate statistical objective analysis scheme. Mon. Weather Rev. 103,246-257. Schroeder, L. C., Boggs, D. H., Dome, G., Halberstam, I. M., Jones, W. L., Pierson, W. J., and Wentz, F. J. (1982). The relationship between wind vector and normalized radar cross section used to derive Seasat-A satellite scatterometer winds. J. Geophys. Res. 87,3318-3336. Sela, J. G. (1980): Spectral modeling at the National Meteorological Center. Mon. Weather Rev. 108,17279- 1292.

140

DUNCAN B. ROSS ET AL.

Smith, S. D. (1980). Wind stress and heat flux over the ocean in gale force winds. J. Phys. Oceanogr. 10,709-726. Snyder, R. L., Dobson, F. W., Elliott, J. A,, and Long, R. B. (1981). Array measurements of atmospheric pressure fluctuations above surface gravity waves. J . Fluid Mech. 102, 1-59. Tracton, M. S.,Desmarais, A. J., van Haaren, R. J., and McPherson, R. D. (1981).On the system dependency of satellite sounding impact-comments on recent impact test results. Mon. Weather Rev. 109, 197-200. Valenzuela, G. R., Laing, M. B., and Daley, J. C. (1971). Ocean spectra for the high frequency waves as determined from Airborne Radar Measurements. J. Mar. Res. 29,6944. Weller, R. R., Rayne, R. E.,Large, W. G., and Zenk, W. (1983). Wind measured from an array of oceanographic moorings and from F/S METEOR during JASIN, 1978. J . Geophys. Res. 88(C14),9689-9705. Wentz, F. J., Christensen, E. J., and Richardson, K. A. (1981). Dependence of sea-surface microwave emissivity on friction velocity as derived from SMMR/SASS. In “Oceanography from Space,” pp. 741-750. Plenum, New York. Wilkerson, J. C., and McNutt, S . L. (1979).Seasat Gulf of Alaska Experiment surface truth data inventory. JPL Document No. 622-99,15 Feb. 1979. Wright, J. W. (1968).A new model for sea clutter. IEEE Trans. Antennas Propag. AP-16,217-223. Wy J. (1969). Froude number scaling of wind-stress coefficients. J. Atmos. Sci. 26,408-413. Yu, T . W., and McPherson, R. D. (1979).Surface pressure analysis using scatterometer-derived wind data from the Seasat-A satellite. Preprint paper, Cunf Numer. Weather Predict., 4 t h Oct. 29-Nov. I , Silver Spring, M D pp. 351-355. Yu,T. W., and McPherson, R. D. (1984).Global data assimilation experiments with scatterometer winds from Seasat-A. Mon. Mather Rev. 112,368-376.

SURFACE AND INTERNAL OCEAN WAVE OBSERVATIONS C. L. RUFENACH AND L. S . FEDOR National Oceanic and Atmospheric Administration Wave Propagation Laboratory Environmental Research Laboratories Boulder. Colorado

J. R. APEL Applied Physics Laboratory The Johns Hopkins University Laurel, Maryland

F. 1. GONZALEZ National Oceanic and Atmospheric Administration Par& Marine Environmental Laboratory Environmental Research Laboratories Seattle. Washington

1. Introduction. . . . . . . . . . . 2. Ocean Surface Waves. . . . . . . . 2.1. Physical Characteristics . . , . , 2.2. Altimetry . . . . . . . . . . 2.3. Synthetic Aperture Radar. . . . . 3. Ocean Internal Waves . . . . . . . 3.1. Observations . . . . . . . . . 3.2. Ancillary OceanographicInformation 3.3. Interpretation. . . . . . . . . 3.4. Theoretical Considerations . , . . 3.5. Amplitude Estimates . . . . . . 4. Summary and Conclusions. . . . . . . References . . . . . . . . . . .

. . . . . . . . . . . . . . . 141

. . . . . . . . . . . . . . . 142 . . . . . . . . . . . . . . . 142

. . . . . . . . . . . . . . . 146 . . . . . . . . . . . . . . . 153 . . . . . . . . . . . . . . . 176

. . . . . . . . . . . . . . .

176

. . . . . . . . . . . . . . . 181

. . . . . . . . . . . . . . . 183 . . . . . . . . . . . . . . . 184

. . . . . . . . . . . . . . .

189

. . . . . . . . . . . . . . . 192 . . . . . . . . . . . . . . . 193

1. INTRODUCTION

Two microwave radars, a radio altimeter and a synthetic aperture radar (SAR), were flown on board Seasat. The usefulness of these radars for ocean wave studies depends on an understanding of the interaction of these radar signals with both the short (capillary and gravity) and long ocean surface waves. The modulation in the SAR signal due to the long waves can be detected as an intensity modulation in the imagery whereas the stretching of the leading edge of the altimeter radar pulses is directly related to roughness of the surface or equivalently the ocean wave height. A theoretical description of this pulse stretching is not given here since it is well understood and has been given by others (e.g., Brown, 1977; Rufenach and Alpers, 1978). However, the interaction of SAR signals with the ocean surface is an area of active research where our understanding is still incomplete. Indeed, ad141 ADVANCES IN GEOPHYSICS.VOLUME 27

142

C. L. RUFENACH E T A L .

ditional experimental and theoretical work is required to consistently infer the djrectional wave spectrum from SAR. High-frequency internal waves are frequently observed in the stratified ocean near the continental shelf. These waves, which occur at a stratified boundary in the water column, modify the capillary and short gravity waves at the ocean surface. These changes in the short-wave field are visible in the SAR imagery since these surface manifestations of the internal waves change the radar cross-sectional modulation. This chapter is separated into two primary areas of discussion: In Section 2 ocean surface wave measurementsare described and in Section 3 internal wave measurements are described. Section 2 is separated into a description of the physical characteristics of the surface waves, altimetric measurements of wave height, and SAR theory and measurements. Described in Section 3 are the inferred internal wave results using SAR. 2. OCEAN SURFACE WAVES

2.1. Physical Characteristics

Ocean surface waves exist over a wide range of heights (centimetersto tens of meters) and wavelengths (hundreds of meters to millimeters). These water waves are governed by Navier-Stokes differential equations for incompressible fluids, which express the balance between inertial, convective, and restoring forces of the wave motion. The restoring force for waves with wavelengths longer than 1.7 cm is the gravitational acceleration g and such waves are called gravity waves, The restoring force for waves with wavelengths shorter than 1.7 cm is surface tension and these waves are called capillary waves. The sea surface is specified by a three-dimensional spatial and temporal displacement field ((x, y, t). Because of the nature of this field, it must be described by a random process that requires statistical analysis. The random field is usually considered locally homogeneous and stationary, which means that ensemble averaging is independent of (x, y , t ) over the area of interest. Therefore, the surface displacementcan be specified in terms of the directional wave-height spectrum X(k, o),which is a function of both the wavenumber k = ( k x , k,,) and radian frequency w = 2nf. The directional spectrum is the Fourier transform of the covariance of the sea surface displacement [ from its mean level at two points in space and time.

JJJ

X(k,w)=(2a)-3

([(x,t)&x

+ r , t +~))exp[-i(k.r -oz)]drdz

(1)

5. SURFACE AND INTERNAL OCEAN WAVE OBSERVATIONS

143

where x = (x, y ) and.the angle brackets represent an ensemble average. The spectrum X(k, W) is never actually measured in practice since simultaneous averages of estimates in time and space would be required. The spectrum given by Eq. (1) is normalized such that the mean-square surface height (c’) is

r or

where $(k) and S(o) are the two-dimensional spatial and one-dimensional temporal spectra, respectively, given by

$(k) = (24-l

I

X(k, W) d o

and

fs

S ( o ) = ( 2 ~ ) - ~X(k,w)dk

Ocean surface waves are considered here to be the superposition of many individual waves, which, in general, may include a wide range of wavelengths L = 2n/k and periods T = 2401 traveling in a wide range of directions 4 = arctan(k,/k,). Furthermore, gravity waves with small slopes in deep water are governed by a dispersion relationship with an intrinsic frequency o moving with the underlying deep water,

,/m

(6)

o=&i

where k = and g is the acceleration of gravity. Indeed, the waves are dispersive: the longer wavelength components travel faster than the Nonlinear, selfshorter ones with a phase velocity = o / k = interaction effects modify Eq. (6) when the wave amplitude c0 increases, i.e., l o k 4 1. Most notable, harmonic wave components are generated and there is a fractional increase of i(l0k)’ in the phase velocity. The one-dimensional temporal spectrum, Eq. (5), is usually measured with wave staffs and accelerometers on buoys or ships (time series of wave height sampled at a single point). This spectrum is dependent on the wind speed, duration, and fetch (distance over which the wind blows). Suppose that a uniform stationary wind is blowing constantly and perpendicular from a long, straight shoreline. Then a wave spectrum that grows in height and wavelength

c

fl.

144

C. L.RUFENACH ET AL.

develops along the fetch, according to Hasselmann et al. (1976):

where f, is the spectral peak frequency,a is the Phillips constant, y is the peakenhancement factor, and Q is the peak width (more precisely Q was separated by Hasselmann et al. for f 5 f, with oa and ob given by the left and right spectral widths, respectively). Equation (7) simplifies to the PiersonMoskowitz spectral shape for fully developed seas when y = 1. It has been shown by Kitaigorodskii (1973)that all wave field parameters, when nondimensionalized in terms of g and U,. should be functions of only the nondimensional fetch parameter 5 = g x / U 2 where x is the fetch and U is the wind speed referred to 10 m above mean sea level. The nondimensional peak frequency v = L'f,/g and Phillips constant a weie determined empirically over the nondimensional fetch range lo-' < 5 < lo4 relevant for both wave tanks (lo-' < t c 10') and open ocean (10' < 5 < lo4).

The shape parameters y, oa,and bb exhibited scatter, but no significant mean dependence on fetch; y = 3.3, aa = 0.07,and q,= 0.09. Although no detailed empirical dependence between v and duration has yet been established,it is clear that ultimately a dependence of v on both fetch and duration is involved. Indeed, a nondimensional duration parameter, gr'/ U, where z' is the duration, can be defined. At high wind speeds, fetch and duration are rarely sufficient to allow full development of the wave spectrum. The parameter v has been experimentally determined to approach 0.13 for fully developed seas ( 5 w lo4) whereas v > 0.13for less than fully developed seas. For some applications, the mean-square displacement (5') of Eq. (3) or equivalently the significant wave height, SWH, is of interest. The expression is SWH(m) = 4((52))1/2 = 9.4 x 10-4v-'0/6U2 (msec-')

(10)

Wave height for a fully developed sea based on v = 0.13, as given by Hasselmann et al. (1976)and as adjusted by Pierson (1977),is SWH(m) = 2.6 x 10-'U2

(msec-')

(1 1)

5. SURFACE AND INTERNAL OCEAN WAVE OBSERVATIONS

145

The above relationships do not consider swell propagating from distant sources. Furthermore, Hasselmann et al. (1980) have shown a detailed directional dependence, which may be useful for certain applications. The two-dimensional spectrum describes the direction of energy flow, Eq. (4), and is usually measured with a pitch-and-roll buoy, an array of pressure sensors, or an SAR. Pitch/roll (P/R) buoy measurements are carried out in the frequency domain, whereas SAR measurements are carried out in the wavenumber domain. The transformation from the wavenumber domain to the frequency domain is given by changing to polar coordinates $(k)dk,dk,

= $,(k,#)kdkd4 = S(o)dw

(12)

The one-dimensional temporal spectrum is then related to the twodimensional spatial spectrum based on Eq. (12) by s(w) = k(dk/do)$k(k, 4 )d4

(13)

It is sometimes reasonable to separate the directional dependence into $k(k, 4 ) = @O(k)f($) where [02nf(4)db = 1 Then S(w) = (k/P#o(k) where 5 is the group velocity [ x $ ( g / k ) for deep water]. The use of satellite altimeter signals scattered from the rough ocean surface is a well-established method of measuring the mean-square displacement of the surface (Brown, 1977). Stretching on the leading edge of the transmitted radar pulse is used to infer the mean-square value, which is usually reported in terms of the significant wave height. Recently, a new method of extracting more detailed ocean wave information has been developed. The surfaceheight probability function p ( [ ) appears in the integrand of the expression for the leading edge of the radar altimeter signal. This integral equation is easily inverted, therefore, to give the probability p ( r ) . The second moment of p ( [ ) , i.e.,

(C2>

=

rzP(c)dr

(14)

is the familiar mean-square displacement defined in Eqs. (2) and (3) and related to significant wave height in Eq. (10). Although the ocean surface often appears to have a Gaussian probability density to lowest order, there are situations that depart significantly from Gaussian. Breaking waves have a bimodal distribution, with height peaks that are harmonically related. Large

146

C . L.RUFENACH ET AL.

swell observed over a small area will produce a probability density with two peaks at c0 = +a, where a is the amplitude of the sinusoidal swell. The skewnessof the surface height probability, directly inferred from the altimeter, will convey more information about the waves than merely the wave height. The SAR capability for measuring the directional wave spectrum quantitatively, Eq. (4), is not as well understood as the altimeter wave-height capability. However, the limited available SAR measurements give accurate estimates of dominant wavelength and direction when observed in the imagery. Directional wave information is not extractable from the other instruments on board Seasat. 2.2. Altimetry 2.2.1. Background. The Seasat radar altimeter is the third in a series of spaceborne radar altimeters dedicated to oceanographic remote sensing. The basic measurementsare the height of the radar above the mean surface and the shape and amplitude of the mean return-power waveform. From these radar measurements and appropriate models for the interaction of the radar signal with the surface, one can extract certain surface and near-surface characteristics such as wind speed and SWH, which are very useful in describing the state of the surface. Although the altimeter designs of Seasat and the earlier altimeters differ somewhat, the basic functions of these radars remain the same. The Seasat system has a shorter pulse width and a higher pulse repetition frequency (Townsend, 1980), which lead to more accurate surface measurements. In addition, the Seasat radar signal processor was designed to provide an on-board estimate of the significant wave height of the ocean waves with only minor postcalibration corrections (Townsend, 1980). This feature is very important because of the goal to be able eventually to extract ocean surface parameters on board in near real time, and therefore eliminate or minimize ground processing of the raw radar data. The purpose of this chapter is to report on a study of the accuracy of ocean surface significant wave height derived from the radar altimeter data and appropriate scattering models. An accuracy study, of course, implies that there is an independent source to compare experimental measurements with and that the accuracy of the independent sources is known. In the past these independent sources have been hindcast estimates based upon numerical models, ship reports, and buoy measurements. Numerical model hindcast estimates and ship-reported measurements were very useful in initial comparison studies. Buoy measurements are considered to be the best source of comparative data. In this study we will consider only buoys as an independent source of surface measurements.

5. SURFACE AND INTERNAL OCEAN WAVE OBSERVATIONS

147

2.2.2. Comparison Considerations. A buoy measurement of wind speed and wave height comprises a point measurement in space and an average over time. The altimeter measurements are essentially averages over space at a specific point in time. That is, although the altimeter data may be averaged over many kilometers of along-track distance, the time required to do this may be less than a second. What this means is that comparisons between the altimeter and buoy data have meaning only if the measurements are nearly coincident in time and space or if the surface is homogeneous with respect to wave height or wind speed over the difference in time and space between the buoy and altimeter measurements. If the altimeter and buoy measurements are required to be exactly coincident in time and space, the common data set becomes extremely small because the altimeter’s groundtrack seldom passes directly over a buoy just when the buoy is reporting a measurement. To increase the common data set to a meaningful number, it is necessary to consider data sets that are not exactly coincident in time and space. Based upon similar wind and wave studies using GEOS-3 satellitemeasurements,the following time/space “window” was used in this study. In regard to space, the point of closest approach (PCA) between the altimeter groundtrack and the buoy location was required to be less than 80 km; for time, the difference between the PCA altimeter measurement and the buoy measurements was required to be less than 1.5 hr. As a result of this time/space window, the two data sets may differbecause of temporal and spatial inhomogeneity in the surface statistics. It is impossible to correct for this problem. There were cases in the data set in which the altimeter-derived wave height showed an along-track gradient in the vicinity of the buoy. There were also a few instances in which the wave height was changing with time as the altimeter groundtrack passed the PCA. However, these cases were not numerous, and furthermore there was no clear pattern to the difference between the two measurement sets. Consequently, although there may be some errors resulting from the noncoincidencein space or time, they are not considered to be a significant error source. With future spaceborne altimeters, this error source can be investigated in much more depth. The buoys used in this comparison (Fig. 1) comprised the NOAA buoy network located along the East, West, and Gulf Coasts of the United States. In addition, limited data were also obtained from Canadian Ocean Station PAPA located at 50”N and 150”W. For reference, the NOAA buoy networks consist of six buoys along the East Coast (Fig. la), three along the Gulf Coast (Fig. lb), and nine along the West Coast (Fig. lc). 2.2.3. Wave-Height Comparisons. The primary effect of ocean waves on the transmitted pulse of a radar altimeter is to stretch the leading edge of this

o

I

0

8

!

SB a

Ki

5. SURFACE AND INTERNAL OCEAN WAVE OBSERVATIONS

149

pulse. The slope of the leading edge responds inversely to the height of the waves: the smaller the slope, the higher the waves. As in a previous study (Fedor et al., 1979), we will consider the SWH to be four times the RMS height of the ocean waves. Earliest efforts to validate the capability of the Seasat radar altimeter onboard microprocessor to measure SWH employed a number of wave-measuring sensors including computer hindcast charts of SWH contours routinely distributed by the U.S. Navy Fleet Numerical Oceanographic Central, buoys, aircraft underflights, and intersecting arcs with the GEOS-3 radar altimeter (Tapley et al., 1979). East of these data sets has its own bias and quality of measurement. The most numerous comparisons with in situ measurements are provided by the buoys (along with instruments deployed at Ocean Station PAPA). The altimeter data were also processed using an algorithm developed by one of the authors (Fedor et al., 1979) as an aid in resolving differences between the buoy and on-board processed estimates on SWH. The Seasat preflight specifications for measuring SWH were accuracies within k0.5 m or 10% of SWH, whichever was larger (Townsend, 1980). A fundamental difference between buoy-measured wave heights and satellite altimeter measurements is that the measurements of the former are averaged temporally while the latter are averaged spatially along the satellite subtrack. To provide comparability between the two sets of measurements the altimeter results represent a spatial average over approximately 50 km along-track distance. A scatter diagram comparing the buoy measurements against the results of the on-board processor estimate of SWH is given in Fig. 2. A clear bias is evident for SWH > 2.0 m. It appears that the on-board processor overestimates SWH by 0.5 m in this range. For SWH < 2.0 m there is some scatter, but the values are well within the preflight specifications. The correlation coefficient between the two sets of data is very high, p = 0.945. Despite the obvious bias, the mean and RMS differences between two sets of data are very small, 28 and 38 cm, respectively. A similar diagram in Fig. 3 compares the buoy measurements with the radar altimeter measurements using the Fedor algorithm. A strong bias is no longer evident. The mean and RMS differences are well within the specifications for the measurement: 6 and 29 cm, respectively. Again the correlation coefficientis high, p = 0.965. These results show that the bias is introduced by the on-board processor and is not in the buoy measurements or raw altimeter waveform. The SWH data from the buoys were obtained from the NOAA National Data Buoy Office (NDBO), Bay St. Louis, Mississippi. These data represent standard output products from NDBO. The Ocean Station PAPA wave-rider buoy data (Earle, 1981) were specially processed for the Gulf of Alaska Experiment (GOASEX) (Wilkerson et al., 1979). Figure 4 shows individual

150

--E z

2 m_

C. L. RUFENACH ET AL.

5.0

-

4.0

-

Total # Points = 51 Mean Diff. = 0.279 m RMS Diff. = 0.377 rn Regression Coeff. = 0.838 Y-Intercept = 0.065 rn Correlation Coeff. = 0.945 RMS Diff. from Regr. Line = 0.329 rn

’

/ -/

3.0-

,/ / O /

%

0 PAPA

A

41001 42001 X 42003 44004 9 46001 % 46005

+

0

1

1.o

I 2 .o

1 3.0

1

4 .O

I

5.0

SWH (On-board Algorithm) (m)

FIG.2. Scatter diagram comparing significant wave-height estimates from the NOAA buoy network and Ocean Station PAPA with estimates from the Seasat altimeter on-board processor (51 observations).

scatter diagrams comparing S WH measurements from three NOAA buoys and Ocean Station PAPA with the altimeter estimate of SWH determined by the Fedor algorithm. The results for the PAPA comparison (Fig. 4a) cover the range of SWH from approximately 1 to 5.5 m. The mean differenceis 7 cm and the RMS difference is 10 cm, both well within the Seasat altimeter specification. No trends or biases are evident in the comparison. The correlation coefficient is 0.998, The comparison of altimeter estimates with NOAA buoy 41001 (Fig. 4b) shows a definite trend, i.e., the buoy underestimates the SWH.

1

6.0

5. SURFACE AND INTERNAL OCEAN WAVE OBSERVATIONS 6.

1

I

Total #I Points = 51 Mean Diff. = 0.065 m RMS Diff. = 0.286 rn Regression Coeff. = 0.902 Y-Intercept = 0.123 rn Correlation Coeff. = 0.965 RMS Diff. from Aegr. Line = 0.266 m

5

-

I

1

151

I

/

’

//

4.

d

3.

-

2.1

-

E

I

A

z

0 3

m, I

$

0

PAPA 41001 42001 X 42003 0 44004 9 46001 X 46005

A

+

1.(

I

1

1

I

1

2.0

3.0

4.0

5.0

6.0

I

#

/

I 1.o

3

SWH (Fedor Algorithm) (m)

FIG.3. Scatter diagram comparing significant wave-height estimates from the NOAA buoy network and Ocean Station PAPA with the Seasat altimeter estimates determined by using the Fedor algorithm (51 observations).

Note that the RMS difference from the regression line is 13 cm compared with the RMS differenceof 29 cm. On the other hand, the comparison for N O A A buoy 44004 (Fig. 4c) does not show a trend. The mean difference between the altimeter estimates and the buoy estimates of SWH is - 7 cm. This is the same order of magnitude as the PAPA comparisons. The scatter is slightly larger for buoy 44004 with an RMS difference of 16 an. Finally, the comparison for N O A A buoy 46005 again shows a trend (Fig. 4d); there is small scatter about the regression line and an RMS difference of 12 cm. These comparisons

152

C. L. RUFENACH ET AL.

6.0

I

5,0

-

4.0 -

I

60

I

I

Total U Points = 11 Mean Dill. = 0.066 m RMS Dill. = 0.098 m Regression Coell. = 0.976 Y-Intercept = 0.002 m Correlation Coell. = 0.998 RMS Dilf. from Regr. Line = 0.092 m /

/ / / / P /

-6

z

8

m,

3.0 -

3

m,

1

I

I

I

Total # Points = 13 Mean Difl = 0 196 m RMS Dill = 0 287 m o- Regression Coell = 0 650 Y-Intercept = 0 444 m Correlation Coell = 0 967 4 0- RMS Difl from

30-

I

R

/

‘a’

I

1

1

I 6.0

1

1.0 2.0 3.0 4.0 5.0 SWH (Fedor Algorithm) (m)

60

--E

1

I

I

/

//

// / ’/ /

// / // /

/ -

30-

0 41001

/ I

I

1.0

6.0

/

%

-

~I

(b) /

I

Total U Points = 11 Mean Difl = -0 073m Dill = 0 164 m o- RMS Regression Coell = 0920 Y-Intercept = 0 209 rn Correlation Coell = 0 977 4 0 - RMS Dill from Regr Line = 0 153 m

’/

I

I

I

)I

2.0 3.0 4.0 5.0 SWH (Fedor Algorithm) (m)

I

I

6.0

I

Total U Points = 8 Mean Dill. = 0.251 m RMS Difl. = 0.263 IT1 5,0- Regression COeff. = 0.713 Y-Intercept = 0.328 m Correlation Coefl. = 0.979 4.0 - RMS Regr. Dill. Line from = 0.121 m

/ // / // / /

-

-

3.0 -

-

-

2.0 -

-

I

044004 1

I

I

I

I

’/

0

/

k . / 1.0 I (dl

46005

I

1

I

I

2.0

3.0

4.0

5.0

SWH (Fedor Algorithm)

(m)

FIG.4. Scatter diagrams comparing significant wave-height estimates from individual buoys with estimates from the Seasat altimeter determined by using the Fedor algorithm: (a) Ocean Station PAPA (11 observations), (b) NOAA buoy 41001 (13 observations), (c) NOAA buoy 44004 (1 1 observations),(d) NOAA buoy 46005 (8 observations).

appear to show definite differences between the various buoys. The difficulty, of course, is an insufficient number of data samples. The scatter is small enough about the regression lines to indicate that the altimeter is able to detect definite differences between the buoys. This poses the interesting possibility in the future of using radar altimeters to calibrate new or refurbished buoys. 2.2.4. Summary. The Seasat altimeter has been shown to be capable of measuring SWH with a precision that exceeds the prelaunch specifications

6.0

5. SURFACE AND INTERNAL OCEAN WAVE OBSERVATIONS

153

of f0.5 m or 10% of SWH, whichever is greater. It is recommended that investigators requesting altimeter SWH data from the NOAA Environmental and Data Information Service reduce the data by 0.5 m for SWH greater than 2.0 m. 2.3. Synthetic Aperture Radar 2.3.1. Background. Of the various microwave sensors, SAR is generally considered to contain the greatest amount of ocean surface wave information. SAR has the potential of measuring ocean wavelength, wave direction, and wave height. However, the transfer function between the radar image and the ocean wave field is not, in general, a single one-to-one mapping. Indeed, on occasion, ocean waves are not imaged at all by SAR, implying that no wave information is available. The lack of wave patterns in the radar imagery can be attributed to two effects: first, the contrast, or equivalently, the intensity modulation in the image could be smaller than the system noise level; second, if the transfer function is highly nonlinear a gray image is formed. These limiting cases are dependent on the radar and ocean wave parameters, but typically, the first case occurs for smaller wave heights and longer wavelengths, whereas the second occurs for higher wave heights and shorter wavelengths. SAR is a phase-coherent radar that utilizes the Doppler shift, or equivalently its phase history produced by the uniform platform velocity, to locate targets in the flight direction. If the targets move during the time interval required to form the phase history, then its history is modified and the target locations differ from the ones expected for stationary targets. Suppose that an ensemble of targets are moving uniformly in the flight direction; then there is no relative position error between the targets. However, suppose that targets exist that are spatially separated in the flight direction with different radial velocity components. Then the radar senses differential Doppler histories in the flight direction which are encountered when the orbital motion of the long ocean waves travel in the flight direction. The wavelike patterns observed in SAR imagery can be caused by two mechanisms: (1) the usual, “real” cross-section modulation Acr due to tilt and hydrodynamic modulation by the long ocean waves; and (2) an “artificial” cross-section modulation due to ocean wave motion-orbital velocity and acceleration of the long waves. Furthermore, the importance of motion could be dominated by the stochastic character of wind waves. Random drifting scatterers riding on the long waves can cause a dispersion of the radial velocities of these scatters, which can degrade the image formation process. A measure of this randomness of the scene is the scene coherence time. No conclusive measurements are currently available that establish the dominant

154

C. L. RUFENACH ET AL.

mechanism. However, ocean wave motion is most important for long ocean waves traveling along the direction of radar platform motion, while real crossscatterers riding on the long waves can cause a dispersion of the radial platform motion. Synthetic aperture radar itself has an inherent integration time required to resolve targets along the flight direction (azimuth resolution). This integration time can be modified during processing; however, the azimuth resolution is also modified. In other words, the integration time, or equivalently the time interval that a target is illuminated by the radar beam, is longest if the full azimuth bandwidth is utilized in processing. Alternately, if only a fraction of the total available bandwidth is used, then the resolution is degraded, or equivalently a shorter effective integration time, However, for this case, only a fraction of the total available bandwidth is available to form the image, implying that several fractional bandwidth subimages can be incoherently averaged over a period equal to the full integration time needed to form the total image. This type of processing is called mixed integration or multiplelook processing, which always leads to a degraded resolution over the onelook (full-bandwidth)processing. Indeed, if the full bandwidth is utilized in image formation of a stationary scene, then multiple-look processing can be used to decrease the noisy background (speckle)in the image without loss of scene detail, provided the scene detail is not undersampled. However, for a moving ocean surface, the optimum processing is more complicated. The effect of moving point targets on SAR is well understood and has been investigated over the last 10 years (e.g., Raney, 1971; Shuchman et al., 1981). However, the extension of these point-target results to imaging of ocean waves (distributed scene) is complicated since not only must the results be extended from a point target to imaging of a distributed scene, but the ocean motion, which includes the scene coherence and long-wave parameters (orbital motion and phase velocity), must be considered. The interaction of the SAR with these ocean wave motions has been investigated by numerous authors: e.g., Larson et al. (1976), Alpers and Rufenach (1979), Shuchman et al. (1978), Jain (1978), Tomiyasu (1978), Rufenach and Alpers (1981), Valenzuela (1980), Harger (1980), Alpers et al. (1981) Raney (1980), Hasselmann (1980), and Shuchman (1980). However, the radar interaction mechanisms due to these motions are sometimes interpreted differently by different authors or their underlying assumptions may differ on occasion. For example, Valenzuela (1980), Jain (198 l), and Shuchman and Zelenka (1978) concluded that the influenceof the long-wave phase velocity on the image formation is important whereas others such as Alpers et al. (1981) argued that this velocity is unimportant or second order. The relative importance of the randomness of the ocean wave scene compared to the systematic orbital motion as the dominant mechanism for image degradation is also interpreted differently.

5. SURFACE AND INTERNAL OCEAN WAVE OBSERVATIONS

155

Raney (1980) investigated how a scene with a random fade is imaged by SAR, deriving expressions for azimuth resolution in terms of scene coherence. Rufenach and Alpers (198 1 ) derived an expression dependent on both scene coherence and orbital motion. They argued that the orbital motion dominates and that the relevant scene coherence time is on the order of 1.0 sec in contrast to 10 to 100 msec suggested by Raney. Furthermore, Swift and Wilson (1979) and Alpers and Rufenach (1979) have suggested that the secondharmonic component of the ocean waves may be present in the imagery under certain conditions. The limited available imagery has not been interpreted in terms of this second-harmonic component. The real cross-sectional modulation is maximum for the long ocean waves traveling in the range direction, in contrast to the maximum sensitivity for ocean surface motions, which occurs for motion along the direction of flight, the cross-range direction. A quantitative two-dimensional description of the cross-sectionalmodulation including both tilt and hydrodynamics is not well developed. Some experiments specifically designed to measure the modulation transfer functions on the open ocean have been performed (e.g., Alpers and Jones, 1978; Wright er al., 1980). These experiments show that the dominant scattering mechanism is two-scale Bragg scattering as originally described by Wright (1968) and Bass et al. (1968). This model is based on a surface wave field divided into two regions of different wavelength scales: short waves represented by the Bragg resonant condition; and long waves represented locally by tangent surface segments called facets of dimension D, which are small compared with the long wavelength but large compared with the short wavelength. Therefore,the Bragg scattering theory can be applied in the local reference system of moving tilted facets. This two-scale model describes cross-sectionalmodulation caused by tilt and hydrodynamic effects. The tilt modulation is due to changes in the local incidence angle induced by the slope of the long ocean waves. The hydrodynamic modulation is due to hydrodynamic interaction between the short Bragg scattering waves and the long waves, which results in an asymmetrical distribution of the short waves relative to the crests of the long-wave field (e.g., Keller and Wright, 1975). 2.3.2. Theory 2.3.2.1. ArtiJcial modulation. Suppose that a radar platform is moving along the x direction at a range r with a velocity V. Furthermore, assume that a single point target located at xo, yo in a horizontal plane is riding on a monochromatic long ocean wave with wavenumber k = (kx,k,,), angular frequency w, and wave amplitude with the wave field traveling at an angle with respect to the flight direction. An artist’s conception of the relevant geometry is given in Fig. 5, including the angle of incidence 8. The form of the

co

156

C. L.RUFENACH ET AL. z

I

b Serest

Satellite

z

, ~

/

SAR Antenna /

/

/

R

I

Phase Histories of Points A & 0 7 -

\

a

FIG.5 . Geometry for observation of the ocean surface by Seasat synthetic aperture radar. In (a) the geometry for Seasat SAR observations of the ocean surface is defined in the x y plane; in particular, the observations are of long gravity waves at a point (x,y) traveling at an angle Q, with respect to the SAR flight path and having a wave vector k, where k = 2n/L and L is the ocean wavelength. In (b) note that SAR image resolution along the y or range direction is obtained by a short radar pulse length. In (c) resolution along the x or azimuth direction is obtained by observing the different phase histories of points A and B.

point-target intensity response at the image plane ix(x,xo) along the flight direction, following Rufenach and Alpers (1981), is i,(x,xo)a =-T:~erp[-<(x-xo-~ 2 Pa Pa

>'I

(15)

V ur

where x and xo are the azimuthal positions of the target in the image plane and on the surface, respectively. T, = IZr/(2Vpa)is the integration time, pa = v/Bais the theoretical azimuthal resolution for a stationary target, Bais the azimuthal bandwidth, and

is the resolution of a point target, due to harmonic motion of the long ocean waves. The radial components of orbital velocity and acceleration are u, and a,, respectively, and 1 is the radar wavelength. The radar transmits pulses as it moves along the x direction. These pulses travel down to the surface point target and return to the radar receiver. The pulses are modulated with a linear

5. SURFACE AND INTERNAL OCEAN WAVE OBSERVATIONS

157

frequency called a chirp modulation, which can be represented by a quadratic variation of phase; the 'radar geometry causes a natural quadratic phase variation in the azimuth or along-track direction. Therefore, the surface is scanned in the range direction (r) at one-half the speed of light and in the azimuth direction (x) at a much slower platform velocity I/. If the same procedure is followed as in developing Eq. (1 5 ) and if coupling between the two orthogonal intensities is neglected (Hasselmann, 1980), the point-target intensity response ir(r,ro) along the range direction is where r and ro are the range positions of the target in the image plane and on the surface, respectively. The transmitted pulse width is T,, p; = c/B, is the one-way range resolution, c is the velocity of light, and B, is the radar system bandwidth. For the SAR flown on board the Seasat satellite, Y 2 7 km sec-', Ba 2 1kHz, pa 2 6 m,and pr = 2pi = 2c/B, (Jordan, 1980). Note that pr is the slant range resolution and pa is the one-look resolution or equivalently the fullbandwidth resolution. The conversion from slant range r to ground range y is p,, = pr/sin 8, where the angle of incidence 8 E 20" for Seasat. The smearing or degradation in azimuthal resolution caused by ocean wave motion, as given by Eq. (16),has been extended to include the influence of the number of multiple looks N, and the scene coherence time z, (Rufenach and Alpers, 1981):

Suppose that the scene is coherent, Tx/z,= 0, and that the orbital acceleration is negligibly small, (n/AN)T:ar << 1. Then Eq. (18) reduces to the standard multiple-look expression for stationary targets, P b N = NPll

(19)

The usual processing for the Seasat SAR in which N = 4 (four looks) implies pbnr2 24 m. The influence of a moving point target on radar imagery is well understood. However, the extension of these results to imaging a distributed surface such as the ocean surface is more complicated. First, the assumptions that are required to evaluate the intensity modulation differ between different workers; second, the description of the interaction of ocean wave surface with the radar signals is complicated and incomplete as required for a quantitative description of the image formation process. The imagery must be described in terms of radar signals scattered from a distributed surface. Therefore, the responses of the individual point targets

158

C. L. RUFENACH ET AL.

must be superimposed to obtain the distributed surface result. The scattering surface is considered statistically white for typical SAR resolution cell sizes, of say meters, assuming that the backscattered signal is due primarily to the short Bragg waves. The scattering surface is then rough at microwave radar frequencies, which implies uniformly distributed phases. Therefore, the intensity due to the ocean surface is

w,Y.).(=.I[[

X o ) W Yo)@,,

Y o ) dxo dY0

(20)

where g(x0, yo) is the radar scattering cross section per unit area for a point target located within dx, dy,. The usual horizontal coordinates y, called the ground range, must be converted to the radar slant-range coordinate I such that dy = &/sin 8 and dy, = &-,/sin 8. In the present work, the variables x, y are used to describe the ocean surface wave field and the variables x, r are used to describe the radar signals. Equation (20) can be evaluated in closed form under certain conditions. Suppose that the effects of orbital acceleration and scene coherence are negligibly small, then pbN = Np,; furthermore, suppose that the radar resol1.itions piN and pr are small compared to the long ocean wavelength. Then it is reasonable to represent the resolution cells as delta functions:

1 The two-dimensional image intensity, following Alpers and Rufenach (1979) is then

where a[x - (r/V)ur, r] is slowly varying within a resolution cell. The magnitude function 11 + (r/V)(d/dxo)urlcauses a modulation due to the orbita velocity gradient along the flight direction. This modulation is sometimes called velocity bunching, as illustrated in Fig. 6, where the anipiitude q of (r/ Wdurldxo) is rj

= ( r / ~ ) c , o cos k @J(sin

8sin

+ cosz e

(24)

The influence of velocity bunching is strongly dependent on q. This bunching under certain conditions can lead to pronounced contrast in the imagery. The displacement Ax causing velocity bunching [see Eq. (IS)] can be

1

L

112 L

312 L

2L

Real Surface, xo

FIG.6. Illustration of velocity bunching. (a) Surface elevation associated with an ocean wave traveling in azimuthal direction. (b) Azimuthal shift of scatterers in the image plane; xo is the aximuthal axis on.the real surface, and x on the mapped surface, i.e., in the image plane. Shift x - xo is given by velocity of scatterers in range direction. It is proportional to wave amplitude i0 and, for wT/2 << 1, also to wave frequency w. (c) Relative power (intensity) per azimuthal resolution cell as function of azimuthal position xo for different values of q.

expressed as

AX = x - XO

=(~/V)U,

(25)

which is independent of radar wavelength 2. The effect of orbital acceleration is also important in the image formation process. However, the acceleration smearing is dependent on radar wavelength [see Eq. (16) or (18)]. The image degradation due to orbital acceleration can be minimized in radar system design by selecting the shortest radar wavelength available for a given radar antenna. A sharp image with pronounced modulation could then be

160

C. L. RUFENACH ET AL.

produced by the velocity bunching, as illustrated in Fig. 6. This effect is maximum for CD = 0". Furthermore, Fig. 6 illustrates one condition required for a linear transfer function (between ocean wave field and intensity modulation), namely, when q is small, e.g., q 2 0.3. The linear condition is important for obtaining the directional wave spectrum without distortion (one-to-one mapping). Unfortunately, the linear condition only occurs over a narrow range of ocean radar parameters (e.g., Alpers et al., 1981). Suppose that long Ocean waves are propagating along the same direction as the platform motion, i.e., Q, = 0". Equation (24) can then be expressed as q lo=o = (r/'V)Cowkcos8

(26)

which shows that the velocity bunching parameter q is independent of range since the platform height, h = r cos 8, implies

v le=o = (h/VCowk

(27)

Equation (27) illustrates that velocity bunching is independent of range for CD = 0. Therefore, long-crested azimuthal traveling ocean waves should be

imaged with the same modulation over the range dimension of the image, provided the second-order effects such as orbital acceleration and scene coherence are negligibly small. 2.3.2.2. Real modulation. The image formation caused by ocean surface waves when the ocean wave motion is negligible is called the real crosssectional modulation or the modulation transfer function. A theoretical twoscale model that describes this modulation, including tilting and straining (hydrodynamicinteraction) of the short Bragg waves, has been developed (e.g., Keller and Wright, 1975;Alpers and Hasselman, 1978). This theory is similar to the two-scale tilt modulation model previously developed (Wright, 1968). This real modulation is the dominant component when the surface wave motion is negligible. This condition is most likely to exist when the long ocean waves are traveling in a cone centered on the range direction (Alpers et al., 1981). Equation (23) then simplifies to

b, r)=

Y , 8,

(28)

where K = (n/4)T,?T i N p , p : is a constant for a given radar system. The real modulation to first order is then A1 = KAo ITiLT + KAa lwmt

(29)

where Anla ITlLT =m

P Q

W a IHYDR = fid

where s is the RMS slope;

"pQ

(30)

(31) = gradTapQ iik/bPQ is the tilt mod-

5. SURFACE AND INTERNAL OCEAN WAVE OBSERVATIONS

161

ulation in terms of the spatial rate of change of the radar cross section (grad, cPQ) along the horizontal direction that the long waves are traveling, Zk; P and Q are the indices associated with transmitter and receiver antenna polarizations, respectively; j = and 6" is the hydrodynamic modulation, a complex quantity (e.g., Keller and Wright, 1975). The total real modulation is therefore tG = jmPQ+ tGH. Tilt modulation. The tilt modulation caused by tilting of the long waves changes the local slope of the short Bragg waves. This tilting is a function of the position of a scatterer on the long wave. The maximum modulation occurs on the forward (toward radar) face of the long wave, 90" out of phase with the wave amplitude. The dependence of the tilt modulation on azimuthal angle Q, has received little attention. This dependence is needed to determine which mechanism is important for imaging ocean waves. Elachi and Brown (1977) considered the azimuthal dependence based on Valenzuela's (1968) work. Alpers et al. (1981) also considered this modulation in terms of the orthogonal directions Q, = 0" and n/2. These models show that the tilt modulation is a factor of about 10 larger for waves traveling in the range direction Q, = n/2 relative to azimuth traveling waves Q, = O", based on infinitely long-crested waves with 8 = 20"relevant for the SAR flown on board Seasat. In this section, a model of this tilt modulation to first order in slope is developed. The tilt modulation is due to the tilting, or equivalently the slope, of long ocean surface waves. Since these slopes are usually small on the open ocean, this approximation is normally very good. Suppose the ocean surface is taken as a random two-dimensional rough surface i(x, y ) with zero mean. The coordinate system is chosen so the mean plane corresponds to z = 0 with increasing z out of the surface, rather than the one-dimensional, long-crested sinusoidal wave field used in the previous artificial modulation section. Therefore, tilting in two orthogonal dimensions is important, as illustrated in Fig. 7. The orthogonal slopes of the rough surface are (, and 5, and the total RMS slope is s = ,/((,') + ((,'). These slopes can be expressed in terms of the angular deviations as shown in Fig. 7,

0;

r, = -tanycosQ, ry = -tanysinQ,

(32) (33)

where tan y = -s. The tilted-plane coordinates are given by y', z' and the reference-plane coordinates are given by x, y, z. Therefore, the local angle of incidence 6 to first order in local slope is XI,

+ lxtan6cos6 + rYtan6sin6) sin 8 = sin I9( 1 - i,ctn I9 cos 6 - c,, ctn 8 sin 6)

cos I9 = cos6(1

(34)

(35)

162

C. L. RUFENACH ET AL.

ii

I I

\ \ \

Y

---

X’

FIG.7. Geometry for scattering from a two-dimensional rough surface ((x,y). The z = 0 plane corresponds to the m:an surface. The unit normal to the rough surface is ii; the radar wavenumber vector is ko.

where 6 is the azimuthal angle between the reference coordinate and the incidence plane. Furthermore, the local Bragg wavenumbers can also be expressed in terms of the local slope kx, = - 2k,(sin 8 cos 6 - [, cos 8)

(36)

- 2ko(sin8 sin 6 - 5, cos 8)

(37)

k,.

=

where k, is the radar wavenumber. The total Fragg wavenumber kB = can also be expressed as k, = - 2k, sin 8. Furthermore, when the long-wave slopes are negligible, i.e., 5, = 5, z 0, then kB reduces to the standard expression, kB = -2ko sin 8. Alpers et al. (1981) investigated the tilt modulation in terms of the tilt angles in and perpendicular to the reference plane of incidence. However, here a slightly different approach is used by analyzing the modulation in terms of the slopes in and perpendicular to the reference plane (Cx, 5,). These slopes can be defined in terms of two sphericalgeometry angles (y, @), as given in Eqs. (32) and (33) and illustrated in Fig. 7. The radar cross section for a point target based on a perfectly conducting rough ocean, assuming that the radar ray paths are sufficiently removed from nadir (specular reflection negligible) and sufficiently removed from grazing (shadowing negligible), is given by

d m ,

~PQ(L ly98, CS) = G&,, ly,8, W’(kx,, ky,)

(38)

163

5. SURFACE AND INTERNAL OCEAN WAVE OBSERVATIONS

where Y(k,, k y ) is the directional wave spectrum whose spectral density is evaluated at the local Bragg wavenumbers k,,, k,.. TpQ.is the scattering coefficient in terms of small slopes which reduces to Rice’s (1951) result provided 1, = 1, = 0. These scattering coefficients based on a perfectly conducting surface (Brown, 1978; Bahar, 1981), are THH = 4nk: cos4 8(l

Tv,

= 4nk$(l

+ 41, tan 0 cos 6 + 4lYtan 8 sin 6)

+ sin‘ 8)’(

1-

(39)

)

21, sin 20 cos 6 - 21, sin 28 sin 6 1 + sin26 1 + sin’e

(40)

TVH = 0 (41) Equations (39) and (40)assume that the angle between the reference plane and the local reference plane is small, causing negligible cross-polarization. This condition is not always satisfied, since SAR measurements of ocean waves have been observed with cross-polarized antennas. However, the analysis of th’e cross-polarization is complicated and beyond the scope of the present analysis. The tilt modulation is given by the total derivative of oPQwith respect to the slope s along the horizontal direction iik = cos @ax sin @ay: = grad.,

-

cpQiik = da,p cos

4,

+ + -sin

dcrPQ

d1Y

(42)

where grad, OPQ = (daPQ/dCx)zx + (dSPQ/dry>zy For a side-lookingradar, where 6 = n/2, Eqs. (36) and (37) simplify since the radar antenna is then pointed along the y direction. These equations can be further simplified for the geometry assumed by others. For example, consider long-crested, range-traveling waves, where @ = 4 2 , then

which is a special case of Eq. (42) previously given by Keller and Wright (1975) in a slightly different form since ticy = -d8. Substitution of Eqs. (38) through (40) into Eq. (43) gives, in terms of tilt modulation, 1 ~ C H H= 4tanO + 4ctn8 mHH = --

(44)

OHH aCy

and 1 aovv m,, = -bvv

81,

=

-

2sin28 1 + sin’e

+ 4ctnO

(45)

164

C. L. RUFENACH ET AL.

which is consistent with results evaluated numerically in Alpers et al. (1981); e.g., the minimum value for the horizontal polarization is 8 when 0 = 45" in Eq. (44). Hydrodynamic modulation. The hydrodynamic modulation is caused by the spatial dependence of the spectral energy of the short waves over a long ocean wavelength. This modulation of the spectral energy at the short wavelengths fe.g., 10 to 30 cm) has been investigated by several workers. Recently, this modulation was measured at a Bragg wavelength of 13 cm in a region off the west coast of California for winds up to 10m sec- and wave periods from 2 to 18 sec (Wright et al., 1980). The incidence angle 8 was selected to minimize the tilt modulation. The real modulation m for vertical polarization varied from about 5 to 20 and the phase varied between 20 to 40"forward of the long-wave crest. The hydrodynamic theory presently available predicts a maximum modulation of 4.5. This contrasts to the larger measured modulations of 5 to 20. Several workers have considered the modification of the short waves by the long waves. Keller and Wright (1975) developed a relaxation-time theory for isotropic short waves. If the short waves are perturbed from equilibrium they will relax back to the previous unperturbed state at an exponential rate defined by the relaxation time constant p - l . Alpers and Hasselmann (1978) developed a similar dependence using an action-density approach. Recently, Phillips (1981) derived an expression based on action density for an anisotropic short-wave spectrum for an inviscid fluid ( p = 0),which means the maximum spectral density of the short waves occurs at the crest of the long waves. This expression for hydrodynamic modulation includes both longwave direction @ and a wind direction @,,, relative to the radar look direction: mH = ms(@,@w

f h(@)

(46)

where

"

1

2 df cos(@- Ow) ms(@,O W , f )= - sin(@- a,,) - 2 9f(@w) d@w and

(47)

where f is the angular spectrum of waves symmetrical about the wind direction, C is the phase speed.of the long waves, and Eis the phase speed of the short waves. Equation (47) contains the short-wave dependence and Eq. (48) contains the dependence of the long waves. Phillips has suggested that the direction distribution of the waves f (QW) may not be important unless it is narrow. Indeed it is thought that the assumption of isotropic short waves as

5. SURFACE AND INTERNAL OCEAN WAVE OBSERVATIONS

165

assumed by Keller and Wright (1975), and at least implicitly by Alpers et al. (1981), is reasonable. The widening of the directional wave spectrum as the wavelength decreases has recently been reported for a series of measurements in the North Sea (Hasselmann et al., 1980). The hydrodynamic modulation then simplifies to

m = $sinZ@

(49)

provided

f(@,)

=1

and

c>>C

This simplification of Eqs. (46)-(48) is in agreement with Alpers and Hasselmann (1978) and Alpers et al. (1981). However, Eq. (49) gives a maximum modulation of 4.5, whereas the measurements show modulations of 5 to 20. These measurements are much larger than expected based on Eq. (47). Therefore, the theory needs to be reexamined with the hope of bringing it into better agreement with the larger measured values. One possibility is to include a wind-dependent factor for the spectral density at the shorter gravity waves while another is to reexamine the tilt-modulation theory in more detail. Available measurements show a wind dependence for these short gravity waves (Wright and Keller, 1971).

2.3.3 Wave Measurements, Spaceborne SAR has provided quantitative information on dominant wavelength and direction of the long-surface-wave field. However, before this imagery can be fully exploited to provide routine synoptic information on the long-wave field, both the conditions under which the wave field causes wave-like patterns to form in the imagery must be quantitatively defined, and if the wave spectra are required, then the conditions when the transfer function between the ocean wave field and the image intensity is linear must be specified. In addition, an optimum SAR system and processor should be investigated for future satellite systems. This section describes field observations of ocean surface waves by Seasat and airborne SAR, with emphasis on the satellite measurements. The ocean surface wave information extracted from SAR is conveniently separated into three physical quantities: the dominant ocean wavelength, wave direction, and the directional wave spectra. First, the accuracy of SAR-derived dominant wavelength and direction estimates must be considered, given the existence of dominant ocean wave signatures in a SAR image. Second, the dependence of the detectability of these dominant wave systems on the scene parameters needs to be defined. The scene parameters are the oceanographic, meteorological, radar, and geometric parameters that characterize a particular measurement. Third, a

166

C. L. RUFENACH ET AL.

description of the transfer function relating the ocean wave directional spectrum to a SAR image intensity spectrum is needed. The first two categories are discussed in Sections 2.3.3.1 and 2.3.3.2, and the third in Section 2.3.3.3. These three categories are, of course, related. In fact, it is clear that precise knowledge of the transfer function could yield answers to the first two categories. Nonetheless, this artificial separation has proven useful. 2.3.3.1. Dominant wavelength and direction. Straightforward comparisons of in situ ocean measurements and radar measurements are reported here from three major field experiments: GOASEX (the Gulf of Alaska Seasat Experiment), JASIN (the Joint Air-Sea Interaction experiment), and Duck-X (an experiment performed near Duck, North Carolina). These comparisons have been summarized by Vesecky and Stewart (1982)in plots presented in Figs. 8 and 9. Details of the data reduction and analysis for each of the three individual data sets are given by Gonzalez et al. (1981), Vesecky and Stewart (1982),and Beal(l981). Each data point is the resuit of two different procedures. The first procedure, used to obtain in situ estimates,involved computing a wavelength by inserting the observed peak frequency of the ocean wave spectra into the

I

I

I

‘

300

400

0

0 GOASEX 0 Duck - X 0 JASIN

400 500!

300 -

200 -

100

~

100

200

500

Wavelength by Surface Observation (rn)

FIG.8. Comparison of dominant ocean wavelength as estimated by Seasat SAR and by surface P/R buoys. The average percentage difference between the SAR and insitu estimates is f 12%.(From Vesecky and Stewart, 1982.)

5. SURFACE AND INTERNAL OCEAN WAVE OBSERVATIONS

100"

2000

167

300" 360"

Direction by Surface Observation (degrees true) FIG.9. Comparison of dominant ocean wave direction as estimated by Seasat SAR and by surface P/R buoys. The SAR estimates show no significant bias with respect to the surface estimates, and the mean difference between the two measurements is f 15". (From Vesecky and Stewart, 1982.)

appropriate ocean surface gravity-wave dispersion relation. Most of these in situ observations were made with wave-rider buoys, which provided only onedimensional energy spectra; only a few estimates of the directional energy distribution were available from P/R buoys. This explains the fewer number of data points plotted in Fig. 9. The second procedure involved taking a twodimensional optical Fourier transform (OFT) or digital Fourier transform (DFT) of a portion of SAR imagery corresponding to a 10- to 25-km2 area centered at or near the position of the in situ wave measurements. The wavelength and direction associated with each local peak in the resulting image intensity spectra were then assumed to be the dominant wave systems present and were paired with the corresponding surface observations to provide each data point in Figs. 8 and 9. Examples of such a Seasat ocean wave scene, an OFT and DFT of that scene, and a P/R buoy record taken coincident with the SAR measurements are presented in Fig. 10. The limited available measurements show that the SAR-provided dominant wavelength and direction estimates yield more detailed information than the corresponding in situ estimates. There are several aspects of the data taken in tota that support this view. The agreement between the limited available SAR and in situ measurements is within the accuracy of the surface observations: the average percentage difference between estimates is +12%, and the mean difference in wave

168

C. L. RUFENACH ET AL.

plr, BUOY Rev I126 I3 Sept 1978

1730 GMT Significant wave height 2 7 rn Wind speed 3 8 m h c Wind direction 2 4 4 '

01 0

I

1

0.004 0006 0.012

1 1 1 0.016 0.020 0.024 0.028

K ( m-9

(d)

PITCH/AOLL BUOY RECORD

FIG.10. (a)The 15 x 15-km SAR sceneacquired on September 13,1978, during GOASEX,and optically processed by the Jet Propulsion Laboratory. (b) Optical (OFT) and (c) digital (DFT) Fourier transforms of the SAR scene in (a). In each representation. distance from the center is proportional to wavenumber k = 2n/L. The two local peaks evident in each transform represent two dominant wave systems characterized by similar wavenumbers (approximately 0.0225 and 0.0250),but whose direction differs by about 20". (d) P/R buoy spectral estimates S(k) computed from data acquired coincident with the SAR imagery of (a). Note that, in contrast to the Fourier transforms, only one dominant wave system is indicated at wavenumber k z 0.024 rad m-'. The original data provides spectral estimatesin terms of the temporal wave frequency;these data have been transformed to wavenumber space using the Jacobian appropriate to the linear surface gravity wave dispersion relationship. The wavenumbers in (b),(c),and (d) are plotted in terms of spatial frequency K = l/L.

direction estimates is f 15%. Recent simulated SAR wave spectra suggest that the dominant ocean wavelength will be shifted (biased) toward longer wavelengths over a certain range of ocean/radar parameters (Alpers, 1983). This range corresponds to a nonlinear transfer function, which requires a

5. SURFACE AND INTERNAL OCEAN WAVE OBSERVATIONS

169

Monte Carlo method to obtain the SAR spectra. Figure 8 tends to support this bias toward longer wavelengths for part of the SAR-inferred wavelengths. Additional analysis of measurements are required to see if this bias may be more significant on occasion. Biases in the wave direction should also be present since only the azimuthal SAR wavelength is biased. Finally, there are strong indications that SAR gives greater detail than the P/R buoys. Two examples in point are presented in Figs. 10 and 11. In each case, the sea state was evidently characterized by two dominant wave systems somewhat similar in wavenumber and direction. The SAR detected both systems, which the P/R buoy was unable to resolve. It is conceivable that the indications of a multiple wave system in the cases are not real, and are instead an artifact of the SAR system. This does not seem likely, however, since there is good agreement between a number of SAR and in situ observations of single dominant wave systems that were taken under similar conditions. The accuracies claimed for SAR assume that the scene parameters are favorable, in the sense that wave signatures are, in fact, present in the imagery. The important question of what constitutes favorable scene parameters for detectability is discussed below. 2.3.3.2. Detectability. A number of scene parameters may affect the detectability of a dominant-wave system: among them are oceanographic and meteorological parameters (wavelength, significant wave height, wind speed

10

Wave Directional Distribution JASlN FIA Area 4 Aug 1978 0600 UT Buoy

SAR Image. Rev 547

Atlantis II Pitch/Roll Buoy

I 240'

I 210"

11 180'

I

I 270'

I 330"

300"

360'

True Direction from which Waves Arrive 1

60"

I

90"

1

1

1200

150'

I

180"

I

210"

Angle between Wave Direction and SAR Flight Path

FIG.11. Comparison of the ocean wave directional distribution derived from P/R buoy measurements and the image intensity spectrum obtained as the result of performing the digital Fourier transform of a Seasat SAR image. The distribution refers to ocean wavelengths near the dominant wavelength of about 170 m. (From Vesecky and Stewart, 1982.)

170

C. L. RUFENACH ET AL.

and direction, atmospheric stability);radar parameters (frequency,resolution, integration time); and geometric parameters (range, incidence angle, and radar-wave and radar-wind angles). For this reason a relevant theory and the associated dependences are required for a quantitative understanding of the image formation process. A good measure of the quality of SAR wave imagery is the fractional modulation of the backscattered intensity. An example which illustrates the different image formation processes is the complex ocean wave field present during the GOASEX experiment on September 26, 1978. Three separate identifiable wave systems traveling in different directions with different wavelengths (nominally 250, 130 and 20 m) were directed by surface measurements (Gonzelez et al., 1981). An analysis of these surface measurements indicates approximately equal energies in each system, SWH z 0.6 m, since the total SWH z 1.1 m. The longer wavelength (250-m) system was not detected in the satellite or aircraft imagery whereas the shorter wavelength (130 m) system was detected by both the satellite and aircraft radars. Furthermore, the shortest wavelength (20-m) system was detected by the aircraft radar whose resolution is pa = 3 m, whereas the satellite radar whose resolution was paN= 25 m did not detect this sytem as expected because of resolution limitations. The detectability is dependent on both the real and artificial modulation with significant azimuthal degradation occurring when ocean wave motions are important. These resolution limitations are not easily obtained when the wave motion is important since a closed form expression is unavailable. The modulation and hence the degradation is given by substituting Eqs. (15)-( 18) into Eq. (20). The degradation is most important for azimuth traveling waves (4 = 00). This case is illustrated in Rufenach and Alpers (1981) for scene parameters similar to the Seasat (L-band) radar with a range of ocean wave lengths and wave heights. For the complex wave field observed on September 26 c0 is approximately 0.21 m, based on narrow wave systems. Therefore, a small azimuthal modulation is expected for the 250-m system and a larger modulation is expected for the 130-m system based on extrapolating the results of Rufenach and Alpers. Furthermore, a significant degradation at I = 0.25 m is expected for the 20-m system. The real modulation expressions [see Eq. (29)] show increased detectability as the long-wave slope increases. The wave systems on September 26 were traveling primarily in the range direction (@ > 45") for both the Seasat and aircraft radar imagery selected here (r/V ratios within a factor of 3). The important result is that the 250-m wave system was not detected by either radar. This nondetection is interpreted as insufficientwave slope for real modulation detection whereas the 130-m wave system is interpreted as sufficient wave slope for detection. An example which illustrates the importance of ocean motion effects on detectability is the case of azimuth-traveling ocean waves. Indeed, theory and

5. SURFACE AND INTERNAL OCEAN WAVE OBSERVATIONS

171

measurements suggest that relatively low wave heights or wave slopes are required to detect these waves. Therefore, it is significant that the Seasat analysis detected only two azimuthal wave systems generated by distant storms that were hundreds of kilometers away from the measurements and resulted in nearly monochromatic ocean wave swell. Thus, one of these wave systems, approximately 240 m in length, traveling in azimuth and apparently generated about 2 days earlier by Hurricane Gilma, was detected off the coast of Baja California, after traveling some 2000 km across the northeast Pacific (Fig. 12). In the second case, Gonzalez et al. (1981) reported that a 500-m azimuth-traveling wave, evidently generated in the Southern Ocean (Fig. 13), was detected in the northeast Pacific Ocean. These nearly monochromatic wave systems evidently experienced less smearing due to nonlinear mapping than the wider spectral wave systems usually associated with wind-driven wave fields. 125" I

W

115'

1200

I

110'

105"

I

\

Waves Detected at

.

30"

25"

\ 2OC

15" 0 0000 GMT 0 1200 GMT

Wave Hindcast Position 16 July for 0300 GMT I 1200

I 115O

I 110"

FIG.12. Seasat SAR detection of azimuth traveling waves generated by Hurricane Gilma approximately 2 days earlier at a point about 2000 km away. L = 240 m and @ 2 0.

172

C. L.RUFENACH ET AL.

FIG.13. Digital Fourier transform of SAR scene acquired August 13,1978, on Seasat Rev 674 at a point in the northeast Pacific approximately 17"N and 115"W. The azimuth orientation is straight down in this presentation, and the spectral peak at k = 1/2nL = 6.4 x radm-' or k = 1/L = 0.002 m-', corrpponding to a 500-m-long azimuth-traveling wave believed to have originated in the South Pacific. The radial axis is in units of.h-(m-').

A dual-frequency SAR (X and L band) was mounted on the Canadian CV580 aircraft and flown in ihe Puget Sound region during the GOASEX experiment (September 1978). This SAR observed ocean waves traveling in the azimuth direction on flight lines (1) and (4) as illustrated in Fig. 14. The

GOASEX and Seasat results illustrate that azimuth waves can be detected by both aircraft and satellites. 2.3.3.3. Directional wave spectrum. A quantitative description of ocean surface wave fields is needed for verifying and/or forecasting input to marine wave models (Earle, 1981). Ideally, the two-dimensional wave spectrum with fine resolution is desired. In practice, few measurements of the directional

5. SURFACE AND INTERNAL OCEAN WAVE OBSERVATIONS

173

S easa t HEA DING

3

4 SYSTEM LATFORM

*

Seasat

**ERIM CV-580

**ERIM CV-580

-L (m) %9, -

---

L-BAND )

-BANI

L

RELATIVE WAVE HEADING

i

(m) Ob9) --

102

159

98

356

158

97

355

CROSSWAVE

( 1)

152

152

110

318

142

115

323

UPWAVE

(2)

242

162

103

221

168

96

214

UPWAVE

(3)

283

177

ioa

185

177

108

185

CROSSWAVE

(4)

138

109

136

167

109

136

DOWNWAVE

(5)

141 101

39

153

113

51

DOWNWAVE

(6)

333 62

FIG.14. CV-580 aircraft and satellite SAR wave measurements during September 23, 1978; GOASEX Rev 1269. Airborne observations were made with six different viewing angles as illustrated. @,isthe true azimuth heading and @A is the long-wave azimuth heading relative to the aircraft direction (see Fig. 5). From P/R bouy: L = 151 m; CP = 93". Seasat data, optical Fourier transform; CV-580 data, digita1 Fourier transform.

174

C.L. RUFENACH ET AL.

properties of the waves are available in the region of interest and at the time desired, Therefore an operational imaging radar which consistently images the long ocean waves would be useful since at least the dominant wavelength and direction could be extracted. Eventually, when the nature of the transfer function is better understood and optimum radar systems are developed for wave imaging, it may be possible to provide the directional wave spectrum. A spaceborne SAR could provide global directional information as well as measurements useful in determining the spatial evolution of the wave field (Beal, 1981;Shuchman and Kasischke, 1981). This type of measurement could lead to a better understanding of wave dynamics including interaction with current boundaries boundaries and bathymetry effects. To extract directional wave spectra using SAR one must correct for radar system distortions including the effect of the finite resolution of stationary scatterers as well as the nonlinear transformation between the ocean wave field and the intensity modulation in the image. Past oceanographic experiments have demonstrated that SAR can provide information on the general shape of the directional wave spectrum. Furthermore, wave measurements can be obtained under a variety of wave conditions including hostile environments such as hurricanes (Elachi et al. 1977). Monaldo and Beal (1981) have corrected the intensity wave spectra using Seasat imagery. This finite resolution is caused by a faster spectral decrease than expected based on an infinitesimally small resolution at the high wavenumbers. In particular, if the azimuthal antenna beamwidth is approximated by a uniform rectangular be’am,then the point target response is of the form sin x’/x’, where the width of the spectrum is a measure of the resolution and the shape is a measure of the correction in the wave spectrum. Monaldo and Beal(l981) and Beal(l981) show that this correction is of a Gaussian form with width k, 12 ~ / 6 0m-l. Unfortunately, once the correction has been performed, the intensity spectral estimates may show azimuthal wavenumber smearing directly related to the local sea state. In other words, the corrected spectra show azimuthal smearing that is most pronounced at the high wind speeds, consistent with the theory given by Eq. (18),provided the high winds imply larger long waves and orbital motions. Therefore, if the intensity spectral estimates are used to extract qualitative information about the ocean wave spectrum, the high-frequency portion of this intensity spectrum, say wavelengths shorter than 100 m, would underestimate the ocean wave spectral estimates because of the radar’s finite resolution (Beal, 1981). Spectral analyses of wavelike patterns in SAR imagery have been performed and compared with P/R buoy measurements and other surface measurements in or near the area of radar measurements. McLeish et al. (1980) and Vesecky and Stewart (1982)provide two of the more comprehensive comparisons that

5. SURFACE AND INTERNAL OCEAN WAVE OBSERVATIONS

175

have been undertaken. McLeish et al. showed a radar directional spectrum that was narrower than the fitted model based on the P/R buoy estimates. It was also noted that the radar system directional resolution was higher than the P/R buoy resolution. Wave-height spectra obtained by a laser profile meter exhibit spectral shapes similar to the radar-extracted intensity spectra on most occasions. However, some significant discrepancies are present on occasion. Vesecky and Stewart suggest that the local wind has little effect on the wave formation process in the imagery except for long waves traveling in regions of very low surface winds. They claim that the Bragg (short) waves may not scatter sufficient energy at these low wind speeds, say 2 m sec-' or less. Furthermore Vecesky and Stewart have suggested the intensity spectra more closely approximate the wave-height spectra than the wave-slope spectra, after including a correction for finite resolution. 2.3.4. Conclusions. Among the various microwave radars developed to measure ocean waves from either aircraft or satellites, synthetic aperture radar imagery is considered to contain the greatest amount of information. When ocean wavelike patterns are observed in the imagery they are in agreement with surface measurements of dominant wavelength and direction. The ocean surface waves are observed more often when they are traveling perpendicular to the flight direction (in the range direction). Imagery of ocean waves is smeared or degraded in the flight direction because of the coherent integration time (- 1.0 sec) required to form the image. For this situation, wave motion causes the modulation transfer function to become nonlinear unless orbital acceleration and velocity are negligibly small. This nonlinearity imposes a fundamental limitation on extracting the directional wave spectrum from SAR. Linearity is required for interpreting the intensity (radar) modulation in terms of the directional wave spectrum. The intensity modulation is caused by two mechanisms: first, artificial modulation due to the orbital motions described above; and second, the real cross-sectional modulation due to the geometric tilting and hydrodynamic straining of the Bragg scatterers riding on the long ocean waves. Artificial modulation is dominant for the long ocean waves traveling along the flight direction, whereas the real modulation dominates for waves traveling cross-flight (in the range direction). The modulation-transfer function is likely to be nonlinear except for a narrow cone of long-wave directions centered on the range direction. If possible, future SAR systems should be designed in an attempt to ensure linearity and detection of the long waves. This means a radar with the shortest possible radar wavelength to minimize the degraded azimuth resolution caused by orbital acceleration.

176

C.

L. RUFENACH ET AL.

3. OCEAN INTERNAL WAVES 3.1. Observations

Internal gravity waves are frequently observed in stratified waters on the continental shelf as highly coherent groups having well-defined but variable wavelengths and generally propagating shoreward. It is well known that there are often surface signatures accompanying the underlying oscillations which take the form of regions of either enhanced or reduced small-scale roughness, or both, depending upon the amplitude and phase of the waves. Such surface signatures have been observed with a variety of remote sensors, including photographs and scanner images on satellites, as well as synthetic aperture radar images made from aircraft (Apel et al., 1975a,b, 1976; Elachi and Apel, 1976). The Seasat spacecraft, launched in June 1978, was equipped with a SAR having a wavelength of 25 cm, a mean incidence angle of 23.4, and a spatial resolution of 25 m. This instrument was originally intended to provide directional spectra of long-length surface gravity waves, especially under storm conditions. However, SAR also images many features on the sea surface that modulate the short-wavelength (-30 cm) ocean roughness, since waves of this length are effectively Bragg scatterers under the existing radar illumination conditions. It was therefore expected that the radar would image surface manifestations of internal waves (Brown et al., 1975). Such has apparently proved to be the case. Figure 15a shows a map of the Baja California region in the vicinity of Bahia San Juanico on the Pacific side of the peninsula. Figure 15b illustrates an optically processed SAR image of that region made during pass 150 on July 7,1978, at approximately 04:OO local time. The general low reflectance of the sea surface indicates that very light wind conditions existed during this predawn pass. The image reveals a considerable number of periodic striations on the water surface. Superimposed on the map of Fig. 15a is a line-drawing interpretation of the features on Fig. 15b that displays the periodic nature of both the striations within the packets and the intervals between packets. Visible are several wave groups located landward of the 200-m isobath, oriented approximately parallel to the depth contours. These features are interpreted as being due to underlying quasiperiodic internal wave fields. An analysis of the packets visible in the SAR image yields the following characteristics, which are deduced by direct measurement of the image in Fig. 15b: 1. The waves occur in well-defined packets or groups, typically four in

5. SURFACE AND INTERNAL OCEAN WAVE OBSERVATIONS 26'

I

I

0

177

112"

1

\

10 20 30 40 50

/ 25.

1

113"

FIG.15a. Map and line-drawinginterpretation of several packets of internal waves in Bahia San Juanico off Baja California,as observed on July 7, 1978, with the Seasat SAR. Packets are labeled lS, 2 s,...; lN, 2N,....

number, propagating shoreward and separated by an average interpacket distance D = 18.7 km, with a range of 15 to 23 km. 2. The crests are generally oriented along isobaths and have an average leading-crest length in a packet, C = 55 km, with a range of 36 to 76 km. 3. Within a given packet, the crest lengths are longest at the front of the wave group and shortest at the rear. 4. For the packets that are visible, the individual wavelengths I average 390 m in length, with a range of 200 to 1600 m. The waves exhibit a general decrease in wavelength from the front to the rear of the packet. This feature is illustrated on Fig. 16 for the four groups located along the south shore of Bahia San Juanico and numbered sequentially from west to east as 1s to 4s. 5. For the packets, the average angular spread of propagation direction A8 is 12",with a range of 7 to 20". The individual packet characteristics may be represented to a reasonable extent by a spectral analysis of the surface signature of a given packet. Figure 17 illustrates an enlargement of a portion of the well-defined packet 2s located near Santo Domingo del Pacifico. This group is propagating toward shore

FIG.15b. SAR image for a portion of the region shown in Fig. 15% illustrating quasiperiodic variations in surface radar reflectivit) associated with underlying internal waves.

5. SURFACE AND INTERNAL OCEAN WAVE OBSERVATIONS

179

.

\ Y

&

400

P,

.s T

O

O

1 2 3 4 1 2 3 D I S T A N C E FROM FRONT O F W A V E PACKET s (km)

FIG.16. Variationsin wavelengthsof internal waves from front to rear of four packets located along the southeastern portion of Bahia San Juanico. Packets are numbered sequentially from west (seaward)to east as shown in Fig. 15a. A monotonic decrease in wavelength is a ubiquitous feature of nonlinear internal waves.

at an angle of about 78" true (approximately ENE). The image shown is 14.54 km on a side and has a fundamental spatial resolution of 25 m. The two largest visible waves have lengths of 1000 to 1600 m, while the trailing waves are near 400 m in length. This information is summarized succinctly in the two-dimensional Fourier transform' of the image sector in Fig. 17, as shown in Fig. 18a. The concentrations of image intensity, i.e., radar backscatter, are located at angles of approximately 78 and 258"T, with full widths at half-maximum, (A& 2 12". The five dominant wavenumbers are at 0.35,0.71, 1.2, 1.8, and 2.6 cyc km-', coinciding with the wavelengths apparent in the visual image (the peak at 0.35 cyc km-' may well be an artifact). Figure 18b shows the angular distribution of the two-dimensional transform, taken along a constant wavenumber of 1.19 f 0.10 cyc km-'; Fig. 18c gives the wavenumber distribution obtained from a scan at a constant angle of 78 & 5". The latter clearly shows the multiple-peaked wave number distribution dominated by peaks at the five wavenumbers mentioned above. Although this spectral analysis strictly represents the spatial variation of only backscattered radar power, it has been shown by Ape1 and co-workers

' The fundamental pixel size used is 28.2 m, and the image and transform arrays have 512 pixels on a side.

180

C. L. RUFENACH ET AL.

FIG.17 Enlargement of packet 2s seen in Fig. 15a, showing details of a portion of the packet. The section shown is approximately 14.54 km on a side and contains 2 waves having lengths of about 1400m, followed by approximately 10waves with lengths less than 500 m. Dark stripes are regions of increased surface roughness overlying internal wave troughs, followed by light regions caused by smoother-than-average surface wave fields.

that the surface roughness overlying coherent internal waves bears a definite phase relationship to the underlying internal wave field (Apel and Holbrook, 1980). In particular, the rough surface regions appear to be phase-locked to the downgoing or trough phases of the internal oscillations; these are followed by an anomalously smooth surface region immediately behind the rough portion. Indications of this rough-to-smooth transition are clearly visible in the long waves of Fig. 17 (Elachi and Apel, 1976). Thus the patterns of surface roughness and smoothness serve to delineate the underlying internal wave fields, at least insofar as the fundamental internal spatial frequencies are concerned, and a spectral analysis of the surface signatures such as presented here may reasonably be taken as representing the dominant internal wavenumbers and directions.

5. SURFACE AND INTERNAL OCEAN WAVE OBSERVATIONS

181

Nor1 h

FIG.18a. Two-dimensional digital Fourier transform of the SAR image of packet 2s (Fig. 17), showing spatial frequencies of surface roughness variations as a function of east (k,) and north (k,) components of the wave vector. The three largest maxima correspond to the variable lengths of waves seen in Fig. 17. This spectrum is related to the distribution of dominant wave vectors in the underlying internal waves.

3.2. Ancillary Oceanographic Information Other properties of the ocean in this region that are pertinent to internal wave generation are the vertical density distribution, the characteristics of tidal currents, and the presence of bathymetric features that protrude into the thermocline. Figure 19 shows vertical profiles of density anomaly, a, = ( p - 1) x lo’, and Brunt-Vaisala frequency N , where

N 2= -(g/P)(aP/W and where p is the density and g = 9.8 m sec-’. The profiles were calculated from historical data for July 1978, in the region of interest (Bell et al., 1974). A shallow mixed layer of 20 to 30 m in depth and a maximum buoyancy frequency Nm8,/2n of approximately 8.3 cyc hr-l are apparent. Tidal height data for July 1978 are shown in Fig. 20, for the tide station at Bahia Magdalena. The tidal heights show mixed semidiurnal and diurnal tides with a range approaching 1.4 m. On continental shelf waters, such amplitudes may generate tidal currents in excess of 1 knot; indeed, tidal current tables give ebb and flood speeds of 0.50 and 0.65 m sec-’, respectively, on July 7.

1.50

>-

I

k

1

1

= 1.19 f .ICyC km-'

In

I\

z

: W

1.00

I(e)

2 l-

0.50

a

J

w

f

a

o.oo--d---

1 ' L---

1

3.0

t v)

: 2

z

2.0

I (Ikl)

; a

1.0

J

w

IL

Ox)

2

4

8

6

2

WAVENUMBER

12

10

14

(cycles km-'1

FIG.18band c. (b) Angular distribution of surface roughness variations obtained by evaluating Fig. 18a along a constant wavenumber. The narrowness of the angular spread indicates a nearuniform direction of propagation for this section of the packet. (c) Wavenumber distributions of surface roughness variations obtained by evaluating Fig. 18a along a constant angle. The peaks correspond to the dominant wavenumbers visible in Fig. 17.

0

1

1

1

'

1

'

"

\

\

$

1

-.

50-

91

d 4

100-

X

klu

150-

200

I

I

I

FIG.19. Historical density anomaly and Brunt-Vaisala frequency during July for the region of Fig. 15. A shallow mixed layer and highly stable water characterize the upper ocean.

5. SURFACE AND INTERNAL OCEAN WAVE OBSERVATIONS

183

Y

i=

T I M E t (day)

FIG.20. Tidal heights for early July 1978, for the region of Fig. 15. Associated ebb and flood current speeds are near 0.50 to 0.65 m sec-', respectively.

Bathymetric charts for the region of the Seasat image illustrate a broad continental shelf of under 200 m in depth, extending some 80 km to sea. A sequence of offshorebanks whose depths are as shallow as 15 m lie beyond the shelf break, and their positions with respect to the internal wave packets suggest that these play some role in the wave-generation process (Halpern, 1971; Haury et al., 1979; Farmer and Smith, 1978). 3.3. Interpretation

To explain the characteristicsof the surface signatures,it is assumed that the waves are generated on a semidiurnal basis by tidal flow over the offshore banks, or along the shelf break near 200 m, through one of two possible mechanisms. The first, shear flow instability, invokes the existence of a strongly baroclinic tidal flow in the vicinity of the banks or the shelf for a few hours near the peak tidal flow onshore, during which time an internal wave packet is generated and propagates shoreward (Proni et al., 1978). This process is repeated approximately every 12; or 25 hr, depending on the semidiurnal or diurnal nature of the tides. The linear theory for shear flow instability shows reasonably good agreement with experiment for internal waves at the continental shelf edge south of Long Island (Tsai and Apel, 1979). The second mechanism is similar to the first in terms of the tidal periodicity of the packets, except that it assumes the wave packet is generated during offshore flow as a lee wave consisting of a few to several oscillations. The condition for generation is that the flow speed over the bank should exceed the intrinsic speed of propagation for internal waves. The wave packet may be linear or nonlinear at its point of formation, depending on the Froude number of the flow (Maxworthy, 1979). Either of these two mechanisms may result in a series of nonlinear, coherent internal wave packets, which then propagate shoreward as radiation fields, to be absorbed via breaking and turbulence in water whose depth approaches the mixed-layer depth. The problem at hand is to deduce something of the

184

C. L. RUFENACH ET AL.

generation and propagation characteristics of the individual packets from the SAR images through the application of a simple hydrodynamic theory. 3.4. Theoretical Considerations

The propagation of weakly nonlinear internal waves in a stratified medium is known to be governed by the Korteweg-deVries equation or its generalizations (Keulegan, 1953; Benjamin, 1966,1967; Ono, 1975; Kubota et al., 1978). For a two-dimensional ( x , z ) wave field (with z downward), the displacement q(x,z, t ) within the thermocline may be written as ~ ( xz,, t ) = W ( z ) A ( x t, )

(50)

where A ( x , t ) is the separation function for the wave amplitude given as a departure from an undisturbed streamline, and W(z) is the vertical eigenfunction or structure function, valid in this depth regime for either the linear or nonlinear problem. For waves with long horizontal scales L,and small but finite amplitudes such that H / L and A / H are both small compared to unity, the double amplitude A(x, t ) satisfies the Korteweg-deVries equation, A,

+ co(A, + yA,,, + otAA,) = 0

(51)

where co is the phase (and group) speed of infinitesimal-amplitudewaves at zero wavenumber and x, z, and t subscripts refer to partial derivatives. The environmental parameters y and a are interpreted below. In the absence of a mean current, the structure function W(z)satisfies the eigenvalue equation,

W,,,+ k:"2(z)/o,z

- 11 W,= 0

where k, = 2 ~ / 1 ,is the linear wave number, w, is the radian frequency for mode n, and N ( z ) is the Brunt-Vaisala frequency as defined previously. The boundary conditions for a rigid lid model have W(0)= W ( H )= 0, where H is the depth of water. Solutions to Eq. (52) yield both eigenfunctions W,(az)and eigenvalues w,(k), which define families of the dispersion relation o = o,(k), with n = 1, 2,.. .. Figure 21 shows examples of the two lowest-order eigenfunctions obtained using the Brunt-Vaisala profile of Fig. 19 and evaluated at a wavenumber k1/2x = 2.6 cyc km-'. Figure 22 illustrates the linear phase and group speeds, cpl= w/k and cgI= dw/ak, respectively. Note that for the lowest mode, both speeds are asymptotic to co = 0.716 msec-' as k + 0. Henceforth only this lowest-order mode will be considered, and the modal subscript n will be suppressed. Figure 23 illustrates the dispersion relation. The waves observed in Fig. 15, whose average wavelength is

5. SURFACE AND INTERNAL OCEAN WAVE OBSERVATIONS

185

0 NORMALIZED EIGENFUNCTIONS, W ( o z )

FIG. 21. Vertical eigenfunctions for the two lowest-order modes, evaluated at a linear wavenumber k,/2n = 2.6 cyc km-'.

FIG.22. Linear phase and group speeds for the two lowest-order modes, n calculated from the density profile of Fig. 19. I

7

-Nrnox 8-

I

1

I

=

1 and n = 2, as

_i

-

2

-2 0

6-

t N

3

4-

0 >-

z

5

2-

L

O

ea

1

I

0

2

4

I

I

6

8

1

i0

HORIZONTAL WAVENUMBER k,/Er(cyc

km-')

FIG.23. Dispersion relation for the two lowest-order modes. The short waves in Fig. 15 correspond to wavenumbers near 2.6 cyc km-'.

186

C. L. RUFENACH ET AL.

approximately 390 m, have k1/2n = 2.6 cyc km-’, cpl= 0.52, cgl = 0.27 msec-’, and a period T = 2n/w = 0.21 hr. Returning to Eq. (51), the environmental parameters a and y are functions of the density profile and eigenfunctions and are given by

where the expressions on the right-hand side are the parameters evaluated for a two-layer model of upper and lower depths h, and h 2 , respectively, and are included for illustrative purposes only. To interpret Eqs. (53)-(59, the nonlinear dispersion equation that is equivalent to Eq. (51) is used. Differentiate between the linear phase and group speeds cpl= o/kl and csl = aw/dk,, respectively, and their nonlinear duals cpand cg. Then for periodic (but not necessarily sinusoidal) oscillations, the nonlinear speeds may be obtained directly from the Korteweg-deVries equation using arguments found in Whitham (1974):

o / k , = cp = co(l - ykf = cpl

c%o/dk,

+ 2CoaA/3

= cg = co(l - 3yk; = cgl

+ 2aA/3)

+ 2coaA/3

(56)

+ 2aA/3) (57)

The quantities cpl= co(l - yk:) and cgl= co(l - 3ykf) are long-wavelength quadratic approximations to the full linear dispersion relation (Fig. 22). Thus, from Eq. (56) the coefficient y gives the wavenumber y-1/2 for which the quadratic approximation, 1 - yk:, to the full dispersion term vanishes. For the case at hand, numerical integration gives y = 1860 m2,or 1/2 ~ y = ” 3.69 ~ cyc km-’. These quadratic approximations are shown for the lowest-order mode in Fig. 22; the approximations are poor beyond k1/2n E 3 cyc km-’ for the phase speed and 1 cyc km-’ for the group speed. The nonlinear parameter a measures the degree of amplitude “cohesion” brought about by the tendency for a finite-amplitude wave to travel faster, behaving as if it were propagating on a thermocline that is deepened by the wave’s own amplitude. For the present example, it has the numerical value of 1.25 x m-’, or a-’ = 80 m. From Eq. (56), a wave of double amplitude

5. SURFACE AND INTERNAL OCEAN WAVE OBSERVATIONS

187

80 m would have more than 13times the phase speed of an infinitesimal wave; an internal wave with A of order 80 m would thus be strongly nonlinear in this geographical region. Very large waves that have the speed increment, 2c0aA/3, of order 0.40 m sec-', have been directly observed with current meters in the Sulu Sea in the Philippines (Ape1and Holbrook, 1980; Holbrook et al., 1980). There are several steady-state solutions known for the internal KortewegdeVries equation (Keulegan, 1953; Benjamin, 1966, 1967; Ono, 1975; Joseph, 1977; Whitham, 1974). The best known is the long-wavelength soliton solution, which, for water deep compared with the thermocline depth but still shallow, has the simple form A(x,t ) = - A , sechz[2(x - c,t)/L]

(58)

describing a single bell-shaped thermocline depression moving to the right at constant speed cp,with the parameter L characterizing the width of the pulse. In the absence of dissipation and horizontal divergence, the solitary wave propagates indefinitely without change of shape or amplitude. A second steady-state solution incorporates the periodic cnoidal function, cn,(c$), one of the Jacobian elliptic functions (Abramowitz and Stegun, 1964), and is given by A(x, t ) = A, - A,~n;[1/2(k,x - ot)]

(59)

This solution also moves without change of shape but is characterized by an infinite number of down-going troughs of double amplitude - A , , with the requirement for zero mean displacement to be determined by the constant A,, which is generally much smaller than A , . The cnoidal wave is a nonlinear generalization of the cosine, to which it reduces when the nonlinear parameter mz + 0. It has a so-called stretched wavelength A, defined by , I= A(m2) = 4K(m2)/kl 2 271/kl

(60) where K(m2)is the complete elliptic integral of the first kind with argument mz, and where 0 I mz I1. As before, k, = 271/A, is the linear wavenumber. As the degree of nonlinearity increases, the wavelength A increases from its smallamplitude value of 271/kl until, with complete nonlinearity, mz + 1 and A + 00, leaving only an isolated pulse as the solution. In this limit, cnrn(4) m-i sech(4)

and the solitary wave is recovered. The asymptotic approach to the soliton solution allows one to relate the small-amplitude wavenumber k, to the soliton width parameter L. Clearly kl = 4/L. Thus the cnoidal solution to Eq. (51) is a generalization of the solitary wave, encompassing it at one limit and the ordinary cosinusoidal solution at the other limit of infinitesimal amplitude.

C. L. RUFENACH ET AL.

188

This lower limit also allows one to discuss groups or packets of cnoids in exactly the same fashion as for cosines. The nonlinear parameter m2 is given by (Whitham, 1974) m 2 = aA/3yk: = (c0aA/3)/(c0- cpl)

(61)

which is the ratio of the nonlinear speed increment to the dispersive linear phase speed decrement. The invariance of the steady-state solutions results from a balance between ordinary linear dispersion on one hand, and amplitude cohesion on the other. While longer waves travel faster, so do larger ones. The wavelength and amplitude variations in the observed internal wave packets therefore reflect the tendency for the longest and largest Fourier components to move out faster than the shorter, smaller ones. This suggests that an approximate model function to describe the observed internal wave fields can be constructed from cnoidal solutions to the Korteweg-deVries equation, without asserting that the model constitutes a rigorous solution to the governingequations. Parameters of the model can be fitted to the observations and information thereby deduced. The model function advanced is given by q(x, y, z, t ) = 0, =

s

- c,t < 0

-Ao(s - c,O%(x,

(62)

Y )W(az)

x cnf [1/2(k, s - wt

+ &)I,

s - C,t

>0

This function describes a cnoidal wave packet propagating along a horizontal arc s = s(x, y) in the xy plane. The modulation envelope, - Ao(s - c,t) of the oscillations, which determinesthe packet amplitude, shape, and dimensions,is downgoing and commences at s - c,t = 0. The curvature of the packet isophase fronts in the horizontal plane is described by the function R,(x, y) and may be empirically determined directly from the SAR imagery. W(az)is the vertical eigenfunction with a characteristic scale of 1/a. The nonlinear parameter m2,as given by Eq. (61), is a function of both amplitude and the linear phase speed decrement, and connects the envelope function A. , the phase speed, and the stretched wavelength through Eqs. (56), (60),and (61). It is by these relationships that the variable wavelength is introduced into the model function. Phase $, adjusts the onset of the oscillation to some convenient value and is taken as $,, = 2K(rn2). Equation (62) is not an exact solution to the Korteweg-deVries equation due to the inclusion of the amplitude modulation function but is in error only by a factor of order Ao/T’ compared with 2A0/?jwhere Tpis the duration of the packet, of order 2 hr. In general, the ratio exceeds 20 and hence the error is small, The function models the salient features of continental shelf internal waves as seen at a large number of sites around the world.

5. SURFACE AND INTERNAL OCEAN WAVE OBSERVATIONS

189

3.5. Amplitude Estimates

It is in principle possible to estimate the amplitude of internal waves from satellite observations of the surface signatures by at least two methods. The first makes use of the nonlinear group speed increment mentioned above in conjunction with measurements of interpacket separation to deduce the amplitude from Eq. (57). It will be called the time-of-flight method. The second makes use of the observed variation of wavelengths within a given packet, invoking properties of the cnoidal solution to the Korteweg-deVries equation to deduce the degree of nonlinearity and from this, amplitude. This will be called the variation-of-wavelength method. The first measures average speed and amplitude between any two packets separated by a tidal cycle in time and the attendant propagation distance in space; the second measures the local, near-instantaneous properties of a single packet. Such deductions are not expected to be highly accurate, of course, but they can provide factor-oftwo estimates if the nonlinear effects are appreciable. These concepts have been applied to the SAR imagery of Figs. 15b and 17a, and the results are interpreted in terms of the cnoidal model function and its auxiliaries, Eqs. (56), (57), and (60)-(62). 3.5.1. Time-of -Flight Method. The quantity determined here from the SAR imagery is the interpacket separation D, which together with the assumed semidiurnal tidal period z = 12.42 hr yields an average nonlinear group speed (c,). In this case, the average separation D between centroids of the offshore packets is 22 km, which gives (c,) = 0.49 m sec-'. By integrating Eq. (57) over a tidal period, the double amplitude (assumed constant) is obtained

from which one may in principle obtain the packet-average amplitude, A. For the case at hand, however, the characteristics of the bathymetry and density distribution mitigate against the application of the method, mainly because the nonlinear speed increment vanishes in some region between the packets near the 200-m isobath and those packets inshore, near H = 100 m. This can be understood from Eq. (53), where the two-layer model predicts a = 0 when hl = h2. The equivalent two-layer depth is approximately 75 m (Fig. 21), which implies a = 0 near the 150-m isobath. Solitary waves go through some sort of an ill-understood transition at such a point, but presumably propagate at near-linear speeds in the vicinity of the transition. The theoretical linear group speed for these packets, when calculated as an intensity-weighted average over the dominant spectral components, has a

190

C. L. RUFENACH ET AL.

value cg, = 0.49 m sec-' which is the same as the average nonlinear group speed derived from the image, within the errors associated with the method. Thus the nonlinear increment in this case is too small to be deduced from Eq. (63),and the time-of-flight fails. 3.5.2. Variation-ofi Wavelength Method. The starting point for the second method is Fig. 16, from which we have extracted an averaged and smoothed wavelength variation, A = A(s), for the three southernmost packets, 1s to 3S, which are in moderately deep water. The origin for s is taken ahead of the lead wave at one-half its length. Table I lists these wavelengths, which range from 700 m at the leading edge to 272 m at the trailing edge. From this, the smallamplitude asymptotic wavelength is obtained by extrapolation as A1 = 270 m. The standard deviation of A for the data set is approximately f50 m. Thus the wavelength stretch is A(350)/1, = 2.59 = 2K(0.995)/n. From Eq. (60) one calculates K ( m 2 )and hence m'(s); from Eq. (61)and the data for Fig. 22 comes A&). As can be seen from Table I, the estimated nonlinearity and amplitude range from 0.995 and 30 m at the leading edge, to 0.03 and 3 m at the trailing edge. The values at the approximate packet centroid are 0.63 and 50 m. The considerable surface roughness implied by the intensity variations in the SAR image lends credence to such large amplitudes. It is likely, however, that these

TABLE I. SMOOTHEDNONLINEAR WAVE-PACKET PARAMETERS'

350 1050 1540 1950 2320 2660 2980 3285 3575 3860 4135 m

700 490 410 370 340 320 305 290 285 275 212 270

0.625 0.559 0.518 0.493 0.47 1 0.456 0.443 0.430 0.425 0.415 0.413 0.41 1

0.995 0.943 0.848 0.747 0.626 0.509 0.395 0.252 0.199 0.070 0.030 0.000

30 50 56 56 51 44 36 27 19 7 3 0

Parameters: s, distance from front of packet; I , local smoothed wavelength; cp,, linear phase speed; m', argument of complete elliptic integral; A,, amplitude of nonlinear internal wave envelope.

I

-

I

I

1

I

1

I

1

I

I

I

I

1

I

I

1

1

1

1

-

-10-

E

-40-

-

-50-

-

I

5

-20;

+* -30-

z

W V

a J

a

e -60

I

I

I

I

I

I

I

1

I

I

1

I

I

I

FIG. 24. Calculatd vertical displacements from a damped cnoidal internal wave model using numerical values obtained from the data of Figs. 15 and 16. The model incorporates the decrease in wavelengths apparent in Fig. 16 as well as the nonlinear increase in phase and group velocity deduced from Fig. 15.

192

C. L. RUFENACH ET AL.

are overestimates because spreading and dissipation of wave energy are obviously occurring in the fields visible on Fig. 15b. The data of Table I have been used to calculate the spatially varying vertical displacement according to the model function, Eq. (62), and the results are shown in Fig. 24. Several features are worth mentioning. First, the monotone wavelength variation of the wave train is clear, being incorporated into the cnoidal function via the specification of the nonlinearity. Second, the halfwidths of the troughs are constant, in essential agreement with observations elsewhere (Ape1et al., 1975a;Holbrook et al., 1980).Third, the leading wave at s = 350m, while the most nonlinear, is not the largest but has something of the form of a precursor, a phenomenon seen frequently in such packets; this is due to its high speed and long length. Fourth, the oscillations in the remaining main body of the packet are essentially rank ordered, just as the soliton evolution theory prescribes. However, while solitons, in order to be considered as such, must be separated from their neighbors by something in excess of s z 3L, the cnoidal function is not so restricted. 4. SUMMARY AND CONCLUSIONS The Seasat SAR has returned imagery of continental shelf internal waves that appear to be generated by semidiurnal tidal flow over bathymetric features. The nonlinear, packetlike characteristics of the waves are similar to those observed in the New York Bight and the Sulu Sea. By using measurements of the spatial properties of the wave patterns in conjunction with a cnoidal wave model, it is possible to obtain reasonable estimates of wave amplitude and degree of nonlinearity. It should be emphasized, however, that this model is not viewed as a rigorous theory but rather a convenient and simple way of interpreting the data and connecting them to analytical expressions. More sophisticated interpretations of the remotely sensed data may lead to a greater understanding of the physics of internal waves. Such interpretations must proceed through a more rigorous theoretical model than the one used here, presumably fashioned along the lines of inverse scattering theory (Osborne and Burch, 1980) or solutions to an integro-differential equation of the type advanced by Kubota et al. (1978). This work is in progress for the Sulu Sea data. ACKNOWLEDGMENTS

This research was supported by the Seasat Project within NOAA and partially by the NASA Ocean Processes Branch under Work Order W-15,084.

5. SURFACE AND INTERNAL OCEAN WAVE OBSERVATIONS

193

REFERENCES

Abramowitz, M., and Stegun, I. A. (1964).“Handbook of Mathematical Functions.” U.S. Govt. Printing Office, Washington, D.C. Alpers, W. (1983). Monte Carlo simulations for studying the relationship between Ocean wave and synthetic aperture radar image spectra. J. Geophys. Res. 88,1745-1759. Alpers, W., and Hasselmann, K. (1978).The two-frequency microwave technique>formeasuring ocean wave spectra from an airplane or satellite. Bound. Layer Meteorol. 13,215-230. Alpers, W., and Jones, W. L. (1978).The modulation of the radar backscattering cross section by long ocean waves. Proc. Int. Symp. Remote Sensing Environ. 12th, Manila pp. 1597-1607. Alpers, W. R., and Rufenach, C. L.(1979). The effect of orbital motions on synthetic aperture radar imagery of ocean waves. IEEE Trans. Antennas Propag. AP-27,685-690. Alpers, W.R., Ross, R. B., and Rufenach, C. L. (1981).On the detectability of ocean surface waves by real and synthetic aperture radar. J. Geophys Res. 86,6481-6498. Apel, J. R., and Holbrook, J. R. (1980). The Sulu Sea internal soliton experiment, Part A: Background and overview. EOS 61,1009. Apel, J. R., Byrne, H. M., Proni, J. R., and Charnel], R. L. (1975a).Observations of oceanic internal and surface waves from the earth resources technology satellite. J. Geophys. Res. 80, 865881. Apel, J. R., Proni, J. R., Byrne, H.M., and Sellers, R. L.(1975b). Near-simultaneous observations of intermittent internal waves on the continental shelf from ship and spacecraft. Geophys. Res. Lett. 2, 128-131. Apel, J. R., Byrne, H.M., Proni, J. R., and Sellers, R. L. (1976).A study of oceanic internal waves using satellite imagery and ship data. Remote Sens. Enoiron. 5, 125. Bahar, E. (1981). Full-wave solution for the depolarization of scattered radiation fields by rough surfaces of arbitrary slope. IEEE Trans. Antennas Propag. AP-29,443-454. Bass, F. G., Fuks, I. M., Kalmykov, A. I., Ostrovsky, I. E., and Rosenberg, A. D. (1968).Very high frequency radiowave scattering by a disturbed sea surface. IEEE Trans. Antennas Propug. AP-16,554-568. Beal, R. C. (1981).Spatial evolution of Ocean wave spectra. In “Spaceborne Synthetic Aperture Radar for Oceanography”(R. C. Beal et al., eds.), pp. 110-127. Johns Hopkins Univ. Press, Baltimore, Md. Bel1,T. H.,Mays A. B., and deWitt, W. P. (1974). Upper ocean stability: A compilation of density and Brunt-Vais&lBfrequency distributions for the upper 500 m of the world oceans. Vols. I and 11, NRL Report 7799,Naval Research Laboratory, Washington, D. C. Benjamin, T. B. (1966).Internal waves of finite amplitude and permanent form. J. Fluid Mech. 25, 241-270. Benjamin, T . B. (1967). Internal waves of permanent form in fluids of great depth. J . Fluid Mech. 29,559-592. Brown, G. S.(1977).The average impulse response of a rough surface and its applications. IEEE Trans. Antennas Propag. AP-25,67-74. Brown, G. S. (1978). Backscattering from a Gaussian-distributed perfectly conducting rough surface. IEEE Trans. Antennas Propag. AP-M,472-482. Brown, W.E., Jr., Elachi, C., and Thompson, T. W. (1975). Radar imaging of ocean surface patterns. J. Geophys. Res. 81,2657-2667. Earle, M. D. (1981). Problems in ocean wave hindcasting, In “Spaceborne Synthetic Aperture Radar for Oceanography” (R. C. Beal et al., eds.), pp. 98-109. Johns Hopkins Univ. Press, Baltimore, Md. Elachi, C., and Apel, J. R. (1976). Internal wave observations made with an airborne synthetic aperture imaging radar. Geophys. Res. Lett. 3,647-650.

194

C. L.RUFENACH E T A L .

Elachi, C. E., and Brown, W. E. (1977). Models of radar imaging of the ocean surface waves. IEEE Trans. Antennas Propag. AP-25,84-95. Elachi, C., Thompson, T. W., and King, D. (1977).Ocean wave patterns under hurricane Gloria: Observations with an airborne synthetic aperture radar. Science 198,609-610. Farmer, D., and Smith, J. D. (1978).Nonlinear internal waves in a fjord, In “Hydrodynamics of Estuaries and Fjords” (J. Nihoul, ed.). Elsevier, Amsterdam. Fedor, L.S., Godbey,T. W., Gower, J. F. R., Guptill, R., Hayne, G. S., Rufenach, C. L.,and Walsh, E. J. (1979). Satellite altimeter measurements of sea state--an algorithm comparison. J . Geophys. Res. 84(B8), 3991-4002. Gonzalez, F. I., Shuchman, R. A., Ross, D. B., Rufenach, C. L., and Gower, L. F. R. (1981). Synthetic aperture radar wave observations during GOASEX. In “Oceanography from Space” (J. F. R. Gower, ed.), pp. 459-467. Plenum, New York. Halpern, D. (1971). Semidiurnal internal tides in Massachusetts Bay. J. Geophys. Res. 76,6573. Harger, R. 0.(1980). The side-looking radar image of time-invariant scenes. Radio Sci. 15, 749-765. Hasselmann, D. E., Dunckel, M., and Ewing, J. A. (1980). Directional wave spectra observed during JONSWAP 1973. J. Phys. Oceanogr. 10,1264-1280. Hasselmann, K. (1980). A simple algorithm for the direct extraction of the two-dimensional surface image spectrum from the return signal of a synthetic aperture radar. Int. J. Remote Sens. 1,219-240. Hasselmann,K., Ross,D. B., Miiller, P., and Sell, W.(1976). A parametric wave prediction model. J. Phys. Oceanogr. 6,200-228. Haury, L. R., Briscoe, M. G., and Om, M. H.(1979). Tidally generated internal wave packets in Massachusetts Bay. Nature (London) 278,312-317. Holbrook, J. R., Apel, J. R., and Tsai, J. J. (1980).The Sulu Sea Internal Soliton Experiment, Part B: Observations of large-amplitude nonlinear waves. EOS 61, 1009. Jain, A. (1978).Focusing effectsin synthetic aperture radar imaging of ocean waves. Appl. Phys. 15,323-333. Jain, A. (1981). SAR imaging of ocean waves: Theory. IEEE J. Ocean Eng. OE-6,130-139. Jordan, 0.(1980).The SEASAT-A synthetic aperture radar system. IEEE J. Ocean Eng. OE-5, 154- 164. Joseph, R. I. (1977).Solitary waves in a finite depth fluid. J . Phys. A 10, L225-L227. Keller, W.C.,and Wright, J. W. (1975). Microwave scattering and straining of wind generated waves. Radio Sci. 10,139-147. Keulegan, G. H. (1953).Characteristics of internal solitary waves. J. Res. NBS 51, 133-140. Kitaigorodskii, S. A. (1973). The physics of air-sea interaction (translated from Russian). U.S. Dept. of Commerce, National Tech. Info. Service, TT-72-50062. Kubota, T., KO,D. R. S., and Dobbs,.I (1978). Propagation of weakly nonlinear internal waves in a stratifiedfluid of finitedepth, J. Hydronaut. 12,157-168. Larson, T. R.,Moskowitz, L.I., and Wright, J. W. (1976). A note on SAR imagery of the ocean. IEEE Trans. Antennas Propag. AP-24,393-394. McLeish, W., Ross, D., Shuchman, R. A,, Teleki, P. G., Hsiao, S.V., Shemdin,0.H., and Brown, W. E. (1980). Synthetic aperture radar imaging of ocean waves: Comparison with wave measurements. J. Geophys. Res. 85,5003-SO1 1. Maxworthy,T.(1979). A note on the internal solitary waves produced by tidal flow over a threedimensional ridge. J. Geophys. Res. 84,338-346. Monaldo, F. M., and Beal, R.C. (1981).The effects of high sea states on the SEASAT SAR ocean wave spectra. Proc. IUCRM Symp. Wave Dyn. Radio Probing Ocean Surf. (in press). Ono, H. (1975).Algebraic solitary waves in stratified fluids. J. Phys. SOC.Jpn. 39, 1082-1091. Osborne, A. R., and Burch, T. L.,(1980). Internal solitons in the Andaman Sea. Science 208, 451-460.

5. SURFACE AND INTERNAL OCEAN WAVE OBSERVATIONS

195

Phillips, 0. M. (1981). The structure of short gravity waves on the ocean surface. In “Spaceborne Synthetic Aperture Radar for Oceanography” (R. C. Beal et al., eds.), pp. 24-31. Johns Hopkins Univ. Press, Baltimore, Md. Pierson, W. J. (1977). Comments on a parametric wave prediction model. J. Phys. Oceanogr. 7 , 127-137. Proni, J. R., Apel, J. R.,Byrne, H. M., Sellers, R. L., and Newman, F. C. (1978). Oceanic internal waves from ship, aircraft and spacecraft: A report on the New York-to-Bermuda Sensing Experiment. NOAA Atlantic Oceanographic and Meteorological Laboratories. U.S. Govt. Printing office, USGPO 1978-796-417-1 16-10. Raney, R. K. (1971). Synthetic aperture imaging radar and moving targets. IEEE Trans. Aerosp. Electron. Syst. AES7,499-505. Raney, R. K. (1980). SAR processing of partially coherent phenomena. I n t . J. Remote Sens. 1, 29-51. Rice, S. 0. (1951). Reflection of electromagnetic waves from slightly rough surfaces. Commun. Pure Appl. Math. 4, 351-378. Rufenach, C. L., and Alpers, W. (1978). Measurement of ocean waveheights using GEOS-3 altimeter. J . Geophys. Res. 63, 501 1-5018. Rufenach, C. L., and Alpers, W. (1981). Imaging ocean waves by synthetic aperture radars with long integration times. IEEE Trans. Antennas Propag. AP-29,422-428. Shuchman, R. A. (1980). Processing synthetic aperture radar data of ocean waves. In “Oceanography from Space” (J. Gower, ed.), pp. 477-496. Plenum, New York. Shuchman, R. A., and Kasischke, E. S. (1981). Refraction of coastal ocean waves. In “Spaceborne Synthetic Aperture Radar for Oceanography” (R. C. Beal et al., eds.) pp. 128-135. Johns Hopkins Univ. Press, Baltimore, Md. Shuchman, R. A., and Zelenka, J. S. (1978). Processing of ocean wave data from a synthetic aperture radar. Bound. Layer Meteorol. 13, 181-202. Shuchman, R. A., Kasischke, E. S., and Klooster, A. (1978). Synthetic aperture radar ocean wave studies (Final Rep. 131700-3-F). Environ. Res. Inst. of Mich., Ann Arbor. Shuchman, R.A., Ma!Tett, A. L., and Klooster, A. (1981). Static modeling of SAR imaged ocean scene. IEEE J. Oceanic Eng. OE-6,41-99. Swift, C. T., and Wilson, L. R. (1979). Synthetic aperture radar imaging of ocean waves. IEEE Trans. Antennas Propag. AP-27,725-729. Tapley, 8. D., Diamante, J. M., Douglas, B. C., Goad, C. C., Kolenkiewicz, R., Marsh, J. G., Martin, C. F., Smith, S. L., 111, Townsend, W. F., Whitehead, J. A., Byrne, H. M., Fedor, L. S., Hammond, D. C., and Mognard, N. M. (1979). SEASAT altimeter calibration: initial results. Science 240,1410-1412. Tomiyasu, K. (1978). Tutorial review of synthetic aperture radar (SAR) with application to imaging of the ocean surface. Proc. IEEE 66 (5), 570-583. Townsend, W. F. (1980). An initial assessment of the performance achieved by the SEASAT-I radar altimeter. IEEE J . Oceanic Eng. OE-5(2),80-92. Tsai, J. M., and Apel, J. R. (1979). Tidally-induced shear-flow instability as a source of internal waves on the continental shelf. NOAA Pacific Marine Environmental Laboratory, Contribution No. 415. Valenzuela, G. R. (1968). Scattering of electromagnetic waves from a tilted slightly rough surface. Radio Sci. 3, 1057-1066. Valenzuela, G. R. (1980). An asymptotic formulation of SAR images of the dynamical ocean surface. Radio Sci. 15, 105-1 14. Vesecky, J. F., and Stewart, R. H. (1982). The observation of ocean surface phenomena using imagery from the SEASAT synthetic aperture radar. J. Geophys. Res. 87,3397-3430. Whitham, G. B. (1974). “Linear and Nonlinear Waves.” Wiley, New York. Wilkerson, J. C., Brown, R. A., Cardone, V. J., Coons, R. E., Loomis, A. A., Overland, J. E.,

196

C. L. RUFENACH ET AL.

Peteherych, S., Pierson, W. J., Woiceshyn, P. M., and Wurtele, M. G. (1979). Surface observations for the evaluation of geophysical measurements from SEASAT. Science 204, 1408-1410. Wright, J. W. (1968). A new model for sea clutter. IEEE Trans. Antennas Propag. AP-16, 217-223. Wright, J. W., and Keller, W. C. (1971). Doppler spectra in microwave scattering from wind waves. Phys. Fluids 14,466-474. Wright, J. W.,Plant, W. J., Keller, W. C., and Jones, W. L. (1980). Ocean wave-radar modulation transfer functions from the West Coast Experiment. J . Geophys. Res. 85,4957-4966.

CIIAFTER 6

SEASAT MICROWAVE WIND AND RAIN OBSERVATIONS IN SEVERE TROPICAL AND MIDLATITUDE MARINE STORMS PETERG . BLACK

R . CECIL GENTRY

Atlantic Oceanographic and Meteorological Laboratory Hurrirane Research Division Miami Florida

Department of Physics and Astronomy Clemson University Clemson South Carolina

.

. VINCENTJ . CARDONE

JEFFREY D. HAWKINS’

Oreanweather. Inc . White Plains . New York

Atlantic Oreanographir and Meteorological Laboratory Hurricane Research Divison Miami. Florida

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 2. The Nature of Severe Marine Storms . . . . . . . . . . . . . . . . . 2.1. Tropical-Cyclone Structure . . . . . . . . . . . . . . . . . . . 2 2. Severe Midlatitude Marine Storms . . . . . . . . . . . . . . . . 3. Microwave Measurements in Severe Marine Storms . . . . . . . . . . . . 3.1. Remote Sensing of Tropical-Cyclone Cloud and Precipitation Patterns . . . . 3.2. Microwave Remote Sensing of Ocean Surface Winds . . . . . . . . . . 4. Seasat Observations of Rain Rate and Microwave Attenuation in Tropical Cyclones . . . . . . . . . . . . . . . . . . . . . . . 5 . Seasat Surface Wind Observations in Tropical Cyclones . . . . . . . . . . . 5.1. Aircraft Data . . . . . . . . . . . . . . . . . . . . . . . . 5.2. Cloud-Motion Winds . . . . . . . . . . . . . . . . . . . . . 5.3. SASS Winds and Methods of Comparison . . . . . . . . . . . . . . 5.4. SASS Alias Removal in Tropical Cyclones . . . . . . . . . . . . . . 6. Analysis of Individual Storms and Comparison of Seasat Data with “Surface-Truth’’Data . . . . . . . . . . . . . . . . . . . . . 6.1. Hurricane Fico. July 1978 . . . . . . . . . . . . . . . . . . . 6.2. Hurricane Ella. September 1978 . . . . . . . . . . . . . . . . . 6.3. Hurricane Greta, September 1978 . . . . . . . . . . . . . . . . . 6.4. Selected Typhoons . . . . . . . . . . . . . . . . . . . . . . 6.5. Hurricane Ella Model Fields . . . . . . . . . . . . . . . . . . 6.6. QEIIStorm . . . . . . . . . . . . . . . . . . . . . . . . 6.7. Discussion of Errors in Wind Measurement . . . . . . . . . . . . . I. Seasat Observations of Sea Surface Temperature Near Tropical Cyclones . . . . 8. Application of Seasat Observations to Operational Marine Storm Forecasting Needs . . . . . . . . . . . . . . . . . . . . . 8.1, Tropical-Cyclone Forecast Applications . . . . . . . . . . . . . . . 8.2. Extratropical-Cyclone Forecast Applications . . . . . . . . . . . . .

198 200 200 204 206 206 207 208 215 216 217 217 218 218 218 229 238 246 248 249 258 264 265 265 270

’

Present address: Naval Ocean Research and Development Activity (NORDA). Bay St.Louis. Mississippi. 197 ADVANCES IN GEOPHYSICS.VOLUME 27

.

Copyright @ 1985 by Academic Press Inc. All rights of reproduction in any form reserved. ISBN 0-12-018827.9

198

PETER G . BLACK ETAL.

. . . . . . . . . . . . . . . . . . . . . . . . . . .

9. Conclusions. References

. . . . . . . . . . . .

273 274

1. INTRODUCTION This overview presents initial results of studies concerning Seasat measurements in and around tropical and severe midlatitude cyclones over the open ocean and provides an assessment of their accuracy and usefulness. Sensors flown on Seasat provided complementary measurements of surface wind speed and direction, rainfall rate, significant wave height and wave length, and sea surface temperature. These measurements were made with the Seasat-A Satellite Scatterometer (SASS), the Scanning Multichannel Microwave Radiometer (SMMR), the Seasat altimeter, and the Seasat Synthetic Aperture Radar (SAR). This is the first time that such a sophisticated array of microwave instruments has been used to study tropical cyclones. Up to now, polar-orbiting and geostationary satellites carrying visible and infrared radiometers have provided data for tropical cyclone studies. Primarily, these satellites have been used to assess storm intensity and characteristics of the inflow and outflow layers by means of cloud morphology and cloud tracking, respectively. Seasat, however, has opened new horizons for satellite-based measurements of winds and precipitation in tropical cyclones. The potential for using these data in hurricane forecasts and warnings is far reaching. Some possible studies are (1) determination of the radius of gale-force winds, (2) measurement of initial wind and precipitation fields for input to dynamic prediction models, (3) measurement of initial wind fields for wave and storm surge prediction models, and (4) estimation of storm rainfall for storm-related flood and inundation forecasting. For the case of the severe midlatitude marine storm, these data are also important in composing timely and accurate warnings of position as well as wind and wave distributions to aid shipping interests. In addition, Seasat can provide real data inputs to regional and global circulation models. This capability is especially important for the Southern Hemisphere in rapidly developing situations where very little data exist. A tropical cyclone is a warm-core, low-pressure system that develops over the warm tropical ocean in conjunction with an upper level anticyclone(highpressure system). In its mature state, the tropical cyclone has intense winds that blow counterclockwise in the Northern Hemisphere and clockwise in the Southern Hemisphere around its clear, calm “eye” region. A tropical cyclone derives its energy from warm, moist air flowing into it at low levels. This

6. SEASAT WIND/RAIN OBSERVATIONS

199

energy is released in towering convective clouds concentrated in the storm's eyewall, a nearly circular ring of clouds near the center, and in outer rainbands. If maximum winds, located close to the center, exceed 33 m sec- ', the cyclone is called a hurricane, typhoon, or cyclone,depending on the area of the world. If maximum winds are 17.5-32 m sec-', the system is called a tropical storm. Weaker systems are called tropical depressions or disturbances. Midlatitude marine storms, on the other hand, are low-pressure systems of larger scale than tropical cyclones (diameter usually 1000 km or more) and develop along the boundaries between warm and cold air masses in conjunction with an upper-level trough in the westerly jet stream. The boundaries between the warm and cold air masses are swept ahead of the midlatitude system in the form of fronts, which may generate smaller scale squall systems. Generally, the regions of strong winds and heavy rain are spread out over a larger area than in tropical cyclones. The high winds, seas, and precipitation, as well as the large gradients of these parameters, make the tropical cyclone the severest test of Seasat's capabilities. This study summarizes the limits of these measurements. The objectives of this study of Seasat measurements in storms were to determine the following parameters: 1. Biases and root-mean-square (RMS) errors of individual SASS and SMMR wind measurements when compared with individual high-quality surface wind observations from aircraft, ships, and buoys, with special emphasis on high-wind areas ( > 15 m sec - '). 2. Biases and RMS errors of SASS and SMMR wind fields when compared with subjective analyses of the storm wind fields. 3. Magnitude of attenuation corrections derived from SMMR data applied to SASS measurements and to evaluate the SASS performance in measurement of high winds through heavy precipitation. 4. Capability of SMMR to measure sea surface temperature (SST)in storm areas by comparing individual surface observations and SST fields with SMMR estimates.

Other studies have attempted to determine additional parameters: 5. Bias and RMS error in significant wave-height observations by altimeter in storm areas. 6. Ability of SAR to image the dominant waves generated by hurricane winds.

The second set of objectives to be accomplished, once the first had been achieved, were as follows:

200

PETER G. BLACK E T A L .

1. Determine the capability of Seasat to define storm center locations to within 60 km or less and to define the extent of gale-force and hurricane-force winds for storm warnings. 2. Demonstrate the usefulness of Seasat-derived wind fields and fields of latent heat release in the initializing of storm forecast models.

During its lifetime, Seasat acquired data over most of the Northern Hemisphere tropical cyclones in 1978. It completed’ 126 passes over 21 hurricanes and typhoons and 179 passes over 20 tropical storms. In addition, there were 64 passes over the tropical depression stages of these storms. About 75 of the passes were selected for processing after surveying the availability of surface truth data. About 10 of these have been selected for detailed analysis and will be discussed in this overview. Since Seasat’s lifetime spanned the Northern Hemisphere summer months, only one severe midlatitude storm occurred for which high-quality surface truth data were available. Data for this case were processed and results compared with the tropical storm cases. A large data set of virtually untouched data remains available for researchers interested in exploiting the use of microwave remote sensing in tropical cyclones, as well as in other midlatitude marine storms, especially in the Southern Hemisphere. In Section 2 of this review, general tropical-storm structure is discussed. The progress, to date, with microwave remote sensing of tropical storms is reviewed in Section 3. In the sections that follow the accuracy is assessed of derived wind, precipitation, and sea surface temperature fields in and around tropical cyclones. In a final section initial efforts to demonstrate the usefulness of Seasat data for operational applications, such as storm warning and forecasting, are reviewed.

2. THE NATURE OF SEVERE MARINESTORMS 2.1. Tropical-Cyclone Structure

Hurricanes have been called “the greatest storms on earth” (Gentry, 1970) and, over the years, considerable effort has been devoted to the study of their dynamics and to the development of models for forecasting their intensity and tracks. As a result, much attention has been focused on the hurricane boundary layer, because it is there that convective processes are fueled that drive the hurricane’s circulation through the transport of heat and moisture from the ocean. Furthermore, it is the winds at the surface that cause the

6. SEASAT WIND/RAIN OBSERVATIONS

20 1

damage at landfall (either directly or indirectly by the storm surge). Unfortunately, most critical hurricane forecasts have to be made while the storm is still at sea and usually well removed from the observational networks. In situ measurements of the surface winds under such conditions currently are not practical on a large scale. Therefore, a means of obtaining measurements of these surface winds is of paramount interest to researchers and forecasters. The hurricane is the most destructive of natural phenomena, While tornadoes have stronger winds, those in hurricanes last longer and affect much larger areas. In addition, hurricanes can develop over all tropical oceans of the world, except the South Atlantic. In the United States alone, over 17,000 deaths have been caused by hurricanes since 1900 (Dunn and Miller, 1964), and in many areas the number is much higher. In the United States, the average annual damage from hurricanes was $745 million during 1971-1980, according to records at the National Hurricane Center as published in the annual hurricane summary articles (e.g., Lawrence and Pelissier, 1981). Although other marine storms are not so dramatic, they are more frequent, affect more and larger areas, and cause great losses to shipping and along exposed coasts. Figure 1, an airborne radar presentation of Hurricane Anita (1977), illustrates some distinctive features of a hurricane. The darkest shadings, which represent heavy concentrations of water drops, are areas where the rainfall is heaviest. The open circular area in the center is the eye of the storm. There, the winds are light and variable, cloudiness is much less than in the surrounding areas, and there is little or no rain. Immediately outside the eye, however, is the eyewall with its massive clouds, which frequently extend from about 150 m above the surface upward 9-15 km or more. In most mature hurricanes, the strongest winds and the most intense rain are associated with the eyewall. The spiral bands in Fig. 1, called rainbands, are typical of most hurricanes. Like the winds, they tend to rotate around the storm, although much less rapidly. The rainfall, squalls, wind speeds, and gustiness are much greater in and beneath a rainband than in the areas between bands. Wind speeds along the bands frequently vary by 8 m sec-' in 15 km and the variation is even greater perpendicular to the bands (Gentry, 1964; Willoughby et al., 1982; Jorgensen, 1984). The variation of wind speed with radial distance in a hurricane is highly variable. Figure 2a-g shows some examples of the typical range in radial profiles of tangential wind in hurricanes. Small, intense hurricanes can exhibit profiles, like Hurricane Allen on August 8 in Fig. 2a, where the peak winds occur only a few kilometers from the center, exhibiting horizontal shears of

202

PETER G. BLACK E T A L .

1-50 k m - q

I

I

I

a *

I

FIG.1. Radar map of Hurricane Anita, September 1,1977,06:27GMT,from lower fuselage Cband weather radar on board NOAA WP-3D Orion aircraft flown at 700-mbar pressure altitude. Contour intervals in decibels are, in ascending order, 25 (thick solid line), 32, 39, 46, and 53 (solid shading). These contours represent rainfall rates of 1, 3, 10, 35, and 120 mm hr-', respectively.

about 60 m sec-' per 10 km. Other storms can exhibit secondary wind maxima at various radii from the center (Willoughby et aZ.,1982),as typified by Hurricanes Allen on August 5 and 9 and David on August 30 in Fig. 2b-d, respectively. Figure 2e represents the more typical profile for a mature hurricane, exhibiting peak winds at 25 km. Figure 2f and g illustrates profiles that are typical of large storms. For these cases, maximum wind radii ranging from 50 to 150 km have been measured. Thus, storm sizes, and the associated horizontal wind shears, can vary greatly from storm to storm, with the entire circulation of a small-sizestorm out to the radius of gale-forcewinds being contained within the clear eye of a large-size storm, such as Hurricane Ginger in Fig. 2g. The expanse covered by hurricanes also varies greatly. If the horizontal scale is defined by the area of subnormal surface pressure, its diameter is frequently about 1000 km,but individual storms may differ by a factor of 2 or 3. Maximum winds measured in a few hurricanes have exceeded 80 m sec-'.

6. SEASAT WINDIRAIN OBSERVATIONS 8o

203

r

ALLEN 8 AUG 80 600 mbar

20

It

ALLEN 9 AUG. 80

700 mbar

60

,

GINGER 28 SEP. 71 850 mbar

'0

20

40

60

80

100

RADIUS (km)

120

140

160

FIG.2. Typical wind profiles in mature hurricanes.

The average radial extent of hurricane-force winds is typically about 100 km, but gale-force winds (17 m sec-l or more) may extend 500 km from the center (Dunn and Miller, 1964). Rainfall can be extremely heavy. Dunn and Miller (1964) report one storm near Miami, Florida, that dropped 3.4 cm of rain in 10min. This is an unusual rate of rainfall, but the flood potential of a tropical cyclone can be very great. Simpson and Riehl (1981) write that inside the 200-km radius about the hurricane center, computed rainfall from a moderate storm in one day has been shown equal to the average annual discharge of the Colorado River at its point of largest flow. Just as with the winds, however, rainfall does not occur uniformly over the entire area of the storm. Even in the inner 200-kmradius,

204

PETER G . BLACK E T A L .

the percentage of the area covered with active radar echoes is quite small, typically < 10% (Malkus et al., 1961; Jorgenson, 1984). Tropical cyclones form over tropical waters where the sea surface temperature is 26°C or higher (Palmen, 1948), and they occur most frequently during the warmest four months of the year. Tropical storm formation usually proceeds from a preexisting tropical disturbance that consists of organized cloud and wind patterns. However, only about 10% of all tropical disturbances reach storm intensity, in spite of the superficially similar synoptic conditions associated with each disturbance. Thus, the forecaster has the extremely difficult problem of detecting in advance the disturbances that will develop (Simpson, 1971). This difficulty, again, emphasizes the need for wind measurements, including those near the surface. The average life span of hurricanes is about 1 week, but individual storms may survive indefinitely (longer than 4 weeks), if they remain over warm tropical water. The primary reason for the short average life is the tendency for tropical cyclones to either strike land or move under the influence of the large-scale midlatitude circulation systems and into the cooler, more hostile, environment of middle latitudes. 2.2. Severe Midlatitude Marine Storms

Whereas the tropical cyclone obtains its energy from the warm moist air flowing into it at low levels, the other type of intense marine storm derives its energy either from the surrounding air currents or from contrasting air masses with different temperatures flowing into the storm. As a result, the maximum winds are rarely much above 40 m sec- the gradients in wind speed are much less than in tropical cyclones, and the rainfall is usually not as concentrated. The temperatures in the center of the storm are frequently colder than the average environmental temperatures. Usually the distribution of winds and rain are more asymmetrical than in tropical cyclones. On the other hand, the expanse of the storm may be two or three times as great as in a hurricane. The storm that buffeted the oceanliner Queen Elizabeth I1 (QE I1 storm) is typical of intense storms. Its structure is discussed in Section 6.6. The nature of the origin and movement of extratropical cyclones has been a subject of intense interest since the eighteenth century. The principal tracks and mean frequencies of cyclones and preferred areas of cyclogenesis have been well documented for the Northern Hemisphere (Klein, 1957). Much less is known about Southern Hemisphere cyclones because of the scarcity of observations. Indeed, a preliminary analysis of the impact of data from drifting buoys deployed in the Southern Ocean during the First GARP Global Experiment (FGGE), on operational surface weather analysis, suggests that

',

6. SEASAT WIND/RAIN OBSERVATIONS

205

the mean frequency and intensity of cyclones there are much greater than had been thought (Guymer and Le Marshall, 1981). In high and midlatitudes, severe extratropical massive storms (hereafter referred to as SEMS) extract a steady toll of life and property. Recently, a SEMS crossed the path of 302 boats participating in the FASTNET Race in the Irish Sea (Rice, 1979). Fifteen sailors died in the heavy seas, 5 boats sank, and 18 were abandoned. Coastal residents are also susceptibleto rising water caused by SEMS. For example, in 1953 a storm surge associated with a storm in the North Sea flooded parts of the Netherlands and drowned 2000 people. In most coastal regions outside the tropics, design criteria for port and harbor installations and offshore structures on the continental shelf are determined by severe surface winds, wave heights, and storm surges associated with SEMS. Recently, there has been renewed interest in SEMS. First, it is becoming apparent that advanced numerical weather prediction techniques have met with only limited success on SEMS compared with other weather systems(e.g., Leary, 1971; Bosart, 1981). Second, it appears that most of the Northern Hemisphere’s deep cyclones, which, in their aggregate, define those major features of the general circulation known as the Aleutian and Icelandic Lows, begin their life cycle as explosively developing extratropical cyclones. SEMS, which deepen at a rate of at least 1 mbar hr-’ for 24 hr, have been termed “bombs” by Sanders and Gyakum (1980). Finally, the physical mechanism responsible for the formation and intensification of SEMS appears to be rather more complicated than the classical view, which states that extratropical cyclogenesis is a manifestation of a wavelike baroclinic instability on the tropospheric thermal gradient associated with the polar front. The climatology of “bombs” has been studied by Sanders and Gyakum (1980), who find a preference for their formation on the western sides of the Atlantic and Pacific Oceans, near or just north of the Gulf stream and Kuroshio currents (Fig. 3), respectively. Gyakum (1981) studied the nature and evolution of a particularly severe bomb which occurred in the North Atlantic during the Seasat mission. He found the system to possess several features of intense hurricanes, such as a deepeningrate of over 60 mbar in a 24hr period, hurricane-force surface wind speeds, a clear eye surrounded by intense convection, and a deep tropospheric warm core. He concluded that, while baroclinic forcing provided an initial mechanism for formation, bulk heating of the troposphere caused by cumulus convection was a significant factor in the intensification. This finding has important implications for the design of numerical weather prediction and general circulation models. It also indicates a potential for improvement in marine forecasts from operational, remotely sensed, surface marine wind data (Cane and Cardone, 1981). These considerations are discussed in Section 8.2.

206

PETER G.BLACK ET AL.

FIG. 3. Distribution of "bomb" events during three winter seasons. Isopleths represent smoothed bomb frequencies in units of number per 5" latitude/longitude square.

3. MICROWAVE MEASUREMENTS IN SEVERE MARINE STORMS 3.1. Remote Sensing of Tropical-Cyclone Cloud and Precipitation Patterns

Measurements of various tropical storm parameters have been obtained from satellites through several methodologies. Most widely used and accepted is the Dvorak (1975) method to infer storm intensity and center locations from visible and infrared cloud morphology from the Geosynchronous Operational Environmental Satellite (GOES) and polar-orbiting satellites. Kidder et al. (1978), Grody et al. (1979), and Rosenkranz et al. (1978) have attempted to use the Scanning Microwave Sounder (SCAMS) on Nimbus to infer storm intensity through measurement of the upper-level warm-temperature anomaly over the storm core. Alder and Rodgers (1977) and Allison et al. (1974) have attempted to use the Electrically Scanning

FIG. 3. Distribution of "bomb" events during three winter seasons. Isopleths represent smoothed bomb frequencies in units of number per 5" latitude/longitude square.

6. SEASAT WIND/RAIN OBSERVATIONS

207

Microwave Radiometer (ESMR) to infer rainfall rates in typhoons. More recently, use of passive microwave radiometry to infer tropical cyclone rainfall has been summarized by Rodgers and Adler (1981). However, the most widely used and accepted technique is to infer rainfall rates from infrared cloud-top temperatures using methods such as that developed by Griffith et al. (1978). One of the most promising approaches to the precipitation problem has been described by Jung (1980). He used infrared-derived cloud-top temperatures over several typhoons to determine the cloud height. Using this with simultaneously observed microwave brightness temperatures from Nimbus-5 and -6, he derived rainfall rates. Several sensitivity tests to different drop size distributions and errors in cloud-top temperatures were used to derive error estimates for the calculations. 3.2. Microwave Remote Sensing of Ocean Surface Winds The use of passive and active microwave techniques for remote sensing of ocean surface winds is well documented (Barrick and Swift, 1980). For airborne passive observations, several authors (Ross et al., 1970; Nordberg et al., 1971; Ross and Cardone, 1974) have shown that the ocean brightness temperature is strongly correlated with wind speed through the creation of roughness, foam, and breaking waves (whitecaps). Still others (Guinard et a!., 1971; Krishen, 1971; Jones ec al., 1977) have shown that radar backscatter from the sea can be used to measure wind speed (through the proportionality of the Bragg off-nadir radar return to the wind-dependent spectrum of water waves a few centimeters long) and wind direction (through the anisotropic scattering characteristic of the sea surface). The first airborne passive and active observations of a hurricane were conducted during Hurricane Ava (Ross et al., 1974). However, in this case, the L-band radar (1.3 GHz) observations were used only to generate wave images. This type of observation was later quantified in 1976 (Weissmanet al., 1979) demonstrating that L-band radar cross sections obtained in Hurricane Gloria exhibited a weak dependence on wind speed. The concept of combining active and passive microwave observations was first implemented using a 13.0-GHz system (S-193 RADSCAT) on the NASA Skylab. During this experiment, the first such hurricane measurements were reported (Ross et al.. 1974; Cardone et al., 1976). The latter report also documented results for tropical storm Christine, which clearly demonstrated the ability to sense wind speed in hurricanes with a microwave scatterometer after regions of heavy rain were eliminated using the radiometer. This same concept was incorporated in the design of Seasat, although with the

208

PETER G . BLACK ET AL.

considerably coarser spatial resolution of 50 km for Seasat in contrast to 20 km for Skylab. A microwave altimeter was used in the GEOS-C experiment to measure winds up to 25 m sec-', as well as rain rates and significant wave height with a nadir-pointing, K-band instrument (Brown, 1979; Fedor et al., 1979). The method was refined using Seasat data (Fedor and Brown, 1982). Rufenach and Alpers (1978) employed these methods to measure wind and waves in tropical cyclones, based upon measurements made in Hurricane Caroline (1975). 4. SEASAT OBSERVATIONS OF RAINRATEAND MICROWAVE ATTENUATION IN TROPICAL CYCLONES

Rainfall was one of the most critical observations made by Seasat in tropical cyclones, SASS radar backscatter measurements were corrected for the effect of attenuation from rain to improve the accuracy of the winds measured in rain areas, Corrections were effective for cell area average rain rates of up to 10 mm hr-l. This observation is also intrinsically important for the specification of rainfall in tropical cyclones. The method of SASS attenuation correction adopted for production runs over tropical cyclones was developed at Kansas University (Moore et al., 1982). This method computed a 13.7-GHz attenuation value for SASS from SMMR measurements at 18 GHz as compared with the alternate Wentz algorithm, which used the 10 SMMR channels. The SMMR measurements were made only on the right side of the spacecraft,which prevented the use of a large data set. S M M R attenuation values were computed based upon average brightness temperatures measured over a 54-km2grid box. These values were interpolated to SASS cell locations. A comparison of attenuation values computed over Hurricanes Fico and Ella, with and without information from the 37-GHz channels, is shown in Fig. 4A and B. A slight improvement in the algorithm was realized when 37-GHz data were eliminated because they saturated at high rain rates and contaminated the calculations. Rainfall rates were computed using several techniques, two of which have been compared for Hurricanes Fico and Ella. In addition, the SMMR-derived rain rates have been compared with NOAA airborne radar-derived rain rates for Hurricane Ella. The techniques evaluated were developed by Wentz (1981b) and Chester (1981). The Wentz algorithm derives rain rates in two ways. First, information from all frequencies are used to compute an average attenuation over the 50-km2 SMMR grid. This average is then converted to a rain rate. The second method is to use only the 37-GHz brightness temperature and to derive an attenuation from a model-function-derived

209

6. SEASAT WIND/RAIN OBSERVATIONS

100

1

1

I

I

1

1

I

I

94

88 i3 ; 82 L

ln ln

o-----

76

WENTZ f

-.-.-.

70

rJ

z

s 64

-----..............

(A1 1 Frequencies)

WENT22 (All but 3 7 G H t ) KANSAS1 (18 and 37GHt) KANSAS2 (18GHz)

1

OVERLAP

b-

58 52

A 4

100 94

88 i3 ;82 k

fln 76 5 70 ln z

2 +

1

2

3

1

I

I

1

4

5

ACROSS-TRACK

1

I

6

7

8

9

10

11

I

1

I

I

I

1

S M M R GRID 3 CELL

1

-

-

58 64

52

I

1

1

2

1

1

4

5

ACROSS-TRACK

I

6

I

7

SMMR GRID 3 CELL

1

1

1

1

8

9

10

It

FIG.4. Comparison of four methods of computing SASS attenuation from SMMR brightness temperatures for Hurricanes Fico (A) and Ella (B).

power law. Then a rain rate is calculated for each 37-GHz footprint, which has a resolution of 18 x 28 km. These steps are followed to maximize SMMR resolution. In contrast, the Chester algorithm uses a simple linear least-squares regression to derive rain rates from 37-GHz brightness temperatures. In the case of Hurricane Ella, it was possible to derive a distribution of rainfall rates from calibrated airborne C-band radar, which was flown on

1

210

PETER G. BLACK ET AL.

board the NOAA WP-3D research aircraft. Actually, two radars were used for the composite,which added to the credibility of the results. Figure 5 shows the resulting composite. Comparing this with Wentz's 37-GHz-derived rain rates for individual SMMR cells, shown in Fig. 6, one sees a remarkable degree of comparison. In fact, this technique is the only one capable of resolving rainband structure. The values saturate at 7-8 rnrn hr-', however. Figure 7 shows the results from the Chester algorithm averaged to SMMR grid 4, which has a resolution of about 30 km. One sees, however, that this is not sufficient to resolve rainband scale events. The W P 3 D radar rain rates were used to help tune this algorithm. The values above 8 mm hr- are somewhat artificial, in fact, because the corresponding brightness temperatures have very little variation with rainfall at those relatively high rain rates. Thus, the large

32

31

30

,-,

29 0

FIG.5. Radar composite of HurricaneElla, September 1,1978,13:20to 14:30GMTfromlower fuselage and nose C-band weather radars on board NOAA WP-3D Orion aircraft flown at a 150to 350-m altitude.

21 1

6. SEASAT WIND/RAIN OBSERVATIONS

73

72

71

t

33

33

32

32

31

31

30

30

29 74

29 73

72

71

I

FIG.6. Rain rate in millimeters per hour derived, according to the Wentz algorithm, from “ungridded” 37-GHz brightness temperatures measured by the SMMR as it passed over Hurricane Ella during Rev 952 on September 1, 1978, 13:30 GMT.

gradients at the periphery of the rain pattern seem artificial. Both the Wentz and Chester techniques, however, do resolve the location of the eye very well. The Chester algorithm generally derives rain rates that are lower than the Wentz algorithm, except for a few very high points. This difference occurs because the 37-GHz brightness temperatures saturate at high rates and yield values that appear to increase rapidly above background values. For comparison, Fig. 8 shows the results from the original Wentz algorithm for SMMR grid 3. This analysis corresponds to the type of pattern that is used to derive the attenuation values applied to the SASS. It is not difficult to see why some problem is encountered in the attenuation correction procedure, since the details of the banded structure and the resulting variability in the attenuation corrections have been smoothed out. However, the average rain rates computed by the Wentz algorithm agree very well with the WP-3D radar rain rates averaged to the 50-km SMMR cell. These comparisons are shown in Fig. 9.

212

PETER G. BLACK ET AL. I

33

32

31

30

29

72

71

70

FIG.7. Rain rates in millimeters per hour derived according to the Chester algorithm, using 37-GHz SMMR data averaged to grid 4 (about 30 km2)for Hurricane Ella, Rev 952, on September 1, 1978, 13:30 GMT.

Similar analyses for Rev 331, Hurricane Fico, are shown in Figs. 10 and 11 for the Wentz algorithm, only. Again, one can see the high degree of averaging that occurs with the 50-km resolution in Fig. 11, totally obscuring the rainband features, which show up well in the ungridded data (Fig. 10). In the high-rain-rate areas, more realistic rainfall rates result when the 50-km resolution and the lower frequency information are used. However, near the eyewall, lack of resolution results in large underestimates of the rainfall. In this case, the 37-GHz, high-spatial-resolution techniques yield more realistic rain rates. Griffith has recently applied her satellite rain estimation technique (Griffith et al., 1978) with IR data to GOES and Seasat VIRR data coincident with the microwave observations. This technique has been recently applied at the NOAA, NWS National Hurricane Center, Miami (on an experimental basis), to operational rainfall estimation for landfalling hurricanes (B. Jarvinen,

33

1 33

32

32

31

31

30

30

29 I

72

71

29 1

FIG.8. Rain rates in millimeters per hour derived according to the Wentz algorithm, using 37and 18-GHz SMMR data averaged to grid 3 (about 50 km2) for Hurricane Ella, Rev 952, on September 1, 1978, 13:30 GMT. The storm center is indicated by an X.

FIG.9. Comparison of W P 3 D airborne radar measurement of rain rate, averaged over grid 3 boxes with Wentzalgorithm results for grid 3. Hurricane Ella, September 1,1978, Rev 952; 50-km cell.

214

PETER G. BLACK E T A L . 153”

1

I

3”

I

I 0

153’

1

FIG.10. Rain rate in millimeters per hour derived, according to the Wentz algorithm, from “ungridded” 37-GHz brightness temperatures measured by the SMMR as it passed over Hurricane Fico during Rev 331 on July 20, 1978,04:30GMT.

personal communication, 1982). Comparisons between simultaneous GOES and VIRR images for Hurricane Fico, Rev 280, were made in an effort to verify that the technique would work equally well with VIRR data as with the GOES data. An excellent comparison resulted. Therefore, this work has continued to derive VIRR rain rates over several typhoons and tropical storms. Derivation of attenuation values for use with both the SASS and the altimeter is accomplished from the rain values. Further evaluation of this technique is in progress. The satellite IR-derived rainfall rates will be compared with airborne radar rates for the Hurricane Ella case, as well as for two other hurricane cases. This technique offers the potential to estimate SASS attenuation values for the left-sideportions of the SASS swath not observed by the SMMR. Thus, we can use a large amount of typhoon and hurricane data that had been discarded earlier due to lack of an attenuation correction method.

215

6. SEASAT WIND/RAIN OBSERVATIONS

'B 18"

0

lFlC0 R e v 331

I

1

I

153'

151'

14

FIG.1 1 . Rain rates in millimeters per hour derived according to the Wentz algorithm,usin 37and 18-GHz SMMR data averaged to grid 3 for Hurricane Fico, Rev 331, on July 20, 78, 04:30 GMT. The centers of the 50-km2grid boxes are indicated by solid dots and the storm center is indicated by a hurricane symbol.

5.

SEASAT SURFACE WIND OBSERVATIONS IN TROPICAL CYCLONES

Present in situ methods of deriving surface winds in tropical storms often involve techniques of limited accuracy. For example, surface winds derived from operational aircraft reconnaissance flights are frequently based on Beaufort state-of-the-sea relationships. However, these relationships are questionable in hurricane-force winds because they lack documentation. Further, the sustained flight-level wind at a 3300-m altitude is often used to approximate the peak gust near the surface. In addition, minimum surface pressures have been used to infer maximum surface winds (Atkinson and Holiday, 1977). In fact, to date, the only reliable method of obtaining the mean surface wind speed has been through low-level penetration of the

216

PETER G . BLACK ET AL.

hurricane with aircraft that are equipped with inertial navigation systems. Then, flight-level wind measurements have been extrapolated to the surface using a boundary-layer model. The use of microwave remote-sensing methods from satellites offers the opportunity to measure surface winds without the need for dangerous, in situ measurements. Three hurricanes were selected for detailed analysis to evaluate the accuracy of Seasat-derived wind data. These were Hurricanes Fico in the eastern North Pacific, Ella in the western North Atlantic, and Greta in the western Caribbean Sea. The former was selected because of the availability of Air Force reconnaissance data in the inner core during Rev 331. The latter two were selected because of the simultaneous availability of Hurricane Research Division (formerly National Hurricane Research Laboratory) research flight data during Revs 952,966, and 988 for Ella, and Revs 1175,1182, and 1196 for Greta. Surface truth data from ships and from GOES cloud motions were also composited with respect to the storm center. The latter were available mainly outside the central dense overcast of the storm, i.e., beyond about 200 km, whereas the aircraft data were available in the inner core within 150 km of the center. Therefore, the three data sets were complementary in that they defined a wind field over the entire storm.

5.1. Aircraft Data The Air Force data in Fico were available only at the 700-mbar altitude. It has been established by others that for mature, steady-state hurricanes, there is relatively little wind shear with height between the boundary layer and about 400 mbar (LaSeur and Hawkins, 1963; Hawkins and Rubsam, 1968; Hawkins and Imbembo, 1976). Therefore, the Air Force 700-mbar winds were assumed to be representative of top of the boundary-layer winds. Powell(1980) has recently shown, based on his boundary-layer model and the work of Bates (1977), that the 19.5-m-level wind is about 20% lower than that at the top of the planetary boundary layer (PBL). Therefore, a factor of 0.8 times the flight-level wind was used to derive the neutral stability, 19.5-m wind, which is the assumed level for the SASS winds. Furthermore, the Cardone boundary-layer model was run for Fico using the aircraft-measured maximum wind, radius of maximum wind, and the prevailing synoptic-scale pressure gradient. The resulting surface wind directions were used as guidance for the analysis, with heaviest weighting going to ship reports. A 0.9 correction was applied to the GOES-derived cloud motions. The rationale for using this 0.9 factor is explained in Hawkins and Black (1982). For the Ella and Greta cases, the aircraft observations were made near cloud base, very close to the top of the PBL. The boundary-layer model

6. SEASAT WIND/RAIN OBSERVATIONS

217

(Powell, 1980) was used to reduce these Inertial Navigation System (INS)derived flight-level winds to the surface. Detailed sea surface temperature fields were derived for the stability analysis from airborne expendable bathythermographs (AXBT) and airborne infrared radiation thermometer data. The reduced aircraft data were supplemented with cloud motions and ship winds at the larger radii.

5.2. Cloud-Motion Wnds Tracking of clouds using images from geostationary satellites to derive free air winds has been commonplace for many years. Problems arise, however, in obtaining such winds near hurricanes, because many of the clouds best suited for tracers at low levels do not persist or maintain their identity for even 30 min. When they do, they are obscured by high-level clouds much of the time, even though they may be viewed briefly through breaks in the clouds. To overcome these difficulties, experiments at the Goddard Space Flight Center sponsored by the National Aeronautics and Space Administration (NASA) and the National Earth Satellite Service (NESS) of NOAA have shown it feasible to obtain many low-level winds near hurricanes by tracking cloud motions if the imagery is taken at short intervals of 7.5 min or less (Rodgers et al., 1979). Hasler et al. (1977) had reported that cumulus cloud motions approximate, within amean vector difference of 1.3 m sec-', the speed and direction of the ambient flow at the cloud-base level compared with aircraft observations in the trade-winds regime over ocean areas. The cloud-tracking technique was applied for Hurricanes Ella and Greta and many winds were obtained at about the same time as the respective Seasat overpasses.

5.3. SASS Winds and Methods of Comparison The SASS algorithm used in this study selects backscatter observations from paired forward- and aft-looking antennas such that the individual observations are less than 50 km apart. The wind solution derived from the model function is that located at the midpoint between the paired observations. An explanation of the SASS algorithm is given in Chapters 3 and 4,when interpretation of instrument observations and ocean surface wind observations, respectively, are discussed. The surface truth winds are derived in several ways. First, and simplest, the wind at the midpoint between the cell pairs is interpolated from an analysis and compared with the SASS wind. The second method involves area

218

PETER G . BLACK ET AL.

averaging the wind over each cell footprint that is used in the pairing. Then the two area-weighted surface truth winds are averaged. A third technique was used with the Greta data. Here, analyses were made of the wind speeds derived from the SASS measurements. The surface truth wind was then compared with the wind speed for the corresponding position interpolated from the SASS analysis. Results from all three comparisons are presented. It should be mentioned in passing that the SASS algorithm can also average the backscatter observations using the bin method. This involves averaging all the fore-beam backscatter observations and all the aft-beam backscatter observations within a bin of a prescribed size, such as 0.5 or 1” latitude on a side. The resulting averages are then used to compute the wind located at the center of the bin. This method was not used in tropical cyclone work because of the resolution that would have to be sacrificed to obtain a more stable backscatter measurement. 5.4. SASS Alias Removal in Tropical Cyclones

The SASS algorithm does not return a unique wind-vector solution. Two to four aliases result from only two independent backscatter measurements. The unwanted aliases were eliminated subjectively by selecting the direction closest to the tangent to a circle centered at the known storm location. The alias with relative inflow was selected when two aliases were close to the tangent direction. At distances greater than 200 km, the alias most consistent with the surface truth wind direction was chosen. A method for objective alias removal in tropical storms must await further research. OF INDIVIDUAL STORMS AND COMPARISON OF SEASAT DATA 6. ANALYSIS WITH “SURFACE TRUTH”DATA

6.1. Hurricane Fico, July 1978

Hurricane Fico developed from a tropical disturbance that was about 830 km south southeast of Acapulco, Mexico, on July 7,1978. While moving in a general westerly direction it gradually intensified and became a hurricane at 13.6”N and 111.4”W on July 10. It continued moving westward, alternately intensifying and weakening. By July 17, Fico had attained maximum winds of 59 m sec-’. On the 20th, it was about 410 km southeast of Hawaii when Seasat passed above. At about the same time, the Air Force reconnaissance aircraft made measurements at the 700-mbar level. By the next day, Fico’s track began to curve northwestward and the storm started weakening.

219

6. SEASAT WIND/RAIN OBSERVATIONS

The surface truth data selected and processed as described in Section 5 are plotted in Fig. 12. Streamlines and isotachs analyzed to fit these data are shown in Fig. 13. For comparison with these data, the four aliases of the SASS wind solution are shown in Fig. 14. Figure 15 shows the selected “best” alias plotted in meteorological convention,’ and Fig. 16 shows the analyzed SASS streamline and isotachs. The surface truth and SASS streamline analyses for Fico (Figs. 13 and 15) are in generally good agreement. The one problem that emerges, however, is the existence of the artificially sharp wind shifts due north of the center in the SASS analysis (Fig. 15). These shifts appear to be a model

153’

151”

FIG.12. Distribution of surface truth data used for comparison with SASS data from Seasat Rev 331 for Hurricane Fico. Aircraft and ship wind data were composited over a 12-hr period. Two hurricane reconnaissance flights occurring between 0O:OOand 12:oO GMT, July 20, 1978, were used. Triangles with no wind barbs indicate that an aircraft wind-speed measurement was reported, but no direction was available. Cloud motions were centered at 04:OOGMT and based on If-hr motion vectors.

’ One crossbar is 5 m sec-’, one-half crossbar is 2.5 m sec-I, and a penant is 25 m sec-’

220

PETER G. BLACK ET AL.

FIG.13. Isotach and streamline analyses for the 19.5-mlevel in Hurricane Fico, derived from the data in Fig. 12, representative of conditions for 04:30GMT,July 20,1978.

function deficiency and arise when the number of aliases returned from the wind solution changes from two or three to four. Further discussion of this is in Section 6.7.3. The surface truth and SASS-derived isotach fields also are in good agreement, up to wind speeds of 20 m sec- '. Above that the high-wind areas are clearly very much underestimated. The low estimates are due to coarseness of cell resolution and underestimation of the attenuation corrections caused by heavy precipitation. Table I summarizes the statistics for the comparison of surface truth fields with SASS wind fields for Fico. The table summarizesthe statistics for regions with and without attenuation corrections (AC) for intervening precipitation and water vapor. These statistics are presented in plotted form in Figs. 17a and 17b. For the precipitation-free regions, the RMS errors are about 2.0 m

6. SEASAT WIND/RAIN OBSERVATIONS

22 1

FIG.14. SASS wind aliases for Hurricane Fico, Rev 331,04:30 GMT July 20,1978. Numbers indicate the average wind speed from the four aliases at each observation point.

TABLE I . STATISTICAL SUMMARY OF ERRORS FOR COMPARISON OF SASS WINDS WITH SURFACE TRUTH WINDSFOR HURRICANE Fico, REV33 1

No precipitation

No. of points E" speed RMS speed SD speed E" direction RMS direction SD direction

Precipitation

All data

No AC

AC

No AC

AC

No AC

AC

21 1 1.5 1.9 1.1 2.1 13.2 13.0

21 1 1.8 2.0 1.o 2.1 13.2 13.1

64

- 5.6 7.1 5.3 1.o 14.1 14.1

64 -3.1 5.2 4.2 0.5 14.0 14.1

215 -0.1 4.1 4.1 1.9 13.4 13.3

215 0.6 3.1 3.0 1.8 13.4 13.3

E = mean error(SASS- surface truth) = bias.

222

PETER G . BLACK ET AL.

,

18'

16

FlCO R e v 331

14'

FIG.15. SASS wind aliases for Rev 331 that were closest in direction to the observed surface truth wind direction.

sec- for speed and 13" for direction, with standard deviation (SD)of i-1.0 m sec- and & 13.1'", respectively. The speed bias is significant at 1.8 m sec- for Fico. In the areas affected by precipitation, the attenuation correction reduces the R M S speed errors by about 30%, while leaving the direction errors unchanged. Even so, the speed errors are about double the errors for precipitation-free regions, exhibiting a larger negative bias of - 3.1 m sec-'. This bias is evident from the scatter plots in which the precipitationcontaminated points are seen to consist mainly of high-wind points. Other studies (Jones et al., 1982)have shown that SASS tends to underestimate high winds. Underestimated attenuation and poor spatial resolution appear to have further compounded the problem in this case. The directional errors for the precipitation areas also increase somewhat over the precipitation-free areas, ranging from 14 to 24". The statistics for all data considered together show little bias. This characteristic,however, is due to a positive bias, at winds

'

6. SEASAT WIND/RAIN OBSERVATIONS

223

FIG. 16. Isotach and streamline analyses for Hurricane Fico based on SASS wind aliases for Rev 331 that were closest in direction to the surface truth analysis.

below 18 m sec-', of about 2 m sec-', and a negative bias, at higher wind speeds, of about 3 m sec-'. The RMS errors in speed and direction average out to about 3 m sec-' and 13". In the preceding comparisons, the surface truth winds have been interpolated to the point of the SASS wind solution. We now examine the case where cell area averages are computed. The cell pattern for Fico is shown in Fig. 18, with every other row of cells depicted. To determine if wind-speed statistics could be improved by improving the resolution, wind directions estimated from the SASS dealiased wind field were input to the algorithm as known quantities and wind speeds for each cell were computed. Surface truth winds averaged over the cell footprint were then compared with forwardlooking and aft-looking cells. The latter were larger for Fico, a descending orbit. The results are presented in Table I1 and as scatter plots in Figs. 19a and 19b for the fore and aft beams in Fico. The same tendency to overestimate

224

PETER G . BLACK ET A L .

L

I'

REV 331 v - POL

, # I

/

M

= 0.6771 = 0.3132

B = 5.3938 R

. ==

QUALITY 1 QUALITY 2

FIG. 17a. Scatter plot for Rev 331 over Hurricane Fico of surface truth wind speeds interpolatedto SASS wind locations versus SASS wind speeds. SASS observationswere obtained in the vertical polarization mode. Pluses indicate regions of cold cloud-top temperatures and large attenuation corrections. Circles indicate rain-free areas. The correlation coefficient is 0.91. TABLE11. ERRORSTATISTICS FOR FORWARD AND AFTBEAMS FOR Fico, REV 331 Aft beam

Forward beam

No. - points E RMS SD

No P

P

All

No P

P

All

119 2.4 2.9 1.5

52 -0.3 2.4 2.4

171 1.6 2.7 2.2

15 2.1 2.5 1.4

33 -0.7 1.9 1.8

108 1.2 2.3 2.0

225

6. SEASAT WIND/RAIN OBSERVATIONS

%o-

-

am-

m-

- -

g- -216

5c -u

g1w0

-

9i w 2 !

H I lo -

REV

v-

72

fl

331 POL

= 1.0tIlll

B = -3.6Y37 R = 0.9362 o.

r-"

T GROUND TRUTH WIND DIRECTION

I

=

QUALITY 1 QUALITY 2

lDEGI

FIG. 17b. Scatter plot for Rev 331 over Hurricane Fico of surface truth wind directions interpolated to SASS locations versus SASS wind directions.

wind speeds below 18 m sec- is seen in the scatter plots, as was the case for the original SASS-derived winds. This bias is tabulated as a function of wind speed in Table 111, averaged for both fore and aft beams. The primary result shown in this comparison is a reduction of the high-wind bias error in precipitation regions by a factor of 2 compared to the comparisons in Table I and Fig. 17a. In fact, the errors in the precipitation regions tend to be somewhat less than in the precipitation-freezones. This result suggests that a large portion of the high-wind errors are due to resolution problems. Limited comparisons of the nadir cell wind speeds with surface truth wind speeds have been made. Preliminary results for Fico and Greta indicate very large errors exist in the SASS algorithm for the nadir cells. Errors of 40-50% were found in both the Fico and Greta data. A sample comparison of shipderived surface winds in Fico with the nadir SASS winds is given in Fig. 20.

PETER G. BLACK ET AL.

9.

-

u-

10-

17

-

-

1.

I-

U-

ll

-

' l r -

FIG.18. SASS antenna beam pattern showingall Doppler cell locationsfor every other row of cells for the forward- (foreshortened)and aft-lookingbeams as the Seasat subsatellitetrack passed east of Hurricane Fico, Rev 331,July 20,1978. The region of overlapping cell locations from the fore and aft beams is the region where SASS wind-vector solutions can be obtained.

227

6. SEASAT WIND/RAIN OBSERVATIONS 40

36 32 -28

-'p

E24

U J

g - 20 z

$16 U

v)

12 8

4

0 0

4

8

12

16 20 24 28 FIELD WINDS (rnsec-')

32

36

40

FIG. 19a. Scatter plot of high-resolution SASS-derived wind speeds versus surface truth windfield-derived speeds at individual forward-looking cell locations for Hurricane Fico, Rev 331. Speeds were derived from measured radar cross sections at the cell location given the SASSderived wind direction. TABLE111. FOREAND AFTBEAMBIAS ERRORAS A FUNCTION OF WINDSPEEO FOR Fico, REV331 B(m sec-')

Speed range (m sec-')

2.1 1.4 - 1.3

< 15 15-20 20-25 z 25

- 3.0

Wentz (1981a) has recently used his SMMR algorithm (Wentz, 1983) to compute wind speed from the 10.7-GHz brightness temperatures. His initial results for Fico are shown in Fig. 21 from data averaged over the 90-km resolution SMMR grid 2. The agreement between the SMMR winds and the area-averaged surface truth (over the 90-km SMMR cell) agree to within 3 m

228

PETER G . BLACK ET AL. 40

I

1

1

I

1

I

I

1

c

3 6 r

32

t

:I-@-,

l2

I

I

16

20

1 4

'0

12

8

32

FIELD WINDS FIG.19b. Same as Fig. 19a, except for aft-looking cells.

32 30

I

I

I

100

200

300

I

I

I

I

I

I

I

400

500

600

700

800

900

1000

:t "0

20.5.N

ALONG-TRACK DISTANCE ( k m l

12.0'N

FIG.20. Comparison of SASS winds derived along the subsatellite track for Rev 331 at nadir incidence with surface truth winds based on merchant-ship reports.

229

6. SEASAT WIND/RAIN OBSERVATIONS

8.2

8.8

0-7

i' looNR.2~-yq-j--~,s R FICO e v 331 1 -

-

8.8

7.9

8.5

7.4

7.2

6.2

3.5

I

1

I

4.1

I

I

I

I

I

I

FIG.21. Wind field derived from SMMR for Hurricane Fico, Rev 331, July 20, 1978.

'

sec- for Fico. In the region away from the storm, good agreement is found in the shape and location of the isotachs. SMMR's basic disadvantage is the large cell resolution (90 km) at the 10.7-GHz frequency, where the brightness temperature sensitivity to wind speed is strongest. 6.2. Hurricane Ella, September 1978

Hurricane Ella formed over the Atlantic Ocean and had moved to a point about 500 km southeast of Cape Hatteras, North Carolina, at the time of the Rev 952, September 1, 1978. It had intensified rapidly and had maximum

230

PETER G . BLACK E T A L .

winds of 55 m sec-'. The strong winds were concentrated within 100 km of Ella's center. The intense gradients of the wind speed made accurate measurements difficult for the SASS instrument with its relatively low resolution. Northeast of the center, radially outward from the eyewall, the wind speed changed by 33 m s e t - ' in 55 km. 6.2.1. Comparisons with Surface Truth Fields Derived from Ship and Aircraft Data. As discussed in Section 5, the surface truth data for this storm came from aircraft measurements, low-level clouds, and surface ships. This time, aircraft data were obtained by the NOAA-RFC research aircraft at 170to 540-m altitudes. The surface truth winds are plotted in Fig. 22a, and

7'2"

-

FIG.22a. Surface truth wind data for the 20-m level in Hurricane Ella, September 1,1978. The dots represent measurements by NOAA-RFC research aircraft, the triangles are measurements by Air Force reconnaissance aircraft, and the circles are cloud motion measurements based on short-interval (7.5 min) GOES images processed on the NASA Goddard AOIPS facility.

6. SEASAT WIND/RAIN OBSERVATIONS

23 1

analyses of these data are in Fig. 22b. The SASS winds, including the aliases, are shown in Fig. 23. The best-fit aliases in meteorological format are shown in Fig. 24, and analyzed fields are in Fig. 25. The SASS streamlines are in good qualitative agreement with the surface truth streamlines, except that, as in Hurricane Fico, there are artificially sharp wind shifts that seem to be a function of the number of aliases. See Section 6.7.3 for further discussion. There is excellent agreement, as there was in Fico, between the isotach fields for wind speeds up to 20 m sec-'. Above that, the high-wind areas are very much underestimated. In the case of Ella, much of this is due to cell resolution being too coarse. Underestimation of the attenuation corrections probably also contributes. Table IV summarizes the statistics for the comparisons of the surface truth fields with SASS wind fieldsfor Ella for regions with and without precipitation and with and without attenuation corrections for intervening precipitation and 72'

7 0"

14" -

/

32" 7

\

30°\

28" -

FIG.22b. Streamline and isotach analyses of data in Fig. 22a.

232

PETER G. BLACK E T A L .

FIG.23. SASS wind aliases for Hurricane Ella, Rev 952, 13:OO GMT, September 1, 1978. Numbers indicate the average wind speed from the four aliases at each observation point.

Iv. STATISTICAL SUMMARY OF ERRORS FOR COMPARISON OF SASS WINDS TABLE WITHSURFACE TRUTHWINDSFOR HURRICANE ELLA,REV952 ~~

No precipitation

No. of points

E speed RMSspeed

SD speed

E direction RMS direction SD direction

Precipitation

All data

No AC

AC

No AC

AC

No AC

AC

185 -0.2 1.7 1.7 - 3.5 18.7 18.4

185 -0.1 1.7 1.7 - 3.5 18.7 18.4

112 -0.3 3.3 3.3

112 0.3 2.9 2.9 0.9 24.1 24.2

297 -0.2 2.5 2.5 -1.6 20.1 20.1

297 0.0 2.2 2.2 - 1.9 20.9 0.9

1.5 22.3 22.3

233

6. SEASAT WIND/RAIN OBSERVATIONS

FIG.24. SASS wind aliases for Rev 952 that were closest in direction to the observed surface truth wind direction.

water vapor. The statistics are also presented in plotted form in Figs. 26a and 26b. The RMS errors for the precipitation-free region are 1.7 m sec-' for speed and 19" for direction. The speed bias is near zero. In the areas affected by precipitation, the attenuation correction reduces the RMS speed errors by about 15%, leaving the direction errors relatively unchanged. Even so, the speed errors are nearly double the errors for precipitation-free regions; i.e., about 2.9 m sec-'. This doubling is also evident in the scatter plots in which the precipitation-contaminated points are seen to consist mainly of high wind points. The strong gradients of wind speed in Ella also occur mostly in the areas of high winds. The RMS directional errors increase to 24". The statistics for all data considered together show little bias. This, however, is due to a positive bias at winds below 18 m sec-' of about 2 m sec- and a negative

'

234

PETER G . BLACK ET AL. 140

52"

30"

28"

I

ELLA R e v 1952 /

1

1

I

28

700

FIG.25. Streamline and isotach analysesfor Hurricane Ella based on SASS wind aliasesfor Rev 952 that were closest in direction to the surface truth analysis.

bias at higher wind speeds, as was the case with Hurricane Fico. The RMS errors average out to 2.2 m sec-' and 21". This also is considered excellent. 6.2.2. Comparison between SASS Winds and Cloud Motions. The winds from tracking low-level clouds and from the SASS data for Ella on September 1,1978,were available for larger areas than shown in Figs. 22a and 22b. SASS winds and cloud motions were compared in the following way. Isotachs were drawn subjectivelyto provide a best fit to the SASS wind speeds and also to the cloud-derived wind speeds. There was relatively little random scatter in the SASS wind speeds because the mean of the absolute values of the differences between individual and analyzed values was only 0.6 m sec-' with a standard deviation of 0.7 m sec-'. The GOES wind speeds were reduced by 10% to make them more nearly approximate the winds at the 19.5-m level and were then compared with the

235

6. SEASAT WIND/RAIN OBSERVATIONS

REV 952 v - POL fl

B R

.

0.8932

= 1.3536 = 0.8791 z

QURLITY 1 QURLITY 2

FIG.26a. Scatter plot for Rev 952 over Hurricane Ella of surface truth wind speeds interpolated to SASS wind locations versus SASS wind speeds. SASS observations were obtained in the vertical polarization mode. Pluses indicate regions of heavy precipitation based on airborne weather radar. Circles indicate rain-free areas. The correlation coefficient is 0.88.

SASS wind speeds, as explained in Hawkins and Black (1982). The comparisons were made between values at grid points of the two analyzed isotach fields. A summary of the differences for various portions of the storm is given in Table V where is the mean of the absolute values of the differences, and E is the mean of the differences. The correlation coefficient between SASS and GOES wind speeds was 0.64 for 99 pairs of values interpolated at grid points from analyses in all quadrants. Isogons were also analyzed subjectively for both the SASS and GOES wind directions. For the SASS winds, the alias at each point was selected

REV 952

v - POL ll

B R

a +

= 0.3009 14.4262 = 0.9648 =

QUALITY 1 QUALITY 2

FIG. 26b. Scatter plot for Rev 952 over Hurricane Ella of surface truth wind directions interpolated to SASS wind locations versus SASS wind directions.

TABLE v. DIFFERENCES BETWEEN SASS AND Northwest quad ~

X 0.9 GOES WIND SPEEDS*

Northeast quad

Southern quad

~~

I E(m ~ sec-') E (m sec-') Percentage of cases where SASS is greater Range of SASS speeds (m sec-') Range of GOES speeds (m sec-I) a

1.8 (0.5)b

1.4 (1.0)

1.8 (1.1)

+ 1.8 (0.5)

+ 1.3 (1.2)

+ 1.4 (1.6)

100

92

82

4-12

4-13

4-15

6-10

6-1 1

5-12

Positive values of E mean SASS winds are greater. Values in parentheses represent the SD.

237

6. SEASAT WIND/RAIN OBSERVATIONS

subjectively after we considered which of the two to four aliases seemed reasonable under the existing hurricane conditions. When there was still doubt about which to select, the alias that most nearly agreed with the appropriately placed surface truth data was selected. Once the aliases were selected, comparisons were made between the analyzed values of the SASS and GOES isogons (lines of constant wind direction). The average difference between the values of the isogons interpolated at grid points for the SASS and GOES fields from the smoothed analyses was 17.5"with a SD of 13.6". Table VI lists values by quadrants. The correlation coefficient for 109 pairs of values interpolated at grid points from the isogon analyses in all quadrants was 0.81. If the wind direction differences between GOES and SASS are interpreted as real differences as a function of height, the change between the wind directions at the GOES level (cloud base or about 500 m) and the surface (SASS level, 19.5 m) is such that the angle of inflow decreases with increasing height in all quadrants, except the northwest. In that area, the angle of inflow increases with height. In most cases, one would expect the angle of inflow in a hurricane to decrease slightly with height in the subcloud layer. Hurricanes for which detailed flight data have been available show that some of the quadrants have outflow even at low levels, especially at radii greater than 100 km. However, it is surprising to find as much outflow in the northwest quadrant as suggested by the SASS winds. Of course, a different alias for the points in the northwest quadrant can be selected to obtain indications of inflow from the SASS data. However, the inflow angles are so large as to be unreasonable, and the differences from the GOES data are much larger. Furthermore, for both the aircraft and cloud motion winds, the major area of inflow was to the south of Ella with the angle of inflow being relatively small in the northwest quadrant. Figure 27 shows the wind speeds computed by Wentz (1981a) using his SMMR algorithm (Wentz, 1983) for wind speeds from the 10.7-GHZ brightness temperatures for Hurricane Ella. The agreement with surface truth was worse than for Hurricane Fico. SMMR speeds are too low by nearly 10 m sec-' near the center. TABLE VI. MEANDIFFERENCES j? BETWEEN DIRECTIONS OF SASS AND GOES WINDS' Northwest quad

Northeast quad

Southern quad

25" (20")

16" (10')

~~

-

-10"

E

a

(14")b

Positive value means winds veer with height. Standard deviation is given in parentheses.

238

PETER G . BLACK E T A L .

35"N

30°N

25ON

FIG.21.

6.3. Hurricane Greta, September 1978 Hurricane Greta, September 13-20, 1978, offered a good opportunity for comparing the SASS winds with winds measured by low-flying hurricane research aircraft. Five Seasat revolutions were close enough to the storm for the scatterometer to make pertinent measurements and two aircraft flights were made by the NOAA aircraft to obtain wind measurements for comparison. The Seasat revolution times of closest approach to Greta are listed in Fig. 28 along with the times of the two research flights and a graph of the variation with time of the minimum surface pressures (a measure of intensity) in Hurricane Greta. Greta developed from a tropical wave which moved into the

239

6. SEASAT WIND/RAIN OBSERVATIONS I I

780918 FLIGHT # H

I

I

I

I

I

I160 I

16/00

1

1

I182

I175 I 16/12

I

17/00

1

I

17/12

1

I 1

I189 I 18/00

DATE /TIME (GMTI

I

1 1

I

I196 SASS REVOLUTION I 18/12

1

19/06

I 19/12

FIG.28. Minimum central pressure as a function of time for Hurricane Greta, September 16-18, 1978. Times of NOAA-RFC research flights and Seasat passes are indicated.

Caribbean on September 13 and caused winds at Barbados that gusted to 22 m sec-'. It was named at 12:OO GMT, September 14, while just north of The Netherlands Antilles, and reached hurricane intensity at 12:OO GMT September 16 while south of Jamaica. The storm moved in a general westerly direction across the Caribbean. Rev 1160 was about 20 hr before the first aircraft flight, but the other four revolutions were either during or between the flights. The hurricane attained its minimum central pressure of 947 mbar (59 m sec- maximum winds) early on September 18, as reported by an Air Force reconnaissance plane. 6.3.1. Comparisons with Aircraji Surface Truth Data. The areas for which data are available from SASS and from the aircraft are indicated in Fig. 29. In each case, the area has been outlined relative to the position of Hurricane Greta's center at the time the respective data were obtained. Figure 29 shows that fairly complete coverage of the storm was achieved by both Seasat and by the research aircraft. Caution needs to be exercised in this analysis, however, because the hurricane was changing in intensity with time, as shown in Fig. 28, and the time between Revs 1160 and 1196 is about 60 hr. Furthermore, about 30 hr elapsed between the two aircraft flights. The variations of intensity with time were used to interpret the differences that are found between the various wind sets. The analyses of the surface truth data were composed of winds that were from low-flying research aircraft and reduced to the 19.5-m level, x 0.9 cloud motions, and surface-ship winds. The distributions of these surface truth data are shown in Fig. 30. Streamlines and isotachs of the composited data are shown in Fig. 31. The SASS data are shown in Fig. 32, plotted in

240

PETER G . BLACK ET AL. TRACK

............. -------

FLIGHT #

780916 780918

FIG.29. Location of areas of SASS wind solutions and aircraft flight tracks relative to the center of Hurricane Greta, September 16-18, 1978.

meteorological convention, and the analyzed streamlinesand isotachs for this SASS data composite are shown in Fig. 33. All flight-level aircraft winds were reduced to the 19.5-m level with the Powell (1980) boundary-layer model, as was done for the Ella case. Comparisons between winds are made in three ways. First, 66 of the aircraft wind speeds, interpolated to grid points from the analyses in Fig. 30, are compared with interpolated values from the SASS wind analysis for the same grid point locations relative to the hurricane center. Efforts were made to compare aircraft and SASS data that most nearly agreed in time. Therefore, when SASS passes overlapped in the composite, the pass closest in time to the aircraft data was chosen for the composite field. The SASS and aircraft winds for Greta are plotted in a scatter diagram and are shown in Fig. 34. For these 66 pairs of data, the standard deviation of the differences between respective pairs was 3.9 m sec-* and the RMS was 3.8 m sec- l , The correlation coefficient between the aircraft and SASS winds was 0.86. For the pairs of values where the aircraft wind speed was < 31 m sec-', the average error was 0.5 m sec-' and the RMS was 3.0 m sec-'. The SASS winds were slightly stronger in the areas of weak winds and the aircraft winds were stronger in the areas of strong winds.

6. SEASAT WIND/RAIN OBSERVATIONS

24 1

FIG.30. Composite of research flight data from September 17 and 18,1978, cloud motions and merchant-ship observations for Hurricane Greta. Winds are representative of the 20-m level.

A further comparison was made between the aircraft wind speeds and the SASS wind speeds. An aircraft wind speed every 2 min of flight in or near the

hurricane was compared with the interpolated value of the SASS wind speed for the position of the aircraft. The mean differences, standard deviations of the differences, and the R M S errors are tabulated in Table VII for all cases where the aircraft wind speed was <25 m sec-'. For higher wind speeds, the differences tend to be larger. Data in Fig. 28 show that flight 780916 coincides most closely in time with Rev 1175and flight 780918 with Rev 1196. The mean differencesfor these two comparisons were -0.1 and 0.7 m sec- ', respectively. For Rev 1160,aircraft wind speeds from both flights were higher in the mean than the SASS wind speeds. However, the storm intensified considerably between the overflight of Rev 1160 and the aircraft flights. The larger mean errors of - 2.1 and - 5.2

242

PETER G . BLACK ET AL.

FIG.31. Streamline and isotach analyses for Hurricane Greta of surface truth data in Fig. 30.

for this orbit are probably due to real differences in the storm’s temporal evolution. The three sets of error statistics for Rev 1182 are associated with three types of polarization combinations used for the SASS wind solutions. The SASS data for all the other hurricane overflights examined have been characterized by remarkable consistency in the speeds for adjacent points, because only cases using vertical polarization have been examined. Investigation showed that the lack of consistency for 1182 is at least partially due to the mixture of polarization used. In 1182 some of the values were derived using horizontally polarized readings (H) from each antenna beam, some using vertically polarized readings (V) from each beam, and some using V from one beam and H from the other. Adjacent values from different mixtures do not agree closely, as will be discussed in Section 6.7.2.

FIG.32. Composite of “correct” SASS wind alias from several Seasal orbits relative to the center of Hurricane Greta. TABLEVII. COMPARISON OF WIND SPEEDS ( 1 9 3 LEVEL) ~ DEDUCED FROM AIRCRAFT AND SASS MEASUREMENTS

Revolution number 1160 1175 1182

(H Pol) (V Pol) (V/H Pol)

Flight 780918 Wind speeds < 25 m sec-‘

Flight 780916 Wind speeds < 25 m sec-’ na E SD RMS

n“

d

89 12 79 99 45

-2.1 -0.1

SD

RMS

3.0 1.5

3.6 1.4

13

-5.2

1.6

5.4

-

-

-

-

0.8 0.6 3.8

4.3 4.3 2.4

4.4 4.3 4.6

73 71 58

0.0 0.7 -1.8

4.3 3.7 2.5

4.3 3.8 3.1

0.3

7 7 10 20

-3.3 6.9 -1.6 0.7

1.6 1.8 1.2

3.6 7.1 1.9 1.7

1189

(Western) (Nadir) (Eastern) 1196

2.9

2.3

-

29

-

-

-

-

-

-

-

-

-

-

-

’n is number of pairs.

1.6

244

PETER G. BLACK E T A L .

FIG.33. Streamline and isotach analyses for the SASS data relative to Hurricane Greta shown in Fig. 32.

Another comparison was made using the data from the various Seasat overflightsin Greta. When the data areas are located relative to the hurricane center, there are large areas for which collocated SASS data are available from Revs 1160, 1182, and 1189 (Fig. 29). Collocated pairs of data for the respective sets were compared, and a summary of the differences is provided in Table VIII. The maximum winds and the size of Greta were greater at the time of Rev 1189 than at the time of Rev 1160. It should be expected that the speeds for Rev 1189 exceeded those in Rev 1160 at the same radial distance from the center. No ready explanation is available for the fact that Rev 1182 had higher speeds than 1189, unless they were caused by use of different polarizations in 1182 (Section 6.7.2). 6.3.2. Comparisons with Cloud-Motion Winds.As in Hurricane Ella, the cloud-motion winds for Greta were compared with the SASS winds that were

245

6. SEASAT WIND/RAIN OBSERVATIONS

:I 34

f f

r

28

26

.. ......,’. . /

u)

22

*’,

I

9’

:

.

...‘.:A / . /* ,, .‘ ’ /’

,’

,,

....’, ,.

.,’.,.‘ / .. /

.

.

om

d .

, /

7’

*’*

v1

2

.

_‘

6 42

I

-,,’ 1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

I

I

TABLEVIII. MEANDIFFERENCES I N SASS WINDSPEEDSFROM VARIOUS REVOLUTIONS FOR PAIRS OF DATA COLLOCATED RELATIVE TO CENTER OF HURRICANE GRETA Revolutions

n

E

1160- 1 189 1189-1 182

74 79

- 2.3

- 2.2

obtained at approximately the same time. Comparisons were made in a manner similar to that described in Section 5.2. The SASS wind speeds, again, differed little from the analyzed cloud motions. The mean of the absolute values of the differences was 0.4 m sec-’ with a SD of 0.7 m sec-’. The differences in the values at grid points for the two analyzed wind fields are listed in Table IX. The data for this storm are summarized for two areas, north and south of 19”N. The correlation between 50 pairs of values from the anaylzed fields at grid points over both areas is 0.76. The differences between the SASS and GOES wind speeds were less for Greta than for Ella, possibly because gradients in the wind speeds were less for Greta. That is, the coarse resolution of the SASS instrument would be a greater handicap for the Ella case.

246

PETER G. BLACK ETAL. TABLE IX. DIFFERENCFS BETWEEN SASS WIND SPEeDS AND x 0.9 GOES WIND SPEEDS'

Area North of 19"N 1.1 (0.8)b +0.3 (1.3) 7-14 7-18

IEI(m sec-') B (msec-') Range of SASS speeds (m sec-') Range of GOES speeds (m sec-')

South of 19"N 1.1 (1.5) +0.9 (1.6)

4-14 5-17

' Positive value means SASS is greater. Values in parentheses represent the SD.

The selected individual aliases for SASS winds differed from the smoothed analysis on the average by 5.8", with a SD of 6.4". Comparisons between the SASS and GOES directions are summarized in Table X. The correlation coefficient for 65 pairs of directions at grid points distributed over the entire area was 0.86. If the difference between SASS and GOES wind directions, which ranged from 10-30", is considered real, then in the mean, the angle of inflow was greater for the SASS winds than for the GOES winds. As shown earlier, this finding is in agreement with indications from boundary-layer theory and other hurricane observations. Table X indicates that veering was strongest just north of the storm center (located at latitude 15.5") and smallest just south of the center. 6.4. Selected Typhoons

In a complementary study, R. Brody, of the Naval Environmental Prediction Research Facility, Monterey, California (personal communication, 1981) compared SASS-measured winds within four regions of eight TABLE X. MEANDIFFERENCES (deg) BETWEEN SASS AND GOES WINDDIRECTIONS'

Latitude belts

E IEl a

25.5 24.5

24.0 22.5

6(10)b 11(5)

W5) ~

4

)

22.0 19.0

18.0 15.5

15.0 13.5

13.0 11.0

25.5 11.0

19(4) 19(4)

29(12) 29(12)

5(17) 12(12)

23(9) 23(9)

17(13) 18(11)

Positive value means winds veer with height.

* Values in parenthesesrepresent SD.

247

6. SEASAT WINDiRAIN OBSERVATIONS

western Pacific typhoons. These regions are defined in Fig. 35. Using 15- and 25-m sec- wind radii estimated by typhoon forecasters at the Joint Typhoon Warning Center (JTWC),Guam, using Air Force reconnaissance data, Brody interpolated wind speeds to SASS cell locations, comparing these estimates with the SASS attenuation-corrected wind-speed estimates. His results are summarized in Table XI. This shows that the SASS underestimate of winds increases with increasing wind speed and precipitation effects within the typhoon inner core. Surface truth ship observations, totaling 76, were available only outward from the 15-m sec-' radius. These comparisons

Region of as seen on satellite photo

FIG.35. Areas within 15-m sec- ' wind radii of the center of a typhoon used for comparison of SASS winds and winds estimated from Joint Typhoon Warning Center wind distributions. Region A is between 15- and 25-m sec-' wind radii without heavy cloud cover as derived from polar-orbiting satellites. Region B is between 15- and 25-m sec-' wind radii with heavy cloud cover. Regions C and Dare between the center and the 25-m sec-' wind radius without and with heavy cloud cover, respectively. XI. COMPARISON BETWEEN SASS WINDSAND JTWC WARNING-DERIVED WINDS' TABLE Mean wind speed Area (see Fig. 1)

Sample size

SASS

JTWC

Mean difference

Standard deviation

Bias JTWC-SASS

A B C

139 12 27 21

17.8 17.3 18.9 19.5

19.9 19.7 21.6 30.2

3.8 2.5 8.6 10.8

2.4 2.1 2.5 3.6

2.8 2.4 8.6 10.8

D

Values in meters per second.

248

PETER G . BLACK ET AL.

indicated that SASS was biased slightly high by 1.5 m sec-'. or IS%, with a standard deviation of 2.1 m sec-'. For ship-SASS wind comparisons in Typhoon Carmen, using an earlier version of the SASS algorithm, K. Tsuchiya of the National Space Development Agency of Japan, Tokyo (personal communication, 1979) also found a high bias for winds near 10 m sec-' and a lower bias for higher winds up to 15 m sec- He found a standard deviation of 1.3 m sec-' for winds of 10-15 m sec-'. The final versions of the algorithm reduced the overall biases, but the tendency for low biases at high winds remained.

'.

6.5. Hurricane Ella Model Fields

SASS winds were also compared with hurricane PBL model-generated winds for the Ella case. Input parameters for the model were the radius of maximum wind measured by aircraft, the maximum wind, and the environmental pressure gradient. Model-derived wind fields were first compared with the aircraft-derived wind fields. Essentially zero bias was found, with RMS speed differences of 1.9 m sec- and RMS direction differences of 20". Results suggest that these errors are not random. In the southeast quadrant, model wind directions are biased low, suggesting too much inflow. In the northwest quadrant, model-wind directions are biased high, suggesting too much outflow. Model and SASS winds were compared using the two different interpolation schemesdescribed earlier. Results are shown in Table XI1 and in Figs. 36 and 37. One can see from Table XI1 that averaging the surface truth over the footprints, rather than simple interpolation, reduces the differences substantially, most notably at the higher winds. The cell-averaging procedure reduces the bias to nearly zero over the entire range of wind speeds. The RMS speed errors are reduced by about 50% over straight interpolation for winds above 20 m sec-l. Average RMS speed

'

TABLE XII.

COMPARISON OF

FOOTPRINT AVERAGING AND SIMPLE INTERPOLATION

Footprint average

Mean RMS Mean

RMS Mean RMS

Interpolated wind

Speed

Direction

Category

N

Speed

Direction

-0.51 2.34 0.36 2.40 -0.30 2.43

3.31 29.64 3.19 26.29 1.10 20.83

V > 20 m sec-'

20

V 2 15 rn sec-'

45

V > lorn sec-'

137

-3.21 4.39 -1.44 3.33 -0.91 2.80

2.32 28.63 2.84 25.75 1.05 20.51

249

6. SEASAT WIND/RAIN OBSERVATIONS

301

/

/

25

0 W W

% 0 I

I5-

I v)

s lo5-

O/

1

I

I

5

I0

I5

I 20

I

I

25

30

MODEL WIND INTERPOLATED TO CELL MATCH LOCATION (rn sec-’ )

FIG.36. Scatter plot of SASS wind speed versus Cardone boundary-layer model wind for Hurricane Ella, interpolated to SASS wind solution location for Rev 952, September 1, 1978.

errors for the storm, as a whole, are 2.4 m sec- with direction RMS errors amounting to about 25”. These errors are similar to those derived from in situ surface truth and suggest that a hurricane PBL model could be used to simulate surface wind fields in tropical storms and could be used with SASS observations for determining a detailed wind field over the entire storm. This procedure would be especially useful if only part of the storm were observed with SASS. Perhaps an iterative technique of fitting model wind fields to SASS wind fields could be derived to match the SASS observations with the initial guesses of maximum wind and radius of maximum wind. 6.6. Q E I 1 Storm A small, low-pressure system moved off the New Jersey coast on September

8,1978, and deepened explosively in the next 36 hr, causing the loss of a fishing trawler off the Grand Banks on September 9 and damage to the oceanliner Queen Elizabeth I1 (QE 11) 2 days later. Despite the network of NOAA data buoys and the relatively high density of transient ship reports off the U S . East

250

PETER G . BLACK ET A t .

25

-

-pro€

1

n w w

%

I5-

n

s P

v)

2

10-

5-

ELLA Rev 9 5 2

/ /

/

//

/

I

1

I

I

I

I

MODEL FOOTPRINT-AVERAGED WIND SPEED (rn sec-' )

FIG.37. Scatter plot of SASS wind speed versus Cardone boundary-layer model wind for Hurricane Ella, area averaged over SASS cell location for cell pairs used in the wind solution.

Coast, operational weather analyses and forecasts grossly underestimated the rate of deepening in this period. Operational surface wave analyses and forecasts were too low by a factor of 2-4 (Cane and Cardone, 1981). The QE I1 storm is an example of a bomb. Gyakum (1981) has performed an intensive study of the dynamics of the storm. Cane and Cardone (1981) studied the potential impact of Seasat scatterometer-derived winds on improved specification and prediction of the intense surface winds and sea states associated with this storm. The QE I1 storm was well observed by Seasat (Figs. 38a-38e). Particularly interesting data were returned from Rev 1066 near 12:OO GMT on September 9, 1981, where the left-side SASS scan viewed the early stages of the storm's development. Rev 1080, about 24 hr later, viewed the storm near maximum intensity. Rev 1093and 1094,24hr after Rev 1080,viewed most of the storm's circulation, including areas of high surface winds through a cloud-free atmosphere. Figure 39 compares the time history of maximum surface wind and minimum central pressure in the storm derived by postanalysis of Seasat and ship-log data, and the history indicated in operational NOAA analyses. The failure of the conventional real-time data base to resolve the storm

FIG.38a. Surface pressure analyses for time periods when SASS data were obtained over the QE I1 storm. 12:OO GMT, September 1978 map tirne-16:22 GMT SASS overpass time.

FIG.38b. Surface pressure analyses for time periods when SASS data were obtained over the Q E I1 storm. 00:00GMT, September 10, 1978, map time-04:22 GMT SASS overpass time.

FIG.38c. Surface pressure analyses for time periods when SASS data were obtained over the QE I1 storm. 12:OO GMT, September 10,1978, map time-14:Ol GMT, SASS overpass time.

FIG.38d. Surface pressure analyses for time periods when SASS data were obtained over the QE I1 storm. WOO GMT, September 11,1978, map time-0359 SASS overpass time. 252

FIG.38e. Surface pressure analyses for time periods when SASS data were obtained over the QE I1 storm. 12:00GMT, September 11, 1978, map time-16:31 GMT,SASS overpass time.

FIG.39. Time change of minimum central pressure and maximum wind for the QE I1 storm. 253

254

PETER G. BLACK ET AL.

intensity led to poor initial-state specifications for numerical weather prediction. The strongest winds measured by SASS in the QE I1 storm were in Rev 1080, near 12:OO GMT on September 10, 1978. Figure 40 is a reanalysis of the surface pressure field for that time. The ship Euroliner (plotted near 43"N, SOOW) precisely fixed the location of the storm center and its central pressure. The SASS winds resolved the storm center as an area of wind speeds of 10-12 m sec-' with winds of 25-30 m sec-' (effective 19.5-mwind speeds) surrounding the center out to radial distances of about 220 km. The surface wind field in Rev 1093/1094, derived by detailed postanalysis of conventional data, was used to evaluate the performance of SASS geophysical evaluation algorithms (Boggs, 1981) in high winds. Figure 41 shows the location of the swaths, as well as the surface streamline and isotach analysis. A comparison of SASS wind speeds (from the SASS-1 model function) and wind speeds reported by ships (including the QE 11) within the fields of view of 1093/1094 is shown in Fig. 42. The differences seen are comparable to the errors characteristic of the ship data (Cardone et al., 1980). Comparisons of

FIG.40. Postanalysis of pressure field for the QE I1 storm for 12:OOGMT, September 10,1978, with location of SASS swath.

6. SEASAT WINDiRAIN OBSERVATIONS

255

GREENLAND,

FIG.41. Streamline and isotach analyses based upon SASS winds from revolutions for the QE I1 Atlantic storm on September 1 1 , 1978. Boundaries of wind solutions from these revolutions are shown by the dashed lines.

SASS and field estimates of the surface wind speed and direction are shown in Fig. 43a and b, respectively. These comparisons suggest that SASS has achieved measurement accuracies of at least f2 m sec- in speed and f20" in direction in winds as strong as 25 m sec- For this case, SASS seems to underestimate surface winds up to 20 m sec-', in contrast to the hurricane cases where it appeared that SASS overestimated winds in this range of speeds. SMMR wind data for the QE I1 revolutions have also been studied extensively by the SMMR Evaluation Task Group. Figure 44,from Wentz et al. (1982), is a comparison of collocated SMMR and SASS wind measurements for the QE I1 revolutions. The SMMR brightness temper-

'.

256

PETER G . BLACK E T A L .

mi

SPOT REPORT COMPARISON REVS 1093/1094 v-POL SEPT 11,197e 0 BEAUFORT ESTIMATE (CORRECTED) 0 ANEMOMETER (ADJUSTED TO 19.5W

l4

I

OE 2

SASS ALGORITHM T (SASS-SPOT1 -1.58

b

--I .56

0 22

240

3t

/

OL

/

::

I:

15

Ilg

GROUND TRUTH WIND SPEED

I:

4:

$7

3

(m sec-'1

FIG. 42. Scatter plot of SASS wind speeds from Revs 1093 and 1094 versus individual merchant-ship wind-speed reports within the area of the QE I1 storm, September 11, 1978. Corrected Beaufort estimates, according to Cardone er ul. (1980), and anemometer-measured winds reduced to the 19.5-m level are compared.

atures are known to be contaminated by proximity to land and rain, and in daytime revolutions, by the contribution of reflected solar microwave radiation (sun glint). The data in Fig. 44 exclude cells at which the SMMR algorithm specified rain and at which the sun angle is < 15" for SMMR wind speeds that are < 15 m sec-'. Otherwise, sun angles < 10" are excluded. There is more recent evidence that SMMR wind estimates can be degraded for sun angles as large as 25" (Wentz, 1981b). Nevertheless, the SMMR winds in the QE 11 revolutions display sensitivity to at least 25 m sec-' and suggest that, at least for SMMR retrievals away from land or rain and not affected by sun glint, wind-speed measurement accuracy of f2 m sec-' is achievable with a passive microwave system.

257

6. SEASAT WIND/RAIN OBSERVATIONS

--

24

'$21

-E

18

c3 W

w

a I5 n z -

= 12 UJ v) U

m 9

M = 0.8096 B = 1.5744 R = 0.8618 0 = QUALITY 1 + = QUALITY 2

6

3

o l 0

/

I

3

I

I

6

9

I 12

I 15

I

1 21

18

GROUND TRUTH WIND SPEED

1

24 fm sec-' I

1

I

27

30

324 288 I

-

252

0

g216 0 k-

E

I80

0 0

2 144 v,

2

108

M = 0.9566

72

36

'0

36

72

I08

144

180

216

252

288

324

360

GROUND TRUTH WIND DIRECTION (DEGI

FIG.43. Scatter plot of SASS wind speed from Rev 1093 (a) and direction (b) versus surface truth values for the QE I1 storm. The pluses are points where heavy cloud cover was observed from satellite cloud photographs.

258

PETER G.BLACK ET AL.

SASS WIND SPEED (m sec-1)

FIG.44. Scatter plot of SMMR wind speed versus SASS wind speed for Revs 1066,1074,1080, and 1094 over the QE I1 storm. [From Wentz et al. (1982).]

6.7. Discussion of Errors in Wind Measurement

The wind speeds and directions measured by Seasat compare quite well with the data assembled for surface truth, as shown in the error tables prepared for the various storms. There are some problems with both, although most are not too serious. 6.7.1. Accuracy of Surface Truth Data. The winds that were measured by aircraft and used in this study were all obtained with the aid of highly calibrated inertial navigation systems. The spot winds should have mean errors < 1 m sec- *. The wind directions,as long as the speeds are > 8 m sec- ', should have mean errors < lo". There are other problems however, which arise before these winds can be used for comparison. First, winds are measured at flight level and must be reduced to the 19.5-m level. While the best means available were used for accomplishing this reduction, undoubtedly some small errors were introduced. A more serious problem arises from the lack of coincidence in time. The Seasat made all of its measurements for a storm in about 3 min. The aircraft spent 3 or 4 hr making measurements for part of the same area. SASS winds are equivalent to long-term averages in time (perhaps 10-60 min) and about 30 km in space. By contrast, the aircraft winds are representative of speeds averaged over a period of 30 sec, or about 3 km in space. Furthermore, if an aircraft measures an extreme wind 1 hr after the Seasat overflight, that extreme wind may not have existed when the satellite was passing.

6. SEASAT WIND/RAIN OBSERVATIONS

259

The cloud motion winds also have errors. In general, the cloud motion winds were obtained by averaging the cloud movement over about 30 min, with that 30 min coinciding very closely to the time of the Seasat overpass, so the time differences are not as critical as for the aircraft data. There always remains, however, the question of how closely low-level cloud motions correspond to the winds at the top of the boundary layer. Limited comparisons have been made with aircraft data in other hurricanes. These studies have indicated that the cloud-motion wind speeds agree with aircraft measurements that were made within a few hours of the time that the clouds were tracked to within about 2.5 m sec-' (Rodgers et al., 1979).There have not been sufficient cases to fully support this conclusion. The other surface truth data used here are from surface ships and buoys. There were very few buoy reports available. The surface-ship winds taken from merchant ships have well-known deficiencies. In general, not much emphasis was put on those reports in the hurricane comparisons reported in this study. Because of all the possible deficiencies in the surface truth data, especially those concerning time differences, it is remarkable that. the mean differences between surface truth and Seasat data were so small. 6.7.2. Accuracy of SASS Wind Speeds. In general, the SASS-measured speeds are higher than the surface truth speeds when the wind speed is less than 15 m sec- I. However, SASS underestimates aircraft-measured speeds when the wind speed is greater than 25 m sec-'. Exceptions to the last statement were the data from Rev 1182 over Hurricane Greta using HH or VH/HV mixtures of polarity. There, the SASS speed measurements were in reasonable agreement with surface truth for all speeds up to about 30 m sec-', although there was still a tendency for them to be high in the low ranges and low at speeds greater than 20 m secThe differences in the wind speeds measured by the various mixtures of polarization require further comment. Comparisons were made using only data where the three polarization mixtures (HH, VH or HV, and VV) were used to derive winds near the same location relative to the storm center. The wind-speed differences are greater for the higher wind speeds within 220 km of the hurricane center and especially for areas of high incidence angle. A schematic plot of the rows of SASS wind solutions that were parallel to the subpoint track for Rev 1182 is shown in Fig. 45. The hurricane center was between bands a and b and just north of 15" latitude. The differences between the mixtures of polarity were summarized for various areas and the results are shown in Table XJII. There were no data for the VV mixture in bands a or b; hence the comparisons are limited to the other bands. Table XI11 suggests that differences in wind-speed values for the various combinations of polarization were functions of wind speed and incidence

'.

260

PETER G . BLACK E T A L .

FIG.45. Schematic representation of SASS data stratified according to increasing incidence angle bands from a to f for Rev 1182 over Hurricane Greta, September 17,1978. TABLEXIII. MEANWIND-SPEEDDIFFERENCES FOR MIXTURFS OF POLARIZATION FOR HURRICANE GRETA HH

- VV

Zone

(msec-')

Band c Band d Band e Band f Latitude belt 21-25" Latitude belt 17-21" Latitude belt' 13-17" Latitude belt 9-13" Area of 220-km radius centered on hurricane

0.9 0.4 1.8 4.7

(HV or VH) - VV (m set-')

0.1

0.6 0.2 1.1 3.1 0.0

1 .o

0.7

4.0

2.4

1.o

0.9

3.7

2.2

Hurricane center was in this belt.

angle. With the addition of similar data for Humcane Ella from Revs 966 and 988, these polarization differences were further investigated. An analysis of the wind speeds was prepared for each revolution for the V H (or HV) values since these values were more uniformly distributed over the entire storm area than were the HH and VV values. Differenceswere then calculated between the VH and each of the HH and VV values and plotted as a function of incidence angle and wind speed in Figs. 46 and 47, respectively. To facilitate drawing isolines of equal differences for these graphs, mean values were calculated for the differences for various incidence angle and wind speed intervals. These averages are plotted in Fig. 46 (representing 307 comparisons)and in Fig. 47 (representing41 1comparisons)and for HH versus V H and V H versus VV, respectively. Both Figs. 46 and 47 show that the differences are relatively small (less than 1m sec- l ) when the wind speed is less than 15 m sec- I . At wind speeds greater than 15m sec-', the differences vary rapidly with wind speed and less rapidly with the incidence angle. Since the

6. SEASAT WIND/RAIN OBSERVATIONS 55

-

50

-

g45

-

-

26 1

0

I

2

3

4

REVS 966 988

5

W

e 2 l

z40-

3 '

0

z

W

Q

u 35

-

=I1

-0.1 -0.1 -0.3

0

0.5

-3.y 2

-0.1 -0.1 - I 1.6 -0.3\-1.5

0

25

-1

I

,

Iw

5

10

I5

1

0.4

, 20

b

I

I

25

30

, 35

WIND SPEED (m sec-' ) HH POLARITY

FIG.46. SASS wind-speed differences (m sec- I) at a given location for HH polarization minus HV or VH polarization as a function of incidence angle and SASS HH polarization wind speed. Average values are plotted based on data from Revs 966 (Ella),988 (Ella), and 1182 (Greta).

FIG.47. Same as Fig. 46, except for HV or VH polarization minus VV polarization.

262

PETER G . BLACK E T A L .

wind-speed data used in developing the algorithms were mostly less than 15 m sec-', this may explain why the algorithms do not handle well the problem of the mixed polarizations at high wind speed (Boggs, 1982). Comparisons of the research aircraft data with the HH, HV or VH, and VV polarization data were also made. Table XIV shows an example of these comparisons for Rev 988 over Hurricane Ella. These comparisons show that the HH winds are high and that the HV and VV values are slightly low. Typical maximum HH winds for Greta and Ella were 35 m sec-', while typical maximum VV winds were 25 m sec-'. Furthermore, the RMS values for HV are larger than for HH or VV. These comparisons suggest that a problem exists with the algorithm for deriving winds using horizontal polarization or a combination of horizontal and vertical polarization, especially at high wind speeds. It is, therefore, suggested that only vertical polarization values be used for severe storm analyses. 6.7.3. Accuracy of Wind Directions. Once an alias was selected for the wind direction, the SASS directions agreed quite well with those of surface truth. In most cases, when there was a bias, the SASS directions indicated more inflow than the directions measured at the top of the boundary layer. This agrees with boundary-layer theory and other observations for hurricanes. Selecting the proper alias does, at the present, require knowledge from other sources. In the case of a hurricane, the assumption of cyclonic flow around a known center location is generally sufficient for this purpose. It has already been mentioned (Section 6.1) that there is a problem with artificially sharp wind shifts due north of the center in Hurricane Fico and in the region northeast, northwest, southwest, and southeast of the center of Hurricane Ella. These shifts appear to be a model function deficiency and arise when the number of aliases returned from the wind solution changes from two or three to four. This occurs when the wind is changing direction relative to the antenna beam-pointing angle. In other words, when one antenna is looking directly upwind or downwind and the other is looking crosswind, a two- or three-solution case arises. As the wind shifts away from TABLE XIV. COMPARISON OF SEASAT AND AIRCRAFT WINDS MR HURRICANE ELLA,SEPTEMBER 3 AND 4,1978"

Polarity

HH VH(HV)

vv

Number of data points

Mean difference AC - Seasat (m sec-I)

RMS (msec-')

11 27 (13) 23 (18)

-3.1 1.4 (4.6) 1.1 (3.4)

6.0 (7.0) 5.8 (4.7)

4.4

'Numbers in parenthesesare for wind speeds >25 m sec-'.

263

6. SEASAT WINDiRAIN OBSERVATIONS

this situation, a four-solution case arises, with the need to select one of four aliases. A wind direction jump results. Since the beams are oriented SW-NE and NW-SE, the two- or three-solution case arises for SE, NE, NW, and SW wind directions. This situation could probably be minimized by some smoothing of the wind field. However, caution must be used in calculating vorticity, divergence, or wind stress curl, as artificial maxima and minima in the calculated fields will result. To examine these artificially sharp wind shifts, Table XV was prepared in which the errors were stratified according to whether the antenna orientation was upwind-crosswind, downwind-crosswind, or straddling the wind direction, either in an upwind-downwind orientation (perpendicular to the subpoint track), or in a crosswind orientation (generally parallel to the subpoint track). These statistics show that, in general, the speed errors are constant with orientation. However, the RMS directional errors for the upwind-crosswind and downwind-crosswind orientations tend to be larger than those for the straddle orientations. This is probably because most of the up- and downwind situations are two- and three-alias cases, whereas the straddle situations are four-alias cases. A smooth transition does not occur from the two- to three-alias case to the four-alias case. Furthermore, it was found that in all cases in Hurricanes Ella (Rev 952) and Greta (Rev 1182) when only two aliases were selected by the algorithms, the wind directions for these aliases were within 5" of the values 117, 293, 26, or 206". These values are the antenna viewing azimuths for an ascending orbit at 15-35" latitude. Furthermore, for the more common TABLE XV. VARIATION OF SASS ERRORS WITH ANTENNA AZIMUTH ANGLES TO THEWINDDIRECTION RELATIVE Up-Cross

Fico E speed RMS 8 direction RMS

Straddle up

NoP"

P

NoP

5-20b

>15

5-20

1.7

P >15

Straddle cross

Down-Cross

NoP

P

NoP

P

5-20

>15

5-20

>15

2.0 6.9 15.3

-3.8 5.4 3.8 14.3

1.8 2.1 12.4 17.8

0.9 1.7 10.7 12.5

2.0 2.2 0.3 9.1

-4.2 5.2 -1.4 7.9

1.5 1.8 -4.0 14.6

-3.3 5.6 -1.3 16.7

-0.6 1.8 2.9 20.3

1.0 2.0 5.2 24.7

-0.8 1.8 16.6 25.3

-0.3 2.4 8.8 18.1

0.2

0.0 3.5 2.0 16.6

-0.1 1.5 -5.1 8.2

0.2 3.8 -13.9 34.4

Ella

C, speed RMS 8 direction RMS a

No P, No precipitation;P, precipitation. Wind speed range.

1.7 -11.5 17.4

264

PETER G. BLACK ET AL.

wind-vector solutions resulting in three or four aliases, the average direction of two of the wind aliases separated by less than 45" in direction corresponded to the above antenna viewing azimuths. Therefore, wind directions along the antenna orientation appear to be be preferentially selected. This may explain why wind direction errors will be preferentially larger for wind directions oriented at 45" to the antenna orientation. In the case of the Greta data, an analysis was made of the differences between aliases selected from two-alias reports and the aliases selected from four-alias reports at nearby points (within 55 km). The mean difference in direction was 25". 7. SEASAT OBSERVATIONS OF SEASURFACE TEMPERATURE NEAR TROPICAL CYLONES

Sea surface temperature patterns have been examined over the North Atlantic before, during, and after the passage of Hurricane Ella through the area using the Wentz SMMR algorithm. This algorithm relies mainly on the 6-GHz brightness temperatures. This frequency has a very coarse resolution, on the surface, of about 150 km. This channel also suffers from large antenna sidelobes, which prevent meaningful measurements from being made close to land. Very large land brightness temperatures contaminate the measurements over the ocean from the main lobe. It was found that when the main footprint is closer than 300 km from land in the cross-track direction or 600 km in the along-track direction, contamination occurs. Signal contamination also results from reflected microwave energy in sun glint areas (Wentz, 1981b). To avoid this problem, only night-time passes were used in the study. Orbits used for the study were Revs 902,945,952, and 988, all of which passed over the same geographical area. Surface truth sea surface temperatures were composited from ship data, AXBT data taken during 3 days of flights in the vicinity of Ella, and Polymode Experiment XBT data from ships in the area. Analyses were made corresponding to within 12 hr of the Seasat overpass time. Sea surface temperatures were then integrated over the 150-km footprint of SMMR grid 1. Figure 48 shows the alignment of swaths 1-4 over the area of interest, with SMMR-derived SST using the Wentz algorithm. The Ella storm track is superimposed. Figure 49 shows the surface truth sea surface temperature analysis for the area corresponding to Rev 945. Figure 50 shows the comparison of surface truth SSTs (indicated by triangles) and SMMR-derived SSTs. The solid symbols represent points that are contaminated by land. These comparisons, plus those from the other revolutions, support Wentz's error analysis over the North Pacific, indicating RMS errors on the order of f0.8"C.-

6. SEASAT WIND/RAIN OBSERVATIONS

265 38

36

14

12

10

8

!6

FIG.48. Sea surface temperature measurements and SMMR cell locations based on 6-GHz brightness-temperature measurements for Rev 945, in the vicinity of Hurricane Ella, September 1, 1978.02:15 GMT.

8. APPLICATION OF SEASAT OBSERVATIONS TO OPERATIONAL MARINE STORMFORECASTING NEEDS

8.1. Tropical-Cyclone Forecast Applications Hawkins and Black (1982) have recently shown that Seasat measurements can be used to identify the gale-force wind radii in tropical cyclones as well as the radius of 25 m sec- wind. Every hurricane warning contains an estimate

'

266

PETER G . BLACK ET AL. 64

30

L3

'26

36

a ~

34

.27-

32

30

-

28

.2B

'29

1

ELLA

-

30 26

'30 SEA SURFACE TEMPERATURE I*CI 12 GMT TBOBJt TO 12 GMT 780901

-

30

/ 16

?4

I2

?O

68

I

I

66

64

FIG. 49. Composite SST distribution for the 24-hr period 12:OO GMT, August 31, to 12:00GMT,September 1, centered on the time of Rev 945. The Hurricane Ella track and position at 0O:OOGMT, September 1, is shown with the position of the Gulf Stream (dash-dot line) and a cold eddy (dashed line) determined from Polymode XBT survey and a prior 5-day mean SST analysis.

of the extent of gale-force (17.5 m sec-') and hurricane-force (33 m sec-') wind for the four quadrants of the storm. In addition, typhoon warnings also contain an estimate of the radius of 26-m sec-' winds, since the probability of occurrence of winds above this threshold requires that certain actions be taken. It would, therefore, be of very great benefit if some observational data on these radii could be provided. Frequently, the gale-force radius is beyond the maximum radius for which normal reconnaissance is flown. In addition,

26

6. SEASAT WINDDAIN OBSERVATIONS

267

REV 945 CELL # 1

26 25

24

1

30

2

3

4

5

6

7

8

9

4

=,-[I CELL#2

0

1

10 W I-

1 I1

1

1

1

1

1

12

13

14

15

16

30FCELL K 3

% 29

FIG.50. SMMR-derived SST plotted as a function of cell number from north (left) to south (right) and for swaths 1 (top) to 4 (bottom). Uncontaminated values are indicated by an open circle, land-contaminated values from sidelobes by a solid circle, and precipitation-contaminated values from sidelobes (primarily in the vicinity of Ella) by solid squares. Area-averaged surface truth SSTs are indicated by the inverted triangle.

Seasat-derived radii would have the advantage of being actual surface measurements, not extrapolations from a higher aircraft altitude. Hawkins and Black compared SASS-derived gale-force wind radii with advisories issued by the main storm-advisory centers for the Atlantic, eastern Pacific and western Pacific regions. Nine revolutions were used for the East Pacific, eight for the Atlantic, and four for the West Pacific. This was especially worthwhile, since statistics for SASS winds at near gale force were

268

PETER G . BLACK ET AL.

nearly unbiased and showed RMS speed and direction errors of 1.5 m sec-' and 20", respectively. Table XVI summarizes the mean differences between SASS and advisory gale-force wind radii, Advisory radii are overestimated, in general, by 30 to 50%. In the Atlantic, the advisory radii in the NE and NW quadrants are overestimated, while in the SE and SW quadrants the advisory radii are slightly underestimated on the average, based on this limited data set. This difference is probably due to intentional overwarning in the forward quadrants closest to land. The average overestimate is about 100 km, and the R M S is about 60 km. The Seasat scatterometer-derived radii for the extent of gale-force winds near tropical cyclones revealed several flaws in forecast advisory values. The radii of gale-force winds determined by SASS were consistently less than forecast. Advisory values exceeded those of SASS in 72% (56 out of 68) of the individual quadrant by quadrant comparisons presented in this study. Only 1 out of 31 cases in the East and Central Pacific indicated a forecast radius smaller than that independently observed by Seasat. These 31 cases include a mixture of advisories made with and without reconnaissance data. Thus, the advisories were conservative overestimates caused by high-quality surface wind data being unavailable to the forecaster in a real-time mode. The SASS measurements often detected large asymmetries in the distribution of the gale-force wind field. They were in direct contrast to forecasts specifyingone value to delineate the radius of gale force winds in all quadrants. This is clearly evident for the East and Central Pacific cases involving Hurricane Fico. Forecasters typically try to avoid this problem by using several rules of thumb to help specify wind field asymmetries. Westwardmoving storms are usually assigned semicircular asymmetries, with the larger radius to the right of the storm track (i.e., for Fico, 2Wkm northern

TABLE XVI. ADVISORY MINUSSASS GALE-FORCE WINDRADII(km)'

Quadrant

East Pacific

PI Atlantic

IEI West Pacific

PI a

NE

SE

sw

NW

109 (69) 105 55 (70) 74 -3 (110) 75

147 (106)

154(100) 160 - 15 (66) 47 - 12 (125) 100

100 (73) 100 -67 (107) 105 7 (34) 25

110

- 14 (64) 42

- 10 (26) 55

Mean and standard deviation (in parentheses) and average error, IEl.

6. SEASAT WIND/RAIN OBSERVATIONS

269

semicircle- 125-km southern semicircle). Prevailing synoptic patterns may suggest a slightly rotated pattern, such as 200-km northeast semicircle and 125 km elsewhere. These semicircular advisories represent the best guess possible without the benefit of low-level reconnaissance data. This study indicates that the technique is far superior to the circular methods. But SASS observations also reveal that substantial errors can occur in forecasts specifying asymmetries. Several conclusions can be drawn about forecast errors for storms in different ocean regions. In general, Atlantic and western Pacific errors were relatively small and probably resulted from the availability of reconnaissance data and/or proximity to land. Eastern and Central Pacific comparisons for Fico were degraded when reconnaissance data became available. This occurred when the 700-mbar reconnaissance-derived values were increased by over a factor of 1.5-2.0 to represent the surface extent of gale winds as the storm neared Hawaii. A more correct method of reduction would have yielded values very close to those independently sampled by SASS. Mean error statistics for each quadrant are influenced strongly by individual errors because of the limited number of cases. Some tentative conclusions can be reached by taking these statistics into account. Forecasts delineating semicircularasymmetriesusually have more trouble with winds in the northern sector of the storm. In the majority of cases, the error is greater in the northeast quadrant (typically area of highest winds). Forecasts utilizing circular symmetries routinely produced very large errors in the southern quadrants. The conservativenature of these forecasts is at fault. The forecast must be applicable to the quadrant of the storm containing the largest radius of gale winds. The accuracy of this value in other quadrants suffered appreciably. Seasat surface winds could have provided tropical-cyclone forecasters around the world with an invaluable forecast aid. This would have been especially true for storms off Australia, India, and the East Pacific where reconnaissance data are nonexistent, or minimal at best. This “proof of concept” oceanographic satellite has shown that active microwave radars can detect gale winds created by tropical systems. Since SASS can work in the severe meteorologicalconditions associated with these storms, the technology has been validated. An operational scatterometer with three antennas per side to eliminate wind-direction aliases would benefit a much larger user community than tropical meteorologists, alone. It is, thus, recommended that a polar-orbiting scatterometer be launched as soon as feasible, possibly on a modified NOAA TIROS-N type meteorological satellite. Chang (1981) has recently conducted numerical studies to investigate whether SASS data could be used to initialize the new generation of hurricane dynamical prediction models. He studied cases using only initial data

270

PETER G . BLACK ET AL.

as well as initial data followed by periodic update of the model at selected time steps. The impact of the satellite-sensed winds on the intensity forecasts of tropical cyclones is evaluated by a simulation study with an axisymmetric numerical model. The parameterized physics in the forecast model are deliberately made different from those in the model that generates the observations. Model-generated “observations” are assimilated into forecasts by 12-hr dynamic initialization. A series of 24-hr forecasts with and without assimilation of satellite-sensed winds are conducted and compared with the “observations.” Results indicate that assimilation with marine surface (or low-level) wind alone does not improve intensity forecasts appreciably, that a strong relaxation coefficient in the initialization scheme causes model rejection of the assimilation, and that an attenuating relaxation coefficient is recommended. However, when wind observations at the outflow level are included in the assimilation, forecasts improve substantially. The best forecasts are achieved when observations over the entire lower troposphere are assimilated. Additional experiments indicate the errors in the satellite observation contaminate the forecast. But the assimilation of inflow and outflow winds still improves the intensity forecast if the satellite observation errors are less than, or about the same magnitude as, those in the initial wind field. A more limited experiment was also conducted by Fiorino and Warner (1981) in which they initialized a 12-hr forecast for Hurricane Eloise (1975) with surface wind fields derived from composited ship reports. The fields were used as initial conditions for the forecast in a manner similar to the way Seasat winds could be used. The winds were used to diagnose a surface pressure field. However, results were inconclusive, since only a 12-hr integration was performed, during which no improvement over conventional methods resulted. Neumann and Pelissier (1981) have shown that dynamical-forecast models show relative improvements only for forecast periods greater than 36 hr. 8.2. Extratropical-Cyclone Forecast Applications

The early stages of the QE I1 storm provide a striking example of the potential impact of Seasat data on improved weather and state-of-sea forecasts. Cane and Cardone (1981) show how the satellite data could have detected the explosive development of the storm about 24 hr before it was seen in real-time conventional data. Early detection would contribute to forecast improvements as follows: First, marine forecasters and ship routers would be given early warning of the severity of developing severe marine storms and

6. SEASAT WlNDiRAIN OBSERVATIONS

27 1

nowcasts and short-range forecasts would improve. Second, numerical weather prediction models would be provided with initial-state specifications during the deepening stage. While numerical models still do not simulate well the role of convective-scale processes in the development of severe storms, frequent updating of model initial states with satellite data should lead to improved weather forecasts in the 1- to 5-day time frame. Improved weather forecasts would lead naturally to improvements in seastate forecasts. Cane and Cardone (1981) have reported a series of simulation experiments specificallydesigned to assess the potential impact of marine surface wind data on numerical weather prediction. The experiments were performed with the analysis scheme and general circulation model of the NASA Goddard Laboratory for Atmospheric Sciences. Care was taken to duplicate the spatial coverage and error characteristics of conventional surface, radiosonde, ship, and aircraft reports. These observations, suitably degraded to account for instrument and sampling errors, were used in an anal ysis-forecast cycle that resembles those used by major meteorological centers. A series of five 72-hr forecasts were made using the analyzed fields as initial conditions. The forecast error growth was found to be similar to that in operational numerical forecasts. Further experiments simulated the time-continuous assimilation of remotely sensed marine surface wind and temperature sounding data, in addition to the conventional data. The wind data were fabricated directly for model grid points that were intercepted by a Seasat scatterometer swath and were assimilated into the lowest active level (945 mbar) of the model with a localized successive correction method (SCM). The temperature sounding experiment assimilated error-free data that were fabricated along the actual Nimbus orbits. Forecasts were made from the resulting analysis fields and the impact of the simulated satellite data assessed by comparing these forecast errors with those of the control forecasts. The results of these experimentsfor the lowest layer wind and for the surface pressure are shown in Figs. 51 and 52, respectively. There are notable impacts in the extratropical Northern Hemisphere, particularly in the data-sparse North Pacific and in North America, downstream from the Pacific. The assimilation of error-free sounder data (again by the SCM) gave impacts comparable with the wind data, suggesting that surface wind data alone may be as valuable as temperature soundings for numerical weather prediction. Figure 52 also shows that the effect of nominal SASS errors ( k2 m sec-' in magnitude, &20° in direction) on the impacts derived from wind data were negligible (at least insofar as these errors are uncorrelated). Available Seasat results indicate that the operational SASS errors are of this magnitude. Examination of the individual cases shows that the impacts result from

272

PETER G . BLACK ET AL. 4

r

0

r

N. AMERICA

I

DAYS

2

3

0

EURASIA

1

DAYS

2

3

FIG.51. Forecast errors out to 72 hr in the Goddard general circulationmodel for six regions of the world using simulated SASS surface winds as initial data. Data are integrated using a successive correction method (SCM).Forecasts were compared with the control (C) using conventional data and with simulated error-free remote temperature sounding data (T).

improvement in the forecast of specific features rather than a uniform global improvement. These types of forecast impact tests, utilizing real Seasat data, have begun at several centers and preliminary results are expected soon. Several studies have already been completed that show that Seasat wind data when combined with conventional ship data (Yu and McPherson, 1979) or satellite cloud-tracked winds (Endlich et al., 1981) can provide improved surface pressure field specifications in data-sparse marine regions. The spectacular improvements in Southern Hemisphere numerical weather predictions observed during FGGE (Guymer and LeMarshall, 1979; Leslie et al.. 1981) have been attributed, mainly, to improved initial surface pressure fields made possible by drifting buoy data. This suggests that significant improvements will also

273

6. SEASAT WIND/RAIN OBSERVATIONS 5 r

--..- -.*..--o..-

CONTROL

-Q- PERFECT WINOS [SCMI WINDS (SCMI WITH NOMINAL SEASAT-1 ERRORS PERFECT TEMPERATURES

4-

L 1 2 3

OO

,.a.

c,

*..*a

P OO

oar

U i 2 3 oar

FIG.52. Same as Fig. 51, except for sea-level pressure errors for North America and the North Pacific, only.

be derived with the assimilations of surface wind (and derived surface pressure fields), based upon real Seasat data sets that were obtained in Northern Hemisphere basins. 9. CONCLUSIONS Comparisons of the SASS-derived winds for Hurricanes Fico, Ella, and Greta, and the QE I1 case, show that the wind speeds and directions are accurate to within the design specifications as long as the wind speeds are less than about 26 m sec-'. That is, the wind speeds are accurate to within 2 m sec-' or lo%, whichever is larger, and the directions are accurate to within 20". The cases reported herein, where the errors were larger, can be explained as caused by inadequate computation of the attenuation, poor sensor resolution relative to observed gradients, or lack of time correspondence with surface truth data. In the case of Hurricane Greta data, there were suggestions that SASS might measure winds relatively well up to 30 m sec-l, In general, SASS tends to measure light winds too high and intense winds too low. The few comparisons for winds in hurricanes derived from measurements by SMMR revealed errors which exceeded SASS. Selecting the proper alias for the wind direction requires knowledge that is unavailable in the SASS data. There are still some problems with abrupt shifts in the wind directions that are related to changes in the number of aliases. Another problem arises because the SASS wind speed is a function of the polarization used to make the backscatter measurement. This problem seems

274

PETER G. BLACK E T A L .

to be most serious with the higher incidence angles and the higher wind speeds. In spite of all the reservations expressed above, the data seem accurate enough to be very useful in severe marine storm forecasting and research.

ACKNOWLEDGMENTS This work was sponsored jointly by NOAA/NESS, Oceanic Sciences Branch; by NOAA, Atlantic Oceanographic and Meteorological Laboratory, Hurricane Research Division; by NASA, Langley Research Center; and by NASA, Jet Propulsion Laboratory. The authors gratefully acknowlege this support. This work would not have been possible without the pioneering algorithm development by Dr. Dale Boggs, Jet Propulsion Lab; Dr. Frank Wentz, Remote Sensing Systems: and Mr. Emidio Bracalente, Langley Research Center. Much of the work reported here was done in collaboration with Dr. W. Linwood Jones, Mr. L. C. Schroeder, and Mr. Willian Grantham, Langley Research Center. Program support has been provided by Mr. John W. Sherman, 111, NOAA/NESS/OSB, and by Dr. George Borne, Dr. David Lame, and Dr. James Dunne, Jet Propulsion Lab. The help of Mr. John Wilkerson, NOAA/NESS/OSB, in coordinating many of the activities and materials, especially the Seasat Storms Workshop, that resulted in this summary is gratefully acknowledged. The cooperation of Mr. Edward Rodgers, NASA/Goddard Space Flight Center, and Mr. Joe Steranka, GE-MATSCO, in producing GOES cloud-motions data is also sincerely appreciated. The results of this study are also due in no small part to the vision of Dr. John Apel, Johns Hopkins University, Dr. Willard Pierson, City University of New York, and Dr. Richard Moore, University of Kansas, who were instrumental in making Seasat possible. The assistance of Claire Black and Angel Tillman in typing the manuscript is gratefully acknowledged, as is the assistance of Constance Arnhols for editorial work, Dale Martin in drafting the figures, and Tom Tatnall in photographing the figures.

REFERENCES Adler, R. F., and Rodgers, E. B. (1977). Satellite observed latent heat release in a tropical cyclone. Mon. Weather Rev. 105,956-963. Allison, J. F.,Rodgers, E. B., Wilheit, T. T., and Fett, R. W. (1974). Tropical cyclone rainfall as measured by the Nimbus 5 electrically scanning microwave radiometer. Bull. Am. Meteorol. SOC.55,1074-1089.

Atkinson, G. D., and Holliday, C. R. (1977). Tropical cyclone minimum sea level pressure/ maximum sustained wind relationship for the western North Pacific. Mon. Weather Rev. 105,421-427. Barrick, D. E., and Swift, C. J. (1980). The Seasat microwave instruments in historical perspective. IEEE J. Oceanic Eng. 5,74-79. Bates, J. (1977). Vertical shear of the horizontal wind speed in tropical cyclones. NOAA Tech. Memo. ERL WMPO-39, US.Dept. of Commerce. Boggs, D. H. (1981). The Seasat scatterometer model function: The genesis of SASS 1. Publ. No. 622-230, Jet Propulsion Laboratory, California Inst. Tech., Pasadena, Calif. Boggs, D. H. (1982). Seasat geophysical data record (GDR) users handbook-scatterometer. Publ. No. 622-232, Jet Propulsion Laboratory, California Inst. Tech., Pasadena, Calif.

6. SEASAT WIND/RAIN OBSERVATIONS

275

Bosart, L. F. (1981). The President’s Day snowstorm of 18-19 February, 1979: A subsynopticscale event. Mon. Weather Rev. 109, 1542-1566. Brown, G. (1979).Estimation of surface wind speeds using satellite-borne radar measurements at normal incidence. J . Geophys. Res. 84,3974-3978. Cane, M. A,, and Cardone, V. J. (1981). The potential impact of scatterometry on oceanography: A wave forecasting case. I n “Oceanography From Space”(J. F. R.Gower, ed.),pp. 587-595, Plenum, New York. Cardone, V. J., Young, J. D., Pierson, W. J., Moore, R. K.,Greenwood, J. A.,Greenwood, C., Fung, A. K.,Salfi, R., Chan, H. L., Aforani, M., and Komen, M. (1976). The measurement of the winds near the ocean surface with a radiometer-scatterometer on Skylab. Final Report, Contract No. NAS-9-13642, SUNY Inst. Marine and Atmos. Sci., City College, New York. Cardone, V. J., Broccoli, A. J., Greenwood, C. V., and Greenwood, J. A. (1980). Error characteristics of extratropical storm wind fields specified from historical data. J . Petrol. Tech. May, 872-880. Chang, S. W. (1981). The impact of satellite sensed winds on intensity forecasts of tropical cyclones. Mon. Weather Rev. 109, 539-553. Chester, T. (1981).SMMR rain rate algorithm modifications. SMMR Mini-Workshop IV,Ch. 10. Publ. No. 622-234, Jet Propulsion Lab., Pasadena, Calif. Dunn, G. E., and Miller, B. I. (1964). “Atlantic Hurricanes.” Louisiana State Univ. Press, Baton Rouge, La. Dvorak, V. R. (1975). Tropical cyclone intensity analysis and forecasting from satellite imagery. Mon. Weather Rev. 103,420-430. Endlich, R. M., Wolf, D. E., Carlson, C. T., and Maresca, J. W., Jr. (1981). Oceanic wind and balanced pressure-height fields derived from satellite measurements. Mon. Weather Rev. 109,2009-2016. Fedor, L. S., and Brown, G. S.(1982). Wave height and wind speed measurements from the Seasat radar altimeter. J. Geophys. Res. 87, 3254-3260. Fedor, L. S., Godbey, L. W., Gower, J. F. R.,Guptil, R., Hayne, G. S.,Rufenach, C. L., and Walsh, E. J. (1979). Satellite altimeter measurements of sea state.-an algorithm comparison. J. Geophys. Res. 84,3991-4002. Fiorino, M., and Warner, F. L. (1981). Incorporating surface winds and rainfall rates into the initialization of a mesoscale hurricane model. Mon. Weather Rev. 109, 1914-1929. Gentry, R. C. (1964). A study of hurricane rainbands. NHRP Report No. 69, U S . Dept. of Commerce, Washington, D.C. Gentry, R. C. (1970). Modifying the greatest storm on earth-the hurricane. Underwater Sci. Tech. J . Dec., 204-214. Grifith, C. G., Woodley, W. L., Grube, P. G., Martin, D. W., Stout, J., and Sikdar, D. N. (1978). Rain estimation from geosynchronous satellite imagery-visible and infrared. Mon. Weather Rev. 106,1153-1171. Grody, N. C., Hayden, C. M., Shen, W. C. C., Rosenkranz, P. W., and Staelin, D. H. (1979). Typhoon June winds estimated from scanning microwave spectrometer measurements at 55.45 GHz. J . Geophys. Res. 84,3689-3695. Guinard, N. W., Ransom, J. L., Jr., and Daley, J. C. (1971). Variation of the NRCS of the sea with increasing roughness. J . Geophys. Res. 7 6 , 1525. Guymer, L. B., and LeMarshall, J. F. (1981). Impact of FGGE buoy data on Southern Hemisphere Analyses. Bull. Am. Meteorol. Soc. 62, 38-47. Gyakum, J. R. (1981). On the nature of an explosively developing cyclone in the Northern Hemisphere extratropical atmosphere. Ph.D thesis, Dept. of Meteorology, MIT, Cambridge, Mass.

276

PETER G. BLACK ET AL.

Hasler, A. F., Shenk, W. E., and Skillman, W. (1977). Wind estimation from cloud motion: Phase 1,2 and 3 of an in-situ aircraft verification experiment,J . Appl. Meteorol. 18,812-815. Hawkins, H. F., and Imbembo, S. M. (1976). The structure of a small, intense Hurricane-Inez, 1966. Mon. Weather Rev. 104,418-442. Hawkins, H. F., and Rubsam, D. F. (1968). Hurricane Hilda, 1964 11: Structure and budgets of the hurricane on October 1,1964. Mon. Weather Reo. 96,617-636. Hawkins, J. D, and Black, P. G. (1982). Seasat scatterometerdetection of gale force winds near tropical cyclones. J. Geophys. Res. 88, 1674-1682. Jones, W. L.,Schroeder L. C., and Mitchell,J. L. (1977). Aircraft measurementsof the microwave scattering signature of the ocean. IEEE Trans. Antennas Propag. 25,52-61. Jones, W.L., Schroeder, L. C., Boggs, D., Bracalente, E. M., Brown. R. A., Dome, G., Pierson, W. J., and Wentz, F. J. (1982). The Seasat-A satellite scatterometer: The geophysical evaluation of remotely sensed wind vectors over the ocean. J. Geophys. Res. 87, 3291-3317.

Jorgensen, D. (1984). Mesoscale and convective-scale characteristics of mature hurricanes. I: General observations by research aircraft. J. Atmos. Sci. 41, 1268-1285. Jung, H. J. (1980). The determinationof rainfall rates from satellite measurementsof the thermal microwave emission. Contrib. Atmos. Phys. 3,366-388. Kidder, S.Q., Gray, W. M.,and Vonder Haar, T.H. (1978). Estimating tropical cyclone central pressure and outer winds from satellite microwave data. Mon. Weather Reu. 106,1458-1464. Klein, W.H.(1957). Principal tracks and mean frequencies of cyclones and cyclones in the Northern Hemisphere. Res. Paper No. 40.U.S. Dept. of Commerce,Washington, D.C. Krishen, K. (1971). Correlation of radar backscattering cross sections with ocean wave height and wind velocity. J. Geophys. Res. 76,6528-6539. LaSeur, N. E., and Hawkins, H. F. (1963). An analysis of Hurricane Cleo (1958) based on data from research reconnaissance aircraft. Mon. Weather Reu. 91,694-709. Lawrence, M. B. (1979). Atlantic hurricane season of 1978. Mon. Weather Rev. 107,447-491. Lawrence, M. B., and Pelissier, J. M. (1981). Atlantic hurricane season of 1980. Mon. Weather Reo. 109,201-216. Leary, C. (1971). Systematic errors in operational National Meteorological Center primitiveequation surface prognoses. Mon. Weather Reo. 99,409-413. Leslie, L. M., Mills, G.A., and Gauntlett, D. H. (1981). Recent improvement in limited area numerical prognosis accuracy in Australia. Q. J . R. Meteorol. Soc. 107,629-642. Malkus, J. S.,Ronne, C., and Chafee, M. (1961). Cloud patterns in Hurricane Daisy, 1968. Tellus 13,8-30.

Moore, R. K.,Birrer, I. J., Bracalente,E. M., Donne, G.J., and Wentz, F. J. (1982). Evaluation of atmospheric attenuation from SMMR brightness temperature for the Seasat satellite scatterometer. J. Geophys. Res. 87,3337-3354. Neumann, C. J., and Pelissier, J. M. (1981). Models for the prediction of tropical cyclone motion over the North Atlantic: An operational evaluation. Mon. Weather Rev. 109,522-538. Nordberg, W., Canaway, J., Ross, D. B., and Wilheit, F. (1971). Measurements of microwave emission from a foam covered, wind driven sea. J. A t h s . Sci. 28,429-435. Palmkn, E. (1948). On the formation and structure of tropical cyclones. Geophysica 3,26-38. Powell, M. D. (1980). Evaluations of diagnostic marine boundary layer models applied to hurricanes. Mon. Weather Rev. 108,757-766. Rice, R. B. (1979). Tracking a killer storm. SAiL 10, 106-107. Rodgers, E. B., and R. F. Adler (1981). Tropical cyclone rainfall characteristicsas determined from a satellite passive microwave radiometer. Mon. Weather R w . 109,506-521. Rodgers, E. B., Gentry, R. C.,Shenk, W., and Oliver,V.(1979). The benefits of using short interval satellite images to derive winds for tropical cyclones. Mon. Weather Rev. 107,575-584.

6. SEASAT WIND/RAIN OBSERVATIONS

277

Rosenkranz, P. W., Staelin, D. H., and Grody, N. C. (1978). Typhoon June (1975) viewed by scanning microwave spectrometer. J . Geophys. Res. 83, 1857-1868. Ross, D. B., and Cardone, V. (1974). Observation of oceanic whitecaps and their relation to remote measurements of surface wind speed. J . Geophys. Res. 79,444-452. Ross, D. B., Conaway J., and Cardone, V. J. (1970). Laser and microwave observations of sea surface conditions for fetch limited 17 to 25 m s - winds. I E E Trans. Geosci. Electron. 8,326. Ross, D. B., Au, B., Brown, W., and McFadden, J. (1974). A remote sensing study of Pacific Hurricane Ava. Proc. Int. Symp. Remote Sens. Environ., 9th. Ann Arbor, Mich. pp. 163-180. Rufenach, C. F., and Alpers, W. P. (1978). Measurement of ocean wave heights using the Geos-3 altimeter. J . Geophys. Res. 83,501 1-5018. Sanders F., and Gyakum, J. R. (1980). Synoptic-dynamic climatology of the bomb. Mon. Weather Rev. 108, 1589- 1606. Simpson, R. H. (1971). Thedecision process in hurricane forecasting. NOAA Tech. Memo, NWS SR-53. Simpson, R. H., and Riehl, H. (1981). “The Hurricane and Its Impact.” Louisiana State Univ. Press, Baton Rouge, La. Weissman, D. E., King, D. B., and Thompson, L. W. (1979). Relationship between hurricane surface winds and L-band radar backscatter from the sea surface. J. A p p l . Meteorol. 18, 1023-1034. Wentz, F. J. (1981a). The effect of atmospheric attenuation on microwave scatterometer measurements. Remote Sensing Systems, Tech. Rep. No. 091781. Wentz, F. J. (1981b). The effect of sea surface sun glitter on microwave radiometer measurements. Remote Sensing Systems, Tech. Rep. prepared for Jet Propulsion Laboratory. Wentz, F. J. (1983). Model function for ocean microwave brightness temperatures. J . Geophys. Res. 88,1892- 1908. Wentz, F. J., Cardone, V.J., and Fedor, L. S. (1982). Intercomparison of wind speeds inferred by the SASS, altimeter and SMMR. J . Geophys. Rex 87,3378-3384. Willoughby, H. E., Clos, J. A,, and Shoreibah, M. G. (1982). Concentric eyewalls, secondary wind maxima and the evolution of the hurricane vortex. J. Atmos. Sci. 39,395-41 1. Yu, T., and McPherson, R.D. (1979). Surface pressure analysis using scatterometer derived wind data from the Seasat-A satellite, Preprints, Conf Am. Meteorol. Soc. Numer. Weather Predict., 4th. Silver Spring, Md., pp. 351-355.

’

This Page Intentionally Left Blank

CHAPTER

7

SEASURFACETEMPERATURE DETERMINATIONS JOHN C . ALISHOUSEE. PAULMCCLAIN NOAAINESDISISEL Washington, D.C .

NOAA/hrESDfS/CESL Washington, D. C.

I . Introduction to Remote Sensing of SeaSurfaceTemperature . 1.1. Radiative Transfer . . . . . . . . . . . . . . . 1.2. Transmissivity and Emissivity Considerations. . . . . 2. Microwave Sensing of Sea Surface Temperature . . . . . 2.1. Background . . . . . . . . . . . . . . . . . 2.2. SMMRResults . . . . . . . . . . . . . . . . 3. Infrared Sensing of Sea Surface Temperature . . . . . . 3.1. Background and Previous Results. . . . . . . . . 3.2. AVHRRResults. . . . . . . . . . . . . . . . 4. Summary. . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . .

. . . . . . .

. . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . .

. . . . . . .

. . . .

279 279 280 281 281 282 286 286 287 292 294

1 . INTRODUCTION TO REMOTE SENSING OF SEA SURFACE TEMPERATURE

1.1. Radiative Transfer The propagation of radiation through a nonscattering medium in thermodynamic equilibrium such as the earth’s atmosphere is described by the equation of radiative transfer (Chandrasekhar, 1960):

dJ(v, 6 ) = { - Z(v, 6 )

+ B[v, T ( Z ) ] } K ( vZ)p(Z)sec , 8 dZ

(1)

where I(v, 6)is the intensity of the radiation at frequency v and angle 6, B(v, T ) is the Planck function = (2hv3/c2)(l/[exp(hv/kT) - l]}, k is Boltzmann’s constant, c is the velocity of light, K ( v ,Z) is the absorption coefficient, p ( 2 ) is the density of the medium, and 2 is the vertical distance above the earth’s surface. The radiative transfer equation (RTE)is often written in integral form

where T is the transmittance and appropriate boundary conditions have been applied. The variable of integration has been changed from distance 2 to pressure P. 279 ADVANCES IN GEOPHYSICS,VOLUME 27

ISBN 0-12-018827-9

280

JOHN C. ALISHOUSE A N D E. PAUL McCLAIN

z(v, 0, Po) is the transmittance from the earth's surface (surface pressure, Po) to the top of the atmosphere at frequency v and direction 6. Radiation emitted by the surface is absorbed by the atmosphere and some radiation is reemitted but from a region of the atmosphere that has a temperature different from the surface. The absorption and reemission are described by the integral in Eq. (2). If the emissions from the surface deviate significantly from that of an ideal blackbody, then a quantity called the emissivity must be introduced. Thus the RTE becomes

where I , is the downward component of radiation. Strictly speaking the reflective term in the RTE should be written as an integral over all possible incidence angles. An ideal blackbody, by definition, emits radiation that is described by the Planck function. Many actual bodies emit radiation that does not follow this function. Thus one can define the emittance or emissivity of a body as E(V,

6 ) = I(v,

WW, T )

(4)

where 0 c E 5 1. Note that emittance has been written as a function of frequency and angle. 1.2. Transmissivity and Emissivity Considerations

In the infrared window regions of the spectrum centered at 3.7, 11, and 12 ,urn, emittances tend to be near unity and relatively independent of sea state and salinity. In the spectral interval 3.6-13 pm the emittance of sea water ranges from >0.99 to 0.97 (Hobson and Williams, 1971). The major limitations in deriving SSTs from satellite infrared observations are correcting for atmospheric attenuation by water vapor and accounting for clouds. Low, uniform clouds, which are often at temperatures close to the SST,are particularly troublesome. Thin clouds or partial filling of the field of view by clouds can adversely impact infrared SST determinations. The transmittance at 11 pm may vary from 0.4 to 0.9, depending on the amount of water vapor, while the transmittance at 3.8 pm may vary from 0.7 to 0.9 (Weinreb and Hill, 1980). In the microwave region of the spectrum the emissivity is much less and is a function of sea state. At 5 GHz and vertical incidence the emissivity of sea water can vary from 0.36 for a calm sea to 0.44 for a 20-m sec-' wind, and the

28 1

7. SEA SURFACE TEMPERATURE DETERMINATIONS

transmittance of the atmosphere varies only between 0.99 for a clear atmosphere and 0.98 for a thick, but nonprecipitating, cloud (Porter and Wentz, 1971). The major limitations in deriving SST from satellite microwave radiometric observations are rain, variable surface emissivity,and land effects including man-made radiofrequency interference.

2. MICROWAVE SENSING OF SEASURFACE TEMPERATURE 2.1. Background In the portion of the microwave spectrum used for SST determinations a useful approximation to the Planck function can be developed by assuming hv/kT << 1. For the Scanning Multichannel Microwave Radiometer (SMMR) we have v = 6.6 GHz, h/k = 4.8 x lo-", and T = 300 K, so that hv/kT = (4.80 x 10-")(6.6 x 109)/(3 x 10') = 1.06 x

<< 1

(5)

Thus B(v,T ) =

2hv3 c2[exp(hv/kT)- 11

becomes 2hv3 ~ ' ( 1 hv/kT - 1)

+

upon expanding the exponential, or B(v, T) = 2kv'T/c2. This is the RayleighJeans approximation. Thus the RTE can be expressed in terms of temperature, the so-called brightness temperature

(7) where T,, is the physical temperature of the surface and Td is the downward component of radiation. A more thorough review of the physics of emission from the ocean's surface is given in Swift (1980). Early theoretical efforts were made to model the variability in surface emissivity. Stogryn (1967) used a geometrical optics model to calculate the effect of increased RMS wave slope on surface emissivity. Wagner and Lynch (1972) used a two-dimensional geometrical optics model which included

282

JOHN C. ALISHOUSE A N D E. PAUL McCLAIN

multiple scattering, Wu and Fung (1972), Porter and Wentz (1972), and Wentz (1974, 1975) developed models which included both capillary and gravitational waves. In the same time period, measurements from aircraft and surface platforms were being made which also dealt with variable surface emissivity. Results obtained by Nordberg et al. (1969, 1971) for 1.55-cm wavelength clearly showed the importance of foam as a variable in sea surface emissivity. Measurements from the Argus Island tower by Hollinger (1970,1971) showed the effects of wave-slope variation. Further aircraft measurements by Webster et al. (1976) clarified additional points in understanding sea surface emissivity variations, and measurements by Blume et al. (1978) demonstrated the feasibility of making sea surface temperature measurements remotely using microwave radiometry. 2.2. S M M R Results With this background, the SMMR experiments' were conceived as requiring multifrequencies and dual polarizations to specify the sea surface conditions including its temperature. The SMMR experiments are described by Gloersen and Barath (1977). The performance, calibration, and antennapattern correction for the Seasat SMMR have been discussed extensively in the literature by Njoku (1980), Njoku et al. (1980a,b),and Swanson and Riley (1980).

Two distinct algorithms have been used to convert the SMMR data from brightness temperatures to geophysical parameters. One is essentially a linear regression algorithm. The coefficients for this algorithm were developed from brightness temperatures that were computed from a wide range of atmospheric and oceanic variables. The initial development of this algorithm is described in Wilheit and Chang (1980). When it became possible to compare satellite and surface observations, it was found desirable to modify the initial version. These modifications are described in two SMMR Workshop Reports (1980,1981).

The other algorithm is a nonlinear, least-squares estimation technique that is also iterative. This algorithm is described in Bierman et al. (1978). It minimizes in a least-squares sense the difference between observed and computed brightness temperatures. The model function used to compute the brightness temperature is described in Wentz (1983). Both algorithms yield SSTs of comparable accuracy (SMMR Workshop Report, 1981); however, only the regression algorithm was used in the final data processing and archiving. Most of the subsequent results are from the regression algorithm.

' Essentially identical instruments were flown on the Seasat and Nimbus-7 satellites.

7. SEA SURFACE TEMPERATURE DETERMINATIONS

283

The first SST results from the Seasat SMMR (Lipes et al., 1979) were from the Gulf of Alaska Seasat Experiment (GOASEX). GOASEX (Wilkerson et al., 1979) was a special surface data collecting activity in the Gulf of Alaska during September 1978 to acquire high-quality surface data for verification of Seasat results. For a limited data set, the SMMR-surface comparisons showed a standard deviation of 1.5"C and a cold bias of about 4°C. Bias is defined as &MMR and a cold bias means l&MR c Hofer et al. (1981) analyzed data for July 1978 that had an empirical correction based on the September data set. The comparisons for July covered a geographic area from 25"s to 50"Nin the Pacific and included a wider range of SST and many more comparisons. Hofer et al. (1981) found a cold bias of 1.3"C and a standard deviation of 1.2"C. Subsequent work by the SMMR Evaluation Task Group concentrated on determining and eliminating sources of the bias found in the GOASEX data. When these sources, which include polarization mixing, sun glint, rain, interference from ground transmissions, and proximity to land, are excluded, results improved dramatically. The results obtained by the Evaluation Task Group are summarized in Lipes (1982). These results, which include algorithm improvements and compare only with higher quality surface observations, show a warm bias of 0.1"C and a standard deviation of 0.9"C. Bernstein (1982a) compared temperature fields that were made from individual satellite and surface observations in the Northwest Pacific. Only night-time SMMR data were used. Bernstein's results are presented in Figs. 1

zfc.

zrc

10

20

30

SHIP TEMPERATURE, T

FIG.1. SMMR-derived SST (ordinate) vs ship-derived SST (abscissa). Mean difference and standard deviation are shown. (After Bernstein, 1982a.)

284

JOHN C. ALISHOUSE AND E. PAUL McCLAIN

LONGITUDE ( € 1

FIG.2. SST contours for the period July 7-August 6,1978, as derived (a)from the SMMR and (b) from surface observations. (After Bernstein, 1982a.)

7. SEA SURFACE TEMPERATURE DETERMINATIONS

285

and 2. Figure 1 shows SMMR SST versus ship SST. The agreement between ship and satellite observations is good over a wide range of SST. Figure 2a is a contour map of SST for the region of investigation as derived from the Seasat SMMR. Figure 2b is a contour map of the same area as derived from surface observations (SMO). Figure 2a terminates near the Kuril Islands and Japan to avoid possible land effects. The data reported in Bernstein (1982a) are for the period July 7 through August 6 only. Subsequently, Bernstein and Morris (1983)have analyzed Pacific SSTs for the entire Seasat mission. In the course of this analysis, it was found that corrections for cross-scan biases and monthly adjustments for sun angle were required. Their results are summarized in Table I. Liu (1983) has compared SMMR SSTs with those derived from surface measurements. His comparisons are for both point-by-point measurements and fields derived from each set of measurements. For the point-by-point comparisons, there is a cold bias of 0.9"Cand a standard deviation of 1.7"C. The field comparisons, which are done on 5" x 5" blocks, show a cold bias of 0.8"C and a standard deviation that varies from 0.6"Cfor blocks where there is a high density of ship observations to 1.O"Cwhere there is a low density of ship observations. Wilheit et al. (1984) have compared SSTs derived from the Nimbus-7 SMMR with surface measuremnts made by drifting buoys in the Southern Hemisphere. These authors present results utilizing a variety of calibration algorithms, antenna corrections, and combinations of frequencies and polarization. The most refined results show a standard deviation of f.4"C for both day and night data and a cold bias for night (day) data of 0.8"C(2.1"C). These results are similar to those obtained from the Seasat SMMR. The production of SSTs from the Seasat and Nimbus-7 SMMRs has proved to be a more arduous task than anticipated (both satellites were launched in 1978). Nonetheless, it now seems clear that both instruments have produced results that meet or exceed their initial performance goals, which were to measure SST to an accuracy of 2°C. The data examined so far have been screened to avoid known problenis such as rain, sun glint, and land effects. TABLEI. MONTHLY MEANSMMR AND SURFACE COMPARISONS' Period ~~

Bias ("C)

Standard deviation ("C)

- 0.48

0.52 0.46 0.61

~

July 6-A~gust4 August 5-September 3 September 4-October 3

1.04 0.09

a After Bernstein and Morris (1983). Comparisons are gridded at 2" latitude-longitude resolution.

286

JOHN C. ALISHOUSE A N D E. PAUL McCLAIN

Present analysis does not permit definitive answers as to how to compensate for these effects; yet even without this compensation, it appears that microwave radiometry is capable of making significant contributions to determinations of SST.

3.

INFRARED SENSING OF

SEA SURFACE TEMPERATURE

3.1. Background and Previous Results A number of research and operational environmental satellites have carried scanning radiometer systems measuring emitted terrestrial/atmospheric radiation in the thermal infrared portion of the spectrum. These include the HRIR, THIR, and CZCS on the Nimbus satellites; the SR, VHRR, and AVHRR on the NOAA ITOS and TIROS-N series of satellites;the VISSR on the geostationary operational spacecraft, GOES; and the VIRR on Seasat. The chief characteristics of these sensors are described by McClain (1980a,b). In most cases the atmospheric window from 10.5 to 12.5 pm has been employed, and sea surface temperature has been secondary to night-time cloud imaging. Quantitative inference of SST has also been secondary to the imaging of ocean thermal fronts associated with ocean currents and upwelling (Legeckis, 1978). The higher quality radiometers with two and three atmospheric-window channels, such as the AVHRR (Advanced Very High Resolution Radiometer), on the latest satelliteshave enabled the development of methods to retrieve absolute sea surface temperatures of far better accuracy than was possible generally with single-window systems (McClain, 1980~). Just prior to the advent of multiple-window infrared measurements, McClain (1979) reviewed the then current methods of deriving surface temperatures from satellites and gave examples of operational and research products. The technique of extracting the brightness temperature itself is reasonably straightforward and follows from Eq. (3) in Section 1.1, the reflectivity of the sea surface generally being considered negligible in comparison with other error sources. Onboard calibration of the radiometers has been reliably accomplished for many years by alternately viewing “cold” space, with its known temperature, and a portion of the “warm” housing of the radiometer, in which there are embedded several thermistors for obtaining an independent measure of the warm load (Lauritson et al., 1979). Correction for atmospheric attenuation, principally by water vapor, can amount to 8-10 K for warm, moist atmospheres when using 10.5- to 12.5-pm data (Smith et al., 1970).

7. SEA SURFACE TEMPERATURE DETERMINATIONS

287

Corrections based on empirical schemes have been generally unsatisfactory, and even those based on moisture information from atmospheric sounders carried on the same spacecraft have often been inadequate. Good corrections are possible with coincident radiosonde measurements, but this situation is relatively rare, especially over the open ocean. Verifications of satellite-derived SSTs by means of comparisons with observations from nearly coincident “ships of opportunity” have suffered from two main problems: (1) the satellite obtains an areal average of the “skin” temperature of the sea, while the ship is reporting a point value of the “intake temperature” (i.e., water piped in to cool the engines) from a depth of 10 m or more; and (2) the ship data, because of transmission and other errors, are notoriously variable and of uncertain quality. Temperatures measured in the first meter of depth by buoys and expendable bathythermographs (XBTs) have proved to be considerably better for comparative purposes. In the earlier single-window SST methods a histogram technique was employed to judge if there were sufficient cloud-free scan spots for an SST determination in each 100 x 100-km array of scan spots, and to define a representative brightness temperature (Brower et al., 1976). Later, when atmospheric sounders, such as the Vertical Temperature Profile Radiometer (VTPR) and subsequently the High Resolution Infrared Sounder (HIRS), were carried on the same spacecraft as the radiometer, parametric and discriminant function type cloud detectors were employed to isolate sets of clear SST retrievals and the associated sounder data used in the atmospheric corrections (Walton et al., 1976; Walton, 1980). 3.2. A V H R R Results Far more encouraging than the foregoing experience with the singlewindow radiometers have been the results of tests with multiwindow measurements from the AVHRR on the TIROS-N, NOAA-6, and NOAA-7 satellites. The first AVHRRs delivered 1.l-km (local area coverage-LAC) and 4-km (global area coverage-GAC) resolution measurements in four channels: 0.55-0.90 (0.58-0.68 on NOAA-6 and NOAA-7), 0.725- 1-10, 3.55-3.93, and 10.5-11.5 pm (Schwalb, 1978). The 3.7-pm data from the TIROS-N were generally too noisy for SST determination, but prelaunch modifications to NOAA-6, NOAA-7, and NOAA-8 reduced this noise level to acceptable limits (I 0.2 K) during the first 12 months or so after each satellite launch, but the data become increasingly contaminated by electrical interference thereafter. An “outgassing” procedure was implemented in 1983 and successfully reduced the complex but coherent noise in the 3.7-pm data to levels comparable to those measured immediately after launch. More

288

JOHN C. ALISHOUSE AND E. PAUL McCLAIN

recent experience indicates that the outgassing must probably be repeated every 6-12 months to control this problem. The noise level has been exceptionally low, 10.1 K,in the case of the 11-pm channel on NOAA-6 and NOAA-8, and this also appears to hold for the 11-pm (10.3-1 1.3 pm) and the 12-pm (11.5-12.5 pm) channels on NOAA-7 and NOAA-9 satellites. The third window channel was added to the later AVHRRs to enable more widespread daytime SST retrievals, as the 3.7-pm observations can be contaminated by reflected solar radiation. The newest multichannel SST (MCSST) techniques make use of various of the four or five channels of the AVHHR in the initial cloud-filtering stages of the procedure. Thresholds are established that represent the expected magnitude of the bidirectional reflectance that would be measured in the 0.58to 0.68-pm (channel 1) or 0.725- to 1.1-pm (channel 2) channels in the absence of clouds. This reflectance threshold is a function of solar zenith angle, satellite nadir angle, and the azimuth of the viewed spot, and it is less in channel 2 than channel 1 principally because of the effects of atmospheric haze. Brightness-temperature clamps are also useful, e.g., T3., < 271 K would almost always correspond to either pack ice or cloud. The chief cloud tests, however, take advantage of spatial uniformity characteristics of the ocean surface temperature field and of air masses, and the low noise level in the AVHRR data. Within 2 x 2 arrays of either LAC or GAC scan spots, 2.2 km on a side or 8 km on side, respectively, both the ocean and the atmosphere are generally sufficiently uniform that reflectance and emittance variations among adjacent scan spots are of the same order of magnitude as radiometer noise, or less. When clouds are present, on the other hand, variations in reflectance or brightness temperature among the adjacent scanspots comprising a small array will often significantly exceed radiometer noise levels because of natural variability in cloud amount (i.e., fraction of the scanspot occupied by cloud), cloud optical thickness, or cloud top height. Interchannel brightnesstemperature comparisons are also useful for cloud detection, especially at night and in the particular cases that fail spatial uniformity tests, viz., an extremely uniform cloud that completely fills the radiometer’s field of view (FOV), or a uniform distribution of clouds that are much smaller than the FOV (even irregular arrays of very small clouds can sometimes result in uniform integrated mean radiances from adjacent FOVs). When transmissive (optically thin) clouds or small subresolution cloud elements are present, the radiance reaching the satellite is the net of the Planck radiance of the colder cloud and of the relatively warm sea surface. The Planck radiance is proportional to a higher power of the absolute temperature at 3.7 pm than it is at 11 or 12 pm. Therefore the 3.7-pm brightness temperature will be considerably elevated relative to the other two, especially when the FOV is partially filled with very cold cirrus clouds, as shown in Fig. 3.

7. SEA SURFACE TEMPERATURE DETERMINATIONS

289

SUBPOLAR ATM 900mbar-

__

SUBPOLAR ATM 700mbar

SUBPOLAR A'TM 200 mbar-,-

TROPICAL ATM

900 mbar--,-

TROPICAL ATM 200 mbar ...........

FIG.3. Simulated AVHRR channel 3 minus channel 4 brightness-temperature difference as a function of cloud fraction in the field of view for two atmospheric profiles (subpolar and tropical) and for thick clouds (optical depth = 10) at three different pressure levels (900, 700, and 200 mbar).

The effects of subpixel-size temperature discontinuities on the AVHRR infrared channel outputs are considered in more detail by Dozier (1981). In the case of optically thick nontransmissive clouds, the differing optical properties of clouds at 3.7 pm versus 11 or 12 pm come into play. Laboratory and theoretical studies have shown that such clouds are more reflective at the shorter wavelength, the difference increasing with decreasing mean droplet size (Hunt, 1973). Such clouds are characterized by T3.7 - TI, I -0.7K, whereas under cloud-free conditions the ocean/atmosphere gives values of T3., - T,, from 0 to +3.5 K (see Fig. 3). These conditions have been confirmed with AVHRR measurements from the NOAA-7 satellite. The concept of correcting infrared observations for atmospheric attenuation by obtaining simultaneous brightness-temperature measurements in two or more atmospheric windows goes back to at least 1970 (Anding and Kauth, 1970; McMillin, 1975). Probably the first attempt to verify this approach with radiometric data from space was made by Prabhakara et al. (1974),who used a small sample of 95-km resolution, cloud-free brightnesstemperature spectra in the 11- to 13-pm band from the IRIS (infrared interferometric spectrometer) on Nimbus-4 to determine relative absorption coefficients in three-window channels. These absorption coefficients were then used to check the validity of the method by applying it to 106 IRIS measurements from Nimbus-3 (150-km resolution); the R M S difference between the two sets of data was 1.3"C over a range of 4-29°C. The

290

JOHN C. ALISHOUSE AND E. PAUL McCLAIN

simulations by Deschamps and Phulpin (1980), using vertical profiles of temperature and humidity from several climatological atmospheres and radiosonde observations from a ship located at 45"N, 16"W, indicated that absolute temperatures of rather good accuracy (0.14 to 0.39 K) could be expected from multiple-window methods if the noise levels of the radiometric system were sufficiently low: noise-equivalent temperature differences (ie., NEAT) of 0.1 to 0.2 K. A seasonally and geographically diverse set of 59 cloudfree, maritime radiosonde observations was used by McClain (1981)as input to atmospheric transmittance models (Weinreb and Hill, 1980) to calculate the 3.7-, 11-, and 12-pm brightness temperatures corresponding to the filter-response functions of the radiation detectors of the AVHRR. Scatter diagrams of, for example, 7&, - T,, versus T3.7- Tll, T,, - T,,, or T3.7- T,, were constructed for satellite viewing angles (with respect to the nadir) of 0 and 45", and for sea/air temperature differences of 0 and + 5 K. These diagrams exhibited linear relationships and extraordinarily small scatter of the 59 points. Testing of one of the multiwindow equations [viz., = T,, t 1.489 (T3,7- T,)- 271.851 obtained from these scatter diagrams with NOAA-6measured brightness temperatures resulted in a bias of - 0.63"C and an RMS difference of 1.06"Cin a set of 41 matchups with buoy and XBT temperatures (McClain, 1981). Slight modification of this equation on the basis of a temperature-dependent bias correction computed from these initial comparisons yielded a bias of +0.01 K and an RMS difference of 0.91"C when a new and independent sample of 69 buoy/XBT matchups was used (McClain, 1980~).With the launch of NOAA-7, which carried an additional channel comprising the remaining half of the 10.5- to 12.5-pm window, it became possible to test a split-window simulation equation = T,, + 2.492(T,, - T 1 2 ) - 273.48). Night-time tests of the dual-window equation above and a triple-window simulation equation [Kfc = Tll 0.953(T3,7 - T12)- 272.541, and day and night tests of the split-window simulation equation, using substantial numbers of satellite/fixed-buoy measurements matched to within 25 km and 24 hr, enabled the derivation of initial operational temperature-dependent bias corrections for all the NOAA-7 simulation equations. These were used from the beginning of operational production of MCSST products in November 1981 until September 1982, at which time the bias corrections were recomputed on the basis of a larger and more seasonally/geographically diverse set of satellite matchups with drifting buoys (Strong and McClain, 1984). The bias and RMS difference statistics before and after incorporating the most recent bias correction terms, and the current operational MCSST equations, are given in Table 11. It should be noted that the satellite and buoy data used in the first of the tests are totally independent of those used in the

zro

(zfc

+

TABLE 11. EVALUATION OF MCSST WITH RESPECT TO I-m TEMPERATURES FROM DRIFTING BUOYS" ~

Original simulation equations

Biascorrected equations

Day

Night

N Bias RMSD

-

39 -0.96" 1.25"

-

N Bias RMSD

52 -0.17" 0.77"

39 -0.35" 0.96"

68 -0.02" 0.49"

-

39 -0.78" 1.10"

-

84

-

-0.01" 0.57"

N

Bias

RMSD

Day

Night 84

-

+0.02"

-

0.79"

-

84 -0.08" 0.62"

Bias-corrected simulation equations (OK in, "C out) SST = 1.0088T1, + 1.5018(T,,7 - TII) - 273.34

SST = 1.0346T1, + 2.5779(T1,- T12)- 283.21 (day) SST = 1.0350T1, + 2.5789(T1 - T,2) - 283.18 (night) SST = 1.017T11 + .9694(T,,7 - T1Z) - 276.58

Locations: North Atlantic, subtropical and midlatitude South Pacific, South Atlantic, and Indian Oceans.

292

JOHN C. ALISHOUSE AND E. PAUL McCLAIN

second. Figure 4 shows daytime and night-time drifting buoy matchups with surface temperatures calculated from NOAA-7 measurements and the current bias-corrected dual and triple-window equations. Bernstein (1982b) obtained comparable results employing 3.7- and ll-pm data from the AVHRR on board the NOAA-6 satellite. Multichannel, multiwindow sea surface temperatures derived from the sensitive and high-quality radiometers aboard NOAA's TIROS-N generation satellites are of the highest accuracy and resolution yet achieved from space. The only real limitation of this technique is that essentially cloud-free fields of view are required, although multiday compositing enables satisfactory mapping of the temperature field in all but the most persistently cloud-covered regions.

4. SUMMARY The Seasat SMMR data have been compared with surface observations on both a point-by-point and a monthly grid basis. The agreement between the averaged and gridded values is significantly better than the point-by-point comparisons. When biases are accounted far the Nimbus-7 SMMR results are quite comparable with the Seasat SMMR results. Possibly because of its much smaller field of view the point-by-point comparisons between AVHRRderived SSTs and surface observations are better than in the case of the SMMR. The SMMR data have been screened to avoid rain, sun glint, and land effects. The AVHRR data have been screened for clouds and sun glint. Both sets of SST determinations use multiple channels to correct for atmospheric effects. The SMMR data require correcting for surface emissivity as well. Both SMMR instruments have the same angular field-of-view; however, because of its lower orbit the Seasat SMMR has an instantaneous field of view (IFOV) of 79 x 121 km whereas the Nimbus-7 SMMR has an IFOV of 95 x 148 km. Both instruments retrieve SSTs on a grid that is nominally 150 x 150 km. The AVHRR has a nadir IFOV of 1.1 km, but most SST retrievals are done with the 4-km resolution data on a 25 x 25-km grid. These retrievals are then composited on a 50 x 50- or 100 x 100-km grid for operational usage. In the foregoing comparisons it has been assumed that the surface observations are correct. Yet there are problems associated with comparing surface and satellite measurements. The surface measurement is typically a point measurement whereas the SMMR has a grid of 150km and the AVHRR 25 km. A ship of opportunity will be measuring at the depth of its water

293

7. SEA SURFACE TEMPERATURE DETERMINATIONS

30

Blor

-O.Ol'C(O.Ol)

-0.02%

-0.Dl'C

i i l l l l i i i i i l l i l i i l l l l l l i l i ' i i l

-5

0

5

10 I5 MCSST - deg C

20

25

30

FIG.4. Comparisons of sea surface temperatures calculated from the current bias-corrected split-window and triple-window simulation equations and NOAA-7 brightness temperatures with 1-m temperatures from drifting buoys. The data are stratified by daytime (*) and night-time (+) matchups; the small dots represent 8 daytime and 16 night-time matchups (within 25 km and 24 hr) with 5 moored buoys in the North Atlantic and Pacific Oceans. These were added to augment the only three drifting buoy reports in the 9-15°C range (change in statistics in parentheses).

intake, while a buoy will be measuring at a depth of about 1 m. For a satellite radiometer, the depth will be less than 1 cm. The differences between satellite and high-quality surface observations (buoys, research vessels, XBTs, and ocean weather stations) are often less than the differences associated with ships-of-opportunity measurements. Thus some portion of the differences between surface and satellite SSTs is due to errors in the surface measurements. Both the microwave and infrared results presented here are state of the art. Each method has certain strengths and weaknesses. The infrared radiometer can achieve a spatial resolution that antenna size and detector sensitivity deny the microwave radiometer. On the other hand the microwave radiometer easily penetrates clouds and fog that block the infrared radiometer. User requirements for SST are quite variable in terms of spatial resolution, frequency of coverage, and absolute temperature determinations versus an ability to detect gradients in SST. Thus it appears likely that each technology can and will make significant contributions in meeting SST user requirements.

294

JOHN C. ALISHOUSE AND E. PAUL McCLAIN

REFERENCES Anding, D., and Kauth, R. (1970). Estimation of sea surface temperature from space. Remote Sens. Enoiron. 1,217-220. Bernstein, R. (1982a). Sea surface temperature mapping with the Seasat Microwave Radiometer. J. Geophys. Res. 87,7865-7872. Bernstein, R. L. (1982b). Sea surface temperature estimation using the NOAA 6 Satellite Advanced Very High Resolution Radiometer. J. Geophys. Res. 87,9455-9465. Bernstein, R. L., and Morris, J. H. (1983). Tropical and mid-latitude North Pacific sea surface temperature variability from the Seasat SMMR. J. Geophys. Res. 88, 1877-1891. Bierman, G. J., Wentz, F. J., 111, and Lipes, R. G. (1978). Modern estimation techniques applied to microwave sensing of the marine boundary layer. Asilomar Con$ Circuits, Syst. Computer Proc., 12th, IEEE Computer SOC.,Pacifc Grove CA 101-106. Blume, H. J., Kendall, B., and Fedors, J. (1978). Measurement of oceanic temperature and salinity via microwave radiometry. Bound. Layer Meteorol. 13,265-308. Brower, R. L., Gohrband, H. S.,Pichel, W. G., Signore, T. L., and Walton, C. W. (1976). Satellite derived sea surface temperatures from NOAA spacecraft. NOAA Tech. Memo. NESS 78, U.S.Dept. of Commerce, Washington, D.C. Chandrasekhar, S.(1960). "Radiative Transfer," Ch. 1. Dover, New York. Deschamps, P. Y.,and Phulpin, T. (1980). Atmospheric correction of infrared measurements of sea surface temperature using channels at 3.7, 11, and 12 pm. Bound. Layer Meteorol. 18, 13 1-143. Dozier, J. (1981). A method for satellite identification of surface temperature fields of sub-pixel resolution. Remote Sens.Environ. 11,221-229. Gloersen, P., and Barath, F. J. (1977). A Scanning Multichannel Microwave Radiometer for Nimbus G and Seasat A. ZEEE J. Oceanic Eng. OE-2,172-178. Hobson, D. E., and Williams, D. (1971). Infrared spectral reflectance of sea water. Appl. Opt. 10, 2372-2373. Hollinger, J. P. (1970). Passive microwave measurements of the sea surface. J. Geophys. Res. 75, 5209-5213. Hollinger, J. P. (1971). Passive microwave measurements of sea surface roughness. ZEEE Trans. Geosci. Electron. GE9, 165-169. Hunt, G. E. (1973). Radiative properties of terrestrial clouds at visible and infrared thermal window wavelengths. Q.J . R. Meteorol. SOC.99,346-369. Lauritson, L., Nelson, G. J., and Porto, F. W. (1970). Data extraction and calibration of TIROSN/NOAA radiometers. NOAA Tech. Memo. NESS 107, pp. 44-46. U.S. Dept. of Commerce, Washington, D.C. Legeckis, R. (1978). A survey of world-wide sea surface temperature fronts detected by environmental satellites. J. Geophys. Res. 83(C9),4501-4522. Lipes, R. G. (1982). Description of Seasat radiometer status and results. J. Geophys. Res. 87, 3385-3395. Lipes, R. G., Bernstein, R. L., Cardone, V.J., Katsaros, K. B., Njoku, E. G., Riley, A. L., Ross, D. B., Swift, C. T., and Wentz, F. J. (1979). Seasat Scanning Multichannel Microwave Radiometer: Results of the Gulf of Alaska Workshop. Science 204, 1415-1417. Liu, C.-T. (1983). Tropical Pacific sea surface temperatures measured by Seasat Microwave radiometer and by ships. J. Geophys. Res. 88,1909-1914. McClain, E. P. (1979). Satellitederived earth surface temperature. 1n"Quantitative Meteorological Data from Satellites"(J. S.Winston, ed.),Ch. 3, pp. 60-68. Technical Note 166, WMONo. 531, World Meteorological Organization, Geneva. McClain, E. P. (1980a). Passive radiometry of the ocean from space-An overview. Bound. Layer Meteorol. 18, 7-24.

7. SEA SURFACE TEMPERATURE DETERMINATIONS

295

McClain, E. P. (1980b). Environmental satellites. In “McGraw-Hill Encyclopedia of Environmental Science,” pp. 15-30. McGraw-Hill, New York. McClain, E. P. (1980~).Proc. Workshop Appl. Exist. Satellite Data Study Ocean Surf. Energ., Univ. Wisconsin, Madison, 19-21 Nov. pp. 169-173. McClain, E. P. (1981). Multiple atmospheric-window techniques for satellite sea surface temperatures. Proc. C O S P A R I S C O R I I U C R M Symp. Oceanogr. Space pp. 73-85. McMillin, L. M. (1975). Estimation of sea surface temperatures from two infrared window measurements with different absorption. J. Geophys. Res. 80,5113-5117. Njoku, E. G. (1980). Antenna pattern correction procedures for Scanning Multi-channel Microwave Radiometer (SMMR). Bound. Layer Meteorol. 18, 79-98. Njoku, E. G.,Christensen, E. J., and Colfield, R. E. (1980a). The Seasat Scanning Multichannel Microwave Radiometer: Antenna pattern corrections-Development and implementation. I E E E J. Oceanic Eng. OE-5, 125-137. Njoku, E. G.,Stacey, J. M., and Barath, F. T. (1980b). The Seasat Scanning Multichannel Microwave Radiometer (SMMR): Instrument description and performance. IEEE J . Oceanic Eng. OE-5, 100-1 15. Nordberg, W., Conaway, J., and Thaddeus, P. (1969). Microwave observations of sea state from aircrft. Q.J . R. Meteorol. SOC.95,408-413. Nordberg, W., Conaway, J., Ross, D. B., and Wilheit, T. (1971). Measurements of microwave emission from a foam-Covered, wind-driven sea. J. Atmos. Sci. 28,429-435. Porter, R. A,, and Wentz, F. J. (1971). Microwave radiometric study of ocean surface characteristics. Final Report, 2 Vols. NOAA/NESS Contract No. 1-35140. Radiometric Technology, Wakefield, MA. Porter, R. A., and Wentz, F. J. (1972). Research to develop a microwave radiometric ocean temperature sensing technique. Final Report, 2 Vols. NOAA/NESS Contract No. 2-35309, Radiometric Technology, Wakefield, MA. Prabhakara, C., Dalu, G.,and Kunde, V. G.(1974). Estimation of sea surface temperature from remote sensing in the 11 to 13 pm window region. J . Geophys. Res. 79,5039-5044. Schwalb, A., (1978). The TIROS-N/NOAA A-G satellite series. NOAA Tech. Memo. NESS 95. U. S. Dept. of Commerce, Washington, D.C. Smith, W. L., Rao, P. K., Koffler, R., and Curtis, W. (1970). The determination of sea surface temperature from satellite high resolution infrared window radiation measurements. Mon. Weather Rev. 98,604-611. SMMR Mini-Workshop 111 Report (1980). JPL No. 622-224. NASA Jet Propulsion Lab., Pasadena, Ca. SMMR Mini-Workshop IV Report (1981). JPL No. 622-234. NASA Jet Propulsion Lab., Pasadena, CA. Stogryn, A. (1967). The apparent temperature of the sea at microwave frequencies. IEEE Trans. Antennas Propag. AP-15,278-286. Strong, A. E., and McClain, E. P. (1984). Improved ocean surface temperatures-comparisons with drifting buoys. Bull. Am. Meteor. SOC.65, 138-142. Swanson, P. N., and Riley, A. L. (1980). The Seasat Multichannel Microwave Radiometer (SMMR): Radiometric calibration, algorithm development, and performance. I E E E J . Oceanic Eng. OE-5, 116-124. Swift, C. T. (1980). Passive microwave remote sensing of the ocean-A review. Bound. Layer Meteorol. 18, 24-54. Wagner, R. J., and Lynch, P. J. (1972). Analytical studies of scattering and emission by the sea surface. Technical Report 17608-6006-RD-00, prepared for Office of Naval Research, Washington, D.C., by the TRW Systems Group, Redondo Beach, CA. Walton, C. W. (1980). Deriving sea surface temperature from TIROS-N data. In “Remote Sensing of Atmospheres and Oceans” (A. Deepak, ed.), pp. 547-579. Academic Press, New York.

296

JOHN C. ALISHOUSE AND E. PAUL McCLAIN

Walton, C. W., Brower, R. L., and Signore, T. L. (1976). Satellite derived sea-surface temperature from NOAA spacecraft. NOAA Tech. Memo NESS No. 78. U.S. Dept. of Commerce, Washington, D.C. Webster, W. J., Jr., Wilheit, T. T., Ross, D. B., and Gloersen, P.(1976). Spectral characteristics of the microwave emission from a wind-driven foam-covered Sea. J. Geophys. Res. 81, 3095-3099.

Weinreb, M. P.,and Hill, M. L. (1980). Calculations of atmospheric radiances and brightness temperatures in infrared window channels of satellite radiometers. NOAA Technical Report NESS 80. National Oceanic and Atmospheric Administration, U.S. Dept. of Commerce, Washington, D.C. Wentz, F. J. (1974). The effect of surface roughness on microwave sea brightness temperatures. Final Report NOAA/NESS, Contract No. 3-5345. Radiometric Technology, Wakefield, MA. Wentz, F. J. (1975). A two-scale scattering model for foam-free sea microwave brightness temperature. J . Geophys. Res. 80,3441-3446. Wentz, F. J. (1983). A model function for ocean microwave brightness temperatures. J. Geophys. Res. 88,1892-1908. Wilheit, T. T., and Chang, A. T. C. (1980). An algorithm for retrieval of ocean surface and atmospheric parameters from the observations of the Scanning Multi-Channel Microwave Radiometer. Radio Sci. IS, 525-544. Wilheit, T. T., Greaves, J., Gatlin, J., Han, D., Krupp, B. M., Milman, A. S.,and Chang, E. (1984). Retrieval of ocean surface parameters from the Scanning Multifrequency Microwave Radiometer (SMMR) on the Nimbus-7 satellite. I E E E Trans. Geosci. Remote Sens. GE-22, 133- 143.

Wu, S. T., and Fung, A. K. (1972). A noncoherent model for microwave emissions and backscattering from the sea surface. J. Geophys. Res. 77,5917-5929.

OCEAN COLOR MEASUREMENTS HOWARD R. GORDON Department of Physics University of Miami Coral Gables, Florida

ROSWELL W. AUSTIN Visibility Laboratory Scripps Institution of Oceanography La Jolla. California

DENNIS K. CLARK AND WARREN A. HOVIS

CHARLES S. YENTSCH

National Oceanic and Atmospheric Administration National Environmental SateNile. Data. and Information Service Washington, D.C.

Bigelow Laboratory for Ocean Sciences West Boothbay Harbor, Maine

1. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . 297 2. The CZCS System. . . . . . . . . . . . . . . . . . . . . . . . 303 3. Response to Oceanic and Atmospheric Conditions . . . . . . . . . . . . 306 3.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 306 3.2. Subsurface Contribution to L,:L,. . . . . . . . . . . . . . . . . 307 3.3. Atmosphericand Sea Surface Contributions to L, :L , . . . . . . . . . . 310 4. Remote Sensing of the Phytoplankton Pigment Concentration . . . . . . . . 313 4.1. Development of the Bio-optical Algorithm. . . . . . . . . . . . . . 313 4.2. Application of the Bio-optical Algorithms to CZCS Imagery . . . . . . . 317 5. Remote Sensing of the Diffuse Attenuation Coefficient of Water . . . . . . . 322 5.1. The Diffuse Attenuation Coefficient . . . . . . . . . . . . . . . . 323 5.2. Development of the “K” Algorithm . . . . . . . . . . . . . . . . 324 5.3. Application of the “K” Algorithm to CZCS Imagery . . . . . . . . . . 327 6. Summary and Conclusions. . . . . . . . . . . . . . . . . . . . . 331 References . . . . . . . . . . . . . . . . . . . . . . . . . . 332

1. INTRODUCTION Anyone who has looked at different waters (for example, ocean water as compared with waters in canals, lakes, and rivers) has undoubtedly noticed considerablevariations in their color. These variations are due to the presence of material suspended or dissolved in the water. In the case of ocean water the material chiefly responsible for producing variations in the color of the water, that is, a variation from the rich blue of open ocean water (e.g., the Sargasso Sea) to the dark green of some shelf waters, is phytoplankton-microscopic photosynthetic plant life which constitutes the first link in the marine food chain. When inorganic suspended material, e.g., resuspended from the bottom in shallow areas, is present in the water along with phytoplankton, the water often has a bright milky green color. 297 ADVANCES IN GEOPHYSICS, VOLUME 27

Copyright @ 1985 by Academic Press, Inc. All rights of reproduction in any form reserved. ISBN 0-12-018827-9

298

HOWARD R. GORDON ETAL.

It is natural to investigate the extent to which such color variations can be used to determine the constituents present in surface waters and their concentration. Since hand-held color photography from the manned space program demonstrated that these color variations could be detected at orbital altitudes through the earth’s atmosphere, the possibility of remote measurement of constituent concentrations using earth-orbiting satellites is immediately suggested. This possibility is most exciting because the existing body of knowledge regarding the distribution of ocean properties has in large measure been restricted by the classical ocean data sampling techniques. Data are usually obtained at ocean stations whose separation has been determined by a preconceived notion of the nature of the horizontal variability of the phenomena under study or, more usually, by the economics of ship operating costs or the amount of available survey time and the ocean area to be examined. The result is the equivalent of viewing the ocean through a twodimensional, low-pass spatial filter. Much of the knowledge of the highspatial-frequency variations of the phenomena is frequently lost as a consequence. Remote sensing of surface properties from aircraft or spacecraft provides the investigator with a means of sampling a large area and retaining the high spatial frequencies, thereby obtaining a synoptic view of the properties at high resolution. Furthermore, by revisiting the region, satellite remote sensing provides a means of sampling the temporal as well as the spatial variations. The simplest situation in which to try to relate water color to constituent concentrations is the open ocean, for which the principal materials influencing the optical characteristics of the water are phytoplankton and their covarying detritus. Phytoplankton contain the photosynthetic pigment chlorophyll a, which strongly absorbs blue light, and through a quantitative assessment of this absorption the presence of phytoplankton can be detected in such waters without the interfering effects of other materials. Chlorophyll a measurements in fact are the mainstay of primary productivity studies. They are used for assessing the distribution of phytoplankton and they aid in the study of biological and biochemical cycling of organic matter, and in the kinetics of plankton growth (Platt et al., 1977). The next level of complexity would be waters containing two noncovarying components, such as phytoplankton and inorganic suspended material in shallow water or in upwelling regions, or phytoplankton and dissolved organic material (gelbstoff)in estuaries and low-salinity seas such as the Baltic. For these, phytoplankton can still be detected to the extent that the other component does not mask the blue absorption. Finally, regions will be encountered in which all of these components will strongly contribute to the water color, and these present the most difficult situations in which to interpret the water color. It should then be possible to employ water color to measure the phyto-

8. OCEAN COLOR MEASUREMENTS

299

plankton concentration (or at least the index of the concentration, chlorophyll a) in the open ocean. As one approaches the coast, the phytoplankton concentrations as well as the concentrations of the dissolved organics and suspended inorganics are expected to increase, but there should be a (coastal) zone for which the phytoplankton concentration retrieval will be only slightly more difficult than in the open ocean. Close to the coast, however, the concentration of all of the constituents often will become so high that quantitative retrieval becomes very difficult. This latter zone is not addressed in this report. Instead, attention is centered on the retrieval from spacecraft imagery of an index of the phytoplankton concentration in those waters for which phytoplankton and their associated detritus play a major role in determining the color of the water. This constitutes about 99% of the ocean’s surface area. One must ask, however, why measure phytoplankton? One obvious justification for the measurement of phytoplankton is that they constitute the first link in the marine food chain. However, to understand more specifically the reasons behind measuring phytoplankton, it is useful to start by reviewing the rather unique position that they occupy in the natural history of the earth. In the early history of the planet, it is believed that the atmosphere and the oceans contained very little dissolved oxygen. However, the oceans had salts and water necessary for life, and as geologic time passed, they accumulated a weak broth of organic matter. It is believed that the first living cells arose from this broth. In doing so, they faced a paradox. With no other sources of energy, the cellular consumption would soon exhaust the broth. The early cells were living on borrowed time. Fortunately, by consuming organic broth (anaerobic fermentive process), carbon dioxide was produced. The early cells evolved a process of using carbon dioxide to produce organic material. The energy needed was sunlight, and the process is photosynthesis. The meaning to the early cells was that they no longer had to live on the organic broth. With photosynthetic machinery they could synthesize and store organic substances. Photosynthesis involves the use of light and water as well as carbon dioxide, and one of the by-products of the process was oxygen. The appearance of oxygen greatly increased the efficiency with which organics could be decomposed. This aerobic respiration allows for almost complete extraction of energy from organic matter. Now the primordial cells could meet their maintenance ration with a small amount of expended energy. Therefore, the importance of oxygen to life as we know it cannot be underestimated: whereas photosynthesis coupled with anaerobic respiration allows organisms to maintain themselves, photosynthesis coupled with aerobic (oxygenated) respiration allow cells enough surplus to multiply and grow. Another benefit to life occurred with the appearance of oxygen. In the atmosphere a layer of ozone formed which shielded the surface of the earth

300

HOWARD R. GORDON ET AL.

from harmful ultraviolet radiation. This allowed organisms to develop complex genetic material without the ultraviolet light destroying the nucleic acids and proteins. This also allowed organisms to live on the land surface of the planet and not use water submersion as protection from the ultraviolet light. In summary, the two principal benefits of photosynthesis are (1) the production of organic material and oxygen from light energy and resulting aerobic oxidation and growth, and (2) the production of ozone, which shields cells from radiation of harmful wavelengths, thereby providing genetic diversity. We cannot conceive of life on earth without plants. Photosynthesis, which annually yields about 150 billion tons of carbon, half of which is produced in the oceans, is characterized by the uptake of simple inorganic substances which are converted to complex organic compounds. It is the affinity of these compounds for oxygen which provides the energy for the rest of life in the sea. In the course of the evolution of photosynthesis, organisms needed to develop antennae for capturing sunlight. These appeared as pigmented proteins which function in a manner similar to colored dyes. The pigmented proteins fill the visible window of the solar spectrum (Fig. 1). Since none of the proteins would survive in the presence of ultraviolet radiation, a step in their

10

--

-k c

e c .u f

e

0.1

1 0.01 400

500

600

0

Wavelength (nm)

FIG.1. The absorption of light by different algal pigments in the “window of clarity” in water absorption. The spectra for the pigments approximate those measured in oioo. For the sake of this illustration,fucoxanthin and peridinin absorptions are considered identical.

8. OCEAN COLOR MEASUREMENTS

301

evolution must have involved partial submergenceof the cells using the water as a protective shield before the ozone was established. But the cells could not have resided too deep in the water column, because water itself absorbs the energy especially at the longer wavelengths of visible light. Thus, the optimal choice for the pigmented antennae had to be somewhere between the ultraviolet and the near infrared, that is, the visible window of sunlight. In the course of their evolution, the algae have virtually filled the visible window with their antennae, establishing firmly their potential to utilize practically all wavelengths in this region for photosynthesis. Yet, even the casual observer recognizes that the visible window of ocean water is never completely filled; he may not even realize that there are plants there at all, since the ocean is neither a broth nor a soup of algae. But why is it not? To ask the above question introduces the reasons behind the measurement of phytoplankton and their activities. To complete this introduction we must at a very elementary level explore some of the major causes for the dominance of certain species of algae-and the present-day biochemical structure of the oceans. Remembering that the primordial oceans are thought to have had very little oxygen, the production of oxygen by photosynthesis presented problems to bacterial procaryotes. The physiology of these problems is not well understood but is manifested in the following way: In some of the ancient procaryotes (evolutionary old) which are still around today, the respiratory rate is accelerated by light and oxygen. It is as if some of these primitive algae cannot keep photosynthesis from reversing itself. As fast as the organic compounds are made in light, they are consumed. The presence of oxygen also changed the nitrogen metabolism, which posed another problem for the procaryotes. In the anaerobic oceans nitrogen was in all probability in the reduced form as ammonia. This form can readily be utilized by the procaryotes. However, in the presence of oxygen the ammonia form of nitrogen became oxidized to nitrate (NO,). Thus, the early organisms had to develop a mechanism for reducing nitrate to ammonia for their protein synthesis. To cope with these problems a group developed which are now called eucaryotes. These organisms have a highly organized nucleus containing genetic information which can solve these problems, and they soon began to dominate the seas. Organisms such as diatoms have a very low respiration rate and hence active photosynthesis easily produces new growth. They can efficiently convert nitrate to ammonia for protein synthesis. It was, so to speak, a new ball game when the eucaryotes appeared. With adequate light and nutrients they could quickly outgrow the procaryotes. But the question remains: why is the ocean not filled with these organisms? The answer to this question is at the crux of why we measure phytoplankton. It depends not only on understanding the physiology of the phytoplankton, but also requires knowledge of the water mass movements on the oceans.

302

HOWARD R. GORDON E T A L .

In a general sense we can classify the modern oceans as an unbalanced medium for phytoplankton growth. By this is meant that the ligld energy necessary for photosynthesis usually penetrates only to a depth above the reservoir of nutrients which lies in deep waters. Thus, the upper photosynthetic layers (euphotic) generally contain too little plant nutrients, such as nitrate nitrogen, which is needed to support vigorous growth. To put it another way, euphotic waters have limiting concentrations of nutrients, because in the process of photosynthesis nutrients are incorporated into new cells faster than they can be resupplied. This situation is characteristic of ocean water masses having a thermocline, that is, two layers at different density. The top, buoyant layer is warmed by sunlight and the water is of low density. The lower layer consists of cold high-density water. The nutrient-light imbalance is corrected when the two layers mix, and the occasions for mixing may occur on time scales of days to months, depending on the source of energy which mixes the layers. These sources of energy are generally wind, currents, and tides in combination with bottom friction. The close coupling between vertical mixing of the two layers and the variability of the energy input, i.e., changing winds, tides, and currents, etc., means that the pattern of phytoplankton growth will reflect the variability of the energy inputs in both time and space, which in the latter case may involve hundreds of kilometers. Therefore, the prediction of events of phytoplankton growth in today’s oceans, which is necessary for the analysis and wise use of this natural resource, depends on having considerable climatological and hydrographic information. Some of the mixing-growth events can be numerically modeled to a reasonable degree of accuracy. Other events, for example those induced by large-scale oceanic circulation, are much less easily modeled. However, in either case, measurements for confirming or disputing the models require large-scale synoptic observations. This is the value of satellite remote sensing: providing data coverage over large areas in a repetitivefashion. This is of great importance in areas in which the rate of change of the phytoplankton population is large, e.g., coastal regions where short-term blooms arise either naturally or after pollution. Moreover,in the coastal and slope waters (the sea of most fisheries),timing of the spring and fall events of flowering are important to the survival of larval and juvenile fishes. This timing is difficult to establish over large areas by conventional shipboard sampling. In conclusion,justification for satellite measurements of phytoplankton is probably best summarized by the following quotation (Ruttenberg, 1981): Synoptic estimates of chlorophyll are important because phytoplankton variability in space and time is a ubiquitous and important feature of the marine environment. [Phytoplankton variability includes not only the density of organisms but also the number of species present (species abundance) and the distribution of individuals among these species

8. OCEAN COLOR MEASUREMENTS

303

(species equitability), but observations of these factors are hardly accessible to shipboard sensing and inaccessible to remote sensing.] This variability influences both practical problems associated with sampling and estimating abundance within the environment and theoretical considerations related to the structure and dynamics of phytoplankton ecology. Also, the variability of phytoplankton communities is thought to hold a key to understanding the relative importance of physical and biological factors in structuring the marine food web. In addition, there is evidence that the successful modeling of phytoplankton dynamics, and the predictive linkage of phytoplankton production to higher trophic levels, has been limited by a lack of synoptic data and by limited sampling strategies.

One must bear in mind, however, that the success of ocean color remote sensing should not be judged on its capacity for addressing these or any other problems alone, but rather on its capacity to complement the suite of available techniques for studying such oceanic biological processes. 2. THECZCS SYSTEM

The Coastal Zone color scanner (CZCS)on Nimbus-7 is the first instrument devoted to the measurement of ocean color and flown on a spacecraft (Hovis et al., 1980). Although instruments on other satelliteshave sensed ocean color, their spectral bands, spatial resolution, and dynamic range were optimized for land or meteorological use. In the CZCS, every parameter of the visible channels is optimized for use over water to the exclusion of any other type of sensing. The signal-to-noise ratios in the spectral channels sensing reflected solar radiance are higher than those required in the past. These ratios need to be high because the ocean is such a poor reflector that most of the signal seen by the reflected energy channels at spacecraft altitudes is backscattered solar radiation from the atmosphere rather than reflected solar energy from the ocean. The CZCS thermal channel utilizes the 10.5- to 12.5-pm region used on many other thermal mappers. This CZCS channel is unique, however, since it is registered with the reflected solar energy bands and has the same spatial resolution (825 m at nadir). The CZCS is a conventional multichannel scanning radiometer' utilizing a rotating-plane mirror at a 45" angle to the optic axis of a Cassegrain telescope. The rotating mirror scans 360"; however, only f40" of data centered on the spacecraft nadir is collected for ocean color measurements. During the rest of the scan, the instrument acquires a view of deep space and of internal instrument sources for calibration of the various channels. A radiometer consists of a flat detector of surface area A, capable of measuring the radiant power P falling on it in a spectral band A1 centered on the wavelength A. When the detector is located at a position specified by the vector r, is aimed in a direction specified by the unit vector -{, and is viewing the universe with a solid angle of acceptance An,it measures the radiance traveling in the direction { defined by L(r, {,1) = P / A A1 An.

304

HOWARD R. GORDON E T A L . SCAN M I R R O R

f

SECONDARY M I R R O R D I C H R O I C BEAMSPLITTER

/

PRIMARY M I R R O R PSEUDOOEPOLARIZER WEDGES

/

FIG.2. The CZCS optical system.

The CZCS has six spectral bands, five sensing backscattered solar radiance and one sensing emitted thermal radiance. Figure 2 illustrates the method by which discrimination of the spectral bands is achieved. The beam is split by a dichroic beam splitter, one portion of the beam going through a set of depolarizingwedges to a small polychromator where the radiance is dispersed and detected by five silicon diode detectors in the focal plane of the polychromator. Radiance in the 10.5- to 12.5-pm spectral band is reflected off the dichroic and then imaged onto an infrared detector of mercury cadmium telluride cooled to approximately 120 K. Table I shows the center wavelengths, the spectral bandwidths, and the minimum signal-to-noise ratio specified for the instrument at the most sensitive gain setting, that is, the gain setting that would be used for the darkest targets. (Prelaunch tests show the instrument has exceeded the specification for signal-to-noise in every channel.) Channel 5 has the same spectral response as channel 6 of the Landsat multispectral scanner series. The gain of channel 5 is fixed and set to produce the same percentage of maximum signal over land targets as the Landsat channel 6. However, the actual radiance for saturation is higher since the Nimbus-7 spacecraft crosses the equator at high noon whereas Landsat crosses the equator at 9:30 AM local time. The 10.5- to 12.5-pm channel measures equivalent blackbody temperature as seen by the sensor with a noise-equivalent temperature difference of less

305

8. OCEAN COLOR MEASUREMENTS

TABLE I. CZCS PARAMETERS FOR VISIBLE-NWR-IR BANDS' Band

I,

AI,

SIN

Ls

1 2 3 4 5

433 520 550

20 20 20 20 100

> 150 > 140 > 12s > loo >250

5.4 1 3.50 2.86 1.34 23.9

670 750

A is in nm; L, is saturation in m W c m P pn-l ster-'.

than 0.35 K at 270 K. Atmospheric interference with this channel, principally from weak water vapor absorption in the 10.5- to 12.5-pm region, can produce measurement errors of several degrees. Temperature gradients, however, should be seen quite well because of the extremely low noise-equivalent temperature difference of the sensor. The CZCS has considerable flexibility to accommodate a wide range of conditions. The first four spectral bands, for instance, have four separate gains that change, on command, to accommodate the range of sun angles observed during a complete orbit and throughout the various seasons. The gains are changed to utilize the best dynamic range possible without saturating overwater targets. Normally, the gain used in the first four channels is determined by the solar elevation angle of the target to be acquired. When a special circumstance is expected, such as a particularly bright material in the water, the gain can be changed to accommodate the special circumstances. In addition to gain change, the CZCS scan mirror can be tilted from nadir to look either forward or behind the spacecraft line of flight. It can tilt in 2" increments up to 20" in either direction. This feature was built into the instrument to avoid the glint caused by capillary waves on the sea surface that would obscure any scattering from below the surface. The angle of tilt of the scan mirror is determined by the solar elevation angle. It is normally tilted to avoid sunlight. In the untilted mode the sensor views the ocean in a swath of 1600-km width on the earth's surface. A secondary effect of the tilting is a widening of the swath to about 2300 km when the scan plane is tilted toward the north by 20", as would be the case for sensing along the U.S.East Coast in summer, and a narrowing of the swath to about 1300km when the scan plane is tilted 20" to the south. Prelaunch calibration of the CZCS was achieved utilizing a 76-cm-diameter integrating sphere as a source of diffuse radiance for channels 1-5 and a blackbody source for calibration of channel 6. The integrating sphere was especially constructed for calibration of the CZCS and was, itself, calibrated from a standard lamp from the National Bureau of Standards, utilizing a spectrometer and another integrating sphere to transfer calibration from the

306

HOWARD R. GORDON ETAL.

lamp to the sphere. This same type of sphere has been used in calibrating the multispectral scanner for Landsat and the Advanced Very High Resolution Radiometer (AVHRR) on the TIROS-N series. In addition to the sphere and the blackbody, a collimator was also used to monitor the CZCS calibration in vacuum testing. Calibration was transferred from the primary calibration standard, the sphere and the blackbody, to the collimator, using the instrument itself. In-flight calibration of the CZCS is accomplished for the first five bands by using a built-in incandescentlight source. This in-flight calibration source was calibrated using the instrument itself as a transfer against the referenced sphere output. The light source is redundant in the instrument so that in case of failure of one of the lights, another one can be ordered to operate on command. After launch, calibration source number one has been used routinely, with source number two tested occasionally to verify its stability. The in-flight calibration suggested that the CZCS detector-amplifier system was stable to within the noise of the digitizer. Because the calibration lamps provide only about one-fourth the signal of the prelaunch calibration sphere, and one-half that of the signal derived from viewing earth, it was felt that the prelaunch calibration should be used for processing rather than the in-flight calibration. About 2 years after launch a decrease in sensitivity of the blue band was noticed, and thereafter a decrease in the sensitivity of the green and yellow bands as well. This decrease was not revealed in the in-flight calibration, suggesting that it was due to contamination of one of the surfaces in the instrument not subjected to the calibration signal. Compensation for this loss of sensitivity, which amounts to about a 7% decrease in the blue per year in orbit (Gordon et al.. 1983b), is being effected in the CZCS processing. Channel 6 is calibrated viewing the blackened housing of the instrument whose temperature is monitored. Deep space is another calibration viewed during the 360" rotation of the scan mirror. All of the CZCS data are archived with the Satellite Data ServicesBranch of the Environmental Data Information Service of NOAA (see Appendix C). A catalog is available that provides the orbital track of the Nimbus-7 spacecraft on a day-to-day basis, with the areas where CZCS was operated indicated on the orbital tracks. In addition, it contains a short description of the imagery, giving such parameters as cloud cover for each image. 3. RESPONSE TO OCEANIC AND ATMOSPHERIC CONDITIONS 3.1. Introduction The CZCS provides estimates of the near-surface concentration of ocean constituents by measuring the spectral radiance backscattered out of the ocean. [A comprehensive review of the principles of ocean color remote

8. OCEAN COLOR MEASUREMENTS

307

sensing can be found in Gordon and Morel (1983)l. This radiance scattered out of the ocean and reaching the top of the atmosphere comprises only a small portion of the total radiance measured at the sensor. In general the sensor radiance L,(I)(1is the wavelength) can be decomposed into L l ( l ) ,the radiance due to photons that never penetrated the sea surface, and t(A)L,(& the radiance due to photons which were backscattered out of the water (the water-leaving radiance) and diffusely transmitted to the top of the atmosphere, i.e., Ld4 = L l ( 4 + @)L,(4

(1)

All of the information relating to the concentration of oceanic constituents is contained in L,(A). The effects of variations in the constituent concentrations on L,(A) and the removal of L,(I) from L,(A)are described in this section. 3;2. Subsurface Contribution to L,: L,

The water-leaving radiance L,@) carries all of the information contained in &(A) concerning the subsurface conditions. In general, L, depends on the concentration of all of the constituents of the water. In particular, L, is strongly influenced by the concentrations of phytoplankton, inorganic suspended material, and dissolved organic material (yellow substances). Following Austin (1974) and Gordon and Clark (1981), the water-leaving radiance is related to the irradiance reflectance just beneath the surface (2) R E"(O-)/Ed(O-) where E,(O-) and &(O-) are, respectively, the up- and downwelling irradiances' just beneath the surface, by

L,

= (1 - p)Ed(O-)R/n2Q

(3)

In this equation, p is the Fresnel reflectance (water-air) of the air-sea interface, n is the refractive index of water, and Q is a factor dependent on the

* Let n be a unit vector normal to the level sea surface and directed into the ocean. Then the downwelling irradiance Ed(z) and the upwelling irradiance E J z ) are defined according to E,(z, A) = J+ L(z,t, EJz,4

=-

*

n dn,

U z ,L 4 5

-

II dQ,

where the plus and minus signs on the integrals denote integration over solid angles dR, about and E&, i) are, directions 6 for which 5 . n is, respectively, positve and negative. Thus, Ed(z,i) respectively, the downward and upward jluxes of radiant energy (within A l ) across a level surface at depth z .

308

HOWARD R. GORDON ET AL.

angular distribution of the upwelling light field. For a totally diffuse upwelling light field, this factor would be x, while observations (Austin, 1980) suggest that it is closer to 5. The importance of this relationship lies in the fact that R can be related directly to the inherent optical properties of the water (Gordon et al., 1975; Morel and Prieur, 1977),i.e.,

R z 0.33bb/a (4) where a is the true absorption coefficient of the medium, and bb is the backscattering coefficient. These coefficients are linearly summable over the constituents: a = a , + 1 ai

where a, and ai are, respectively, the absorption coefficients of water and the ith constituent, and (bb), and (b,)i are the corresponding backscattering coefficients. These coefficients depend on the constituent concentration Ci through ai = ff(Ci) (6) (bb)i

= ff(ci)

where the fi are in general nonlinear. These equations provide a direct link between L, and Ci.

If measurements of L, are available at a number of wavelengths equal to the number of optically important constituents, and the spectral dependence of the functions f;and f p is known, the resulting system of equations in principle can be inverted to find C,. [It is shown in Gordon and Clark (19Sla) that Ed(O-) can be determined from go and the extraterrestrial solar irradiance.] This has been referred to by Morel and Gordon (1980)as the analytic method, and was used by Morel (1980) to extract constituent concentrations from spectral measurements of R. In general, however, the values of f are unknown, or at most poorly known, and an empirical relationship must be used to retrieve Cifrom L&). Successof this approach requires that f f and ff, be approximately constant in space and time. Morel and Prieur (1977) have optically classified sea water according to the constituents chiefly responsible for determining their optical properties. Those waters for which phytoplankton and their cooarying detrital material play the dominant role in determining the optical properties are called case 1

8. OCEAN COLOR MEASUREMENTS

309

waters, while those for which inorganic suspended material (such as that which might be resuspended from the bottom in shallow areas), which do not covary with phytoplankton, play an important role are referred to as case 2 waters. Most open ocean waters are near case 1. These waters are the easiest to treat from a remote-sensing point of view. Most early studies concerning the remote sensing of ocean color (Clarke et al., 1970; Arvesen et al., 1973) were directed toward the extraction of the surface chlorophyll concentration from the spectral radiance upwelling above the sea surface. However, in addition to the pigments shown in Fig. 1, degradation products of chlorophyll a, the pheopigments, are also present. These are produced upon acidification of chlorophyll a as would happen, for example, in the gut of zooplankton feeding on phytoplankton. According to Smith and Baker (1978a), the concentration of pheopigments ranges from roughly 60% of the chlorophyll a concentration in oligotrophic waters to less than 10% of the chlorophyll a concentration for waters with high chlorophyll a (-10 mg m-3). Their concentration generally increases with depth, reaching a maximum below the chlorophyll a maximum (Yentsch, 1965). The pheopigments have absorption characteristics which are so similar to chlorophyll a in the blue that separation of these pigments with an instrument with as few spectral bands as the CZCS is impossible. Because of this, for CZCS studies it is necessary to consider chlorophyll a and the pheopigments together. The sum of the concentrations of chlorophyll a and the pheopigments will henceforth be called the phytoplankton pigment concentration or just the pigment concentration and will be denoted by C . For Morel case 1 waters it is expected that L, will be determined completely by specifying a,, (bb),, and the function fa, and f bfor C , i.e., spectral variations in L, from location to location will depend mostly on C. This implicitly assumes of course that the concentrations of the various pigments in Fig. 1 which can contribute to the absorption near 440 nm remain constant relative to C . This assumption is not in general valid (Bricaud and Morel, 1981)and the result is a natural inaccuracy or “noise” in the bio-optical algorithms described in Section 4.1 below. Because the solar irradiance backscattered out of the ocean may have actually penetrated to significant depths in the ocean, the relationship between C and the water-leaving radiance should depend on the distribution of the phytoplankton with depth z. Gordon and Clark (1980a) have shown that the pigment concentration ( C ) for an optically homogeneous ocean which would produce the same water-leaving radiance as an optically stratified ocean with pigment concentration C(z) is

310

HOWARD R. GORDON E T A L .

where f(Z) = exp[ - 2

ro

Kd(Z’)

dz’]

(9)

and Kd(Z’)is the attenuation coefficient for downwelling irradiance defined by Kd(Z)

= -d[Ln[Ed(z)l]/dz

(10)

zg0 is the penetration depth defined to be the depth at which Ed falls to l/e of

its value just beneath the surface. (Note that in the case of a constant Kd, zgo = l/&) It is the depth above which 90% of the radiance contributing to L, originates in a homogeneous ocean (Gordon and McCluney, 1975). This indicates that remote determinations of constituent concentrations will be limited to estimations of (C) or, in general, (Ci).For Morel case 1 waters, K,, like a and bb, depends strongly on C; thus it should be possible to estimate K , (and zgo) from remote determinations of C. This is described in Section 5. Schemes for extracting (C) from measurements of &(A) are called bio-optical algorithms. 3.3. Atmospheric and Sea Surface Contributions to L,: L,

In a typical satellite remote-sensing experiment solar irradiance F&) at a wavelength A is incident on the top of the atmosphere at a zenith angle 8, and azimuth &, while the scanner measures a radiance L,(Iz)at a nadir angle 0 and azimuth 4. &(A) consists of radiance which has been scattered by the atmosphere and sea surface, and radiance generated by Fresnel reflection of the direct (unscattered) solar irradiance from the rough ocean surface (sun glint). These radiances are in addition to the desired radiance which has been backscattered out of the water t(A)L,(A). The effect of these radiances on CZCS imagery was initially described by Gordon (1976, 1978), leading directly to an algorithm for atmospheric correction. The first application of this algorithm to CZCS imagery was made by Gordon et al. (1979),in which a detailed derivation of the algorithm based on the single scattering approximation was provided (see also, Viollier et al., 1980;Gordon and Clark, 1980b). In the appendix to a paper by Gordon et al. (1983a) some of the effects of multiple scattering on the algorithm are quantified. This section summarizes the method for the removal of these unwanted radiances on a pixel-by-pixel basis. The interactions within the atmosphere responsible for &(A) consist of scattering by the air (Rayleigh scattering) and by microscopic particles suspended in the air (aerosol scattering). In principle this added radiance could be removed if the concentration and optical properties of the aerosol

8. OCEAN COLOR MEASUREMENTS

311

were known throughout an image. The aerosol, however, is highly variable and, unlike the Rayleigh scattering component, its effect on the imagery cannot be predicted a priori. Thus, only very general aspects of the aerosol properties can be used in estimating its contribution. Gordon (1978) has established that to an approximation sufficient for CZCS processing, the atmosphere-surface radiance L , (A) can be divided into its components: L,(A), the contribution due to Rayleigh scattering; La@),the contribution due to aerosol scattering; i.e., L,

=

L,

+ La

(11)

The error in this expression for most CZCS imagery is less than & 10% of La (Gordon et al., 1983a). Adding tL,, the water-leaving radiance diffusely transmitted (Tanre et al., 1979) to the top of the atmosphere, we have

h(1)=

+ La(A) + t(J+)LwO-)

(12)

It has been implicitly assumed that there is no direct sun glitter in the field of view of the sensor, and that photons reflected from the sea surface (without penetrating) have been included in the first two terms. Since L, can be computed from theory given the extraterrestrial solar irradiance, only La@) must be determined to retrieve L,(A) from &(A), since the diffuse transmittance t(A) can be computed with sufficient accuracy without specific information concerning the aerosol properties (Gordon et al., 1983a; Gordon and Morel, 1983). The key to this determination is the observation (Gordon, 1978) that the spectral variation of La is expected to be nearly independent of position for a given image. Thus, given L,(A) at one position on the image, the quantity

s(410) = La(J-)/L(&)

(13)

can be determined everywhere. Gordon (1981a) has demonstrated directly from CZCS imagery that S(1,no) can in fact remain essentially constant over scales of hundreds of kilometers, even in atmospheres with strong horizontal inhomogeneities in the aerosol radiance. A sufficient condition for a positionindependent S(A, A,) is that the normalized size frequency distribution and refractive index of the aerosol (which define an aerosol “type”) be independent of horizontal position. When the aerosol phase function is approximately independent of wavelength, the single scattering approximation shows that S is related to the optical properties of the aerosol through

w,

10)

=

4,~ 0 ) L - ~ 0 ( ~ ) / ~ 0 @ 0 ) 1

x exp{ -Cz0,(4

- Toz(Ao)l(1/P

+ 1/Po)l

(14)

where zoz is the ozone optical thickness, p and po are, respectively, the cosines of the viewing angle and the solar zenith angle, and €(A, A,) is related to the

3 12

HOWARD R. GORDON E T A L .

aerosol optical thickness za and single scattering albedo oothrough

A,)

= oo(n)za(A)/o,(20)za(A,)

(15)

Note that for a constant aerosol “type” (see above), the dependence of za on I will be position independent; hence, E and Swill also be position independent. Using these relationships, L,(A) is given in terms of Lw(Ao)by

’

Lw(A) = [t(n)I- { LdA) - Lr(2) - s(An o ) x

C W O )

- L,@O) - t(Io)Lw(~o)l)

(16)

Idw(&)z 0

(17)

If lohas the property that then Eq. (16)can be solved directly; however, if such a A, does not exist (as, for example, in case 1 waters with (C) 2 1-2 mg m-3), a further relationship among the various L, values is required. Smith and Wilson (1981)have used an empirical relationship derived by Austin and Petzold (1981)for case 1 waters:

Lw(670)= 0.0829L,(443)[L,(443)/L,(550)]-’*661

(18)

solving the resulting set of nonlinear equations iteratively. In the first comparisons between CZCS-estimated and ship-measured values of pigment concentration (Gordon et al., 1980),S(AJ,) was determined from the water-leaving radiance measured at one position in the image from a ship. It is, however, desirable to be able to determine S(A, A,) without resorting to any surface measurements. This requires estimation of S(A,A,) or, equivalently, €(A,A,) from the satellite imagery itself which can be accomplished using the concept of clear water radiance. Gordon and Clark (1981) have shown that for phytoplankton pigment concentrations less than about 0.25 mg m-3 the water-leaving radiance in the green, yellow, and red CZCS bands can be written

L w ( 4= CL,(IllN cos 00 expC-(Tr/2

+ zoz)/cos 001

(19)

where [L,IN, the normalized water-leaving radiance, is 0.498,0.30, and less than 0.015mW cm-’ pm-’ ster-’ for 520,550, and 670 nm, respectively, and zr is the Rayleigh scattering optical thickness. Thus, if a region for which C < 0.25 mg m-3 can be located in an image, Eqs. (12)and (14)can be used to determine ~(520, 670),~(550,670), and ~(670,670); ~(443, 670)can then be estimated by extrapolation. An important aspect of this algorithm is that no surface measurements of either Lw(I) or any properties of the aerosol are required to effect the atmospheric correction with this scheme.

8. OCEAN COLOR MEASUREMENTS

313

4. REMOTESENSING OF THE PHYTOPLANKTON PIGMENT CONCENTRATION 4.I. Development of the Bio-optical Algorithm

To establish means for the retrieval of pigment concentrations from CZCS imagery a field program was initiated by NOAA/NESDIS in 1975. This program consisted of measurements of vertical profiles of upwelled (traveling toward the zenith) spectral radiance [L,] and pigment concentration along with other optical, physical, and biological parameters of importance for the interpretation of Ocean color remote-sensing data (Gordon and Clark, 1980b; Clark et ~ l . 1980; . Clark, 1981). These measurements were made at over 60 locations shown in Fig. 3. The measurements of chlorophyll Q and its associated pheopigments were made fluorometrically using the technique described by Yentsch and Menzel (1963) with the modifications given by Holm-Hansen et al. (1965). The upwelled spectral radiance measurements were made at 5-nm increments with a submersible radiometer covering a spectral range from 400 to 700 nm. The spectral resolution of the instrument was 4 nm. From the measurements of L,(A, z) the attenuation coefficient of upwelled spectral radiance KL(A)defined by

K d A ) = -dCWL,(A, 4)lldz

(20) was computed, enabling determination of the upwelled spectral radiance just beneath the surface L,(A, 0-) from

L,@, 0-1 = L,,(A,4expC + KL(4zI (21) [Note: E,(O-) in Eq. (2) is given by QL,(O-).] L,(A,O-) was then transmitted through the interface as described by Austin (1974) to yield the water-leaving spectral radiance, Lw(A). An example of four such L, spectra and their associated chlorophyll concentrations is presented in Fig. 4 for case 1 waters. Note that the main effect of increasing the chlorophyll concentration on the color of the ocean is a depression of Lw(A)in the blue region of the spectrum, i.e., a shift in color from blue to green. The actual enhancement of L, in the green at high chlorophyll concentrations is due to scattering by phytoplankton and by their detrital material, which covaries with them. Finally, L,(I) was weighted by the spectral responses of the CZCS (spectral resolution of about 20 nm) to provide (I,,@)), the CZCS-weighted waterleaving radiance. This is the component of the upwelling radiance just above the sea surface which carries information concerning subsurface constituents. For comparison between (L,(A)) and C, the optically weighted pigment concentration (C) in Eqs. (8) and (9) was computed. Since K , in Eq. (9) depends on wavelength one would expect (C) to as well; however, the direct

+

FIG.3. Locations at which the in situ data describedin the text were obtained. Circled locations are believed to meet the criteria for Morel's case 1 waters. Sites marked by unencircled flags are case 2. (From Gordon et al., 1983a.)

8. OCEAN COLOR MEASUREMENTS 10

0.0001

o’ooool

1

2 3

315

4

...........

400 450 500 550 600 650 700 Wovelength (nm)

FIG.4. Water-leavingradiance LJA) for several concentrations of chlorophyll a.

computation (Clark, 1981) of ( C ) from the data acquired at the locations in Fig. 3 shows that (C) is the same in all the visible CZCS spectral bands. Furthermore these computations show that there is no statistical difference between ( C ) and the surface phytoplankton pigment concentration. This indicates no significant variation of C within zgo. In all of the empirical algorithms described below, ( C ) was evaluated at 520 nm. Figure 5a shows the relationship between R(13) = (Lw(443))/(Lw(550)) and the pigment concentration ( C ) for the waters in Fig. 3 believed to qualify as Morel case 1, while Fig. 5b give the same quantities using the data from all of the Fig. 3 locations. The lines on these figures are linear regressions on the data. Note the significantly tighter fit for the case 1 waters, especially for ( C ) > 1 mgm-3. At high pigment concentration (Lw(443)) usually becomes too small to be retrieved from L,(443) with sufficient accuracy to be useful. In to this case it is necessary to employ the ratio R(23) = (Lw(520))/(Lw(550)) extract the pigment concentration. This ratio and the associated regression for all of the data (cases 1 and 2) with C 2 1.5 mgm-3 are shown in Fig. 5c. R(23) is less sensitive than R(13)to variations in ( C ) . Both the ratios R(13)

FIG.5. (a) R(13) regression using only the data acquired from the case 1 (circled) stations in Fig. 3; 0.029 e (C) c 5.4 mg m-3. (b) R(13) regression using all of the stations in Fig. 3; 0.029 < (C) c 77.7 mg m-’. (c) R(23) regression using only the data from the stations in Fig. 3 for which (C) was in the range 1.5-21.3 mg m-j. (From Gordon et al., 1983a).

317

8. OCEAN COLOR MEASUREMENTS

and R(23) (but derived from a much smaller data base) were used in processing the imagery presented by Gordon et al. (1980) yielding two pigment displays foreachscene: onefor(C) < 1mgm-3[R(13)]andonefor(C) > lrngm-, [R(23)]. A similar approach is still being used to process CZCS imagery: the regression line in Fig. 5a being used for (C) < 1.5 mgm-’, and the regression line in Fig. 5c for (C) > 1.5 mgrn-j. The rationale for these choices is that low pigment applications would usually involve mostly case 1 waters, while the higher concentrations are likely to be a mixture of case 1 and case 2 waters. All of the linear regressions shown in these figures are of the form Log(C(i,j)) = LogA(i,j) + B(i, j)LogR(i,j) (22) The values of A, B, r2, the standard error of estimate, s, and the number of samples in the regression, N, for the various algorithms are presented in Table 11. The relative error in (C) is approximately los 1. The specific algorithm now being used by NASA to compute (C) is

-

(C) = (C),

if (C), < 1.5

(C) = (C),

if

(C), > 1.5

but

(C), < 1.5

(C) =(C),

if

(C), > 1.5

and

(C), > 1.5

where (C), is in mgm-3 and (C), and (C), refer to algorithms 1 and 3 in Table 11.

4.2. Application of the Bio-optical Algorithms to CZCS Imagery These bio-optical algorithms have been applied to atmospherically corrected CZCS imagery to determine how well the retrieved values of (C) agree with determinations made at the surface from ships. Ideally, one would like the ship measurements to be carried out simultaneously with the satellite overpass. Since the opportunity for such direct comparisons is rare, most studies thus far involve a comparison between the satellite-derived pigments and the surface pigments measured continuously along the ship’s track within 5 12 hr of the satellite overpass. The first such comparisons were carried out TABLE 11. BIMPTICAL ALGORITHM SUMMARY R(ij)

Case

C- range”

Log A

-B

r2

S

N

~(13) R(13) R(23)

1

1 2 1 +2

0.029-5.4 0.029-71.1 1.5-21.3

+0.053 -0.116 +0.522

1.71 1.33 2.44

0.96 0.91 0.93

0.130 0.223 0.098

35 55 14

+

‘C is in r n g ~ ~ .

318

HOWARD R. GORDON ET AL.

by Gordon et al. (1980) and Smith and Wilson (1981). These suggested that (C) could be retrieved from the imagery to within about a factor of 2. Subsequently, Smith and Baker (1982)and Gordon et al. (1983a)have shown that accuracies of the order of +30% in (C) are possible for Morel case 1 waters. An example of pigment retrievals in the vicinity of a warm core Gulf Stream ring south of Cape Cod is presented in Figs. 6-8 (Gordon et al., 1982). Figures 6 and 7, respectively, show imagery of the derived pigment concentration from orbits 3157 (9 June 1979) and 3171 (10 June 1979). The pigment gray scale for these images is linear and ranges from 0 (white) to 1 (black) mgm-3. Clouds are also black. These images have been resampled

FIG.6. Magnified CZCS image of a warm core ring observed from orbit 3157. The ship traversed the center of this ring about 12 hr afer acquisition of the satellite image. The image has been resampled in a manner such that each pixel has the same area. (From Gordon et al., 1982.)

8. OCEAN COLOR MEASUREMENTS

319

FIG.7. Magnified CZCS image of a warm core ring observed from orbit 3171. The ship traversed the center of this ring about 12 hr before acquisition of the satellite image. The image has been resampled in a manner such that each pixel has the same area. This resampling permits direct visual comparison of the eddy's structure and location between the two images. (From Gordon et a[., 1982.)

and remapped in such a manner that all pixels have the same area. This permits direct visual comparison of the positions of spuctures [i.e., the warm core ring (low chlorophyll) centered at about 39.4"Nand 68.9"W, the north wall of the Gulf Stream (low chlorophyll) in the lower portion of the images, and many other structures of lesser contrast] seen in the two images. Waves on the north wall of the Gulf Stream similar to those observed by MolloChristansen et al. (1981) in thermal imagery are clearly evident, and their motion suggests a speed of approximately 4 knots for the Gulf Stream in these images.

320

HOWARD R. GORDON E T A L . 1.5

1

1

1

1

I

I

I

I

I

I

1

1

,

1.0

E

P

Y

H

P

n

3 Oa5 I-

0.0

Cumulative Distance (km) Rhumblh

FIG.8. Comparison between the ship-measured surface pigments (light line) and the CZCSderived pigments from orbits 3157 (heavy line) and 3171 (broken line). (From Gordon et al., 1982.)

The track of the ship RV Athena 11, which traversed the center of the ring at about 0O:OO hr local time on 10June 1979,is shown as a white line through the ring and surrounding shelf and slope waters. The track is almost perfectly bracketed in time between the two images. Comparison between CZCSderived (heavy line) and ship-measured (light line) pigments for the two orbits is presented in Fig. 8. The pigment concentration was measured while the ship PLATESa1 and a2. An example of CZCS imagery showing warm core Gulf Stream rings in a color-encoded format. Plate a1 is an overview of the Middle Atlantic Bight (25 April 1982) showing an old, moderately high chlorophyll ring (lower left corner) about to rejoin the Gulf Stream, a low chlorophyll ring near 39"N and 72"W, and a third ring undergoing strong interaction with the Gulf Stream. Plate a2 provides a full-resolution image of the central ring and the Gulf Stream, showing the richness in structure in the pigment concentration. The color code for both of these images is provided by the color scale. (Courtesy of 0.Brown, R. Evans, and J. Brown, RSMAS, University of Miami.) PLATES b l and b2. A pair of images from the Gulf of California. Plate b l is for 11 March 1979 and Plate b2 for 22 March 1979. Note the complexity of structure in the pigment concentration within the Gulf and the difference in the intrusion of Pacific Ocean water at the entrance to the Gulf on these two days. (Courtesy of 0.Brown, R.Evans, and J. Brown, RSMAS, University of Miami.)

8. OCEAN COLOR MEASUREMENTS

32 1

was underway using a Turner Designs flow-through fluorometer (Model No. 10) to monitor the in v i m fluorescence of water drawn continuously from about 1.5 m beneath the surface. Periodically, discrete samples were drawn from the same source and extractions performed to determine fluorometrically the concentrations of chlorophyll a and the associated pheopigments, and hence calibrate the in uiuo fluorescence-pigment concentration relationship. Atmospheric correction was effected by applying the techniques, described above, to the Gulf Stream near the bottom of the images. The agreement between the ship and satellite pigment determinations is excellent. The most significant departure between the ship-measured and satellite-estimated pigments occurs at distances greater than 550 km on the distance scale (indicated by the right cross on the ship track on Fig. 7). This occurs in a region where the track is parallel to and close to a significantcolor front. Small errors in the ship or satellite navigation or actual motion of the front in the time between the ship traversal and the satellite overpass could account for some of the disagreement. Beyond about 625 km on the distance scale (indicated by the left cross on the ship track in Fig. 7), pigment retrieval is unreliable due to the presence of a thick haze layer, clearly evident in the uncorrected image. Upon approaching this layer the aerosol radiance at 670 nm increases by a factor of 6 over a distance of 30-40 km. Atmospheric correction could be effected through this layer to an extent that surface structure was evident, but not with sufficient accuracy to produce good pigment retrievals. The CZCS-derived values of the water-leaving radiances were within 10% of those measured at two ship stations (one for each orbit) made along the track at the exact time of the satellite overpass. A second example of CZCS imagery showing warm core rings is provided in a color-encoded format in Plates a1 and a2. Plate a1 is an overview of the Middle Atlantic Bight (25 April 1982) showing an old, moderately high chlorophyll ring (lower left corner) about to rejoin the Gulf Stream, a low chlorophyll ring near 39"N and 72"W, and a third ring undergoing strong interaction with the Gulf Stream. Plate a2 provides a full-resolutionimage of the central ring and the Gulf Stream, showing the richness in structure in the pigment concentration. (Plates bl and b2 show further examples of the CZCS pigment products in the same color-encoded format.) An example of pigment retrieval on the continental shelf at somewhat higher concentrations is provided by Gordon and Morel (1983)in Fig. 9. This track from the Gulf of Mexico is identical to that presented in Gordon et al. (1980), but was reprocessed using the current algorithms. It demonstrates that accuracies 2 30% can be obtained with pigment concentrations as high as 5 mg m-'. Further improvements in the pigment retrieval accuracy over this range ( 0-5 mg m-') are unlikely considering that the inherent error in the bio-optical algorithms for case 1 waters appears to be about f30%.

-

-

322

HOWARD R. GORDON ET AL. 5,

I

I

I

I

I

I

1

Cumulative Distance (km) Rhumbline FIG.9. Comparison between the ship-measured surface pigments (light line) and the CZCSderived pigments (heavy line) from orbit 296 over the Gulf of Mexico. (From Gordon and Morel, 1983.)

Emphasis in algorithm development is now being placed on retrieval techniques for case 2 waters. Preliminary retrievals in the Mississippi Delta have been within a factor of 2 using the algorithms described above. Considering their definition, one cannot foresee the existence of a universal case 2 bio-optical algorithm, since phytoplankton do not dominate the optical properties of such waters. Instead, site-specific algorithms are likely. 5. REMOTE SENSING OF THE DIFFUSE ATTENUATION COEFFICIENT OF WATER

Our knowledge of the worldwide distribution of the optical properties of sea water is indeed limited. Only a few major cruises have taken place wherein optical properties have been measured, The sampling, both in space and in time, has therefore been very restricted. Fortunately, except for the immediate coastal regions and for some limited regions affected by a few major rivers, most of the world's ocean water seems to satisfy the criteria necessary to be classified as case 1 waters. Thus, the departure from pure-water properties seems to be due almost entirely to the presence of various species of phytoplankton and their products of degradation. While the species distribution and concentration change from location to location, the types of planktonic pigments involved are much the same. The optical properties of

8. OCEAN COLOR MEASUREMENTS

323

the ocean, then, are primarily the result of the absorption of the chlorophyll, related accessory pigments, and water, and of the scattering properties of these algal organisms, their detrital products, and water. 5.1. The DifSuse Attenuation CoefJicient

The diffuse attenuation coefficient of ocean water is an example of an optical property that may be inferred from ocean color and, as a consequence, may be remotely sensed. It has been used by Jerlov (1951), Smith and Baker (1978b),and others as a means of classifying ocean waters. It is a property that is of biological significance to a variety of problems associated with the penetration of natural daylight into the ocean and is also an important variable in evaluating the propagation of artificial and/or natural light in sea water for various optical communications, bathymetric, and viewing systems. An attenuation coefficient may be defined for the upwelling and downwelling, radiance or irradiance light fields, i.e.,

K, = -d[Ln Q ] / d z ,

(23)

where Q is any of these quantities. Of particular interest here are the attenuation coefficients corresponding to Ed,E,, and L,: K,, K,, and K , . It is typically observed that these attenuation coefficients are approximately equal to one another, i.e., K , z K, K,.3 The common value of thesecoefficients is denoted by K , the diffuse attenuation coefficient. The diffuse attenuation coefficient is most frequently determined from measurements of the vertical profile of the downwelling irradiance. However, in the development of the algorithms to be used for the remote sensing of K , the values of K associated with the upwelling radiance or irradiance were used for two reasons, both related to the presence of the sun in the downwelling light field. First, the upwelling coefficients are less affected by solar position and depth of measurement and may be, therefore, more nearly associated with the inherent optical properties of the ocean. Second, because of the more gradual change in upwelling radiance with angle and because the upwelling light field is less subject to rapid fluctuations caused by the refraction of sunlight at the moving ocean surface, the measurement of the upwelling light field and, therefore, of the resulting K values tends to be more precise.

’

For K, to be included in this set of near equalities, it is necessary that it be corrected for dependence on the solar zenith angle, i.e.,the measured value of K , must be replaced by cos 8&, where €Jbis related to the solar zenith angle t o through Snell’s law: sin8b = (l/n)sin Oo.

324

HOWARD R. GORDON E T A L .

5.2. Development of the “K” Algorithm The use of remoteksensingtechniquesfor the determination of K implies the existence of a relationship between the sensible upwelling radiances available at the remote sensor and the K. A method developed by Austin and Petzold (1981) uses a relationship between K and the ratio of the water-leaving radiances of two wavelengths and was derived from spectral data obtained by a variety of investigators working in a wide range of ocean locations. The relationship has the form

K(A) - K,(I) = A ( I ) R ( l ,3)’@)

(24)

where K @ ) is the total diffuse attenuation coefficient for the water at wavelength I, and K,@) is the value of the diffuse attenuation coefficient for pure sea water (i.e., sea water containing no suspended or dissolved, scattering, or absorbing material). A(1) and B(1) are empirically derived parameters that relate R(l, 3), the ratio of the water-leaving radiances at the wavelengths of CZCS bands 1and 3, to the residual attenuation coefficient,i.e., that due to the suspended and dissolved material in the water. It should be emphasized that the technique is not restricted to determining the optical properties at specific wavelengths utilized by the remote sensor. It can, in fact, be used at any wavelength for which a suitable relationship can be found between the desired property at that wavelength (in this instance, K) and some combination of the radiances leaving the sea surface at wavelengths measured by the remote sensor. Algorithms have been formed which allow the diffuse attenuation coefficients for 490 and 520 nm to be determined using the ratio of the radiance at 443 and 550 nm as shown in Eq. (24). The values of the parameters K,, A, and B are given in Table IIL4 Figure 10 shows 88 determinations of K(490) plotted against the ratio of upwelling radiances at the sea surface for 443 and 550 nm. The solid curve is a TABLE 111. PARAMETERS FOR K VERSUS R(1,3) RELATIONSHIP OF EQ.(24)

‘

I(nrn)

K,(m-’)

A(m-’)

B

rz

N

490 520

0.022 0.044

0.0883 0.0663

- 1.491 - 1.398

0.901 0.995

88 88

The algorithms for K and the pigment concentration are similar in that they both depend nonlinearly on the water-leaving radiance ratio R(1.3). One can, in fact, relate C and K directly, through their respective algorithms. For case 1 waters, this relationship becomes K(490) = 0.022 + 0.079(C)~.8’’.Such a relationship is of course to be expected, since for case 1 waters, C controls the optical properties.

325

8. OCEAN COLOR MEASUREMENTS 1.o

I

I

I

I

I l l

I

I

I

I ’ l l ”

-

--

1.491

+0.022 88 Points,

-

c

‘E Y

8

0.1

Y -9

t1 1

b

t 0.01

.

REASEARCHER GYRE

NEW HORIZON 5 OCEANUS 5 DAVIDSTAR JORDAN 2 SCOR DISCOVERER 10 CINECA V- CHARCOT 18 HARMATTAN 9 NOAAINESS (CLARK) 19 JAPANESE ISLANDS 3

88 I

I

I

0.901

r2 =

.. TOTAL POINTS USED FOR ALGOflITHM

I I I l l

I

I

I

I

I I I I I

plot of Eq.(24) as determined by a least-squares fit to these points. Figure 11 provides a comparison between K,, the attenuation coefficient as determined from in situ measurements, and K,,the values of these coefficients computed from the empirically determined relationship between K and R(1,3). The comparison provides a breakdown of the relative (KJK,) and absolute (K,- K,) agreement between the values derived from the relationship K,, and the various components of the data base from which the relationship was derived, K,. The breakdowns are by investigator, cruise, and size of K (i.e., less than or greater than 0.1 m-l), and are provided for both K(490) and K(520).In the figure, 3 is used to indicate the mean of the determinations of

326

HOWARD R. GORDON E T A L .

h

2 0 ADDITIONAL POINTS FROM ABOVE SOURCES

@

KJ490)

-

0.0883

[:;I

88 points, r * = 0.901

i s

88

TOTAL DATASET USED TO FORM ALGORITHM

I

-

z I

I -I X

I

S

h = 520nm

490nm

0.983 0.201

-0.002 0.025

K t0.1rn” I43 Points)

P= s=

0.961 0.145

-0.003 0.007

K>O.lm-’ (45Poinls)

ii= s

1.025 0.113

0.004 0.021

-

s

0.016

I

I

11

1.003 0.108

I

Xs =

0.976 0.147

-0.002 0.013

I

-0.007 0.022

K(0.1 rn-’ X = ( 4 6 Points) s =

1.016 0,103

0.000 0.007

-

0.989 0,112

-0.004 0.017

K)Olm~’X. ( 4 2 Points) s

-1,491

+ 0.022

-

X=

88

0,001

0,994 0.133

K,(520)

E

[:‘;=I

-1.398

0.0663 -

88 p o i n t s , r 2 = 0.995

Km values obtained from measured Lu or Eu i n upper attenuation length

FIG.11. Summary of K, versus K,. (From Austin and Petzold, 1981.)

+ 0.044

8. OCEAN COLOR MEASUREMENTS

327

K, (490) [m-’l

FIG.12. Comparison between the measured diffuse attenuation (K,) and that computed (K,) from Eq. (24) and the data in Table IV. (From Austin and Petzold, 1981.)

K J K , or K , - K, and s indicates the standard deviation. Figure 12 is a scatter diagram of K, versus K, for 490 nm. The solid line represents the ideal case where K , = K,.

5.3. Application of the “ K ” Algorithm to CZCS Imagery

The data used in forming the algorithm were the result of in situ measurements. As a consequence, the relationships which were developed require a knowledge of the upwelling spectral radiance backscattered out of the ocean in order to obtain an estimate of the near-surface K. The technique for deriving LJA), the water-leaving radiance, from &(A), the apparent spectral radiance at the satellite, has been described in Section 3. The proper accounting for the effects of scattering and absorption in the atmosphere and their effective removal to obtain L,(A) from Lt(A)is equally as important for the estimation of K as for the estimation of pigment concentration.

328

HOWARD R. GORDON ET AL.

A variety of surface validation cruises have been conducted in the Gulf of Mexico and the Atlantic and Pacific oceans. These campaigns were thoroughly instrumented with equipment for the measurement of the optical, physical, and biological conditions of the ocean and the pertinent optical properties of the atmosphere (Austin, 1980). Oceanographic stations were selected to provide data beneath Nimbus-7 in the footprint of the CZCS. Figure 13 compares the values of K measured on these cruises with the values obtained from the CZCS images for the station locations using the in-water K algorithm and the atmospherically corrected water radiances. The measured values of K were determined for the surface waters to the first optical depth, i.e., 1/K. For a homogeneous ocean, this is the depth above 1.0 NEW HORIZON (Vis Lab) GYRE (Vis Lab) m CLARK (NOAAINESS]

0

A

A = 490nm

0.1

0.01 0.01

0.1 K

1.o

MEASURED (m-1)

FIG.13. Comparison of K values derived from CZCS observationswith K values determined from surface-station measurements. The solid points represent cases in which the satellite overpass was coincident with the surface measurements. The open points refer to cases in which the overpass and surface measurements occurred on different days. (From Austin, 1981.)

329

8. OCEAN COLOR MEASUREMENTS

which about 90% of radiance contributing to L,(A) originates: zgo. As described in Section 3.2, this thickness of water may be considered the effective remote-sensing penetration depth. The excellent agreement between the measured and calculated K values as shown in Fig. 13 provides strong evidence that the in-water and atmospheric algorithms are satisfactory. Many applications, however, require a knowlege of the attenuation coefficient to depths significantly in excess of the first optical depth, and the value of remotely sensed K parameters would be abridged if the attenuation properties below this surface layer were completely independent of the surface values. Figure 14 shows a regression of data obtained on 12 stations on a cruise from Japan to Seattle, Washington, in July 1980. The average or effective K over the upper 100 m [K(O-100 m)] has been plotted against the average K over the first attenuation length [K(O-l/K m)], i.e., the first optical depth. While this relatively small sample cannot be construed as providing a definitive relationship, it would appear reasonable to presume that the remotely sensed K values can be used to infer the average value of the diffuse attenuation coefficient over depths that are significantly greater than the effectiveremote sensing depth. Note that in the more turbid waters the surface ,121

1

c

I

I

1

I

1

I

I

I

I

I,1

I

I

I

I

I

1

1

I

1 1

/

.02 -

-

/

-

1

/

/

-

/

O V I

1

1

I

I

I

I

1

1

I

I

I

I

I

I

I

'

'

I

330

HOWARD R. GORDON ETAL.

K values are greater than the effective K to 100 m and that for K less than about 0.058 m-l the surface values are less than the 0- to 100-m K. This is in agreement with the observation that high-turbidity water is usually located in, or at the bottom of, a relatively thin mixed layer above the thermocline. In clearer oligotrophic water, however, the location of the turbidity maximum is generally found beneath a layer of clearer surface water. When this maximum occurs at depths greater than l/K,,,, K(0-100 m) will be greater than R(O-l/K m). Figure 15 is an image depicting the distribution of the diffuse attenuation coefficient at 490 nm off the coast of southern California. The land areas and several small clouds are shown in black. The gray scale at the bottom shows the representation of K values from 0.03 to 0.085 m-'. The image was formed from atmospherically corrected water radiances using Eq. (24) and the

FIG.15. Image of K(490)off the coast of southernCaliforniaprocessed from the atmospherically corrected water radiances using Eq. (24) at each pixel.

8. OCEAN COLOR MEASUREMENTS

331

parameters in Table 111. This image, consisting of 512 lines of 512 picture elements, covers approximately 150,000 km2 of ocean. The power of the remote-sensing technique is obvious when one considers the detail and synopticity of the information presented in the image. 6. SUMMARY AND CONCLUSIONS The techniques for the extraction of the phytoplankton pigment concentration (the sum of the concentrations of chlorophyll a and pheophytin a) and the diffuseattenuation coefficient (K) of the water, from CZCS observations of the “apparent” color of the ocean, have been described in detail. It is shown that under typical atmospheric conditions the pigment concentration can be extracted from the satellite imagery to within about f30% over concentration ranges from 0 to 5 mgm-3 for Morel case 1 water. For these waters, the simultaneous measurement of K provides an indication of the depth in the water ( l / K ) over which the CZCS “weighs” the pigment concentration. Oceanic waters as a rule are case 1; however, case 1 waters can also be found in coastal areas (in the absence of terrigenous influx) for waters of sufficient depth that resuspension of bottom sediment through vertical mixing is inhibited by stratification. Thus patterns of pigment concentration for the open ocean and outer continental shelf can be obtained from CZCS-type imagery, providing the potential for a near-global assessment of these pigments. Also, within the constraints imposed by the satellite’s orbit and by cloud coverage, repetitive imagery can be obtained providing synoptic time series of the pigment concentration with a maximum frequency of one image per day (i.e., Figs. 6 and 7). Furthermore, in the case of the present CZCS, simultaneous and precisely registered thermal infrared imagery is available from the same sensor, allowing direct comparison between color and thermal imagery, Thus, it appears that CZCS-type imagery can meet the principal justifications for the large-scale measurement of phytoplankton discussed in Section 1-to provide a global assessment of pigment concentration, and to isolate and study the physical mechanisms responsible for driving the biological processes, as revealed through synoptic time series. Another important application of such imagery, fisheries research and management, is discussed in detail in Chapter 13. ACKNOWLEDGMENTS This research received support from NASA (NAS 5-22963 and NAGW-273, H.R.G.; NAS 526247, R.W.A.; NAS 5-22948, C.S.Y.), NOAA/NESS (NA79SA00741, H.R.G.; NA8OAA-D00007, R.W.A.),and ONR (N00014-78-C-0566, R.W.A.).

332

HOWARD R. GORDON E T A L .

REFERENCES Arvesen, J. C., Millard, J. P., and Weaver, E. C. (1973). Remote sensing of chlorophyll and temperature in marine and fresh waters. Astronaut. Acta 18,229-239. Austin, R. W. (1974). The remote sensing of spectral radiance from below the ocean surface. In “Optical Aspects of Oceanogaphy”(N. G. Jerlov and E. S. Nielsen, eds.), Chap. 14, pp. 317344. Academic Press, New York. Austin, R.W . (1980). Gulf of Mexico, ocean-color surface-truth measurements. Bound. Layer Meteorol. 18,269-285. Austin, R. W.(1981). Remote sensing of the diffuse attenuation coefficient of ocean water. Special Top. Opt. Propag. AGARD Conf. Reprint No. 300. 7 Rue Ancelle, 92200 Neuilly sur Seine, France. Austin, R. W., and Petzold, T. J.(I981). Thedetermination of thediffuse attenuation coefficient of sea water using the coastal zone color scanner. In “Oceanography from Space” (J. R. F. Gower, ed.), pp. 239-256. Plenum, New York. Bricaud, A., and Morel, A. (1981). Possible variations in the specific absorption by phytoplankton as a result of the discreteness effect and change in pigment composition. I A M A P Sci. Assembly, 3rd, Hamburg. Clark, D. K., (1981). Phytoplankton algorithms for the Nimbus-7 CZCS. In “Oceanography from Space” (J. R. F. Gower, ed.), pp. 227-238. Plenum, New York. Clark, D. K., Baker, E. T., and Strong, A. E. (1980). Upwelled spectral radiance distribution in relation to particulate matter in sea water. Bound. Layer Meteorol. 18,287-298. Clarke, G. K., Ewing, G. C., and Lorenzen, C. J. (1970). Spectra of backscattered light from the sea obtained from aircraft as a measure of chlorophyll concentration. Science 167, 1119-1121. Gordon, H. R. (1976). Radiative transfer: A technique for simulating the ocean in satellite remote sensmg calculations. Appl. Opt. 15, 1974- 1979. Gordon, H. R. (1978). Removal of atmospheric effectsfrom satellite imagery of the oceans. Appl. Opt. 17,1631-1636. Gordon, H. R. (1981a). A preliminary assessment of the Nimbus-7 CZCS atmospheric correction algorithm in a horizontally inhomogeneous atmosphere. In “Oceanography from Space” (J. R. F. Gower, ed.),pp. 257-266. Plenum, New York. Gordon, H. R. (1981b). Reduction of error introduced in the processing of coastal zone color scanner-type imagery resulting from sensor calibration and solar irradiance uncertainty. Appl. Opt. 20,207-210. Gordon, H. R., and Clark, D. K. (1980a). Remote sensing optical properties of a stratified ocean: an improved interpretation. Appl. Opt. 19,3428-3430. Gordon, H. R., and Clark, D. K. (1980b). Atmospheric effects in the remote sensing of phytoplankton pigments. Bound. Layer Meteorol. 18,299-313. Gordon, H. R., and Clark, D. K. (1981). Clear water radiances for atmospheric correction of coastal zone color scanner imagery. Appl. Opt. 20,4175-4180. Gordon, H.R.,and McCluney, W. R. (1975). Estimation of the depth of sunlight penetration in the sea for remote sensing. Appl. Opt. 14,413-416. Gordon, H. R., and Morel, A. Y. (1983). “Remote Assessment of Ocean Color for Interpretation of Satellite Visible Imagery: A Review.” Springer-Verlag, Berlin and New York. Gordon, H. R., Brown, 0. B., and Jacobs, M. M (1975). Computed relationships between the inherent and apparent optical properties of a flat homogeneous ocean. Appl. Opt. 14,417427. Gordon, H. R., Mueller, J. L., and Wrigley, R. C. (1979). Atmospheric correction of Nimbus-7 coastal zone color scanner imagery. Presented at IFAORS Workshop on ‘Interpretation of

8. OCEAN COLOR MEASUREMENTS

333

Remotely Sensed Data,’ Williamsburg, Virginia, May 23-25; also In “Remote Sensing of Academic . Press, New York, 1980. Oceans and Atmospheres”(A. Deepak, 4.) Gordon, H. R., Clark, D. K.,Mueller, J. L., and Hovis, W.A. (1980). Phytoplankton pigments derived from the Nimbus-7 CZCS: initial comparisons with surface measurements. Science 210,63-66. Gordon, H. R., Clark, D. K., Brown, J. W., Brown, 0. B., and Evans, R. H. (1982). Satellite measurement of the phytoplankton pigment concentration in the surface waters of a warm core Gulf Stream ring. J. Mar. Res. 40,491-502. Gordon, H. R., Clark, D. K., Brown, J. W., Brown, 0. B., Evans, R. H., and Broenkow, W. W. (1983a). Phytoplankton pigment concentrationsin the Middle Atlantic Bight: Comparisons of ship determinationsand satelliteestimation. Appl. Opt. 22,20-36. Gordon, H. R., Brown, J. W., Brown, 0.B., Evans, R. H., and Clark, D. K. (1983b). Nimbus-7 CZCS: Reduction of its radiometric sensitivity with time. Appl. Opt. 22, 3929-3931. Holm-Hansen, 0. G., Lorenzen, G. J., Holmes, R. W., and Strickland, J. D. H. (1965). Fluorometric Determination of Chlorophyll. J. Cons. Perm. Int. Explor. Mer. 30,3-15. Hovis, W. A., Clark, D. K., Anderson, F., Austin, R. W., Wilson, W. H., Baker, E. T., Ball, D., Gordon, H. R., Mueller, J. L., El Sayed, S. Y.,Sturm, B., Wrigley, R.C., and Yentsch, C. S. (1980). Nimbus-7 coastal zone color scanner: System description and initial imagery. Science 210,60-63. Mollo-Christansen,E., Cornillon,P., and Mascarenha, A. Da S., Jr. (1981). Method for estimation of ocean current velocity from satellite images. Science 212,661-662. Morel, A. (1980). In-water and remote measureinent of ocean color. Bound. Layer Meteorol. 18, 177-201. Morel, A,, and Gordon, H. R. (1980). Report of the working group on water color. Bound. Layer Meteorol. 18,343-355. Morel, A., and Prieur, L.(1977). Analysis of variations in ocean color. Limnol. Oceanogr. 22, 709-722. Platt, T., Denman, K. L., and Jassby, A, D. (1977). Modeling the productivity of phytoplankton. In “The Sea” (E. D. Goldberg, I. N. McCave, J. J. OBrien, and J. H. Steele, eds.), vol. 6, Ch. 21, pp. 807-856. Wiley, New York. Ruttenberg, S. (1981). Needs, opportunities, and strategies for a long-term oceanic sciences satellite program. NCAR Tech. Note, NCAR/TN-185 +PPR, p. 72. Smith, R.C., and Baker, K.S. (1978a). The bio-optical state of ocean waters and remote sensing. Limnol, Oceanogr. 23,247-259. Smith, R. C., and Baker, K.S. (1978b). Optical classificationof ocean waters. Limnol. Oceanogr. 23,260-267. Smith, R. C., and Baker, K. S. (1982). Oceanic chlorophyll concentrations as determined using Nimbus-7 Coastal Zone Color Scanner imagery. J . Mar. Biol. 66,269-279. Smith, R. C.,and Wilson, W. H. (1981). Ship and satellite bio-optical research in the California Bight. In “Oceanography from Space” (J. R. F. Gower, ed.), pp. 281-294. Plenum, New York. Tanre, D., Herman, M., Deschamps, P. Y., and de Leffe, A. (1979). Atmospheric modeling for space measurements of ground reflectances, including bidirectional properties. Appl. Opt. 18,3587-3594. Viollier, M., Tanre, D., and Deschamps, P. Y.(1980). An algorithm for remote sensing of water color from space. Bound. Layer Meteorol. 18,247-267. Yentsch, C. S. (1965). Relationship between chlorophyll and photosynthetic carbon production with reference to the measurement of decomposition products of chloroplastic pigments. Mem. 1st. Ital. Idrobiol. 18 (Suppl.), 323-346. Ymtsch, C. S., and Menzel, D. W. (1963). A method for the determination of phytoplankton chlorophyll and phaeophytin by fluorescence. Deep Sea Res. 10,221-231.

This Page Intentionally Left Blank

OBSERVATIONS OF THE POLAR REGIONS FROM SATELLITES USING ACTIVE AND PASSIVE MICROWAVE TECHNIQUES C. T . SWIFT

W . J . CAMPBELL

Department of Electrical and Computer Engineering University of Massachusetts Amherst Massachusetts

United States GeologicalSurvey University of Puget Sound Tacoma. Washington

.

D. J . CAVALIERI. P. GLOERSEN. AND H . J . ZWALLY

L. S. FEDOR

National Aeronautics and Space Administration Goddard Space FIight Center Greenbelt. Maryland

National Oceanic and Atmospheric Administration Wave Propagation Laboratory Environmental Research Laboratories Boulder. Colorado

N . M . MOGNARD

S. PETEHERYCH

Groupe de Recherche de Geodesic Spa!iales Centre Nacional &eludes Spatiales Toulouse. Cedex France

Atmospheric Environment Service Downsview Ontario. Canada

.

.

1. Introduction . . . . . . . . . . . . . 2. Sea-Ice Observations by Seasat: A Case Study . 2.1. Meteorological and Surface Observations . 2.2. Seasat SAR Observations. . . . . . . 2.3. Seasat SASS and SMMR Observations . . 2.4. Altimeter Observations . . . . . . . 2.5. Ice-Edge Observations. . . . . . . . 2.6. Discussion and Conclusions . . . . . . 3. Sea-Iceobservations by Nimbus-7 . . . . . 3.1. Introduction . . . . . . . . . . . 3.2. Nimbus-7 SMMR Sea-Ice Algorithm. . . 3.3. Sample Calculations . . . . . . . . 3.4. Conclusions . . . . . . . . . . . 4. Observations of Ocean Waves in the Antarctic . 4.1. Introduction . . . . . . . . . . . 4.2. The Analysis Procedures . . . . . . . 4.3. Results . . . . . . . . . . . . . 4.4. Conclusions . . . . . . . . . . . 5. Seasat Altimeter Observations of Ice Sheets . 5.1. Introduction . . . . . . . . . . . 5.2. The Tracking Algorithm over Ice Sheets . 5.3. Analysis of Data for Greenland Overflights References . . . . . . . . . . . . .

.

335 ADVANCES IN GEOPHYSICS. VOLUME 27

. . . . . . . . . . . . . 336

. . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . .

. . . . . . . . . . . . .

. . . . . . . . . . . . .

. . . . . . . . . . . . .

. . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . .

337 337 339 351 357 365 366 367 367 369 311 373 373 373 314 315 378 379 319 381 387 390

.

Copyright@ 1985 by Academic Press Inc. All rights of reproduction in any form reserved. ISBN 0-12-018827-9

336

C. T. SWIFT ET AL.

1. INTRODUCTION

In the late 1960s, scientists began an intensive program in the microwave remote sensing of sea ice, ice sheets, and snow. This program was prompted by the realization that the kinds and amount of cryospheric data yielded by conventional field programs and the visible and infrared imagery acquired by the meteorological satellites, such as that from the TIROS, Nimbus, and NOAA satellite programs, were limited in providing the sequential synoptic data needed for numerical modeling of key phenomena. Thus, scientists explored the use of microwave sensors, both on board aircraft and satellites, since they provided a means to penetrate the atmosphere. With the exception of snow, cryospheric processes occur in areas of our planet that are dark and/or cloud covered most of each year and in which surface observations are difficult and expensive to perform. Furthermore, many of these processes undergo large-space-scale variations at short time scales; thus microwave remote sensing appeared to be a promising way in which to obtain the sequential data at the appropriate time and space scales for a wide variety of applications. Therefore, the decade preceding the launch of Seasat was one in which the possibility of an all-weather day-or-night capability of observing the cryosphere was explored. Every satellite microwave experiment preceding Seasat, i.e., the Electronically Scanning Microwave Radiometer (ESMR) flown on Nimbus-5 and -6, the active and passive microwave instruments flown in Skylab, and the radar altimeter flown on GEOS-3, was accompanied by field programs to acquire the surface truth data needed to interpret the satellitedata properly. There were many minor and three major international expeditions preceding Seasat which included coordinated surface, aircraft, and satellite observation programs exploring both active and passive ice remote-sensing techniques. The major expeditions which focused on sea ice were (1) AIDJEX (Arctic Ice Dynamics Joint Experiment), Spring 1971 and 1972, and Spring 1975 through Spring 1976; (2) BESEX (Joint U.S./USSR Bering Sea Experiment), Spring 1974; and (3) Skylab Snow and Ice Experiments, Winter and Spring 1973 and 1974. These experiments confirmed that surface truth observations, both in situ and by aircraft microwave remote sensing, were needed for the correct interpretation and application of the complex satellitemicrowave data. Thus, the 2 years preceding the launch of Seasat were spent preparing experiments for acquiring simultaneous active and passive microwave data for a wide variety of cryospheric phenomena. Sea-ice microwave experiments were planned for the Bering, Chukchi, Beaufort, Greenland, and Norwegian Seas, and for Baffin Bay and the Gulf of Bothnia. Ice-sheet experiments were planned for Greenland. Snow experiments were planned for the United

9. POLAR REGION SATELLITE MICROWAVE OBSERVATIONS

337

States, Canada, Norway, Finland, and Switzerland. Unfortunately, the life of Seasat occurred during the Northern Hemisphere summer, and since all of the planned field experiments were in the Arctic and were scheduled to commence in the early Winter of 1978, essentially no surface/aircraft/satellite data ensemble was acquired. Because of the absence of the surface/aircraft/ satellite data sets that had proven valuable in earlier ice microwave analyses, it was with some caution that work began on the analysis of the Seasat ice data. However, as the analysis proceeded, as it was found that the combined active and passive microwave data signatures could be used in a complementary manner to draw conclusions, which in the past required surface truth supporting data. This was especially the case for the sea-ice data acquired in the Beaufort Sea during the repeat-orbit mode of Seasat operations. The following sections present an analysis of selected key parts of the full Seasat ice data set. Most of the key cryospheric capabilities of the microwave instrument ensemble which it was hoped to demonstrate with Seasat have been accomplished. These include the following criteria: 1. Sequential satellite SAR observations can be used to measure sea-ice kinematics to an accuracy hitherto attainable only by using manned drift stations equipped with Navsat gear. 2. Combined active (SASS, SAR) and passive (SMMR) observations of sea ice give self-consistent signatures which can be used to discriminate between sea-ice type with greater confidence than when used separately. 3. Radar altimeter observations of glacial ice sheets can be used to map icesheet topography to hitherto unattainable accuracies. 4. Radar altimeter observations can be used to measure waves and surface wind speeds in the marginal ice zones.

2. SEA-ICE OBSERVATIONS BY SEASAT: A CASESTUDY 2.1. Meteorological and Surface Observations

The sequence of meteorological charts shown in Fig. 1 defines the general area selected for analysis during the first week of October 1978. The analysis of the remote-sensing data is restricted to the x e a of the Beaufort Sea bounded by Banks Island, Canada, and Point Barrow, Alaska. By coincidence, a low-pressure cell developed to the south during the week and interacted with the ice canopy to add interest to the presentation of the remote-sensing results. The sequence of isobars and wind observations shown

9. POLAR REGION SATELLITE MICROWAVE OBSERVATIONS

339

in Fig. la-f clearly indicates that a change in weather conditions occurred over the time frame that is considered. On October 2, 1978 (Fig. la), the distribution of isobars indicates low-pressure gradients, and therefore low winds, as evidenced by the 10- to 20-knot wind-speed observations. In that time period extending from October 5 to October 8, a high-pressure cell developed over Point Barrow, again producing weak pressure gradients and low wind speeds. The high dissipated on October 6, and the pressure gradients to the southwest indicate the movement of a low to the south and west of the area defined by Fig. Id. The final two figures in the sequence (Fig. l e and f) show that the low dominates the weather on October 7-8 in the southern Beaufort Sea, as evidenced by the large pressure gradients and 30-knot winds from the northeast. The passage of this low-pressure cell profoundly influenced the iceedge characteristics, as will be discussed. Ice charts, as derived from Canadian aircraft and surface observations, are shown in Figs. 2a-c for September 28, October 5, and October 12. The first numbers in the keys denote the estimated percentage of multiyear ice and the remaining numbers categorize various second- and first-year ice forms (MANICE, 1980). The charts suggest a general growth of the ice edge over this 2-week time period. It is of interest to note that the western ice edge retreated during the week between October 5 and October 12. This occurred as a result of the rough seas and high winds generated by the storm that passed over the area on October 7 (see Fig. 1). A NOAA satellite visible image of the immediate vicinity of Banks Island is shown in Fig. 3. This cloud-free image was taken at 18:35 GMT on September 2, 1978. It is of interest to note that the image shown here represents a survey of the conditions that existed a full month before the time frame selected for the analysis of the microwave remote-sensing data. The geographical area of interest from the remote-sensing point of view is shown in Figs. 4a and b. Shown in Fig. 4a are the areas bounded by the SMMR swath for repeat orbits 1409and 1452. The area occupied by the SAR defines a region of intercomparison of data collected by three of the four microwave sensors. These two particular orbits match the 3-day cyclical nature of the repeat coverage by Seasat. To further quantify the ice kinematics, repeat orbits 1395,1438, and 1481 were chosen for analysis. The selection of these five orbits permitted observations over a l-week period as indicated in Table I.

2.2 Seasat SAR Observations During the decade preceding the launch of Seasat, a variety of SAR and SLAR (side-looking airborne radar) aircraft remote-sensing flights were flown over Arctic sea ice (Campbell et al., 1975; Page and Ramseier, 1975; Anderson,

FIG.2. Canadian ice charts for the Beaufort Sea area for (a) September 28, (b) October 5, and (c)October 12, 1978.

9. POLAR REGION SATELLITE MICROWAVE OBSERVATIONS

343

FIG. 3. GOES image showing the ice coverage for September 2, 1978. Cloud coverage precluded ice imagery during the first week of October.

TABLE I. SEASAT ORBIT IDENTIFICATIONFOR ICE OBSERVATIONS ORBIT NUMBER

DATE

REPEATSEQUENCE ~

I395 1409 1438 1452 1481

OCTOBER 2, I978 OCTOBER 3, I978 OCTOBER 5, 1978 OCTOBER 6, 1978 OCTOBER 8, 1978

FIG.4. Seasat SASS, SMMR,and SAR data swaths for the week of October 2 to October 8,1978. (a) Swaths for orbits 1409 and 1452; (b) swaths for orbits 1395, 1438, and 1481.

345

346

C. T. SWIFT ET A L .

1966; Johnson and Farmer, 1971; Parashar et al., 1974, 1976; Dunbar and Weeks, 1975; Morra and de Loor, 1976; Bryan, 1976; Campbell ef al., 1978). The most extensive set of aircraft SAR images of sea ice were those obtained during the main AIDJEX 1975-1976. A mesoscale area (- lo4km2)of sea ice containing the manned AIDJEX drift stations was imaged repetitively at time scales ranging from several days to months. A detailed analysis of the AIDJEX sequential mesoscale SAR images is given in Leberl et al. (1979). This was the first study of sea-ice kinematics and deformation of a mesoscale area of sea ice via aircraft SAR. In this study it was found that numerous homolog ice features could be identified as reference points in sequential sets of radar mosaics. Absolute ice-drift accuracieswere essentially limited by the accuracy of the inertial navigation system on board the CV-990. Errors of drift measurements were found to be about k2.5 km. The relative accuracy, however, was much higher because its limit was set by the radar image geometry and the definition of identical features in successive images. The drift of ice features with respect to one another was determined with errors less than k0.2 km. This study showed the sequential SAR imagery of sea ice could be used to acquire ice kinematics data of the accuracy needed for ice modeling and forecasting. However, it soon became apparent that aircraft were logistically incapable of acquiring the synoptic-scale, high-resolution, sequential imagery needed by the modelers-the time and space scales were beyond aircraft capability. The short lifetime of Seasat (June 26-October 10, 1978)was compensated for by the quality of the imagery acquired by the SAR. The L-band (1.27 GHz) SAR, flown on the starboard side of the spacecraft, imaged a 100-km swath 20.5" off nadir at a ground resolution of 25 m. The length of continuous imaging was limited by the diameter of the receiving-station viewing mask, thus producing about 10 min of data, which equals 4200 km on the ground. From the sea-ice point of view, it was indeed fortunate that one of the five SAR readout stations was in Fairbanks, Alaska. The viewing mask of the Fairbanks station covered the entire southern Beaufort Sea up to a latitude of 75"N.This area was overflown frequently by the satellite because it was at the northern limit of the satellite orbit. In an effort to achieve maximum sequential SAR coverage of the area, the SAR experiment team decided that on all orbits in which time was remaining in the SAR duty cycle the SAR be turned on over the Beaufort Sea before readout immediately thereafter in Fairbanks. Because of these factors, the Beaufort Sea is the area of the earth with the most Seasat SAR coverage. Indeed, of the more than 400 SAR runs FIG.5. Seasat SAR images for orbits 1409 and 1452.

9. POLAR REGION SATELLITE MICROWAVE OBSERVATIONS

347

performed by Seasat, approximately one-third cover the Beaufort Sea ice. The optimism regarding the use of the Seasat SAR for sea-ice studies was well justified, Teleki et al. (1979), in an overview discussing the first SAR images, show that the main morphological features of sea ice can be clearly discerned: leads, polynyas, pressure ridges, shore-fast ice, multiyear floes, and mixtures of multiyear and first-year floes. In sequential SAR images of the same area of sea ice there is no difficulty in identifying homolog ice features. Indeed, this is easier to do in the sequential Seasat SAR images than in any sequential aircraft SAR or SLAR images. This may be because the high incidence-angle beam from the Seasat SAR (20.5") provides better ice imagery than the almost glancing beam from aircraft radars. The theoretical resolution of the Seasat SAR is approximately 25 m. The actual resolution in an image produced from a SAR computer-compatible tape (CCT) depends on how the image is processed. The sea-ice images given in Teleki et al. (1979) were optically processed, and each pixel element is approximately 80m across. Most of the early Seasat SAR imagery was optically processed because that was the only process available. About a year after Seasat failed, digital processing for selected images became available. This processing technique is more expensive than optical processing, but it has the great advantage of yielding an image in which pixel resolution approaches the theoretical resolution of the SAR. The first digitally processed Seasat SAR image of sea ice was published in the NASA ICEX Report (1979), where it is compared to an optically processed image. The pixel resolution in this image is approximately 25 m. It is this fine-resolution capability of satellite SAR that makes it the best known tool for observing detailed sea-ice kinematics at various space and time scales, since many of the ice dynamics models demand input data with spatial resolutions in the order of 25-100 m. Figure 5 shows Seasat SAR mosaic images for the same area of the Beaufort Sea obtained on the repeat orbits 1409 and 1452. The area covered, shown in the map in Fig. 4a, extends cross the entire southern Beaufort Sea from Banks Island, Canada, to Point Barrow, Alaska. These are optically processed images produced at JPL with a pixel resolution of approximately 100m. Similar images for the area shown in Fig. 4b obtained during the repeat orbits 1395,1438,and 1481 were used in the present analysis but are not shown. The seven Seasat SAR images used in the study of sea-ice motion by Leberl et al. (1981) are for the same area. These Seasat SAR images give a unique high-resolution synoptic view of the structure of the sea ice cover. All the main morphological features of summer sea ice can be discerned in these images. In the five mosaic images used in this study numerous homolog ice features could be identified, as was also found in Leberl et al. (1981). From the ice-dynamics point of view, this is extremely important since the ice motion can be deduced from sequential images of the

348

C.T.SWIFT E T A L .

same area only if a sufficient number of homolog features can be clearly located in each image. In the Seasat SAR mosaic images shown in Fig. 5, the varying morphology of the summer Beaufort Sea ice pack can clearly be seen. Large multiyear floes dominate in the east, decreasing in size as one scans westward, with predominantly new first-year ice in the west. The shore leads at Banks Island and Point Barrow are easily discernible since the radar image patterns from the capillary and small gravity waves in the leads are very different from those of the ice. From the sea-ice-dynamics point of view, it is important to establish accuracy bounds on the capability of using sequential sets of both optically and digitally processed Seasat SAR images to measure ice motion. Two recent studies presented at the Seasat symposium held at the Spring 1981 meeting of the American Geophysical Union (AGU) have done this. Leberl et al. (1981) used seven optically processed Seasat SAR images acquired during the repeat mode for the area shown in Fig. 4. Using standard satellite radargrammetric techniques, summarized in Leberl and Clerici (1981),and using ground control points on Banks and Victoria Islands, they mapped sea-icelocation for motion studies across the entire southern Beaufort Sea to an accuracy of f0.5 km. This accuracy resulted because the control points were at the end of an image covering a swath of approximately lo00km along track. They found that where a control point existed for an area of 10,000km2, the accuracy improved to f150 m. With seven control points in lo00 km', the location accuracy reduces to the image resolution, that is, f25 m. Figure 6 shows a typical ice-velocity vector field obtained in this study. It covers the period September 28,1978 (orbit 1339), to October 1,1978 (orbit 1382). These data show that significant variations in ice velocity and direction occur at the mesoscale level. In the eastern part of the image area the ice moved eastward toward Banks Island, and in the western part the ice moved southward toward Point Barrow at about twice the speed of the ice in the east. To illustrate how complex the mesoscale behavior of sea ice can be, Fig. 7 shows the ice kinematics which occurred in the study area during the period September 7, 1978 (orbit 1031), to October 7, 1978 (orbit 1468), as deduced from the seven Seasat SAR image mosaics mentioned earlier (Leberl et al., 1981).These orbits are not the same as the ones discussed earlier. The reason is that the optical processing of the SAR data did not occur chronologically and the seven orbits chosen were the first of a set, whereas the choice of the other orbits (1395-1481)was dictated by the availability of not only SAR data but of SMMR,SASS, and altimeter data as well. However, all orbits are sufficiently close in time so that the ice kinematics of the study area is accurately given in Fig. 7.

60.0

-20.0

-100.0

FIG.6. Ice motion between Days 271 and 274 of 1978, measured from Seasat SAR of orbits 1339 and 1382. Time-mark calibrationwith two ground points on Banks Island.

FIG.7. Overall ice motion between Days 250 and 280 of 1978, measured by Seasat SAR using 7 orbits. Land points were used to calibrate the time marks. Accuracy of each ice-point position is estimated at kO.5 km.

9. POLAR REGION SATELLITE MICROWAVE OBSERVATIONS

351

During the period of the five orbits spanning October 2-8, the ice in the eastern sector of the study area (Fig. 4) initially moved eastward toward Banks Island, then southward, then southwestward. The ice in the western sector moved southward and then southwestward. This differential ice motion can be seen in a comparison of the pair of Seasat SAR images shown in Fig. 5. Other aspects of the ice motion such as floe rotation, lead growth, and leads opening behind large multiyear floes can also be seen in a comparison of these images. The other SAR study presented at the AGU Seasat symp0siu.mwas by W. E. Brown et al. (1981). Brown's study was initiated as an attempt to develop a means of determining the geographic coordinates of an arbitrary pixel in a satellite SAR image where no land reference point existed. For precise location of points in SAR images of ocean areas and sea-ice areas far from land, the techniques relying on the presence of features of known geographic coordinates in each image could not be used. An independent a priori technique for image location that does not depend on area maps and having images containing known location points was needed. Brown et al. developed an algorithm that required only the spacecraft ephemeris data and the SAR sampling information to derive the absolute location of an arbitrary pixel in an image. This algorithm was tested using a Seasat SAR image of Los Angeles, California, in which numerous points that had been accurately mapped were identifiable. Brown et al. found that using their calibrated algorithm enabled them to locate an arbitrary pixel in either of these digitally processed images to an accuracy of k 50 m. This technique will be very useful in the analysis of present and future satellite SAR observations of oceans, both open and ice covered.

2.3. Seasat SASS and SMMR Observations The Seasat-A Scatterometer System (SASS) was a dual-polarized precision radar operating at a frequency of 14.6 GHz. Swath width was developed by using four orthogonal fan-beam antennas boresighted 45" to the subsatellite track, resulting in 500-km swaths on either side of the spacecraft. The SASS swath, indicated in Fig. 4,is the starboard swath. The fan beams were further subdivided into 12 resolution cells by means of Doppler filtering. Further instrument details can be found elsewhere (Appendix A; Johnson et al., 1980). An earth-incidence angle of 25" is associated with cell 1, the southern swath edge. SASS cell 3 is coincident with the center of the much narrower SAR swath that is also shown in Fig. 4. During the data passes of interest, the polarization selection mode was fixed but alternated between horizontal and vertical from pass to pass.

352

C. T.SWIFT ET AL.

The Seasat Scanning Multichannel Microwave Radiometer (SMMR) collected data at two polarizations and at five frequencies ranging from 6.6 to 37 GHz. The antenna was an offset parabolic dish 79 cm in diameter with a constant earth-incidence angle of 49". Further instrument details are presented by Gloersen and Barath (1977),Njoku et al. (1980), and Appendix A. The antenna pointed aft of the spacecraft and mechanically scanned to the starboard side, thus overlapping with SASS and SAR swaths as indicated in Fig. 4. Because of the broad range of operating frequencies, the resolution cell size varied. At 18,21, and 37 GHz, the postprocessed data was formatted to 50-km resolution cells, which is comparable to that of the SASS. The 37-GHz data were processed to 25-km resolution, which is suitable for ice-edge detection. At 50-km resolution, 11 cells span the swath, where cell 1 is along the groundtrack and defines the southern edge of the swath. SMMR cell 7 passes through the center of the SAR swath and is contiguous with SASS cell 3. The utility of passive microwave observations was demonstrated several years ago during the AIDJEX (Wilheit et al., 1972; Gloersen et al., 1973). The flight experiments that were undertaken during this program clearly proved that microwave radiometers could discriminate between multiyear sea ice, first-year sea ice, and water. Substantial radiometric contrast was expected between ice and water because the dielectric constant of water is much greater than that of ice. Indeed, the dielectricconstant of water is so large that water appears much cooler than ice at microwave frequencies. The unexpected discovery during AIDJEX was the observation that multiyear sea ice was radiometrically cooler than first-year sea ice. The explanation for this contrast is of a more subtle nature, and is believed to result from volume scattering by voids formed during the drainage of brine pockets. Subsequent experiments have shown that the emissivity of sea ice varies with electromagnetic frequency and polarization. Under the constraint that all fractional species must add to unity, a minimum of two SMMR channels can be used to estimate the relative fractions of water and the two ice types within the footprint. Additional SMMR channels can possibly correct for atmospheric attenuation and variations in surface temperature. An example of brightness temperature contours in the Banks Island-Point Barrow region is shown in Fig. 8 at the beginning of the time period of interest. This figure displays the isotherms at an lS-GHz, horizontal polarization for orbit 1395. The cooler brightness temperatures observed toward the north are indicative of multiyear ice. The large gradients of brightness temperature are characteristic of the ice edge and an associated mixture of water and first-year ice types. The intermediate region presumably reflects brightnesstemperature variations related to a mixture of first-year and multiyear ice

FIG.8. Seasat SMMR brightness temperature field for orbit 1395. Brightness temperatures shown are 18-GHz, horizontal polarization.

354

C. T.SWIFT ET AL.

forms. Indeed, an algorithm based upon weighted values of 18- and 37-GHz SMMR data can be used to estimate the relative percentage of first-year ice, multiyear ice, and water as shown in Fig. 9a. This retrieval utilizes raw brightness temperatures along SMMR cell 7 for orbit 1452,which corresponds to the center of the SAR swath shown in Fig. 5b. The retrieval indicates a relatively low percentage of water content from Banks Island toward the west until the ice edge is encountered. Near the edge, the fractional water content is observed to increase, as anticipated, before the beams intercept land at Point Barrow. The retrieval further indicates that multiyear ice is the predominant species halfway from Banks Island to the ice edge, whereas first-year ice encompasses the intermediate area. More research is needed to establish error bounds on the retrievals. For example, the ice chart for October 5 (Fig. 2b) indicates an abrupt transition from first-year sea ice to nearly 100% older ice, whereas the microwave sensors indicate a more gradual change. Furthermore, the retrieved values of the multiyear fraction shown in Fig. 9a is much lower than that indicated in the ice chart. The corresponding SASS-normalized radar cross section uofor SASS cell 3 is shown in Fig. 9b. A comparison shows that the values are consistent with the SMMR retrievals of Fig. 9a. The regions of predominantly multiyear ice are characterized by a relatively high cross section of - 5 dB. As the satellite passes over the area where the SMMR algorithm indicates a relatively high concentration of first-year sea ice, the cross section drops to a mean value of - 9 dB. When the ice edge is encountered, a further reduction in backscatter is observed, consistent with the known, and relatively low, scattering cross section of capillary waves over the ocean. The detailed structure of uoshown in Fig. 9b exceeds fluctuations associated with the instrument and exhibits repeatability from orbit to orbit. These variations may be associated with geophysical phenomena, and when coupled with radiometric data, may provide a means of subclassifying ice types as suggested by others (Hawkins et al., 1980). As a final measure of consistency among the various microwave sensors, the SAR images of Fig. 5 show a change in overall contrast that occurs midway between Banks Island and Point Barrow. The eastern portion of the image is brighter as a result of a relatively high radar cross section. Water near the Point Barrow area is evident but it is somewhat difficult to classify the darker intermediate region exclusively from SAR imagery. However, a consistent picture is developed when data from additional sensors are utilized. The data of Fig. 9 indicate areas of water, first-year ice, and multiyear ice that are consistent with the intensity variations of the SAR image. The SASS and SMMR therefore provide a potential means of classifying ice-water types at resolutions of the order of tens of kilometers to ease ambiguity problems of detailed interpretations done at resolutions of tens of meters.

9. POLAR REGION SATELLITE MICROWAVE OBSERVATIONS

355

-4.0 J.

-6.0

.

-

-LO

.

b

-10.0 ..

3

-12.0

-14.0

.'

fci

1

2

3

4

5

8

I

a

0

10

101 (XIOO) FIG.9. (a) Seasat fractionalfirst-year ice, multiyear ice, and water along center of SAR swath, as derived from SMMR data. (b) Seasat normalized radar cross section (a') for orbit 1452 along center of SAR swath.

Finally, the instruments that exhibit relatively coarse resolution characteristically provide a more synoptic view of the ice features of interest. As an example, Fig. 10 shows retrievals over a swath extending from Banks Island to Point Barrow. This figure bounds the regions where the relative fractions of

I1

9. POLAR REGION SATELLITE MICROWAVE OBSERVATIONS

357

water, first-year sea ice, and multiyear sea ice exceed a concentration of 50%. The southern boundary of the area indicated as >50% first-year ice generally follows the old ice boundary of Fig. 2b. At this point, it appears that the radiometric retrievals are consistent with classical observations only if an artificial threshold is imposed. In other words, the passive microwave measurements are providing additional information about the radiating properties of sea ice that are not now confirmed by visual observations. 2.4. Altimeter Observations

Because the altimeter is inherently a nadir-viewing device, simultaneous intercomparisons with the other microwave instruments are restricted. However, if one fixed orbit is chosen for analyzing the data collected by wide swath sensors, the nadir track of subsequent orbits will pass through the data swath. This property is illustrated in Fig. 11, where tracks representing the 3-day repeat coverage by the altimeter are shown relative to on the SMMR swath generated during orbit 1452. This type of intercomparison assumes that negligible ice motion occurs between passes. Such intercomparisons would not be valid over a 3-day repeat cycle; however, a one-orbit difference, 1453for example, is only a 90-min lag and the ice is essentially static over this short time period. Furthermore, orbit 1453 intersects the entire SMMR swath. For the radar, the distance to the target is 4 2 where c is the speed of light and t is the time of flight between transmission and reception of the signal. If the target is smooth, then a transmitted pulse is returned undistorted. Its signal strength depends upon the distance to the surface and the electrical properties of the target. The time of transmission is measured from the center of the transmission pulse to the half-power point of the received pulse. If the target is rough such as the ocean surface, the pulse is distorted by early returns from the peaks and delayed returns from the valleys. The rougher the surface, the greater the stretching of the leading edge of the returned pulse (ramp). Therefore, for relatively flat surfaces the slope of the ramp is inversely related to the RMS surface height h. The mean surface level is located approximately halfway up the ramp. Further reflective interaction of the pulse from the rough surface depends geometrically upon the antenna beamwidth and the slope of the surface (S). For the ocean, the surface slope is gentle and the decay of the reflected pulse is determined by the antenna characteristics. The radar altimeter-returned pulse waveform is sampled by 60 gates. The position of the waveform is adjusted so that the half-power point is located between gates 30 and 31. The surface was sampled at a pulse-repetition

9. POLAR REGION SATELLITE MICROWAVE OBSERVATIONS

359

frequency of 1000/sec. An average of 100 pulses were stored to reduce the amount of data processed and to reduce the amount of noise present in the waveform. The automatic gain control was accomplished by comparing the 10-gate average of the waveform power centered on gate 45 with the expected value from a typical low sea-state ocean return. Changes in the average signal power received are related to the backscatter cross section (&) by an appropriate transformation. Gate 45 was chosen to reduce the effect of surface height roughness on the estimation of no. The radar cross section g o was determined at the maximum value of the returned waveform wherever it may have occurred within the gates. Calibration of the altimeter was accomplished by comparisons with NOAA buoy measurements of wave height and wind speed (aois a function of wind speed) by Fedor and Brown (1982). They show that the theoretical model derived by Barrick (1972) holds for RMS wave heights up to 8 m and wind speeds up to 25 m sec-l. Higher values were not measured or could not be inferred by the buoys. Barrick’s ocean model assumes specular point reflections from a homogeneous surface of normally distributed heights and slopes. 2.4.1. Orbit 1453. The three-dimensional waveform plot shown in Fig. 12a has as the axis (1) the sampling gate number, (2) the received waveform power (in millivolts), and (3) the time the average pulse is received (GMT), or the distance, since the satellite is orbiting at a nearly constant velocity along the surface. Figure 12b shows the satellite suborbital track along the surface. Transverse to the track is plotted the backscatter cross section ( 0 ’ ) at the maximum value within the 60 maximum gates. Because of the location of the track point the lowest numbered gates will sample noise; i.e., no signal has yet arrived. For ocean returns, the noise region will be followed by a ramp centered at gate 30.5; then the waveform will plateau and decay under the control of the antenna beamwidth. If the surface is smooth (RMS surface height less than the transmitted wavelength and the surface slopes less than the antenna beamwidth), then the received pulse will have the same form as the transmitted pulse. The ratio of the magnitudes of the two types of returns will be

R = ~ ~ ~ 2 / ~ ~ ” ’2/IRw(0)I x w ~ 2,~ I ~ s ~ ~ ~ (1) I where H is the height above the surface, S is the RMS slope of the ocean surface, x,., is the spatial width of the transmitted pulse, IRs(0)12is the Fresnel reflection coefficient of the smooth surface, and IRw(0)12is the Fresnel re, flection coefficient of sea water. For Seasat the coefficient H S 2 / 2 a ’ ’ Z ~is on the order of 32 dB. Thus for surfaces dominated by smooth areas, it is

w

8

FIG.12. Radar altimeter data for orbit 1453. (a) Altimeter waveform; (b)normalized radar cross section uo.

361

362

C. T.SWIFT ET AL.

expected that there will be an enhancement of the received signal relative to sea water even though the Fresnel reflection has a lower value. The start time for the plots for orbit 1453 is 15:00:06 GMT. Every sixth pulse is plotted in the waveform plot starting at the right-most part of Fig. 12a. The crkaxplot along track (Fig. 12b) begins off Victoria Island. Note that over and about land the returns are very weak, characterized by an increase in the noise level (to the left of the ramp) due to the action of the AGC (automatic gain control). Between Victoria Island and Banks Island the signal is typical of low sea-state conditions, the significant wave height (SWH) is less than 1 m, and the surface wind speeds (WJ are less than 7 m sec-'. Past Banks Island there is a stretch of open water of approximately 56 km. Sea ice is encountered at 15:01:02GMT (= 126.30"W). From this point until 15:01:18 GMT ( 'Y 127.4"W)the returns are from a mixture of smooth and rough surfaces, dominated by the rough. The sharper peaked areas along the track indicate the smooth area is dominant. Note the rough areas (probably open water) are at 15:02:12 G M T (140"W), 15:02:19 G M T (141.2"W),and 15:03:05 GMT to 15:03:13GMT (150.3"W to 152.7"W). The smooth area just east of Point Barrow (153"W) is the last of the ice-dominated areas. 2.4.2. Orbit 1482. The start time for the plots of Fig. 13 is 15:41:24.5 GMT. For this track the area below Banks Island is all open water. The wind values are from 7 to 9.5 m sec-' with the large values just outside the ice boundaries. Ice is encountered at 15:42:37GMT(129.5"W)but the boundary is not well defined. It appears that open water is no longer present at 15:42:42 GMT (130.5"W).From 15:42:42to 15:43:36GMT the region is dominated by ice with occasional large areas of open water. Note the variations in ukax and the pulse-to-pulse waveform variations. This covers an area from 130"W to at least 140"W. From 15:43:36to 15:44:1 1 GMT o l a xis fairly constant and the waveforms show very few sharp dips. This would indicate almost no open water present. Some slope roughing of surface (indicated by striation in upper gate signals) occurs from 15:44:11 to 15:44:26GMT. This is accompanied by a slight decrease in o l a x . If there is any water present it must be very smooth to maintain the level of okax.A definite boundary occurs at 148.5"W.In this region (15:44:26 to 15:44:51 GMT) the pulse-to-pulse variations become much stronger indicating large patches of water. The occasional spikes indicate patches of very smooth ice. A definite boundary region is indicated from 154451 to 154507 GMT. The strong stretching of the pulses in the boundary region result from interactions with a very rough surface. Immediately outside this region, the altimeter is measuring winds of 12 m sec-'. The roughness may be due to rafting or large waves propagating into the ice. At the end of the plot, note the stretching of the ramp. Here the altimeter is measuring a SWH of 5 m and windspeed of 15 m sec-'.

FIG.13. Radar altimeter data for orbit 1482. (a) Altimeter waveform; (b) normalized radar cross section 8

9. POLAR REGION SATELLITE MICROWAVE OBSERVATIONS

365

2.5. Ice-Edge Observations

The location of the ice edge, as inferred from the 37-GHz, horizontally polarized SMMR channel, is shown in Fig. 14. Horizontal polarization gives higher radiometric contrast between ice and water and selection of the highest SMMR frequencygives the maximum spatial resolution of 25 km. The edge is defined as the 175 K isotherm, which is the approximate average brightness temperature of water and ice. This figure reflects the ice kinematics over the 1-week period. Within the area east of 215"W longitude, the average growth rate due to freezing is 25 nautical miles per day. This is approximately three times the rate of movement of the floes shown in the SAR image of Fig. 5 in the

FIG.14. SMMR ice-edge contour for the week October 2 to October 8,1978, as derived from the SMMR data for 35-GHz horizontal polarization.

366

C. T. SWIFT ET AL.

vicinity of northern Banks Island. The edge growth is much slower along the northern slope of Alaska because of the proximity of land and has therefore reached the seasonal maximum. West of Point Barrow, toward the Chukchi Sea, the edge oscillates as a result of interaction with ocean waves and other wind-related effects. Note that when the low-pressure cell passed over the Beaufort Sea on October 7, the edge characteristics changed drastically. Between October 6 and 8, the western ice edge actually receded by an average of 25 km, and as much as 50 km for longitudes between 210"W and 215"W. Further east, the ice edge grew beyond the limit of the SMMR swath. It is of further interest to note that the SAR images for orbits 1438 and 1481 (not shown) revealed a movement near the Banks Island region and a counter northerly movement of floes in the Point Barrow area, again consistent with the results of Fig. 14. The altimeter and SMMR differ with each other in locating the ice edge. Indeed, the altimeter edge is west of that derived from the SMMR brightness temperatures. The reasons for this are not clear at this time; however, it is surmised that the ocean must be completely ice free for the radar backscatter and waveform to appear "oceanlike." 2.6. Discussion and Conclusions

If only a single sensor were available for the study of ice dynamics and possible operational use, the SAR would be the clear choice, provided that constraints such as cost were no object. In view of the 25-m resolution, acceptable (though marginal) swath width, and continuous imagery along the orbital track, the SAR can provide high-quality geophysical data of considerable value to a user. Indeed, the overall value of SAR has no peer when compared to any presently known in situ or other remote-sensing technique. From the results presented, it can be concluded that SAR provides more measurements of the ice motion compared to drift stations or buoys. Indeed, the difference can be expressed in terms of orders of magnitude when considering the spatial resolution and the amount of real estate observed within a matter of minutes. This coverage further implies that ice dynamics can be studied on spatial scales ranging from several square meters to several thousand square kilometers. From the pair of images shown in Fig. 5, it becomes evident that SAR is a valuable tool for ice-feature identification. Although not discussed in detail in this chapter, it is easy to convincethe casual observer that SAR can be used to classify floe size and type and pressure ridge density and orientation. With temporal data, convincing arguments can be made for quantification of lead and polynya area and orientation. The ice edge can be mapped to within the sensible definition of the edge, and tabular

9. POLAR REGION SATELLITE MICROWAVE OBSERVATIONS

367

icebergs can be located and tracked, as evidenced by the obvious signature of the ice island T-3 shown in SAR images near Banks Island. The case presented for the SAR does not, however, undercut the value of the other sensors. Both SASS and SMMR view swaths that are an order of magnitude greater in width. Furthermore, the reduced resolution implies a much lower data rate and therefore gives a relatively economical data base of great value to the user community. For years, ESMR has been used operationally to quantify the iceedge dynamics. As an extension of this capability, it is suggested that SMMR has the potential to quantify relative percentages of water and ice types. On a large scale, SMMR can also be used as an aid for interpreting SAR images; for example, discriminating water from smooth ice. SASS has not experienced the long-term global observations offered by microwave radiometers. Indeed, SASS is the first satellite radar which was used to collect high-quality data over the ice canopy. The results presented herein have shown that SASS detects the observables that SMMR can quantify. Although it may be possible for SASS to compete with SMMR in the future, the real value of SASS is to work in concert with SMMR to subcategorize various ice forms. The altimeter suffers from the disadvantage that data cannot be collected along a swath. The underlying value of the instrument is, however, the capability of detecting surface undulations on the order of centimeters,spatial resolutions less than 1 km for returns from flat surfaces, accurate radar crosssection measurements, and accurate reconstruction of the waveform. This instrument has proved to be invaluable in the area of physical oceanography. In spite of its restricted view of the earth, the altimeter provided a large number of independent observations during the life of Seasat. At this time, the conclusions to be drawn from the ice-related altimeter observations are tenuous. It is certain that the altimeter can define the ice edge in terms of all water/no ice; however, further work is required to characterize the ice edge and ice-roughness response in general.

3. SEA-ICE OBSERVATIONSBY NIMBUS-7 3.1. Introduction

So far the discussion has centered on data comparison case studies, using portions of single Seasat orbits over areas of particular interest. The Nimbus7 SMMR, launched on October 24,1979, is similar in design and operation to the Seasat SMMR. Since Seasat was limited to about 100 days of operation,

368

C. T.SWIFT ET AL.

there is no overlap in coverage between the two spacecraft, which unfortunately precludes any direct intercomparison of data, The best intercomparison that can be presented is separated in time by about a month and is given in Section 3.3. A comparison of the operating characteristics of the SMMRs on board the two spacecraft is provided by Gloersen and Barath (1977). The dual-polarized, multispectral radiances from the Nimbus-7 SMMR are currently used for extracting a number of geophysical parameters on a global basis. The geophysical algorithms specifically designed for use with the Nimbus-7 SMMR for obtaining sea surface temperatures, nearsurface winds, atmospheric water vapor, cloud liquid water, rain rates, snow cover, sea-ice concentration, multiyear ice fraction, and ice temperature are summarized by Gloersen et al. (1984). The discussion in this sectipn will focus on the determination of the sea-ice parameters and how the multichannel data from the SMMR are utilized in overcoming some of the difficulties encountered with the previous single-channel instruments such as the Electrically Scanning Microwave Radiometer aboard the Nimbus-5 spacecraft (ESMR-5). Since the microwave radiances of polar oceans depend strongly on ice concentration, age, and radiating temperature, the accuracy with which any one of these parameters can be determined from the single-channel radiance from a footprint containing a mixture of water, different ice types, and unknown temperature is limited. These limitations have been partly overcome in using the ESMR-5 data to describe the sea-ice extent and areal coverage of the Southern Ocean where there is essentially only a single radiometric ice type, first-year ice. Additionally, monthly climatological surface air temperatures were used in the ESMR-5 ice algorithm to estimate the ocean and ice temperatures; however, such estimates lack the detailed spatial variability to remove completely errors caused by local variations in ice temperatures. These approximations result in an estimated uncertainty of 15% in the computed ice concentration (Zwally et al., 1983). In the Arctic, the presence of a perennial ice cover further complicates the problem of determining sea-ice concentration. The presence of sea ice ranging in age from newly formed thin ice to thick multiyear ice results in ambiguous microwave signals, which pose additional difficulties in accurately calculating sea-ice concentration. The problem of determining ice concentration in the presence of a mixture of radiometrically different ice types is essentially the problem of determining the ice-type distribution. Some attempts in differentiatingfirst-year and multiyear ice cover in the Arctic using the single-channel ESMR-5 data set have met with only limited success (Carsey, 1981; Campbell et al., 1983). The accuracy of computing sea-ice concentration when two radiometrically different ice types are present and when significant spatial and temporal variations in ice temperature occur has been improved with the use of multispectral radiances from SMMR.

9. POLAR REGION SATELLITE MICROWAVE OBSERVATIONS

369

3.2. Nimbus-7 S M M R Sea-Ice Algorithm

A method is described here by which the dual-polarized, multispectral radiances from the SMMR permit the determination of sea-ice concentration, multiyear ice fraction-defined as the fraction of the ice concentration which has survived at least one summer, and an ice temperature which is a weighted mean-thermodynamic temperature of the radiating portion of the ice. This method uses radiance ratios, which reduce the dependency of the calculated ice parameters on thermodynamic temperature variations of the sea ice. Microwave polarization and spectral information are used to determine the ice concentration and to distinguish between first-year and multiyear sea ice. Some results obtained with this dual-polarized, multispectral approach are presented in Section 3.3. 3.2.1. Microwave Polarization. The microwave polarization is defined as the differencebetween the vertical and horizontal radiances divided by their sum at a given wavelength. Since the polarization is defined as a ratio of radiances, the thermodynamic temperature dependency cancels to first order for uniform fields of view and is greatly reduced for mixed fields of view as, for example, a mixture of sea water and ice. The contrast between the polarization of sea ice and that of open ocean is almost an order of magnitude at each of the SMMR wavelengths and makes the polarization a useful parameter for obtaining sea ice concentrations. Another advantage of this parameter is that it is largely independent of multiyear or first-year ice cover (Cavalieri et al., 1984). 3.2.2. Microwave Spectral Gradient Ratio. The problem in discriminating between first-year and multiyear ice types in the Arctic can be overcome to some extent by utilizing the microwave spectral differences between the two ice types. Early aircraft experiments have indicated that at wavelengths less than a centimeter there is good contrast between the radiometric signals of firstyear and multiyear ice (e.g., Wilheit et al., 1972) with further supporting evidence for this spectral dependency provided during the AIDJEX experiments (Gloersen et al., 1973). These observations serve as the basis for distinguishingbetween the two ice types. A SMMR radiance parameter which incorporates both the observed spectral variation of radiance with ice type and the reduction of thermodynamic temperature variations through the ratio of radiances technique is the spectral gradient ratio. This ratio, defined as the difference between the vertically polarized radiances at the 0.81- and 1.7-cm wavelengths divided by their sum, discriminates between open ocean, firstyear sea ice, and multiyear ice. Since the difference between the 0.81- and

370

C. T.SWIFT ET A L .

1.7-cm vertically polarized radiances is positive for open water, close to zero for first-year ice, and negative for multiyear ice, this ratio used with the polarization information is utilized to obtain the ice concentration and multiyear fraction within the field of view of the instrument. 3.2.3. Calculation of the Sea-Ice Parameters. The sea-ice algorithm is derived from a passive microwave radiative transfer model of the radiation that reaches the satellite. The details of the calculations have been presented by Cavalieri et al. (1984). Briefly, the method neglects the cosmic background and the atmospheric radiation; only the radiance from the surface of the earth is considered. Thus, the radiation emanating from a SMMR footprint containing two ice types and open water is expressed as a three-component equation for each SMMR wavelength and polarization. These, in turn, are used with the definitions of polarization and spectral gradient ratio to obtain relationships between these quantities and the two ice parameters, concentration and multiyear fraction. The derived equations contain the radiances from the individual species in the SMMR footprint, for which values can be obtained by observation of other SMMR footprints containing only one of the three components. Thus the algorithm is “tuned” to large-scale averages of each of the three components. The equations currently in use are as follows: for sea-ice concentration (C) in terms of polarization (PR),

C = (53 - 268 * PR)/[40.6

-

1.2 * F

+ (185.6 - 47 * F ) * PR]

(2)

and for multiyear fraction (F) in terms of the spectral gradient ratio (GR),

F = [33 - 40.3 * C - (354 + 104.7 * C) * GR]/(21.4

- 67.2 * GR) * C

(3)

An initial value of 0.5 is used for F in Eq. (2). These equations are then solved iteratively to obtain final values of the calculated concentration and multiyear ice fraction. The concentration is computed at both the 0.81- (coefficients not shown here) and 1.7-cm wavelengths corresponding to spatial resolutions of 30 and 60 km, respectively. Since the multiyear fraction utilizes both the 0.81and 1.7-cm-wavelength channels, the resolution is 60 km. Finally, an ice temperature is calculated at a spatial resolution of 150 km by making use of the 4.6-cm wavelength vertically polarized brightness temperature and an average ice concentration. The 4.6-cm-wavelength radiances are used for this calculation since the emissivity of sea ice at this wavelength is relatively independent of ice type. Examples of the calculated ice parameters using these techniques are presented in the next section.

9. POLAR REGION SATELLITE MICROWAVE OBSERVATIONS

37 1

3.3. Sample Calculations 3.3.1. Sea-Ice Concentration. Using the technique described in the previous section, the ice concentration was calculated from the radiances measured by the Nimbus-7 SMMR during a number of overpasses in the time interval October 25-27, 1978. These values have been mapped onto a polar stereographic projection using a 26-color representation of the concentrations shown in Fig. 15. The Arctic Basin concentrations range from about 68% (near Severnaya Zemlya) to 100%. Although this is somewhat less than the generally accepted values of 98-99%, concentrations as low as 88% have been estimated by Whittmann and Schule (1966) in the Canadian and Eurasian basins during the winter season and polynyas have been mapped in the Chuckchi Sea by Yakelov (1977) from drifting station observations. On the other hand, Koerner (1973) has reported concentrations of 98-99% generally along his traverses of the sea ice from Svalbard to the North Pole and in the Beaufort Sea. More recently, Campbell and others (1983) have observed similar areas of low sea-ice concentration appearing repeatedly in a 4-year data set of ESMR-5 observations. Observations available for comparison are sparse and not consistent with each other, probably more due to variation in time and location of the observations than anything else. The estimated relative accuracy of the calculated ice concentration is 5-9%, although under near-melt conditions, heavy rain clouds, or extensive areas of newly formed ice, the precision can be as poor as 20% (Cavalieri et al., 1984). An analysis of the algorithm showed that a 10 K variation in the thermodynamic temperature of the ice results in a 1% change in the calculated ice concentration; demonstrating the relative insensitivity of the algorithm to such variations. Keeping in mind the relative uncertainty of the computed sea-ice concentrations, it is nonetheless interesting to describe some of the prominent features that occur. Upon examining a series of images (not shown), such as the one in Fig. 15 over a 3-week time period, the large area of 80% ice concentration location near 80"N and 160"E can be seen to appear and disappear three times, i.e. with a period of 5-7 days. This corresponds approximately to the frequency of winter storm system passages over the Arctic basin, which are known to cause variations in the ice concentration. Also noteworthy is the area of low ice concentration in Fig. 15 just south of Crown Prince Christian Land on the east coast of Greenland and extending out to the sea. This is the location of a persistent and well-known polynya. Figure 16 illustrates the ice surrounding Antarctica for the same period in October. In the southern polar region, this marks the beginning of austral

372

C. T. SWIFT ET AL.

summer and the break up of the pack ice surrounding Antarctica as observed north and east of the Weddell Sea and in the Ross Sea. 3.3.2 Multiyear Sea-Ice Fraction. The multiyear ice-fraction distribution illustrated in Fig. 17 shows considerable variation across the Arctic Basin. A comparison of this distribution with earlier studies using aircraft and surface-vesselobservations show differences of up to 20-25% in some regions (Cavalieri et al., 1984). While the absolute accuracy of these results are uncertain, the relative accuracy is estimated to be in the range 15-25%. Even with this large uncertainty, the position of the 20% multiyear fraction isopleth does correspond roughly to the extremity of the pack ice the previous summer. The simple model of multiyear ice used in the present algorithm assumes all the ice which survived at least one Arctic summer is multiyear. In reality, of course, second-year, third-year, and older ice may have different microwave signatures which contribute to the uncertainty in the derived multiyear fraction shown in Fig. 17. On the other hand, Arctic ice dynamics is such that openings occur within the pack allowing for the formation and growth of new ice producing a mixture of first-year and multiyear ice. In spite of these uncertainties, these results are important for understanding the behavior of the Arctic ice canopy through, for example, a time-sequential analysis of the multiyear ice distribution. 3.3.3. Sea-Ice Temperature. The map of sea-ice radiating temperature shown in Fig. 18 presents still another spatial distribution than those in Figs. 15 and 17. Comparisons of sea-ice temperatures so derived with surface data show similar trends when the sparsity of the surface data and predictable offsets are taken into account (Cavalieri et al., 1984). Figure 18 illustrates the benefit of the use of radiance ratios in calculating sea-ice concentration and multiyear fraction, since the space scale of the temperature variations is smaller than could be measured in practice with in situ techniques. Earlier single-channel determinations of ice concentration (cf. Campbell et al., 1983) could not differentiate between the variations of ice temperature and emissivity and so the 20 K variations shown in Fig. 18 would result in errors of about 20% in the single-channel calculation of ice concentrations.

FIG.15. Ice concentration for October 25-27,1978, as derived from Nimbus-7 SMMR. FIG.16. Ice multiyear fraction for October 25-27,1978, as derived from the Nimbus-7 SMMR. FIG.17. Ice surface temperature for October 25-27, 1978, as derived from the Nimbus-7 SMMR. FIG.18. Ice concentration in the Antarctic for October 25-27, 1978, as derived from the Nimbus-7 SMMR.

9. POLAR REGION SATELLITE MICROWAVE OBSERVATIONS

373

3.4 Conclusions

The results presented here illustrate the advantages of a multichannel passive microwave instrument such as the SMMR in providing large-scale observations of polar ice cover at regular intervals. Sea-ice concentration, age, and temperature distributions are important parameters needed for both mass and heat budget studies of the polar regions.

4. OBSERVATIONS OF OCEAN WAVESIN THE ANTARCTIC 4.1. Introduction

The Southern Ocean is thought to have one of the most hostile environments in the world. Snodgrass et al. (1 966) and Munk et al. (1963) measured swells generated by Southern Ocean storms, some of which propagated across the entire Pacific until they impinged the coast of Alaska. Cartwright (1971) and Cartwright et al. (1978) observed Southern Ocean-generated swells at St. Helena Island in the Atlantic at 16"s which, on certain occasions, have caused extreme surf ("roller") that damaged ships and structures within the harbcr. Until Seasat, however, there have been no reported measurements of waves within the Southern Ocean generation areas. Furthermore, due to a paucity of ship traffic, few observations of the surface wind or pressure fields have been obtained, and the intensity of Southern Ocean storms have been poorly known. Recently, Guymer and Le Marshall (1981) have reported on pressure fields observed by drifters in the Southern Ocean. From these data, central pressures of storm systems have been frequently found to be in the vicinity of 950 mbar and lower. Furthermore, high-pressure systems at lower latitudes have often been found to be in excess of 1030 mbar. Such extreme coexisting systems ought to be capable of generation of severe wind and wave conditions. In 1978, Seasat obtained extensive measurements of surface wind and wave fields in the Southern Ocean by means of a radar altimeter. This chapter presents an analysis of the altimeter wave-height and wind-speed measurements from July to October 1978, which is during the Antarctic winter. The significant wave height (SWH) and surfacewind speed for all longitudes in the area south of 35"s latitude to the ice edge are plotted using the Seasat algorithms and these fields are compared with surface weather maps provided by the Australian weather service. The verification of SWH and surface windspeed determinations using altimeter data and algorithms is given in Tapley et al. (1979) and in Fedor and Brown (1982). Their comparisons of Seasat

374

C. T. SWIFT ET AL.

radar altimeter-inferred estimates of SWH and wind speed with buoy measurements yielded a mean difference of 0.07 m with a standard deviation of 0.29 m over a range of 0.5 to 5.0 m SWH, and a mean difference of -0.25 m sec-’ and a standard deviation of & m sec-’ over the range of 1 to 10 m sec-’ wind speed. Those numbers are very similar to GEOS-3 data comparisons (Mognard and Lago, 1979; G. S. Brown et al., 1981), and provide a corroboration of the wave-height and wind-speed measurement capability of a short-pulse spaceborne radar altimeter. Throughout the southern oceans good qualitative agreement is found between the surface winds derived from the Seasat altimeter data and those derived from the synoptic surface-pressure fields of the Southern Hemisphere given in the weather charts. However, this agreement is not as good as that obtained in analyses of Seasat wind and wave data in areas for which more accurate weather data is available (Guymer and Le Marshall, 1981). From September 21-23, 1978, every available ship surface wind-speed report was collected in the Southern Ocean and compared with the altimeter wind-speed measurement within 100 km and 12 hr of the ship reports. The number of ship reports were few, but 11 ship observations agree reasonably well with the altimeter-deduced wind speeds. Where they disagree, generally the ship wind-speed values exceed the satellite values. An interesting aspect of this study is that in the SWH fields that are derived throughout the southern oceans, the values of SWH are much higher than those derived from GEOS-3 altimeter data for the North Atlantic in winter reported by Mognard and Lago (1979). Indeed, the SWH values are observed as high as 10-12 m occurring in some part of the Southern Ocean every few days. A reasonable explanation for this would be that in the southern oceans, with their great unbounded expanse and more intense meteorological regime, the wave field is generated by higher winds with greater fetch. Moreover, the wave conditions in general seem to consist of large-amplitude swells, often mixed with strong wind seas. This thesis can be evaluated by comparing the local wind wave fields as deduced from the altimeter-derivedwind-speed fields and assumption of a fully developed sea, with the SWH fields deduced from the altimeter-return pulse shape. 4.2. The Analysis Procedures

Neuman and Pierson (1966),by examining an extensive set of simultaneous measurements of surface winds and waves observed at Atlantic weather stations “I” and “J,”established a wind-speed to wave-height relationship for fully developed wave conditions. This relationship, SWH = 0.22U2,provides

9. POLAR REGION SATELLITE MICROWAVE OBSERVATIONS

375

a specification of the average of the one-third highest waves referenced to the wind speed as observed by a cup anemometer mounted on the ship's mast 19.5 m about mean sea level. The altimeter radar backscatter cross section oo has been empirically related to a surface wind speed as measured at a height of 10 m. Using a logarithmic model of the boundary layer, the 10-m wind can be related to the 19.5-m wind. Although many studies (for example, Garrett, 1977) have shown that this relationship is not a constant, the altimeter wind is adjusted upward by 8% to approximate the 19.5-m wind and calculated maximum expected wind-wave heights (WW) according to the equation WW = 0.022(1.08U1J2

(4)

where U,,is the wind speed derived from the altimeter. Where WW is found to be below the observed SWH, a swell is assumed present. The total energy (E) of the particular wave field is then calculated from

E = H:,,/16

(5)

Subtracting the energy of the wind waves (WW) then leaves us with an estimate of swell energy, which can be used to calculate the significant height of the swell.

4.3. Results

A comparison of the wave field derived from Eq. (4) with that calculated from the altimeter radar-return waveform shows that most of the time throughout the Southern Ocean the SWH values are greater than the windwave values indicating the usual presence of significant swell. Comparisons of the WW fields with the surface pressure charts show that they generally move to the northeast, namely in the general direction of the motion of the atmospheric cyclone. The calculated wind waves, as one would expect, respond rapidly to variations in the surface wind speed. During September the satellite pattern repeated every 3 days. For each day, the distribution of four ocean parameters was mapped throughout the Southern Ocean; the parameters were surface wind speed, SWH, WW, and swell. From September 21 to September 23, between the latitude - 35"sto the ice edge, a composite map of each of the four ocean parameters was plotted (Figs. 19-22). On each map the values of the corresponding ocean parameters are contoured.

376

C . T.SWIFT ET AL.

Figure 19 shows the surface wind-speed map. Generally the surface wind speed is lower than 10 m sec-'. As in the SWH map, the Atlantic Ocean is relatively calm with wind speed less than 10 m sec-'. In the Indian Ocean, the wind speed is mainly between 8 an: 12 m sec-', with only limited regions lower than 8 m sec-'. In the Pacific Ocean, by far the roughest, at a longitude of 200"E,wind speeds up to 25 m sec-' are measured along individual satellite tracks. This corresponds with the region of 14-m sec-l wind in our smooth map. Wind speeds higher than 12.5 and 15 m sec-' are also observed along several tracks in the Pacific Ocean. In Fig. 20, very few SWH contours are under 2 m. These low values are mainly located in the Atlantic Ocean whereas in the Pacific Ocean values as large as 10 m were recorded. The Pacific Ocean exhibits the highest SWH values in two different regions, respectively located at a longitude 200"E and 260"E. In the Indian Ocean, also, SWH values as large as 6 m are found along two satellite tracks on September 21. The Atlantic Ocean has the lowest sea state, with SWH generally varying between 2 and 4 m. The map of the maximum wind waves, shown in Fig. 21, reproduces the characteristic features of the wind-speed map with the highest wind waves occurring in the Pacific Ocean in the location associated with the maximum observed wind speed. In the sequential daily maps, not shown here, the motion of the wind-wave families corresponds directly to the movement of the atmospheric forcing field. For swell the situation is more complex. However, the swell-minimum map (Fig. 22) exhibits many of the same characteristics as the former map: low levels in the Atlantic Ocean and higher in the Indian Ocean, with one region higher than 6 m, whereas in the Pacific Ocean the swell level is consistently above 6 m. A background level of significant height of about 2 to 4 m is found to be characteristic of the southern oceans. The swell of the Southern Ocean is the result of the action of the atmospheric forcing field. High, localized seas are generated every few days in regions where surface winds with speeds on the order of 15 m sec-' and higher have occurred. These regions are associated with developing, rapidly moving cyclones, or with steep pressure gradient associated with the conjugate action of a subtropical high and of an intense antarctic low (Guymer and Le Marshall, 1981). A time delay of 1 day is observed between the measurement of high wind speed and the formation of a high-amplitude swell. During this day, the significant height reaches an amplitude of 8 to 10 m for cases with a generating surface wind speed higher than 20 m sec-'. Once such seas have escaped the generation region as swell, we observe that they usually move northward and not in the direction of the migrating cyclone that formed them. One case analyzed in detail concerned an intense storm that formed on

9. POLAR REGION SATELLITE MICROWAVE OBSERVATIONS

377

lW€

FIG.19. Southern Ocean map of surface wind speed (m sec-') derived from the Seasat altimeter for September 21 to 23 with altimeter ground tracks superimposed.

September 22,1978, in the region of - 55"s and 200"E and was characterized by surface wind speeds as great as 25 m sec-'. The following day, on September 23, as the cyclone moved toward the northeast, the local seas had grown from 3-4 to 8 m significant height. A swell family was generated along the cyclone trajectory and persisted on the sequential swell maps derived from the altimeter data until September 25 with a significant height on the order of 7 to 8 m. After this date it could still be followed on the Seasat altimeter swell maps propagating north of - 35"S,with a slowly decreasing amplitude.

378

C. T.SWIFT ET AL.

1-

FIG. 20. Southern Ocean map of (m)for September 21 to 23 derived from the Seasat altimeter measurements and showing the altimeter ground tracks.

4.4. Conclusions

The Seasat altimeter has been used to investigate surface wind and wave conditions in the Southern Ocean. From the wind-speed data, high winds consistent with very intense cyclones are found with central pressures of 950 mbar as reported by Guymer and Le Marshall (1981). The waves generated by these storms are found to be correspondingly high and more severe than observed by Mognard and Lago (1979) in the North Atlantic for a similar situation.

9. POLAR REGION SATELLITE MICROWAVE OBSERVATIONS

379

1-

FIG.21. Southern Ocean map of the maximum wind-wave heights (m) deduced from the HI,, and surface wind-speed Seasat altimeter measurements for September 21 to 23.

5. SEASAT ALTIMETER OBSERVATIONS OF ICESHEETS 5.1. Introduction

The continental ice sheets of Greenland and Antarctica, including the floating ice shelves, contain a volume of ice equivalent to about 70 m of sea level. Ice sheets spread outward under the influence of their own weight, moving downhill along the line of maximum regional surface slope (Paterson,

380

C. T.SWIFT E T A L .

FIG.22. Southern Ocean map of the minimum swell (m) deduced from the H,,, and surface wind-speed Seasat altimeter measurements for September 21 to 23.

1981). Detailed maps of surface elevation delineate ice-sheet drainage basins, ice flowlines, ice streams, and grounding lines between the grounded ice sheet and floating ice shelf. Of particular interest is the possibility of repeated mapping of surface elevations to detect ice-thickness changes, which would provide a direct measure of the mass balance of the ice sheet. A positive mass balance (i.e., ice-volume growth) would be associated with ice thickening, and a negative mass balance would be associated with thinning. At present, it is not known whether the ice sheets in Antarctica and Greenland are growing or shrinking. In addition, it is possible that rapid ice-sheet changes might be

9. POLAR REGION SATELLITE MICROWAVE OBSERVATIONS

38 1

initiated either by internal instabilities in the ice or by external changes such as a climatic warming (Thomas et al., 1979). Although the Seasat radar altimeter was not designed for ice-sheet measurements, significant progress has been obtained on understanding the altimeter performance over ice and in processing the data to obtain the most accurate surface elevations of the ice sheet. The characteristics of the performance over continental ice, the methods of analysis, and preliminary results have been given in detail (Brenner et al.. 1983; Martin et al., 1983; Zwally et al., 1983).

5.2. The Tracking Algorithm over Ice Sheets

The radar altimeter on board Seasat delivered more than 600,000 useful elevation measurements over Greenland and Antarctica during its 99 days of sensor operation (June to October 1978). The southern two-thirds of Greenland, the perimeter of East Antarctica, and the northern half of the Antarctic Peninsula are included within the Seasat latitude limits of _+ 72”. In contrast, GEOS-3, which carried a somewhat different radar altimeter, reached a maximum latitude of only 65” (Brooks et al., 1978). Figure 23 shows the density of Seasat altimetry data acquired over Greenland, with the discontinuities being due to “loss of track.” Similar coverage exists for Antarctica to 72”s. It was discovered by early analysis that changes in altimeter range caused by surface undulations were larger than the limits allowable by the altimeter tracker. This lag in the altimeter servo circuit caused a drift from one waveform average to the next, which causes a shift of the center of the waveform ramp from the central gate position indicated by the vertical lines in Fig. 24. Such variations caused errors in the range determined by the altimeter. When the ramp drifted out of the range of the 60 gates, a “loss of track” occurred. Variations within 60 gates are correctable as described below. Considerable variability in the waveform shape was also observed, with some waveforms being very similar to ocean waveforms, and with substantial changes occurring from one 0.1-sec waveform average to the next (see Fig. 24). Frequently, double-peaked waveforms were observed (Figs. 25d and 26). Therefore, a “retracking algorithm” was developed to locate the center of the waveform ramp or ramps, which corresponds to the distance to the reflecting surface or surfaces. A five-parameter fit is made to single waveforms and an eight-parameter fit to double-waveforms (Martin et al., 1983). The smooth curves drawn through the waveforms in Figs. 25 and 26 are the fitted curves. The AH is the shift of the ramp’s center from the central gate and represents

382

C. T.SWIFT ET AL.

FIG.23. Seasat ground tracks over Greenland. Discontinuities of ground tracks represent gaps in data after retracking and editing.

the range error on the sensor data records on the Seasat sensor data records (SDRs). Because each gate corresponds to about 47 cm in range, the maximum AH correction of a tracked waveform is about 14 m. An uncorrected Seasat height profile (indicated elevation), the corrected (retracked) elevation corresponding to the first ramp, and the corrected elevation corresponding to the occasional second ramps are shown in Fig. 27 along two segments of the East Antarctic ice sheet. The respective corrections

383

9. POLAR REGION SATELLITE MICROWAVE OBSERVATIONS

7

8

9

10

11

12

13

14

15

16

17

18

cn t-

FIG.24. Contiguous set of Seasat altimeter-return pulse waveforms over a continental ice sheet. Each waveform is composed of data from 60 gates. During a 23.5-sec time span of these waveforms, track was nearly lost at waveform 15. Altimeter recovered only to lose track at waveform 24.

:I-150

c

50

A

59

0

1

59

0

GATE

GATE

Iz R = 2.76 m

50

n 0 GATE

FIG.25. (a) Typical ocean return waveform fitted with the single-ramp altimeter waveform model. (b-d) Typical continental ice-sheet return waveforms fitted with single-ramp model.

59

384

C. T. SWIFT E T A L . 150

r t

-q

h-AR,=2.84m

-6.12m

850 1

0

GATE

0

r

v

5i

59

GATE

0

59 GATE

FIG.26. Typical continental ice-sheet double-return waveforms fitted with the double-ramp altimetermodel.

are as much as 10 m in the region of the largest surface undulations along the profile. The ice surface undulations shown in the retracked elevations are approximately 10 m peak to peak with a wavelength of about 15 km, in contrast to the smoother uncorrected profiles. It was reasoned that the double peaks could be caused by multiple reflecting surfaces within the beam-limited footprint that are nearly equidistant from the altimeter. Such a situation would be created by undulations either along track or cross track. To confirm this and study the effect of slopes and undulations, a computer model was developed (T. V. Martin, personal communication, 1981) that simulates the intersection of the radar waveform with a model surface, the radar reflection, and the construction of the return pulse waveform by the altimeter. Appropriate parameters to simulate the Seasat altimeter tracking circuit are included in this model. The simulated indicated elevations over a model actual surface with undulations (Zwally et al., 1981)are shown in Fig. 28a. Also shown are sample

9. POLAR REGION SATELLITE MICROWAVE OBSERVATIONS Q

a W

3075-

! i

(3 I

5 km

1

I

I

I

I

I

U

I,

385

-

.Retracked ElevationNo. 1

E.

0

'

lndlcated Elevation Retracked ElevationNo. 2

t

5 W W

-

3025-

0

2a

3

3000~ I 154 155

I

I

I

I

I

I

158

157

158

159

160

161

182

v)

TIME (SEC)

Ratracked Elevation No. 1 Indicated Elevation Retracked ElevationNo. 2

i;:

3025-

W

B2

5a

!

3000 1:s

I

I

I

I

I

I

177

178

179

180

181

182

I 183

TlME(SEC)

FIG.27. Retracked elevation profiles of Seasat altimeter data over Antarctica (near 7 2 3 and 127"E)compared with altimeterindicated elevation profiles before retracking.

waveforms at several places along the profile. Some of the waveforms show the characteristic double peak where two surfaces are nearly equidistant. The undulations on the simulated indicated elevation are smoother and out of phase from these on the actual surface. At some places the indicated elevation is lower than the actual surface, which is caused by the inability of the tracker to keep the middle of the waveform ramp at the central gate. The retracking algorithm is applied to the simulated waveform for the surface in Fig. 28a, and the retracked elevations after correction are shown in Fig. 28b. The undulations OR the retracked elevations are now in phase with the actual surface and all first ramp elevations lie above the actual surface as they should. The residual differences in the indicated and retracked elevations are a function of the local surface slope (Brenner et af.. 1983). For most simulations,these errors are less than 1 m. For Seasat, at a nominal altitude of 800 km, this would imply a local surface slope angle of 0.1". Further tracking refinements could be applied where the data indicate local surface angles

386

C . T. SWIFT ET AL.

3430 1 0

8

16

24

32

40

48

WAVEFORM NUMBER

FIG.28. (a) Simulated altimeter elevation profile (indicated surface) and selected waveforms. Actual surface is realistic ice-surface elevation model (Zwally et al., 1981). Elevation differences are sum of slope-induced error and tracking error. (b) Retracked simulated altimeter elevation profile and actual surface.

9. POLAR REGION SATELLITE MICROWAVE OBSERVATIONS

387

greater than this amount. However, this was not done in the analysis of the Greenland data. The second ramp elevations in Fig. 28b are indications of a second surface within the altimeter field of view. 5.3. Analysis of Data for Greenland Over-ights

A crossover analysis was performed on the Greenland data to examine the altimeter precision, i.e., the repeatability of the measurement as the altimeter crosses the same point on successive orbits. The RMS differenceof heights at 1032 crossovers is reduced from 2.6 to 1.9 m by retracking, implying that an average error of 1.72 m is removed by the retrackingxorrection. Analysis of the quality of the Seasat orbital ephemeris(Marsh and Martin, 1982)indicates that the residual radial orbit error is approximately 1 m. Therefore, the estimated orbit precision of 1.0m and the RMS value of 1.90 m imply that the range precision for these 1032 crossovers distributed over the ice sheet is 1.6 m. When a subset of 151 crossovers over the flattest and smoothest portion of the ice sheet in the vicinity of 72"N and 320"E was examined, the R M S difference was only 0.85 m, which is indicative of the better performance of the altimeter over smooth areas, Removal of the residual orbit error in a test case on the smooth area reduced the RMS difference to 25 cm. This result is indicative of the precision under optimum circumstances. The precision of 25 cm for the lO/sec waveform averages is consistent with a precision of 10 cm for l/sec averages normally used in ocean studies (Martin et al., 1983). Unfortunately, the precision degrades considerably over the more sloping and undulating parts of the ice sheet, as indicated by the overall precision of 1.6 m. The potential for contouring to small contour intervals is illustrated in Fig. 29, which is a 2-m contour-interval plot of part of the smooth plateau region in central Greenland. The elevations in the upper map are based on the "PGS-S4" dynamic orbits and in the lower map the orbits were adjusted to minimize the differences at crossovers. The major apparent surface features in the upper plot are artifacts of the unadjusted orbits. After adjustment, however, numerous undulations are revealed which are characteristic of the surface of much of the ice sheets and can be analyzed to infer various aspects of ice-sheet flow. This illustrates the importance of the removal of orbit errors to obtain an optimum representation of the ice-sheet surface. The 50-m preliminary topographic map of Greenland from retracked Seasat data is shown in Fig. 30. Although the data are corrected for the altimeter tracking errors, they are not corrected for the slope-induced

1

i'?

NORTH LATITUDE 4

*19

h Y *p

b

9. POLAR REGION SATELLITE MICROWAVE OBSERVATIONS

389

iO.0 325.0

305.0

310.0

315.0

320.0

EAST LONGITUDE FIG.30. Preliminary 50-m Greenland topographicmap from retracted Seasat data. Elevations are with respect to GEM-lob geoid and are not corrected for slope-induced errors.

displacements. The preliminary contour map reveals considerably more detail than any existing maps. When the remaining corrections have been applied, the data will be used to delineate ice catchment basins and to determine ice-movement directions. These parameters are important both for successfully planning field investigations and for understanding ice behavior. In addition, numerous detailed studies can be made of surface undulations and roughness in specific regions.

390

C. T. SWIFT ET AL.

ACKNOWLEDGMENTS One of us (CTS) is indebted to Dr. R. H. Thomas and two other anonymous reviewers who carefully read the original manuscript. Their critical reviews were appreciated. Discussions with Dr. R. 0. Ramseir concerning passive microwave retrievals of sea-ice fractions were of great technical value.

REFERENCES Anderson, V. H. (1966). High altitude side-looking radar images of sea ice. Arctic Roc. Symp. Remote Sens. Environ., 4th. Environ. Res. Inst., Ann Arbor, Mich. pp. 845-857. Barrick, D. E. (1972). Remote sensing of sea state by radar. In “Remote Sensing of the Troposphere”(V. E. Derr, ed.), Ch. 12. U. S. Govt. Printing Office, Washington, D. C. Brenner,A. C., Bindschadler,R. A., Thomas, R. H., and Zwally, H. J.,(1983). Slope-induced errors in radar altimetry over continental ice sheets, J. Geophys. Res. 88, 1617-1623. Brooks, R. L., Campbell, W. J., Ramseier, R. O., Stanley,H.R., and Zwally, H. J. (1978). Ice sheet topography by satellite altimetry, Nature (London) 274,539-543. Brown, G . S., Stanley, H. R., and Roy, N. A. (1981). The windspeed measurement capability of spaceborne radar altimeters. IEEE J. Oceanic Eng. OE6,59-63. Brown, W. E., Campbell,W. J., Chavez, P. S.,Schuchman,R. A., and Teleki, P. G., (1981). Seasat SAR coordinate transformationalgorithm with application to sea ice dynamics. Proc. Seasat Symp. Am. Geophys. Union, Baltimore, May (in press). Bryan, M. L. (1976). Interpretation key for SAR (L-Band) imagery of sea ice. Proc. Am. So?. Photogram. Fall Convention, Seattle pp. 406-435. Campbell, W. J., Wayenberg, J., Ramseyer, J. B., Ramseier, R. O., Vant, M. R., Weaver, R., Redmond, A., Arsenault, L., Gloersen, P., Zwally, H. J., Wilheit, T. T., Chang, C., Hall, D., Gray, L., Meeks, D. C., Bryan, M. L., Barath, F. T., Elachi, C., Leberl, F., and Farr, T. (1978). Microwave remote sensing of sea ice in tthe AIDJEX Main Experiment. Bound. Layer Meieorol. 13,309-337. Campbell, W. J., Gloersen, P., and Zwally, H. J. (1983). “Aspects of Arctic Sea Ice Observable by Sequential Passive Microwave Observations from Space.” To be published. Cartwright, D. E. (1971). Tides and waves in the vicinity of St. Helena. Philos. Trans. R. Suc. London Sec. A 270,603-649. Cartwright, D. E., Drives, J. S., and Tranter, J. E. (1978). Swell waves at St. Helena related to distant storms. Q. J. R. Meteorol. SOC.103,655-683. Cavalieri, D. J., Gloersen, P., and Campbell, W. J. (1984). “Observations of Sea Ice Properties with the Nimbus-7 SMMR.” To be published. Dunbar, M., and Weeks, W. F. (1975). Interpretation of young ice forms in the Gulf of St. Lawrence using side-lookingairborne-radar and infrared imagery. Cold Regions Res. Eng. Lab. Res. Rep. 337. Fedor, L. S., and Brown, G. S. (1982). Wave height and wind speed measurementsfrom the Seasat Radar altimeter. J. Geophys. Res. 87, 3254-3260. Garratt, J. R. (1977). Review of drag coefficientsover oceans and continents. Mon. Weuther Rev. 105,915-929. Gloersen, P., and Barath, F. T. (1977). A scanning multichannel microwave radiometer for Nimbus-G and SeaSat-A. IEEE J. Oceanic Eng. OE2.

9. POLAR REGION SATELLITE MICROWAVE OBSERVATIONS

39 1

Gloersen, P., Nordberg, W.,Schmugge, T. J., and Wilheit, T. T. (1973). Microwave signatures of first-year and multiyear sea ice. J. Geophys. Res. 78,3564-72. Gloersen, P., Cavalieri, D. J., and Campbell, W.J. (1981). Derivation of sea ice concentration, age, and surface temperature from multispectral microwave radiances obtained with the Nimbus7 Scanning Multichannel Radiometer. In “Oceanography from Space” (J. R. F. Cower, d.), pp. 823-829. Plenum, New York. Gloersen, P., Cavalieri, D. J., Chang, A. T. C., Wilheit, T.T., Campbell, W. J., Johannessen, 0.M., Kunzi, K. F., Ross, D. B., Staelin, D. H., Windsor, E. P.L., Barath, F. T., Gudmandsen, P., Langham, E., and Ramseier, R. 0.(1984). “A Summary of Results from the First Nimbus-7 SMMR Observations.” To be published. Guymer, L. B., and Le Marshall, J. F. (1981). Impact of FGGE buoy data on southern hemisphere analyses. Bull. Am. Meteor. SOC.62, 38-47. Hawkins, R. E., Livingstone, C. E.,Gray, A. L., Okamoto, K.,Arsenault, L. D., Pearson, D., and Wilkinson, T. L.(1980). Singleand multiple parameter microwave signature of sea ice. Proc. Can. Symp. Remote Sens. 6th, Halifax, May 21-23. Ice and Climate Experiment (ICEX)(1979). Report of Science and Applications Working Group. National Aeronautics and Space Administration, Goddard Space Flight Center, Greenbelt, Md. Johnson, J. D., and Farmer, L. D. (1971). Use of Side-Looking Airborne Radar for sea ice identification. J. Geophys. Res. 76,2138-2155. Johnson, J. W., Williams, L. A., Bracalente, E. M., Beck, F. B., and Grantham, W. L. (1980). Seasat-A satellite Scatterometer instrument evaluation. IEEE J. Oceanic Eng. OES, 138- 144.

Koerner, R. M. (1973). The mass balance of the sea ice of the Arctic Ocean. J. Glaciol. 12, 173- 185.

Leberl F.,and Clerici, E. (1981). Current status of metric reduction of active scanner images. Proc. Congr. I d . SOC.Photogram. 14th 23, Part B3, Comm. 111,435-451. Leberl, F., Elachi, C., Bryan, L., and Campbell, W. (1979). Mapping of sea-ice and measurement of its drift using aircraft Synthetic Aperture Radar Images. J. Geophys. 84. Leberl, F., Raggam, J., Elachi, F., and Campbell, W.J. (1981). ha-ice motion measurements from Seasat-SAR images. Proc. Seasat Symp. Am. Geophys. Union, Baltimore, May. (in press). Manual of Sea Ice Reporting (MANICE) (1980). Atmospheric Environment Service, Toronto. Marsh, J. G., and Martin, T. V. (1982). The SEASAT altimeter mean sea surface model. J. Geophys. Res. 87,3269-3280. Martin, T. V., Zwally, H. J., Brenner, A. C., and Bindschadler, R. A. (1983). Analysis and retracking of continental ice sheet radar altimeter waveforms. J. Geophys. Res., 88, 16081616.

Mognard, N., and Lago, B. (1979). The computation of wind speed and wave heights from GEOS-3 data. J. Phys. Oceanogr, 6,200-228. Morra, R. H. J., and de Loor, G. (1976). Ice detection by SLAR. Res. Rep. 16:3, Sea Ice 1975. Winter Naval Research Board, Norkopping, Sweden. Munk, W. H.,Miller, G. R., Snodgrass, F. E.,and Barber, J. J. (1963). Directional recording of swell from distant storms, Philos. Trans. R. SOC.London Ser. A 255, 505-584. Neuman, G., and Pierson, W. J., Jr. (1966). “Principles of Physical Oceanography.” Prentice-Hall, New York. Njoku, E. G., Stacey, J. M., and Barath, F. T. (1980). The Seasat Scanning Multichannel Microwave Radiometer (SMMR): Instrument description and performance. IEEE J. Oceanic Eng. OE-5, 100-115.

392

C. T. SWIFT ET AL.

Page, D. F., and Ramseier, R. 0.(1975). Applicationof active radar techniques to the study of ice and snow. J . Glaciol. 15, 171-191. Parashar, S. K.,Bibbs, A. W., Fung, A. K., and Moore, R. K. (1974). Investigation of radar discrimination of sea ice. Proc. Int. Symp. Remote Sens. Enuiron., 9th, ERIM, Ann Arbor, Mich. pp. 323-332. Paterson, W.S. B. (1981). “The Physics of Glaciers, 2nd Ed. Pergamon, Oxford. Snodgrass,F. E.,Groves, G. W.,Hasselmann, K., Miller, G. R., Munk, W. H., and Powers, W. H. (1966). Propagation of ocean swell across the Pacific. Philos. Trans. R. SOC.London Ser. A 259,431-497.

Tapley, B. D.,Born, 0.H.. Hagar. H. H., Lorell, J., Parke, M. E., Diamante, J. M.,Douglas, B. C., Goad, C. C., Kolenlciewia, R., Marsh, J, G.,Martin, C. F.,Smith, S.L., 111,Townsend,W.F., Whitehead,J. A., Byrne, H.M, Fedor, L. S., Hammond, D. C., and Mognard, N. M. (1979) Seasat altimeter calibration, initial results. Science 204, 1410-1412. Teleki, P. G., McLeish, W., Schuchman,R. A., Ross, D., Brown, W. E., Jr., and Mattie, M. (1978). Ocean wave detection and direction measurements with microwave radars. Proc. Oceans ‘79. Mar. Tech. SOC.,Washington. D. C. pp. 639-648. Thomas, R. H., Sanderson, T. J. O., and Rose, K. E. Effect of climatic warming on the West Antarctic ice sheet (1979). Nature (London) 277,355-358. Wilheit, T. T., Blinn, J. C., Campbell, W.J., Edgerton, A. T., and Nordberg, W. (1972). Aircraft measurementsof microwave emission from Arctic sea ice, Remote Sens. Enuiron. 2,129-139. Wittmann, W. I., and Schule, J. J., Jr. (1966). Comments on the mass budget of Arctic pack ice. Proc. Symp. Arctic Heat Budget Atmos. Circul. pp. 215-245. Yakovlev, G. N. (1977). “Ice Routes of the Arctic” (Transl. Department of National Defense, Ottowa). Zwally, H.J., Thomas, R. H.. and Bindschadler,R. A. (1981). Ice-sheet dynamics by satellite laser altimetry. Proc. lEEE Int. Geosci. Remote Sens. Symp. 2, 1012-1022. (Also NASA Tech. Memo. 82128,1981.) Zwally, H. J., Bindschadler,R. A., Brenner,A. C.,Martin, T. V.,andThomas, R. H.(1983). Surface elevation contours of Greenland and Antarctic ice sheets. J. Ceophys. Res. 88, 1589-1596.

CHAPTER10

PRECIPITATION IN TROPICAL CYCLONES L. S. FEDOR

CECILIA GIRZGRIFFITH National Oceanic and Atmospkeric Administration Weather Research Program Environmental Research Laboralories Boulder. Colorado

National Oreanic and Atmospheric Adminbrrarion Wave Propagatlon Laboratory Environmental Research Laboralories Boulder, Colorado

1. Introduction. . . . . . . . . . . . . . . 2. Rain Rate Estimates from the VIRR. . . . . . 2.1. Technique . . . . . . . . . . . . . . 2.2. Limitations . . . . . . . . . . . . . 2.3. Results. . . . . . . . . . . . . . . 3. Futurestudies . . . . . . . . . . . . . . References . . . . . . . . . . . . . . .

. . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . .

393 394 394 398 400 415 4 16

1. INTRODUCTION

The estimation of precipitation from satellite data is not new. Barrett and Martin (1981) have reviewed the numerous studies of the past two decades that resulted in the several existing satellite rain estimation techniques. These techniques use visible, thermal infrared, or microwave data. The VIS/IRbased schemes are empirical techniques that estimate rainfall for summertime convection in several geographic locations by use of the polar-orbiting or geostationary satellites. By starting from the radiative transfer equation and making certain assumptions, precipitation can also be estimated from microwave radiation emitted in, for example, the 18- and 37-GHz regions. Because of the coarse spatial resolution of the microwave data, though, researchers have found it fruitful to combine data from the VIS and IR channels with the microwave information to improve the microwave rainfall estimates. Most schemes have been derived for well-defined applications in meteorology, climatology, hydrology, or agriculture. The use of satellite data to estimate precipitation under tropical-cyclone conditions has been fairly limited. The most extensive work has been the operational applications of VIS/IR techniques to landfalling hurricanes (Waters et al., 1978; Scofield and Oliver, 1977). However, prior to Seasat, microwave data had been used to estimate rainfall in a few tropical-cyclonecase studies. Allison et al. (1974) had demonstrated the use of Nimbus-5, 19-GHz data and Adler and Rodgers (1977) had used the same channel to estimate rainfall for the inference of latent heat release. 393 ADVANCES IN GEOPHYSICS. VOLUME 27

ISBN 0- I 2 4 18827.9

394

CECILIA GIRZ GRIFFITH AND L. S. FEDOR

During its approximately 100 days of operation, Seasat made measurements over a number of Pacific tropical cyclones. The Griffith-Woodley rain estimation technique has been used to derive rain rates from the Seasat VIRR, thermal IR data for selected passes over three storms. Prior to this application the rain estimation technique had been derived and applied using GOES data only. It was anticipated, though, that the principal differences between the GOES and Seasat imaging sensors (pixel resolution and the mechanics of the scan) would minimally affect the rain estimates and a test was made to verify this conjecture. The technique and results are described in Section 2. In Section 3 several courses are suggested for future research. 2. RAINRATEESTIMATES FROM THE VIRR 2.1. Technique

The Griffith-Woodley satellite rain estimation technique (Griffith et al., 1978) was derived in south Florida for the estimation of rainfall from summertime convection. The SMS/GOES (Synchronous Meteorological Satellite/Geostationary Operational Environmental Satellite) thermal infrared (10-12 pm) data from the Visible and Infrared Spin Scan Radiometer (VISSR) were calibrated by data from a dense gage network (1 gage/ 3.4 km’) and from a Weather Service radar to produce empirical relationships that infer rainfall from cloud-top temperatures. As originally constructed, the technique requires a sequence of images to determine cloud life histories and the change in cloud area with time. However, when making operational rain estimates for landfalling hurricanes (Waters el al.. 1978),the life-history aspect of this technique is bypassed and estimates are made based on one image. The Griffith-Woodley technique is a totally automated scheme that uses a digital array of infrared temperatures to produce first an estimate of volumetric output of the convection, and second to infer rainfall rates. Raining convective clouds are identified by the threshold temperature of - 20°C. This threshold was chosen to maximize the determination of precipitating clouds, while minimizing the inclusion of nonraining clouds. The inferred rainfall, expressed as either total volumetric output (m3) or areaaveraged rain depth (mm), is calculated as a function of both areal extent of the storm at -20°C as well as the fractional coverage of the storm by colder temperatures. Rainfall rates for each satellite pixel (and subsequently isohyets) are derived by apportioning this calculated volume over the storm as a function of cloud-top temperature. Three assumptions have been made in applying the diagnostic GriffithWoodley technique to the real-time estimation of hurricane rainfall.

10. PRECIPITATION IN TROPICAL CYCLONES

395

1. The empirical relationships derived for Florida convective clouds can be applied to tropical storms. 2. It is valid to treat the tropical cyclone as one large cloud, even though it is composed of individual clouds at different stages in their life cycles. 3. The tropical storm exists at its maximum cloud size on the image of interest. The evidence to support assumption 1 is partial at present. Three comparisons for Atlantic hurricanes have shown that satellite-derived,areaaveraged rainfalls are within 10% of rain gage estimates from climatological networks (- 1500km2 gage-') for periods of 24 and 48 hr. These results give credence to the validity of using Florida relationships for gross (large-area, long-period) measures of hurricane rainfall. But, they leave unanswered the accuracy of the satellite-inferred rainfall intensities. Research is planned to compare rain rates estimated from airborne radar, satellite thermal infrared, and satellite passive mirowave data to assess the validity of rain rates derived from this technique. Assumption 2 is made as a matter of practicality. Individual cumuli and cumulonimbi often cannot be distinguishedby use of the -20°C isotherm, the technique's cloud-definition threshold. Individual clouds can only be determined as overshooting tops at colder temperatures. Likewise, clouds that are obscured from the satellite's viewpoint by a cirrus cover cannot be isolated. In the case of overshooting tops, information about individual clouds is incorporated via the distribution of the colder temperatures. However, convection that underlies cirrus will have an inferred rainfall only if the cirrus is as cold as or colder than -20°C and if the temperature of the cirrus overlying the convection is within the coldest half of the cloud. Assumption 3 is made to specify a point on the curve of the satellite cloudradar echo relationships. This assumption determines that the inferred echo coverage is 0.0667 of the cloud area and that the rain rate is 16.7 mm hr-'. (In the full, i.e., life-history, technique, rain rates range from 8.2 to 23.8 mm hr-' and the inferred echo coverage ranges from 0 to lo%.) This assumption is supportable because clouds producing little rain, clouds producing much rain, and clouds producing no rain are included under the hurricane's -20°C canopy, and the specified values for echo coverage and rain rate are approximately average values over the lifetime of a cloud. For tropical storms the computation of rainfall rate for each satellite picture element (pixel) in the grid is expressed by the relationship Dij = bijRv10-3/2gijCb

(1)

(2) where Dij is the inferred rainfall rate (mm hr-') in the ( i , j ) pixel of the grid, b, is the empirical weighting coefficient of the (i, j) pixel, RV is = 0*557(A,/gij)(bijCa,bdC b)

CECILIA GIRZ GRIFFITH AND L. S. FEDOR

396

the satellite-inferred, volumetric output rate of the storm for one image (m3 hr-l), converts the units from m3 to mm km', gij is the area (km2) of the ( i , j ) pixel, 2 arises from the rainfall apportionment scheme (see later), A M is the area (km') of the storm measured from the image and defined by the ,-2O"C isotherm, k is an index for each temperature increment in the IR cloud as cold as or colder than -2O"C, a k is the fraction of the storm covered by the kth temperature, bk is the empirical weighting coefficient corresponding to the kth temperature, and Cakbkruns over all pixels in the storm that are - 20°C or colder. The factor of 0.557 is the product of the tropical storm rainfall rate (16.7 x lo3 m3 hr-' km-2) and the inferred fractional coverage of echo area for tropical storms (0.0667) multiplied by the product of the apportionment and conversion constants (5 x The weighting coefficient b is the mechanism by which the inferred rainfall is related to the colder temperatures of the storm. It is an exponential function of GOES digital count (DC) and thus is an inverse exponential function of temperature.' The coefficient is of the form b

= exp(c,

+ c2V)/11.1249

(3) where V is either GOES digital count or temperature ("C), and the appropriate values of the constants cl and c2 are given in Table I. The b values range from 1.00 at -20°C to 4.55 for - 110°C. For temperatures warmer than -2O"C, the b values are zero. The purpose of the weighting coefficientsis most easily understood through example. Consider the extreme cases of two tropical cyclones that have identical areas enclosed by the -20°C isotherm. Cyclone A contains no TABLE 1. CONSTANTS FOR THE EMPIRICAL WEIGHTING COEFFIClENTS Digital counts (DC)

C1

c2

'

lS4SDC<176

177IDCI255

0.026667 0.01547

0.11537 0.01494

Temperature ( T ) -2OoCIT<-31"C 1.784059 -0.03094

-32"C
GOES data are in digital counts that take on integer values between 0 and 255, NOAA's National Environmental Satellite, Data, and Information Service (NESDIS) maintains a calibration by which digital counts can be related to blackbody temperature. In unenhanced data, digital counts between 0 and 176 correspond to temperatures between 56.8 and -31.2"C in increments of 0.5"C; between 177 and 255 digital counts, the temperatures range from -32.2 to - 110.2"Cin increments of 1.0"C.

10. PRECIPITATION IN TROPICAL CYCLONES

397

colder temperatures, while cyclone B is composed totally of pixels at - 110°C. In Eq. (2) the term Eakbkis identical to the one for cyclone A (i.e., k = 1, a, = 1.00, and b, = 1.00), but is equal to 4.55 for cyclone B (k = 1, a , = 1.00, and b, = 4.55). Thus the rain volume inferred for cyclone B will be greater than that inferred for A because of B s colder temperature. Now consider a third,cycloneof the same size as A and B, but in which two-thirds of the area is -20°C and the remaining third is - 110°C. The sum Z Ukbk is evaluated as 2.18 (k = 2, Z q b k = 0.667 x 1.00 + 0.333 x 4.55) and thus its rain volume will have a value intermediate to cyclone A's and cyclone Bs. Although the total area of the storm canopy at -20°C is used in the rain computation of Eq. (2), rain does not necessarily fall from the entire canopy. Rain is assumed to fall only from the coldest half of the canopy and is distributed so that half of the rain falls from the coldest 10% of the canopy area, with the remaining half occurring in the next coldest 40% of the canopy. This is called the 10-50/40-50 apportionment (10% of storm area has 50% of the rain, etc.) and is based on radar studies of intracloud rain rate distributions made by Woodley et al. (1975). The factor of 2 in the denominator of Eq. (1) is therefore required to halve the rain volume. To proceed with the calculation in Eq. (2), the evaluation of the four terms is made. 1. The areas of the pixels as cold as or colder than - 20°C are summed to evaluate AM. 2. The digital counts of these pixels are converted to b values through Eq. (3). 3. The fractional coverage of the storm at each temperature (ak) is determined and is then multiplied by the b value that corresponds to the temperature (bk).These products are subsequently summed (I:akbk). 4. E b is evaluated. This term assumes one of two values. The decision criteria for the appropriate value are based on a ranking of the b values. After the b values for the storm are ranked in descending order, two break points (which define the top 10 and 50% values) are determined. For example, if there were 500 pixels comprising the storm, the breakpoints would be defined by the 50th and 250th largest b values. Because the b values are inversely related to temperature, the top 10% of the b values correspond to the coldest 10% of the storm. Similarly, the top 50% of the b values comprise the coldest half of the storm. The b values in the top 50%, but not b. The b values in the top lo%, are summed and are referred to as in the top 10% are summed for Zlo% b. For the pixel of interest, if b, is in the top (i.e., coldest) lo%, Elo%b is used in (2). If b, is in the top SO%, but not the top lo%, E40%b is used. If b, is not in the top SO%, Dij is set to zero.

398

CECILIA GIRZ GRIFFITH AND L. S. FEDOR

2.2. Limitations

The estimation of convective rainfall from satellite data is a fairly controversial topic. The inherent problems concern the ability to remotely sense at nonoptimal wavelength (visible or IR) a quantity (rainfall)that exhibits high variability in space and time. The question is how reliable are the techniques that attempt to infer phenomena at the ground from measurements made at cloud top? There are several sources of error that affect the accuracy of the satellite-inferred estimate. As detailed herein, some pertain to the estimation of gross measures of rainfall and others to rain rate. There are two major sources of error in the computation of gross measures of rainfall, both of which have been alluded to previously. One source of error is cirrus contamination, that is, clouds that are cold enough in the infrared to qualify as raining clouds and that will have rainfall inferred, but which are actually convectively inactive. In the original technique, life-history information offsets this effect. But the error is potentially very great in the hurricane application, where large amounts of convective debris (cirrus) can be present and no incorporation of compensating life-cycle information is made. Another source of error occurs in cases in which most of the rain is produced by clouds that are warmer than -20°C. This condition is not too likely in the hurricane, although it was found by Augustine et al. (1981) to be operating in the tropical Atlantic Ocean west of Africa during phase 2 of GATE, the Global Atmospheric Research Project’s Atlantic Tropical Experiment. The most frequently occurring situation, however, is a combination of these two, that is, a thick layer of convective debris colder than -20°C obscures the active convection from the satellite sensor. Thus, the inference of rainfall in inactive regions tends to be offset by the existence of convection underlying this same debris region. Two additional error sources operate with regard to the generation of rainfall rates and isohyets. The results of Cheng and Rodenhuis (1978) illustrate the difficulty of determining rain rates from satellite data. Visible and infrared measurements from one NOAA I1 Very High Resolution Radiometer image (- 1-km resolution) were regressed against nearly simultaneous radar rain rates. The standard error of estimate for this one case was almost half of the maximum radar rain rate. Although not directly applicable to the Griffith-Woodley technique because they incorporated two wavelengths, these results provide supporting evidence that if the coldest tops do not lie directly over the rain that reaches the ground, then the inferred isohyets will be misplaced. This occurs when there is a vertical shear in the wind that causes the cloud to tilt. Second, the Griffith-Woodley technique infers isohyets by an entirely empirical scheme (through the 10-50/40-50 apportionment), and for single images the scheme produces unrealistically sharp gradients in the resulting products.

399

10. PRECIPITATION IN TROPICAL CYCLONES

The impact of these error sources can be assessed through comparisons of the inferred rain amounts and the inferred gradients with ground data. Table I1 is a current summary of the comparisons of satellite and ground estimates of spatially and temporally averaged rain amount (mm hr-l). Ground data varied in instrumentation type as well as in density and temporal resolution for each of these applications. Gage, radar, and a combined system of gages and radar have been used. Spatial sampling of the gage data ranged from 1 gage every 3 km2 to the several-hundred-squarekilometer spacing of the U.S.climatological network. Radar samples were generally taken at a resolution of 1" in azimuth and 1 nautical mile in range. The temporal frequency of the ground data was as short as 5 min or as long as 48 hr. In compiling these statistics no attempt was made to standardize and/or account for the errors inherent in the various ground data. Three frequently cited statistics (bias, root mean error, and leastsquares fits) are shown. The bias, B, is the quotient of the satellite (S) and ground ( G ) estimate, both accumulated for the total period of the application. The root-mean-square error is defined as ERh4S

= JCs

- G/G)2/N

where S and G are satellite and ground estimates of cumulative rainfall, respectively, at the end of the period, and N is the number of points. The correlation coefficient (R), the slope, and the intercept of the least-squares fit are as usually defined with the ground data as the independent variable. According to these statistics hurricane rainfall estimates exhibit the smallest deviations from perfect correspondence (see the last line of the table) for any of the applications shown. This is consistent with the findings of Griffith et al. (1978, 1981) and Meitin et al. (1981) that, in general, the correspondence

TABLE 11. COMPARISON OF AREA-AND TIME-AVERAGED RAINFALLS (mm hr-*) ESTIMATED BY SATELLITE AND GROUND DATAFOR SEVERAL APPLICATIONS ~

~~

~

_

_

_

Least-squares linear fit

R

Slope

Intercept

0.87 0.74

0.97

- 1.47

1.05

I .05

1.15

0.77 0.90

2.81 0.55

0.90 0.78

0.07 -0.27 0.18 23.93

.oo

0.00

Application

N

B

GATE FACE HIPLEX Dense Sparse Hurricanes Flash flood Perfect correspondence

53 51

0.84 1.28

0.79

15 9 3 2

ERMS

1.08

0.06

1.39

0.48

1 0.99

0.62 1.81 0.89 0.89

1.00

0.00

1.oo

1

.oo

_

400

CECILIA GIRZ GRIFFITH A N D L. S. FEDOR

between satellite rainfall estimates and ground data increases for heavier rainfalls and for longer time scales (e.g., accumulations for periods greater than 6 hr, both of which conditions are well satisfied by these hurricane estimates. Although it is not an error source per se, the applicability of the Florida relationshipsto Pacific storms is unproved. In the Pacific Ocean conventional ground data are limited to island stations and no special experiments comparable to GATE have occurred to provide radar data sets over the Pacific Ocean. If the GATE results of Table I1 are transferable to the Pacific Ocean, a 16% bias toward the underestimation of rain from convection in the Intertropical Convergence Zone is indicated. The gradients shown in the following section should be viewed as strictly experimental. The two error sources outlined above are inherent in the rainfalls mapped for hurricanes. And although the general outline of rainfall is usually found to be reproduced in the satellite isohyets, no assessments comparable to Table I1 have been completed for the inferred isohyets. Preliminary studies on the accuracy of the volumetric comparisons as a function of space and time, though, have been done and are pertinent to the question of the accuracy of the isohyets. Meitin et al. (1981) have shown that differences between ground data and satellite estimates are greatest for the spatial resolution of one GOES IR pixel (- 60 km2) and the GOES nominal temporal resolution (30 min). This would seem to indicate that errors larger than those for the volume assessments will be found in future quantitative assessments of the isohyets. In conclusion, Table I1 and the cited analyses suggest that gross measures of satellite-estimated hurricane rainfall (total volumetric output or areaaveraged rainfall) are at least as reliable as climatological gage data under hurricane conditions. [Dunn and Miller (1960)discuss the several factors that influencegage collection of rainfall; for example, the speed of the winds in the storm, the location of the gage relative to the center of the storm, and the elevation and orientation of the gage and state that in a 50-mph wind the gage may catch only half of the actual rainfall.] However, the reliability of satellite-inferred intensities and isohyets is presently unknown. 2.3. Results 2.3.1. GOES-Seasat Comparison. Before rain estimates were produced from the Seasat VIRR data, a test was run to determine the feasibility of using the GOES-derived relationships with Seasat data. Data were sought in the GOES digital archive that were coincident with Seasat passes over Pacific tropical cyclones. Only one GOES image was found that was nearly concurrent with a Seasat revolution.

10. PRECIPITATION IN TROPICAL CYCLONES

40 1

The principal differences between Seasat and GOES are related to differences in the orbits and scan geometries of each of the satellites, The thermal infrared wavelengths that each satellite's sensor responds to are almost equivalent-each senses the 10.5- to 12.5-pm range. Seasat was designed to have a sensitivity of 0.5 K;the sensitivity of GOES has been previously discussed. Seasat is in an 108" inclination circular orbit 800 km above the earth. The GOES systems are in geosynchronous orbit 35,000 km over the equator and two operational satellites(GOES-East and GOES-West) view the western Atlantic/eastern United States and the western United States/eastern Pacific, respectively. Seasat's VIRR produces an image by scanning across the orbit track, while GOES scans from west to east across its field of view and is mechanically stepped from north to south. At approximately 14:12GMT on July 16,1978,during Seasat's Rev 280, the nadir of the Seasat orbit was almost directly over the eye of Hurricane Fico (which was located at approximately 16"N and 133"W). The corresponding image in the GOES-West archive was at 15:15 GMT. (The subpoint of GOES-West is at 135"Wand the equator.) In the GOES digital data that we received, the VISSR subarea enclosing the hurricane was an array somewhat larger than the array for Fico on Seasat Rev 280. No attempt was made to trim the GOES data to the size of the Seasat scan. The particulars of the GOES and Seasat images are given in Table 111. Pertinent statistics for the rainfall calcualtions are also compiled in Table 111. Most values are comparable for both images. The Seasat storm area and total volumetric output are smaller than the respective GOES-estimated values, but the computed area-averaged depth (i.e., volume divided by area) TABLE111. Fico RAINFALL COMPARISONS FROM GOES VISSR AND SEASAT VIRR DATA

Date of image Time of image Array size Storm area Total volumetric output Area-averaged rain depth Minimum temperature Maximum rain rate

GOES

Seasat

July 16, 1978 1522 GMT 323 elements by 217 lines 1.597 x los km2

July 16, 1978 14:12 GMT 224 elements by 192 lines 6.701 x lo5 km2

1.49 x lo9 m3 hr-'

1.34 x lo9 m 3hr-'

1.96 mmhr-'

2.00 mm hr-'

-73°C

- 80°C

10.62 mm hr-'

13.61 mm hr-'

402

CECILIA GIRZ GRIFFITH AND L. S. FEDOR

are practically identical. All other parameters being equal, the GriffithWoodley technique will compute a smaller volumetric output for the smaller storm area. Table I11 and the histograms of each satellite’s digital counts (Figs, 1 and 2) indicate that the Seasat sensor showed colder minimum temperatures than GOES, It is these colder minimum temperatures in the Seasat data that result in a greater maximum rain rate for the Seasat data. The inferred rain rates are plotted in Figs. 3 and 4. The data for each satellite have been projected onto a Cartesian geographic map. Although major rainfall patterns are comparable, there are differences in details that are due principally to the time difference between the two views. The important rainfall patterns that can be seen in both of these figures are that (1) the highest rain rates are in an annulus surrounding the eye; (2) there is a major rainband that runs south-southwest through southeast of the eye; and (3) there is a hint of a minor rainband that runs from the central canopy in the northeast quadrant, breaks due east of the storm, and picks up again in the southeast quadrant where it lies between the central canopy and the outer band. Because the coldest temperatues are usually found in a tropical-cyclone’s central canopy, the highest rainfall rates should also be found there. In comparing Figs. 3 and 4, the Seasat rain rates near the core of the hurricane are higher than the corresponding GOES rain rates. Seasat’s higher core rain rates are due not only to colder temperatures (Figs. 1 and 2) but also to pixel size. The GOES pixel area is -60 km2 and so nearly constant (within 2%) over the area of the storm. O n the other hand, Seasat’s pixel size in the raw data is smallest at nadir (53 km2), but increases along the scan, becoming about 400% greater and sampling an area of 202 km2 at the end of the scan. As a consequence of the pixel area difference, given the same volume of rain in both the GOES and Seasat pixel near the storm core, the Seasat rain depths will always be greater (refer to Eq. (2)). Differencesin detail between Figs. 3 and 4 are due to actual changes in the convection during the period between the two views of Fico, rather than being a peculiarity of the rain estimation technique. Small cumuli can grow and decay in 15 min, and convective elements projecting above cloud tops (“overshooting tops”) can do likewise. As an example, the Seasat inferred rainfalls show rather high rain rates just southwest of the intersection of the 15th parallel and the 135th meridian, where the GOES data show relatively low rain rates. These tops seen by Seasat apparently dissipate during the hour that elapses until the time of the GOES image. Conversely, the GOES rain rates are greater than the Seasat rain rates in the vicinity of 129.5”W and 9.5”N. Presumably these tops continue to grow during the hour separating the Seasat and GOES images. Additional support for the contention that rainfall details are a consequence of local changes in the convection is provided by two studies of pixel rainfall

10. PRECIPITATION IN TROPICAL CYCLONES

403

m

1800

1600

-

1400

I u

1200

g

-

3

5 c

loo0

n f

. -

E, z

-

800

600

400

200

-

-25-

-40

-

=I1-75

0

Temperature (OC)

FIG.1. A histogram of the frequency of GOES digital counts corresponding to temperatures greater than -20°C for Fico on July 16, 1978, 15: 15 GMT.

404

CECILIA GIRZ GRIFFITH AND L. S. FEDOR 2ooo

-

I -

io

Temperature (OC)

FIG.2. A histogram of the frequency of Seasat digital counts corresponding to temperatures greater than -20°C for Fico on July 16,1978, Rev 280.

10. PRECIPITATION IN TROPICAL CYCLONES

405 -

~

I

I I

~

25.1

-

.

J. I

I

I I I I

I I

1

I I

I

I

1

I I I I I

I I

...

20.(

.LI I

I I

I I

I I I

I I

1I

I I I

I I

I 1 I I I

1

15.(

I I I

I I

I 1 I

I I I

I I

I

I I I I

I I I

1o.t

1I I I

I I I I

I

I I

I

I I

5s I

-145.0

I

I

-140.0

I

-135.0

I

-130.0

I

-125.0

FIG.3. Rainfall rates (mm hrf ') for Hurricane Fico on July 16,1978,15: 15 GMT, inferred from the GOES-West thermal IR data.

correlations. Pixel rainfalls within a tropical storm should be fairly well correlated for two nearly simultaneous images, but rather poorly correlated for images whose time differences are large, say 12 hr. The results bear this out. In Fig. 5 the correlation of Seasat and GOES rain rates via a bin-by-bin comparison was computed once both storms were collocated to the aircraftdefined eye location. The rainfalls were averaged into bins 0.26"(- 25 km) on a side. In the least-squares fit of Fig. 5,83% of the variance is accounted forthe correlation coefficient is 0.91. Figures 6 and 7 on the other hand correlate Fico Rev 280 rainfalls with Fico Rev 251 rainfalls (49 hr earlier) and Rev 331

406

CECILIA GIRZ GRIFFITH AND L. S. FEDOR __

I

25

I I - - J -

.-

-----

I

I

--1-

-145.0

I I

I I

I

I

I I

1 I I

I I

I I I - 1 - - -

I I

I

5

-J.-I

I I I I

I I I

..J . . . I 1

I

.____..

I

I

I I

I I

I

I

I

I

I I

I I

I

I

,

I I

I I I

I

I

I

,

I

I

I

I

I

4

I

I I

I I

I

I

I I I

I

I I I I

--1I

I

I I

-1---

1 I

I I

IC

I

I

..--..---L

I

I

I

I

I

I I

- - J.

I

I

I

I

I I

I

I

I

I I

I

I I I

I

I

I I I

I I

I

1 I

15

~

I

1

2c

~r - - - - -

I I

I

I .J.-I I

I

I I

I

I

I

I

I

I

I

I I I ..

--.----..L I

I I I

I I I

I 1 I

I I

I I I I I I

-1400

I'

: :\'3

, ,

I I I I I I I

I I

-135.0

I

,..;:::

I I I I

I

I I

1

I

I

I

I

I

I .J.-I

I

I

-130.0

I I ~

-125.0

FIG.4. Rainfall rates(mm hr-') for Hurricane Fico on July 16,1978,14: 12 GMT,inferred from the Seasat Rev 280 VIRR data.

rainfall (86 hr later), respectively. For both of these the correlation coefficient is considerably smaller. In the former case the correlation coefficient is 0.21, and in the latter it is 0.51. One effect of the apportionment scheme on the rainfall mapped over the storm can be seen in Fig. 5. The scheme causes tight (and probably unrealistic) gradients in the rainfall in the core of the storm. This in turn produces the jump in both rain rate populations seen at approximately the 4-mm hr-' interval. Additionally, although it is not the case here, if there were a cover of thick cirrus over the eye, rain would be incorrectly inferred over the eye.

407

10. PRECIPITATION IN TROPICAL CYCLONES 0 0

0

I

0

TOTRL

POINTS-

MEAN O I F F . = RMS O I F F . =

/

370

-1.271 1.637

Q

REGRESSION .CBEFF.=

!f

Y-INTERCEPT= 1.012

1.090

CORRELATION COEFF.= 0.908 RMS D I F F . FROM AEGR. L I N E = 1.611

I

/

0

%i

0

/

0 0

Q

p>oO "

I

I

I I

I

I

GOES R R I N R R T E (mm hr-1) FIG.5. A correlation of the GOES- and Rev 280 Seasat-inferred July 16,1978, Fico rain rates. Both data sets were binned to 0.26" x 0.26" grid squares, and then collocated to the eye position.

2.3.2. Seasat Rain Estimates. Rain estimates were made for a total of 10 Seasat passes over three tropical cyclones. Rainfall statistics for these storms are compiled in Table IV and plots of the inferred rain rates are shown for selected passes in Figs. 8-1 1. For the lowest two intervals the rates are coded such that rainfalls from 0 to 2 mm hr- are blank and 2 to 8 mm hr- are sparsely dotted. The densely packed dots and the plus signs represent rainfalls in the range 8 to 12 mm hr-' and >12 mm hr-l, respectively. In storms with maximum inferred rain rates greater than 17 mm hr-' (Table IV),

408

CECILIA GIRZ GRIFFITH AND L. S. FEDOR

/

POINTS. 398 M E A N DIFF.= 0.U30 RMS 0IFF.- U.681 R E G R E S S I O N COEFF.= 0.199 Y - I N T E R C E P T = 2.667 C B R R E L A T I O N COEFF.= 0.212 R M S OIFF. FROH REGR. L I N E = 3.522 TOTRL

~t

/ / /

b

c0

4.000

6.000

12.000

V I R F i RFlIN RRTE FROM CiRaIT a 2 8 0

16.000

20.0(

(mmhr-1)

FIG.6. A correlation of the Fico rain rates inferred from Seasat Rev 280 and from Seasat Rev 251. Both data sets were binned to 0.26" x 0.26" grid squares. The eye positions were then collocated.

these two contour levels represent 8 to 16 mm hr-I and >16 mm hr-l. These cases are noted in the appropriate figure captions. 2.3.2.2.Hurricane Fico. Hurricane Fico was viewed by Seasat on five passes that were virtually directly over the eye. In the first pass (day 193, July 12), Fico had just attained hurricane status at 1290 GMT and was located off the west coast of Mexico. The hurricane had peak winds of 115 knots in the eastern Pacific. By July 18 (day 199) Fico was in the central Pacific with 80-knot winds (Shaw, 1979). Fico traveled steadily westward

10. PRECIPITATION IN TROPICAL CYCLONES

TOTAl

I

POINTS=

H E R N DIFF.= RWS n 1 F F . I

409

/

Y13

/

0.808 3.910

R€GRESSION COEFr.=

/

0.533

Y - I N l E R C E P T - 0.933 CORRELATION COEFF..

4.000

/

0.508

12.000

8.OOU

V I F l R R R l N R A T E (mmhr-1)

-

16. D U O

20.00[

SEASRT O R B I T a?80

FIG.7. A correlation of the Fico rain rates inferred from Seasat Rev 280 and from Seasat Rev 331. Both data sets were binned to 0.26" x 0.26" grid squares. The eye positions were then colfocated.

and at the time of the last pass (day 201, July 20) was almost due south of the island of Hawaii. Figures 8-1 1 are the chronologically ordered plots of the VIRR-inferred rain rates for four passes of Fico. Rainbands and the eye can be seen in these plots. There is a major rainband to the east and south of Fico, for example, that appears in alternate plots. The highest rainfalls have been inferred in the central dense overcast region and are usually close to the eye of the storm.

TABLEIV. STATISTICS FOR SEASAT RAINFALL DATA

Day

Time GMT)

Rev

Area (105km2)

Volume (109m3hr-')

D (mmhr-')

max RR (mm hr-')

min T ("C)

Fico Fico Fico Fico Fico

193 194 195 197 20 1

12:56:16 01:21:58 13:34:13 1 4 11:40 04:26:00

222 229 251 280 331

5.1 1 12.32 4.47 6.70 6.03

1.05 2.44 1.03 1.34 1.16

2.06 1.98 2.3 1 2.00 1.93

15.47 18.34 13.82 13.61 15.65

- 92 - 88 -84 -80 - 83

Agnes Agnes Agnes

207 207 210

11:21:27 22:26:01 22:35:09

42 1 428 47 1

26.36 17.03 12.42

5.16 3.73 2.41

2.20 2.19 1.94

2 1.08 19.00 20.5 1

-93 -93 -86

Carmen Carmen

226 226

11:30:08 22:40:05

693 700

6.22 2.87

1.09 0.55

1.75 1.91

16.69 14.16

-73 -81

Storm

10. PRECIPITATION IN TROPICAL CYCLONES

41 1 18.5N

..

1

.p'

r ,

I-

6. EN 126.3W

I10.5W

FIG.8. A plot of Seasat VIRR-inferred rain rates (mmhr-l] for Hurricane Fico on Rev 222. July 12, 1978. See text for contour intervals. Z0.6N

I

.

Y.

4.011

126. OW

101.9w

FIG.9. A plot of Seasat VIRR-inferred rain rates (mmhr-') for Hurricane Fico on Rev 229, July 13, 1978. The maximum contour is > 16 mm hr-'.

Three of the Seasat passes (Revs 222, 251, and 280) were early morning views, shortly after 05:OO local mean solar time. The remaining two passes were evening views at 17:30(Rev 229) and 18:30 (Rev 331) local time. With the exception of Rev 331 (in which Fico's behavior seems to be anomalous), these storms appear to exhibit the variation in storm area

-

-

412

CECILIA GIRZ GRIFFITH AND L. S. FEDOR 17.8N

I

1 133. BW

9. ON

118.2W

FIG.10. A plot of Seasat VIRR-inferred rain rates (mm hr-') for Hurricane. Fico on Rev 251, July 14, 1978. 20.1N

V

z=p

0 '

'

5.3N

I

142. SW

123

FIG.11. A plot of Seasat VIRR-inferred rain rates (mmhr-') for Hurricane Fico on Rev 280, July 16,1978.

documented by Browner et al. (1977). In a study of 16 days of eight Atlantic storms, Browner et al. found that the cloud area (defined by the -20°C isotherm) of hurricanes and tropical storms shows a diurnal oscillation. The data were composited by local mean solar time and, in the mean, storm area was maximum at 17:OO local time and minimum at 04:OO local time. The same phase of the diurnal oscillation is evident not only in Fico's area but also in the volumes, area-average depths, and maximum rain rates listed in Table IV, with

413

10. PRECIPITATION IN TROPICAL CYCLONES

the exclusion of the Rev 331 data. The inferred volumetric output and areaaverage depths would be expected to show a diurnal oscillation, because each is a direct function of cloud area. The diurnal oscillation is apparent in comparing the early morning figures (Figs. 8,10,11) with one of the evening figures (Fig. 9). In the early morning figure Fico covers a much smaller area than in the evening figure. Although a minimum of 12-hourly imagery is required to document a diurnal variation, data from the first four Fico passes suggest its existence in the central Pacific tropical cyclone as well as in the Atlantic tropical storm. 2.3.2.2. Severe tropical storm Agnes. In the three Agnes passes, the center of this severe tropical storm was in the South China Sea off the coast of China. On July 26, 1978 (day 207), Agnes was about 60 nautical miles southeast of Hong Kong and moving westward. However, by July 29 (day 210) Agnes was approximately 150 nautical miles southwest of Hong Kong and moving eastward. The presence of Typhoon Wendy off Shanghai (1000 miles away) produced the Fujiwara effect,wherein two tropical cyclones move around and toward each other. From July 26 to July 30, Agnes’ track traced an elliptical, counterclockwise loop in the South China Sea, causing more than 500 mm of rain to fall in 8 days at Hong Kong (Bell, 1979). In the plot of rainfall for Rev 428 on July 26 (Fig. 12) the storm’s cyclonic organization can be seen, but 3 days later (Fig. 13) the structure is not as apparent, despite a lower central pressure at this time. Although there does appear to be a central 27.3N

0 c

7.111

I

101.5E

127.-

FIG.12. A plot of Seasat VIRR-inferred rain rates(mm hr- l ) for severe tropical storm Agnes on Rev 428, July 26, 1978. The maximum contour is > 16 mm hr-’.

414

CECILIA GlRZ GRIFFITH AND L. S. FEDOR

I

1

1 4.9N

FIG.13. A plot of Seasat VIRR-inferredrain rates (mmhr- ’) for severe tropical storm Agnes on Rev 471, July 29, 1978. The maximum contour is > 16 mm hr-’.

dense overcast region, there are no well-organized rainbands and the convection appears to consist of numerous cumulonimbi. On the average, Agnes is two to three times larger than Fico and Carmen for the data of this study. This is reflected in the rain volumes (Table IV), although the area-average depths of Fico, Agnes, and Carmen are comparable. Agnes’ minimum temperatures are the coldest of the three cyclones and the maximum rain rates are consequently greater. Bell (1979) remarks that because of an abnormally warm upper troposphere that limited the depth of the storm’s convection, rain rates in Agnes were never very intense and less than 10 mm hr-’ at the Royal Observatory in Hong Kong during most of the period between July 26 and 30. Presumably Bell is referring to rainfall rates determined from a time-integrated gage measurement rather than an instantaneous rainfall rate determined from radar. If so, Bell’s observation is not strictly comparable to the VIRR-inferred rain rates, because of the spatial and temporal differences of the two samples. However, Figs. 12 and 13 indicate that near and over land the maximum rain rates are relatively low, generally being less than 8 mm hr-l. 2.3.2.3. Typhoon Carmen. Carmen was a small typhoon that caused widespread flooding and considerable damage during its lifetime (Staff, Joint Typhoon Warning Center, 1979). In the two Seasat passes (Revs 693 and 700) on August 14,Typhoon Carmen was observed to be smaller, drier, and warmer than Hurricane Fico or the severe tropical storm Agnes (Table IV). The compact, symmetrical shape of Carmen is apparent in the rainfall plots of Figs. 14 and 15.

10. PRECIPITATION IN TROPICAL CYCLONES

415 33.3N

4

'. <-.. ....--.-._.. p.

'

.

I

.

*.

p

.

Q

?

15.9Ei

L

120.4E

FIG. 14. A plot of Seasat VIRR-inferred rain rates (mm hr-') for tropical cyclone Carmen on Rev 693, August 14, 1978. 30.5N

c O

C

3

1

21.5N

122.5E

136.2E

FIG.15. A plot of Seasat VIRR-inferred rain rates (mm hr-') for tropical cyclone Carmen on Rev 700, August 14,1978.

3. FUTURE STUDIES It has been demonstrated that rain rates of tropical storms and cyclones can be derived from Seasat VIRR data. These rain rates are useful in themselves for assessing storm rainfall prior to landfall. But VIRR-derived rain rates can also be used to correct for attenuation in microwave channels sensitive to

416

CECILIA GIRZ GRIFFITH AND L. S. FEDOR

rainfall, such as the altimeter and scatterometer. Uncorrected data from the altimeter, for example, have resulted in unrealistic winds for hurricanes and tropical cyclones. Although the Scanning Multichannel Microwave Radiometer (SMMR) on board Seasat gives a direct computation of rain rates, its view is fixed and limited to one side of Seasat’s scan. Consequently an alternate scheme to generate rainfalls is needed for the other half of the track. Future work with these data is suggested for at least three lines of study. First, comparisons of SMMR- and VIRR-determined rain rates should be made for several storms. Second, the performance of both SMMR and VIRR rain rates for correcting altimeter winds should be assessed. Last, the possibility that for western Pacific storms the relationship between IR temperature and rain rate may differ from the Florida-derived relationship needs to be investigated. REFERENCES Adler, R. F., and Rodgers, E. B. (1977). Satellite-observed latent heat release in a tropical cyclone. Mon. Weather Rev. 105,956-963. Allison, L.,Rogers, E., Wilheit T., and Felt, R. (1974). Tropical cyclone rainfall as measured by the Nimbus 5 Electrically Scanning Microwave Radiometer. Bull. Am. Meteorol. SOC. 55, 1074- 1089.

Augustine, J. A,, Griffith, C. G.,Woodley, W.L., and Meitin, J. G. (1981). Insights into errors of SMS-inferred GATE convective rainfall. J . Appl. Meteorol. 20,509-520. Barrett, E. C., and Martin, D. W. (1981). “The Use of Satellite Data in Rainfall Monitoring.” Academic Press, New York. Bell, G. (1979). Severe tropical storm Agnes, July 1978. Mar.Weather Log 23,227-230. Browner, S. P., Woodley, W. L., and Griffith, C. G. (1977). Diurnal oscillation of the area of cloudiness associated with tropical storms. Mon. Weather Rev. 105,856-864. Cheng, N., and Rodenhuis, D. (1978). An intercomparison of satellite images and radar rainfall rates. Tech. ConJ Hurricanes Trop. Meteorol., l l t h , Am. Meteorol. SOC.,Boston pp. 224226. Dunn, G.E., and Miller, B. I. (1960). “Atlantic Hurricanes,” pp. 105-106. Louisiana State Univ. Press, Baton Rouge, La. Griffith, C. G., Woodley, W. L., Grube, P. G., Martin, D. W., Stout, J., and Sikdar, D. N. (1978). Rain estimation from geosynchronous satellite imagery-Visible and infrared studies. Mon. Weather Rev. 106,1153-1171. Griffith, C. G., Augustine, J. A., and Woodley, W.L. (1981). Satellite rain estimation in the U. S. High Plains. J . Appl. Meteorol. 20,53-66. Meitin, J. G.,Griffith, C. G., Augustine, J. A., and Woodley, W. L. (1981). A standard verification for rainfall estimation from remote platforms. Precipitation Measurements from Space Workshop Report (D. Atlas and 0. W. Thiele, 4s.). NASA, Goddard Space Flight Center, Greenbelt, Md. Scofield,R. A., andoliver, V. J. (1978). Using satellite imagery to estimate rainfall from two types of convective systems. Reprint Volume, Tech. Con5 Hurricanes Trop. Meteorol., llth, Am. Meteorol. SOC.,Boston, pp. 204-21 1. Shaw, S. L. (1979). Central north Paoific tropical cyclones, 1978. Mar. WeatherLog 23,166-172.

10. PRECIPITATION IN TROPICAL CYCLONES

417

Staff, Joint Tyhoon Warning Center (1979). Western north Pacific typhoons, 1978. Mar. Weather Log 23,306-319. Waters, M. P., III,Griffith,C. G., and Woodley,W. L.( 1978). Use of digital geostationary satellite imagery for real-time estimation of hurricane rain potential in landfalling storms. Tech. Conf. Hurricanes Trop. Meteorol.. Ilth, Am. Meteorol. Soc., Boston pp. 198-203. Woodley, W. L., Olsen, A. R., Herndon,A., and Wiggert, V. (1975). Comparisonof gage and radar methods of convective rain measurement. J . Appl. Meteorol. 14,909-928.

This Page Intentionally Left Blank

CHAPTER11

LIVING MARINE RESOURCES APPLICATIONS R. MICHAEL LAURS National Marine Fkheries Service South wesl Fisheries Cenfer La Jolla. California

JOHNT. BRUCKS National Marine Fkheries Service Fishery Engineering and Development Division Notional Space Technology Laboratories N S T L Slation. Mi,rsissippi

1. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . 2. Satellite Ocean Remote-Sensing Applications in Fisheries Research. . . . . . 2.1. Use of Satellite Infrared Thermal Data in Fisheries Research . . . . . . 2.2. Modeling Larval Transport Mechanisms Using High-Resolution SASS Wind-Stress Measurements . . . . . . . . . . . . . . . . . . 2.3. Use of Coastal Zone Color Scanner Data in Fisheries Research . . . . . 3. Utilization of Satellite Data in Fisheries-Aids Products Distribution to Fishermen 3.1. Thermal Boundary Charts . . . . . . . . . . . . . . . . . . 3.2. Sea-Ice Forecast Charts . . . . . . . . . . . . . . . . . . . 3.3. NASAiJPL Satellite Data Distribution System and Fisheries Demonstration Program to US. West Coast Fisheries . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . .

. 419 . 421 . 421 . 429

.

436

. 443

.

443

. 441 . 441 . 450

1. INTRODUCTION Satellite oceanic remote sensing is beginning to play an important role in fishery research and fishery management by providing synoptic oceanic measurements for use in evaluating environmental effects on the abundance and availability of fish populations. Variations in ocean conditions play key roles in natural fluctuations of fish stocks and in their vulnerability to harvesting (Hela and Laevastu, 1961, 1970). Information on the changing ocean, rather than on average ocean conditions, is necessary to understand and eventually model the effects of the ocean environment on fish stocks (Sette, 1961). This knowledge is required to provide the best possible advice in making fishery management decisions and to develop efficient harvesting strategies for fishery resources. The use of satellite remote sensing in oceanography expanded considerably in the 1970s and satellites with dedicated oceanographic sensors were first launched in 1978. Coincident with the developments in satellite oceanography, there have been substantial increases in demand on the marine waters of the United States for activities such as fisheries development and utilization, recreation, and offshore oil exploration. With these increased activities have 419 ADVANCES IN GEOPHYSICS, VOLUME 21 ISBN 0- 12-0 18827-9

420

R. MICHAEL LAURS A N D JOHN T. BRUCKS

come expanded requirements for living marine resource-management programs through legislative mandates such as the Marine Mammal Protection Act of 1972,the Fisheries Conservation and Management Act of 1976,and the Ocean Pollution Research Development and Monitoring Act of 1978. With these responsibilities, there has been an increasing need to design and initiate improved ocean-monitoring programs for resource management. The capabilities of evolving satellite remote-sensing technology, combined with conventional data collection techniques, provide a powerful tool for the efficient, cost-effective management of living marine resources. Remote sensing is not entirely new to fishery scientists nor to fishermen harvesting living marine resources, Mariners learned long ago they could increase their perspective by elevating themselves above the water’s surface. Fishermen have done this by use of Crow’s nests on ships, hot-air balloons, and aircraft. The sensor often used has been the naked eye, usually aided by a telescope or binoculars. Visual forms of remote sensing are common today in many fisheries, e.g., the use of helicopters operating from modern tuna purse seiners fishing on the high seas. In addition, aircraft carrying instruments for making oceanographic measurements have been used for supporting fisheries research studies (Pearcy, 1973; Thomas, 1981; and others), for locating areas favorable for fishing (Squire, 1961, 1972;and others), and for locating schools of fishes (Squire, 1982;and others). The era of space technology brought new perspectives in remote sensing for fisheries. Man acquired the ability to view entire oceans and seas in a matter of minutes. Early satellite remote-sensing investigations for fisheries used information gathered over the ocean by systems designed for making terrestrial and meteorological observations. The objectives of these studies were to determine if certain satellite sensors could measure or identify selected oceanographic features believed to influence the distribution of fish. For example, early feasibility investigations were conducted to evaluate if fish distribution patterns may be related to temperature and color measurements made by satellites, e.g., the ERTS-1menhaden investigation (Kemmerer et al., 1974),the Skylab-3 game fish investigation (Savastano et al., 1974),and the Landsat menhaden and thread herring investigation (Brucks et al., 1977). The recent development of satellite remote-sensing techniques for fisheries applications employs data from active and passive instrumentation which has been or is presently aboard a number of satellites. A wealth of satellite remote-sensing systems exists today. For the most part, however, satellite remote-sensing applications in fisheries have concentrated on the measurements of ocean temperature and color, and computation of ocean transport based on satellite-measuredwind stress. Synoptic coverage of ocean temperature, color, and wind stress by no means represents the entire spectrum of environmental information necessary or required for fisheries applications.

11. LIVING MARINE RESOURCES APPLICATIONS

42 1

However, knowledge of key oceanographic conditions and processes affecting the recruitment, distribution, abundance, and harvests of fishery resources may often be deduced using these data. The purpose of this chapter is to review published living marine resource applications of ocean measurements made by Seasat and the Nimbus-7 Coastal Zone Color Scanner (CZCS). In addition, recent U.S. research involving the use of satellite infrared thermal data in fisheries is described. Satellite ocean remote-sensing applications in fisheries research is treated first and then the utilization of satellite data in fisheries-aid products that are distributed to fishermen.

2. SATELLITE OCEANREMOTE-SENSING APPLICATIONS IN FISHERIES RESEARCH The resolution capabilities of remote-sensing systems carried by spacecraft are not adequate for direct detection of fish schools. This has necessitated fishery scientists to examine ocean features that can be measured with spaceborne sensors and in turn can be used in fishery resource investigations. The use of satellite infrared thermal data, scatterometer wind-stress measurements, and Coastal Zone Color Scanner measurements in fisheries research investigations is yielding results which demonstrate that satellite oceanic remote sensing can be an important tool in fisheries research. 2.1. Use of Satellite Infrared Thermal Data in Fisheries Research

Satellite infrared thermal data are playing an increasingly important role in fisheries research. It is being used by U.S. fishery scientists to (1) define marine habitats of fishery resources using satellite data, which are collected contemporaneously with fishery/biological data and ground truth measurements gathered by research and fishing vessels; and (2) describe and explain variability in circulation and water mass distributions using satellite data alone or in conjunction with physical oceanographic measurements, with a view toward understanding the influence of ocean variability on fishery resources and fishing grounds. 2.1.1. Use of Satellite Infrared Imagery for Describing Ocean Processes in Relation to Spawning of the Northern Anchovy. Satellite infrared imagery was used by Lasker et al. (1981) for describing ocean processes in relation to spawning of the northern anchovy (Engradis mordax). In this study NOAA-6

satellite thermal imagery of the Pacific Ocean adjacent to the United

422

R. MICHAEL LAURS AND JOHN T. BRUCKS

States-Mexico west coast was collected on a daily basis coincident with fine-grid oceanographic ship observations. Shipboard observations included sampling of anchovy eggs, larvae, and adults as well as physical oceanographic measurements, e.g., continuous monitoring of temperature and salinity, discrete surface temperature measurements, and measurement of subsurface temperature profiles. The objectives of the investigation were to relate variations in mesoscale sea surface-temperature distributions with anchovy spawning and to identify and delineate ocean processes that might be important to the survival of fish eggs and larvae, e.g., upwelling (Lasker, 1978) and offshore transport (Parrish and MacCall, 1978). Based on satellite imagery and confirmed by shipboard observations during the study, which was conducted during a peak period in anchovy spawning, there were distinct temperature regimes in the general geographic region where anchovy spawning normally takes place. There were (1) a cold area resulting from upwelling off Point Conception and north, (2) a large warm area which extended from Baja California into the Southern California Bight and approximately 185 km offshore, and (3) a large area of the bight with intermediate surface temperatures. The mesoscale features of surface temperature changed slowly so that for a given day the temperature distribution apparent in a satellite image did not appear to be markedly different from the preceding or following few days. Figure 1 (from Lasker et al., 1981) is an infrared thermal image which has superimposed on it the geographic distribution of anchovy egg catch. The distribution of just-spawned anchovy eggs clearly showed that nearly all spawning was confined to the Southern California Bight and to a band 40 km wide, parallel to the Baja California coast. The seaward extent of spawning in the bight was apparently abridged by the southward intrusion of recently upwelled water indicated by the 14°C isotherm. Also, anchovies were excluded from the water warmer than 17°C beyond 40 km of the Baja California coast. The satellite temperature/anchovy egg relationships were corroborated by mapping the distribution of first-day eggs on sea surface temperature observations made concurrently aboard ship. Relatively high numbers of anchovy larvae were found in plankton hauls taken by another research vessel in the coastal area north of Point Conception in February 1980. Temperatures were very warm for this entire zone in February, with progressively warmer conditions to the south. Winds were light and from the south prior to March 11 and upwelling was minimal. Winds from the north increased on March 12 and persisted. When the area north of Point Conception, where anchovy larvae had been collected in February, was sampled in March, no larvae of any age were found. This suggested to the authors that the extensive upwelling, as indicated by wind data and observed by satellite, carried anchovy larvae out of the coastal zone

11. LIVING MARINE RESOURCES APPLICATIONS

423

FIG.1. Distribution of anchovy eggs superimposed on NOAA-6 infrared thermal image of the Southern California Bight. The 14°C isotherm plotted from satellite gray-scale calibration has been drawn in. Feathery objects are clouds. Square indicates number of anchovy eggs under 1 mz of sea surface. (From Lasker et al., 1981.)

and into the California Current. At the same time adult anchovies were excluded, as was evidenced by lack of fish in trawl samples. The start of upwelling on March 14 and the progressively colder temperatures appeared to have excluded anchovies from that time on. Lasker et al. concluded that anchovy avoid recently upwelled water and that the areal extent of upwelled water may be mapped using infrared satellite imagery. They stated: Because satellites can see vast areas of the ocean day in and day out, they provide the scientist with a synoptic look at processes only seen heretofore on smaller scales and restricted to the time when a ship or ships can cover the area of interest. In this study only a

424

R. MICHAEL LAURS A N D JOHN T. BRUCKS

satellite could have provided us with a synoptic picture of the large scale oceanographic events which were happening over the entire spawning area of the anchovy during the peak of the spawning season. The extensive ground truth provided by the R/V David Starr Jordan confirmed that the 0S"C accuracy of the infrared radiometer aboard most of the NOAA satellites detects accurately the movement of surface layers and shows the sequential developmentof upwellingalong the Californiacoast over large areas of Ocean [Lasker et al., 19811.

Satellite remote sensing has also been utilized by Fiedler (1983) to investigate variations in spawning habitat of the northern anchovy in the Southern California Bight. He found that spawning in the northwestern region of the bight is excluded from a cold-water mass apparent in infrared satelliteimagery south of Point Conception, corroborating the earlier findings of Lasker et al. (1981), and that spawning to the south is confined to coastal waters with moderately high phytoplankton pigment levels as measured by the CZCS. (The latter will be discussed further in Section 2.3.) Fiedler (1983) also concluded that satellite images can be used to improve the sampling efficiency of ichthyoplankton surveys. The effectiveness of using satellite infrared and CZCS imagery (see Section 2.3) to monitor shifts in anchovy spawning habitat off California associated with the 1982-1983 El Niiio warmwater conditions have been demonstrated by Fiedler (1984a,b). 2.1.2. Use of Satellite Infrared Thermal Imagery in Tuna and Billfsh Studies. Satellite infrared thermal imagery has proved to be a valuable tool in tuna research. This has been especially true in studies designed to investigate relationships between tuna distribution and availability and ocean temperature fronts and sea surface temperature. 2.1.2.1. North Pacific albacore tuna distribution in relation to ocean temperature fronts. Albacore tuna (Thunnus alalunga), which are highly mobile and widely distributed in the North Pacific, migrate seasonally into waters off the coast of North America during July through October where they support important commercial and recreational fisheries. Laurs et al. (1984) used Advanced Very High Resolution Radiometer (AVHRR) infrared thermal data from the NOAA-7 satellite and fishery data from logbooks kept by commercial albacore tuna fishermen to investigate relationships between albacore fishing success and oceanic temperature fronts in waters off California. Relationships to ocean-color boundaries were also examined using CZCS imagery (Section 2.3). Laurs et al. found that fishing effort and highest catch rates for albacore tended to be concentrated in the vicinity of temperature fronts, which are believed to result from coastal upwelling (Fig. 2). High catch rates were observed on the warm side of the temperature boundaries, usually very near the boundary but sometimes extending up to 100 km offshore from the boundary or front. Fishing activity was markedly less and catch rates were

1 I. LIVING MARINE RESOURCES APPLICATIONS

425

FIG.2. Distribution of mean daily albacore tuna catches made by commercial fishing vessels superimposedon NOAA-7 AVHRR image of water off central California. Albacore catch rates are expressed as number of albacorecaught per 150-line hours of fishingand are shown as varying sizes of circles. (From Laurs et al., 1984.)

usually nil on the cold side of the temperature fronts (Fig. 2). While it is generally believed by fishermen and fishery scientists that tuna may aggregate in the vicinity of upwelling temperature fronts to feed (Laurs et al., 1977), the use of satellite imagery is allowing fishery scientists to investigate this relationship on space and time scales hitherto not possible. Laurs and Austin (1985) investigated the small-scale migration patterns of albacore in relation to oceanic frontal boundaries using ocean color and infrared satellite data collected contemporaneously with observations made from ships at sea. Nimbus-7 CZCS and NOAA-6 satellite AVHRR infrared data were collected in conjunction with field experiments where acoustic telemetering methods were used to track the horizontal and vertical move-

426

R.MICHAEL LAURS AND JOHN T.BRUCKS

ments of free-swimming albacore, and expendable bathythermograph (XBT) observations were made to determine subsurface ocean thermal structure. Three albacore were tracked for approximately 24 hr and one for about 15 hr. The results showed that (1) total distances tracked ranged from about 40 to 60 km, with all fish remaining in the same parcel of warm water that was separated from waters to the north, south, and inshore by about a 2°C temperature gradient as shown by infrared thermal imagery; (2) tracked fish spent most of the time in waters within and below the thermocline, and only small amounts of time in the upper mixed layer; (3) the fish exhibited marked vertical excursions in depth, with the range being larger during the day than at night; (4) the fish spent most time in waters with temperatures considerably lower than what has been generally believed to be the preferred temperature range for albacore; and (5) when changing depth, the fish, frequently within a 20-min period, passed through a vertical gradient of temperature amounting to 6-7°C or about 3 + times greater than the horizontal temperature gradient at the surface indicated by ship measurements and the infrared thermal imagery. These findings indicate that the reasons tuna aggregate on the warm side of surface temperature fronts-an economically significant phenomenon that has been observed on scientific cruises and is well known by fishermen-may not be related to thermal-physiological mechanisms (Neill, 1976). Instead, Laurs and Austin speculate that one or more behavioral mechanisms related to feeding may be responsible. Ocean-color measurements made by the CZCS in conjunction with the tracking study and with catches made by commercial fishing vessels (Laurs et al., 1984) provide data that support this hypothesis (see Section 2.3). Laurs et al. (1981) report on another study involving albacore tuna which was conducted in winter several months after albacore emigrate from waters near the North American coast. In this study NOAA-6 infrared thermal imagery was used in conjunction with fish catch information and ground truth measurements made by chartered commercial albacore fishing vessels. The investigators found that a marked temperature front observed in the satellite imagery appeared to be a boundary to albacore distribution. There was up to a 20-fold decrease in catch rate on the cool side of a temperature front believed to mark the outer edge of the California Current approximately 700 miles off southern California. 2.1.2.2. Gulf of Mexico tuna 1onglineJisher-yand the Loop Current. Leming (1981) evaluated the feasibility of using satellite imagery to locate the position of the Loop Current in the eastern Gulf of Mexico in an investigation of relationships between the Loop Current and tuna caught by the Japanese longline fishery. The locations of longline sets and species composition of

11. LIVING MARINE RESOURCES APPLICATIONS

427

the catches were recorded by National Marine Fisheries Service (NMFS) observers on board selected vessels of the Japanese longline fleet operating in the Gulf of Mexico. GOES-East satellite infrared images composited weekly and analyzed for major thermal boundaries by the NESS Satellite Field Service Station in Miami, Florida, were used to estimate the position of the Loop Current. An apparent strong correlation was found in early May 1978 between sets targeted on bluefin tuna and the location of surface thermal boundaries of the Loop Current (Fig. 3; Leming, 1981). However, after late May or early June, surface temperature gradients in the Gulf of Mexico were of insufficient magnitude to be detected in the GOES imagery. Curiously, during this period, all fishing for bluefin tuna ceased, and fishing was targeted for yellowfin tuna in the western Gulf of Mexico. For the most part Leming had only limited success finding a relationship between the Loop Current and tuna longline fishing in 1979. According to Leming (1981) the thermal boundary charts he used based on GOES infrared imagery did not contain sufficient resolution in time and space for use in an analysis of catch-rate differences for major pelagic groups of fish in relation to satellite data. He concluded that to correlate catch rates with satellite data the thermal boundary analyses must be expanded to include secondary thermal gradients, ground resolution less than 8 km must be used, and composite time scales less than 1 week are necessary. The GOES provides overall the best data source, however, because the frequency of data allows compositing for cloud-cover removal, a more serious problem than spatial resolution (Maul et al., 1984). 2.1.2.3. Atlantic bluefin tuna distribution in relation to seasonal ocean warming. Roffer et d. (1982) used satellite remote sensing in conjunction with shipboard observations and fishing logbook records in an investigation of Atlantic bluefin tuna (Thunnus thynnus thynnus) off the coasts of the mid-Atlantic states. Using satellite infrared imagery to follow the seasonal northerly progression of the 19-20°C surface isotherms, Roeffer et d . found that the development and duration of the various bluefin tuna fisheries along the East Coast follow the movement of seasonal warming of near-surface waters. 2.1.2.4. Movements of Atlantic sword$sh in relation to ocean features. Carey and Robinson (1981) conducted tracking experiments of a swordfish with acoustic telemetry instruments to investigate the daily patterns in activity. Satellite infrared imagery was collected in conjunction with the tracking to study the movements of swordfish in relation to ocean features. Among the oceanic features apparent in satellite imagery were cold shelf water, warm Gulf Stream waters, and a streamer of cold shelf water pulled off by the Gulf Stream moving past Cape Hatteras. The swimming direction of

FIG.3. Distribution of longline sets relative to the May 1978 position of the Loop Current boundary in the Gulf of Mexico deduced from GOES infrared satellite imagery. (From Leming, 1981.)

11. LIVING MARINE RESOURCES APPLICATIONS

429

the swordfish for the 3-day period it was tracked was not correlated with oceanic surface thermal boundaries obvious in the infrared image. The tracked fish was tagged on the north side of entrained cold shelf water and after tagging it swam across the cold shelf water and the Gulf Stream, and then into the Sargasso Sea. The horizontal swimming pattern of the swordfish did not appear to be influenced by oceanic boundaries between different types of waters as has been observed in albacore tuna (Laurs and Austin, 1985). This difference in the relationships between oceanographic environmental conditions and the movements of swordfish and albacore tuna became apparent largely because satellite imagery was collected contemporaneously with the tracking experiments. 2.2. Modeling Larval Transport Mechanisms Using High-Resolution SASS Wind-StressMeasurements Brucks et al. (1984) conducted a case study using Seasat-A Satellite Scatterometer (SASS) wind data to establish, quantify, and document the extent and variability of wind-induced ocean-flow indices on surface-layer transport. Knowledge of surface-layer transport processes is important in fisheries research because dispersal mechanisms control the distribution of early life stages and thereby influence the recruitment and future harvest of marine organisms with planktonic life stages. The purpose of the case study was to determine whether high-resolution SASS measurements of wind stress could improve estimates of surface layer and larval transport based on geophysical models. The study was conducted in the Gulf of Mexico where surface layer transport plays a key role in the early survival of shrimp and menhaden. Fisheries on these species in the Gulf produce the major portions of the highest US.dollar and largest U.S. volume fishery. In addition, several other economically important commercial fisheries in the Gulf have planktonic life stages which are influenced by prevailing dispersal mechanisms common to all. A schematic representation of the penaeid shrimp life cycle is given in Fig. 4 showing transport of developmental stages from offshore regimes to coastal estuarine zones and the adults returning offshore to spawn. 2.2.1. Description of Model and Preparation of SASS Input Data. Brucks et al. (1984) developed a model to use sea surface wind stress measured by the SASS to calculate surface-layer transport. The Seasat approach developed by Brucks et al. compared to the standard technique for deriving surface transport is shown in Fig. 5. The Seasat approach reduces the number

430

I

R. MICHAEL LAURS AND JOHN T. BRUCKS

ESTUARY

OCEAN

FIG.4. Life cycle of penaeid shrimp showing transport of developmental stages from offshore to coastal estuarine zones and adults returning offshoreto spawn. (From Brucks er al., 1984.)

of assumptions involved in the calculation of the wind-stress vector and presumably provides more accurate estimates of surface circulation. The model is composed of offshore and coastal modules to satisfy deep and shallow water responses to meteorologic driving forces. Hydrographic boundary conditions are essentially fixed by monthly and/or seasonal averages of historical temperature and salinity data. Surface transport is thus a function of the varying wind field. The SASS data set for input to the model of surface-layer transport was compiled by extracting scatterometer wind measurementsfrom 70 revolutions over the Gulf of Mexico during September 1978. The spatial distribution of these 11,248 SASS measurements in the study area available for analyses is shown in Fig. 6. The geophysical data records for scatterometer wind measurements with the direction alias removed were stratified into 26 5-day running averages grided by 9 square. AnaIyses showed that sufficient coverage of the Gulf is attained within 5 days. Longer periods added to the data density, but did not improve the distribution of data. The SASS-measured wind-stress field was used to calculate surface currents by the standard homogeneous, steady-state Ekman (1905) solution. Model outputs consist of 5-day mean current vectors on a 4 x f” grid. A trajectory analysis package is also available to portray current pathways of

ASSUMPTION SURFACE PRESSURE

2Qx+-$op

--

ASSUMPTION

GEOSTROPHIC RELATIONSHIP

WIND

-~EosTRopHIc* IND

-

-

C

ASSUMPTION

ASSUMED SURFACE

*

SQUARE LAW

-

EKMAN TRANSPORT

(TI

SVERDRUP TRANSPORT

@LL

P

I

I

BMy'Cvl?

u-

SURFACE

CIRcuU\TIoN

SEASAT APPROACH EMPl RlCAL RELATIONSHIP SCHEMATIC

I

-

T

-Yf

-1

I

I

SEASURFACE RADAR

M,.

SURFACE STRESS

I

I I

I

SURFACE STRE-.

STRESS

P

'

n

ASSUMPTION

NUMERICAL CIRCULATION

I

FIG.5. Comparison of Seasat and standard technique for deriving surface-layer transport. (From Brucks et al., 1984.)

432

R. MICHAEL LAURS AND JOHN T. BRUCKS 30

25

20

15

-1

0

-95

-85

-80

FIG.6. Spatial distributionof 11,248 SASS measurements of the Gulf of Mexico for September 1978. (From Brucks eta!., 1984.)

eggs and larvae and to define impact areas. The trajectory analysis allows random selection of any number of starting points, tracking of the dispersal routes, and demonstration of the impact area of particle location between 1 and 30 days of drift. The wind-driven surface currents derived from SASS observations can be used independently for studies of wind-driven circulation or can be combined with surface flow estimates from thermohaline models to produce total current transport at the ocean surface. Fields of vertical velocity representing upward velocity into the Ekman layer required to balance the computed Ekman divergence induced by the wind-stress curl may also be output from the model. The vertical movement of water due to wind-stress curl provides insight for the enumeration of environmental factors relevant to dispersal mechanisms for fisheries. Divergencein Ekman transport will cause upwelling of waters possibly rich in nutrients, thereby creating areas of high productivity. Conversely, convergence in the Ekman layer will produce downwelling, forming areas of accumulation of planktonic organisms. The relationship between the curl of wind stress and the vertical movement of water is important also in the delineation of spawning areas. Historically, geographic boundaries of spawning areas are identified generally by those zones high in egg and larvae concentrations and/or high in adult specimens laden with mature eggs or possessing mature, “ripe” ovaries (i.e., ready to spawn). Correlation between

11. LIVING MARINE RESOURCES APPLICATIONS

433

wind-stress curl and concentrations of planktonic eggs and larvae could assist in the definition of spawning areas. In this fashion, source ambiguities could be resolved to show that the observed concentration is due to local spawn or is due to a consequence of environmental redistribution and accumulation of distant spawn. 2.2.2. Estimates of Surface-Layer Transport and Vertical Movement of Water Using SASS Wind Data. Using SASS wind stress to calculate surfacelayer transport processes showed striking spatial and temporal variability in wind-drift patterns and regions of convergence/divergence in the Gulf of Mexico. During the first half of September, surface wind drift was most pronounced in the northwestern Gulf. Initially, surface flow was oriented toward the northwest (Fig. 7a). By midmonth the wind-drift regime increased in aerial coverage and changed direction such that anticyclonicflow prevailed in the western Gulf (Fig. 7b). An obvious change occurred again in the third week as evidenced by the regional shift of predominant wind drift to the southwest quadrant of the Gulf, including the Yucatan Straits, and a change in wind-drift direction to the northwest and north (Fig. 7c). Additionally, the magnitude of the wind-driven circulation throughout the Gulf increased approximately twofold with a mean and maximum velocity of 10.50 and 45.71 cmsec-', respectively. At the close of the month (Fig. 7d), the winddriven circulation regime was sluggish. A southwest drift current, indicating a current reversal, was present in the northwest Gulf, replacing a northerly current identified previously, and an area of prominent wind-driven activity was apparent for the first time in the northeast Gulf. Estimates of the curl of the wind stress based on the SASS measurements showed the Gulf to have a highly dynamic oceanic system of regional convergence and divergence. Using the Seasat data base the Gulf was convergent and divergent about equal amounts of time (approximately 1.5:l), with mean and maximum vertical velocities (cmsec-') of 5.2 x and 31.4 x respectively. Using the standard method and data base the Gulf was almost always convergent (approximately 4: l), with lower mean and maximum vertical velocities (cmsec-') of 3.0 x and 14.2 x respectively. Brucks et al. used the SASS model derivations of wind-driven surface-layer transport processes to define potential enrichment zones of spawn material and predict the areas of coastal impact of eggs and larvae as controlled by environmental forces. Processes conducive for the offshore accumulation of planktonic organisms were found mainly in the western and eastern Gulf (Fig. 8). The western distribution was concentrated generally in the west central Gulf with projections to the south, west, and north. Distributions in

434

R. MICHAEL LAURS AND JOHN T. BRUCKS

20

30

i'

b ; p j ..... .

. ....... . . . . . . ...

. . .,

...............\ ..T<. . . . . . .. .. ... . __......._,.... _. __......__.._.._. ................ :x.:::::.--- ... ..,

.....,..... ............ ......._.. .......-........-.-- ...

.

.

... ... ... ...

FIG.7. Winddriven circulation patterns show variability resulting from SASS data for four 5day periods during September 1978. (a) Day 263=days 261-265 (or September 20 = September 18-22); wind-stress average (dynes = mean 0.93, rnax 4.00; current-speed average (cmsec-') = mean 10.50, max 45.71. @) Day 270 = days 268-272 (or September 27 = September 25-29); wind-stress average (dynes = mean 0.29, max 2.75; current-speed average (cm sec-') = mean 3.14, max 31.44. (c) Day 250 = days 248-252 (or September 7 = September 5-9); wind-stress average (dynes an-') = mean 0.48, max 1.83; current-speed average (cm sec-') = mean 5.42, max 20.87. (d) Day 256 = days 254-258 (or September 13 = September 11-15); wind-stress average (dynes an-') = mean 0.60, max 2.04; current-speed average (cmsec-') = mean 6.87, max 23.28. (From Brucks er ul., 1984.)

the east were separated although large, discrete areas prevailed in the eastern Gulf and Yucatan Straits. Coastal accumulations, summarized in Fig. 9, occurred in the northwest Gulf off Texas, Louisiana, and Mississippi; the southwest Gulf off Mexico; and in the east Gulf off Florida. Trajectories due to oceanic and wind effects shown in Fig. 8 were merged to compare final impact areas to areas of known nursery grounds (Fig. 10). It was shown that predicted impact areas were in close proximity to nursery grounds in Louisiana, Texas, and Florida. 2.2.3. Usefuiness of Seasat SASS Wind-Stress Measurements in Modeling Lurval Transport Mechanisms. Brucks et. al. concluded that Seasat afforded

435

11. LIVING MARINE RESOURCES APPLICATIONS

30-

-

-

25

-

2015 -100

-95

-90

-85

-80

FIG.8. High-probability area for offshore waters rich in planktonic eggs and larvae due to accumulations by wind-driven convergence of surface waters (shaded regions). Surface current trajectories are superimposed to show 30-day dispersal pathways for September 1978. (From Brucks et a/., 1984.)

new technology for fisheries application to monitor, model, and predict environmental pathways by which offshore spawn of estuarine-dependent shellfish and finfish find their way into coastal nursery grounds. Synoptic and repetitive direct measurements of wind stress provided enhanced capability to determine wind-driven environmental events, including a pertinent measure of inherent variability, that influence recruitment processes of marine organisms and provide a means to correlate significant interactions between subregions in the Gulf of Mexico. The effect of dispersal mechanisms is common to all major fisheries that have planktonic life stages and knowledge of transport processes can, therefore, help satisfy basic mission requirements of the NMFS for management of national fisheries and for the negotiation and establishment of international agreements concerning fisheries that span national boundaries.

436

R. MICHAEL LAURS AND JOHN T. BRUCKS

COASTAL CONVERGENCE (WEEKLY)

UJ

FlRST WEEK

SECOND wEu(

THIRD WEEK

FOURTH WEEK

FIG.9. High-probability area for coastal waters rich in eggs and larvae for September 1978. (From Brucks et al., 1984.)

2.3. Use of Coastal Zone Color Scanner Data in Fisheries Research

The concept of using ocean color for finding fishing grounds is not new. Fishermen have used variations in ocean color for centuries to locate oceanic areas believed to be favorable for catching fish. The potential value to fishermen of synoptically acquired ocean-color data from space was first demonstrated by Kemmerer et al. (1974). In this study ocean-color measurements from Landsat were used to predict areas of high probability of occurrence of menhaden in the Gulf of Mexico. When the Nimbus-7 satellite with the CZCS was launched in 1978there was increased enthusiasm and interest by fishery scientists to utilize satellite oceancolor measurements in fisheries research. Cram (1979) speculated on the use of CZCS measurements in the management of a pelagic fishery. However, applications of CZCS imagery to fisheries research has only recently been realized with the increased availability of CZCS data and improved access to hardware and software to process the color imagery. Ocean-color measurements from the CZCS are being used in fishery resource applications for (1) the location of Ocean fronts, effluents, and circulation features, (2) quantitative determinations of ocean color that are

11. LIVING MARINE RESOURCES APPLICATIONS

437

PRIME ESTUARINE NURSERY AREAS WITHIN US. TERRITORY [3 MODEL PREDICTION OF WEEKLY IMPACT AREAS DERIVED

FROM OCEAN I WIND COMBINATIONS ADDITIONAL IMPACT AREAS RESULTlNG FROM MONTHLY MODEL PREDICTIONS

FIG. 10. Predicted coastal recruitment areas compared to known locations of estuarine nursery grounds for shrimp and fishes. (From Brucks ef ol., 1984.)

directly related to chlorophyll and sestonic concentrations, and (3) the identification of water masses. 2.3.1. Use of CZCS Measurements in Albacore Tuna Studies. Distribution of albacore catches in relation to color boundaries. CZCS imagery and albacore tuna catch data obtained from daily logs submitted by fishermen were used by Laurs et al. (1984)to investigate the relationships between albacore fishing success and ocean color boundaries off the U.S.Pacific west coast. The ratios of CZCS channel 1 to channel 3 radiance [blue/green ratio or R(13)] were used to determine the locations of ocean color boundaries after atmospheric contamination was removed from the imagery using the algorithm of Smith and Wilson (1981). R(13) was also converted to phytoplankton pigment concentration using the algorithms of Clark (1981)and Smith and Baker (1982). Normalized catch data for 5-day periods, from 2 days before to 2 days after each Nimbus-7 pass, were plotted on the CZCS images. In nearshore waters color imagery showed a sharp color front marking the boundary between the coastal and oceanic water masses and corresponding to

438

R. MICHAEL LAURS AND JOHN T. BRUCKS

the temperature front visible in the AVHRR images (see Section 2.1.2). The boundary generally had a meandering north-south distribution with intrusions of oceanic water into coastal water. Chlorophyll concentrations were usually less than 0.30 mg mP3 offshore of the color front and were greater than 0.50 mg m-3 inshore of the front. Strong color fronts and chlorophyll concentrations > 1.0mgm-3 were also observed in the waters nearer thecoast associated with aged upwelled waters. Albacore fishing effort was distributed mostly in the oceanic waters with lesser amounts in the coastal waters. The fishing effort tended to be highest along or in the vicinity of the color boundary separating coastal and oceanic waters and in some cases up to about 100 km from the boundary in oceanic waters. Also, high fishing effort was concentrated in the fingers of oceanic water that intruded into the coastal water. Figure 11 (from Laurs et al., 1984) shows a plot of albacore catch rates superimposed on a false color image of the bluelgreen color ratio, R(13). The catch rates were highest in the bluish oceanic waters near the color boundary marking the interface between oceanic and coastal waters. The shoreward intrusions of oceanic waters had particularly large catches concentrated at the color boundary. Catch rates within the greenish coastal waters were low or nil. Satellite data and albacore fishery data were also examined from a large oceanic region centered about 500 miles off the coasts of Washington and Oregon. No temperature fronts were visible in AVHRR imagery; however, a diffuse and broken color front was apparent in the center of a CZCS image covering the region. The oceanographic boundary defined by the color front marked an area of high fishing activity and large mean catches in relatively productive water with chlorophyll concentrations of 0.30-0.40 mg m-3. Sea surface temperature was observed to increase gradually from 12 to 20°C over about 8” of latitude. The satelliteimages and concurrent albacore catch data examined by Laurs et al. (1984) clearly demonstrate that the distribution and availability of albacore are related to oceanic fronts. They also substantiate the .conventional wisdom of many fishermen who use temperature and/or color “breaks” to locate potentially productive fishing areas for albacore. The results show that in nearshore regions commercially fishable aggregations of albacore are found in warm, blue oceanic waters near temperature and color fronts on the seaward edge of coastal water masses. Shoreward intrusions of oceanic water are particularly favorable sites for albacore aggregation. In offshorewaters during late summer, commercial concentrations of albacore were found associated with oceanic boundaries that are marked by color FIG.11. Distribution of mean daily albacore tuna catches made by commercial fishing vessels superimposedon Nimbus-7 CZCS blue/green color ratio and phytoplankton pigment concentration in waters off central California. (From Laurs et nl., 1984.)

11. LIVING MARINE RESOURCES APPLICATIONS

439

This Intentionally Left Blank frontsPage detectable from satellites but lacking sea surface temperature gradients. The availability of albacore in offshore waters appears to be higher in relatively productive waters. The temperature signature denoting the boundaries of the relatively more productive waters, if present earlier, may be lost due to seasonal warming. It was noted earlier (Section 2.1.2.1) that Laws and Austin (1985) speculate that behavior mechanism@)related to feeding may be responsible for tuna aggregating on the warm side of temperature boundaries. To support this argument, the authors used CZCS measurements along with a knowledge that tuna are visual feeders. The distribution of ocean color boundaries, apparent in CZCS imagery collected concurrently with albacore acoustic tracking, showed a gradient nearly coincident with the sea surface temperature gradient patterns visible in AVHRR imagery. The diffuse attenuation coefficient (k) and chlorophyll concentration measured by the CZCS also showed a similar pattern to the gradient in sea surface temperature, with lower values in the warmer waters and higher values in the cooler waters. All tracked albacore remained in clear, warm oceanic waters and did not cross the boundaries into turbid, cool coastal waters. However, the tracked fish moved vertically through water temperatures that were 3 + times greater than the gradients in surface temperature apparent in the AVHRR imagery (Section 2.1.2.1). These results suggested to the authors that the influence of water clarity on the detection and capture of prey may play a key role in the mechanism@) underlying the aggregation of tuna on the warm side of ocean fronts. The aggregation of commercial concentration of albacore in clear water on the oceanic side of fronts in nearshore areas found by Laurs et al. (1984) may reflect an inability of albacore to capture efficiently large, mobile prey in turbid coastal water and a dependence on food that has migrated or has been dispersed across the coastal-oceanic boundary. In offshore regions, the aggregation of albacore in relatively productive waters presumably occurs because relatively higher amounts of food organisms are present, yet the waters are clear enough for the albacore to detect them. Studies have shown that both infrared and visible color data from satellites can define environmental limits on the spatial distribution of fishable aggregations of albacore and can do so more effectively than ship or aircraft data as used in the past. No other observational perspective so convincingly reveals the shapes, sizes, and continuity of mesoscale oceanic features, which are important in determining the distribution and availability of this highly migratory species. 2.3.2. Use of CZCS Imagery in a Study of Spawning of the Northern Anchouy. Fiedler (1983) extended the study of Lasker et al. (1981) in an investigation of the distribution of northern anchovy ?pawning observed on

11. LIVING MARINE RESOURCES APPLICATIONS

439

fronts detectable from satellitesbut lacking sea surface temperature gradients. The availability of albacore in offshore waters appears to be higher in relatively productive waters. The temperature signature denoting the boundaries of the relatively more productive waters, if present earlier, may be lost due to seasonal warming. It was noted earlier (Section 2.1.2.1) that Laws and Austin (1985) speculate that behavior mechanism@)related to feeding may be responsible for tuna aggregating on the warm side of temperature boundaries. To support this argument, the authors used CZCS measurements along with a knowledge that tuna are visual feeders. The distribution of ocean color boundaries, apparent in CZCS imagery collected concurrently with albacore acoustic tracking, showed a gradient nearly coincident with the sea surface temperature gradient patterns visible in AVHRR imagery. The diffuse attenuation coefficient (k) and chlorophyll concentration measured by the CZCS also showed a similar pattern to the gradient in sea surface temperature, with lower values in the warmer waters and higher values in the cooler waters. All tracked albacore remained in clear, warm oceanic waters and did not cross the boundaries into turbid, cool coastal waters. However, the tracked fish moved vertically through water temperatures that were 3 + times greater than the gradients in surface temperature apparent in the AVHRR imagery (Section 2.1.2.1). These results suggested to the authors that the influence of water clarity on the detection and capture of prey may play a key role in the mechanism@) underlying the aggregation of tuna on the warm side of ocean fronts. The aggregation of commercial concentration of albacore in clear water on the oceanic side of fronts in nearshore areas found by Laurs et al. (1984) may reflect an inability of albacore to capture efficiently large, mobile prey in turbid coastal water and a dependence on food that has migrated or has been dispersed across the coastal-oceanic boundary. In offshore regions, the aggregation of albacore in relatively productive waters presumably occurs because relatively higher amounts of food organisms are present, yet the waters are clear enough for the albacore to detect them. Studies have shown that both infrared and visible color data from satellites can define environmental limits on the spatial distribution of fishable aggregations of albacore and can do so more effectively than ship or aircraft data as used in the past. No other observational perspective so convincingly reveals the shapes, sizes, and continuity of mesoscale oceanic features, which are important in determining the distribution and availability of this highly migratory species. 2.3.2. Use of CZCS Imagery in a Study of Spawning of the Northern Anchouy. Fiedler (1983) extended the study of Lasker et al. (1981) in an investigation of the distribution of northern anchovy ?pawning observed on

440

R.MICHAEL LAURS AND JOHN T. BRUCKS

four intensive egg surveys of the Southern California Bight. AVHRR and CZCS data were used to describe mesoscale patterns of sea surface temperature and phytoplankton pigment concentration. Spawning anchovy were excluded from cold (< 13.5-14°C) upwelled water to the south of Point Conception, a feature defining the northern boundary of this spawning area. This pattern was observed in February-April 1981 and February 1982, corroborating the results of Lasker et al. for April 1980. Anchovy spawning to the south of San Diego in 1980-1982 was confined to a narrow band along the coast, with occasional extensions farther offshore. Sea surface temperature increased gradually offshore, sometimes exceeding 17"C, but no temperature fronts were observed in the AVHRR imagery to explain the spawning distribution. CZCS images, however, showed relatively high phytoplankton pigment concentrations in a coastal band with a sharp chlorophyll front defining the seaward extent of spawning activity (Fig. 12). In February 1982, spawning extended farther offshore in a jet of cool water with a high pigment concentration. The spatial distribution of northern anchovy spawning can thus be defined by mesoscale patterns in satellite sea surface temperature and phytoplankton pigment images (Lasker et al., 1981; Fiedler, 1983). While neither parameter alone is sufticient, both together may define the spatial distributions nearly completely. In general, the northern extent of spawning in the Southern California Bight, and the offshore extent north of Santa Catalina Island, are limited by cold, upwelled water advected south of Point Conception (Figs. 1 and 13). Spawning activity to the south is limited by low phytoplankton pigment levels in oceanic water found 20-100 km offshore, rather than by temperature (Fig. 12). However, these factors do not directly determine spawning success. Larval anchovy survival is thought to depend upon aggregations of nutritionally suitable food organisms in a stratified water column (Lasker, 1981). Satellite observations of relatively warm surface temperatures along with moderately high pigment levels may indicate a stratified water column with a mature phytoplankton community dominated by dinoflagellates. On the other hand, a well-mixed water column would be indicated by colder surface temperatures caused by upwelling or storm mixing; unsuitable food conditions would be indicated either by the low phytoplankton pigment levels of unproductive oceanic water or by very high levels associated with surface diatom blooms in recently upwelled water (Fiedler, 1983). 2.3.3. Pelagic Fishery in the Southern Benguela Current. Shannon et al. (1983) report on ocean-color experimentsconducted off South Africa to relate

satellite radiance measurements to the distribution of chlorophyll and to fish shoals. In the study, CZCS images were selected as being representative of seasons and events in the southern Benguela Current. Based on a relatively

11. LIVING MARINE RESOURCES APPLICATIONS

441

FIG.12. Distribution of anchovy eggs superimposed on phytoplankton pigments (mg m-3) from Nimbus-7 CZCS for the Southern California Bight. (From Fiedler, 1983.)

high correlation (r = + 0.89) between near-surface chlorophyll concentrations measured aboard ship and those determined from CZCS data, the authors concluded that the satellite method could be used with confidence in the Benguela Current region to produce contoured fields of chlorophyll concentrations. The chlorophyll distribution patterns deduced from the CZCS radiances were found to be generally consistent with the known distribution and migration patterns of the main pelagic fish species off South Africa. For example, the location of a color front observed in the southern region between Cape Agulhas and Cape Point coincided approximately with the outer limit of the fish-catch locations of the five fish species that comprise the South Africa pelagic fishery.

442

R. MICHAEL LAURS AND JOHN T. BRUCKS

FIG.13. Distribution of anchovyeggs superimposedon sea surface temperature from NOAA-6 AVHRR for the Southern California Bight. (From Fiedler, 1983.)

The authors caution that before definiteconclusions can be drawn about the usefulness of satellite ocean-color imagery for the management of pelagic fish stocks in the southern Benguela Current, more CZCS images must be examined and additional work is needed on the feeding requirements of the different life stages of the various fish species. They do go on to state: Nevertheless, it does Seem from the information available that the CZCS is a potentially powerful management tool. It could be used to provide an environmental index for fish availability,which in turn could be used to refine CPUE (catch-per-unit-effort)estimates or to direct fishing effort. It is also possible that chlorophyll measured by satellite could be used as a biological environmental input for fish recruitment models. Most certainly the accuracy of chlorophyll determinationfrom satellites is adequate for it to be used in conjunction with thermal imagery and limited in situ monitoring to detect anomalous or episodic events [Shannon et 01.. 19833.

11. LIVING MARINE RESOURCES APPLICATIONS

443

3, UTILIZATION OF SATELLITE DATAIN FISHERIES-AIDS PRODUCTS TO FISHERMEN DISTRIBUTION Commercial fishermen have always been concerned about safety at sea and in making the best catch for the amount of time expended in search of productive fishing areas. In the last decade, commercial fishing on the high seas has become an increasingly competitive and economically risky business. To ensure a profit it has become necessary for the fishermen to utilize available technological and scientific knowledge to improve their catches. Accordingly, modern commercial fishermen require timely, reliable, and accurate meteorological and oceanographic information. Although the use of satellite imagery in the preparation of fisheries-aids products has been limited, it has the potential to contribute significantly in meeting the increased needs of fishermen for environmental information. The first application of satellite-received data in fisheries-aids products was in a program initiated in 1971, wherein San Diego-based purse seine tuna fishermen operating in the eastern tropical Pacific were provided oceanographic and weather products prepared specifically to meet their requirements (Laurs, 1971). The tailored fisheries-aids products were transmitted via radio facsimile to cooperating fishermen on the fishing grounds who in turn radioed ashore surface weather observations and expendable bathythermograph subsurface temperature observations. In this program visual and infrared satellite images received by automatic picture transmission (APT) at the NMFS Laboratory in La Jolla, California, were analyzed in conjunction with surface observations. The APT photos were used to assist in determining the locations and movements of severe weather conditions which affected safety and/or hampered fishing operations. In addition they were used to define the location of the Intertropical Convergence Zone and regions of ocean surface temperature gradients which are important indicators in locating potential fish-productive ocean areas. Subsequent to this endeavor, other projects and programs have used or are using satellite data in fisheries-aids products which are distributed to fishermen by a variety of mechanisms, including radio facsimile transmission, voice broadcast, U.S. mail, and telephone telecopier. Fiedler et al. (1984) review fisheries applications of satellite data in the eastern North Pacific. 3.1. Thermal Boundary Charts

Operational application of satellite data to commercial fishing operations began along the Pacific coast in 1975 (Breaker, 1981). Charts showing the locations of oceanic thermal boundaries are derived from satellite infrared imagery and provided to commercial and recreational fishermen for use in locating potentially productive fishing areas. Initially the charts were

444

R. MICHAEL LAURS AND JOHN T. BRUCKS

distributed by telephone telecopier and the U.S. mail to locations where fishermen would have the opportunity to examine them before starting a fishingtrip. Radiofasimile has also been used to broadcast the charts directly to boats at sea since 1980. The thermal boundary charts are produced by the (1) NOAA Satellite Field Services Station in Redwood City, California,which produces two charts for the area between 28"N and 40"N; (2) NOAA National Ocean Services Center in Seattle, Washington, which produces a chart for the area between 40"N and 52"N; and (3) NOAA Alaska Ocean Services Unit in Anchorage, Alaska, which produces charts covering the area from 48"N to 75"N. Examples of sea surface thermal analysis charts off the Pacific west coast are found in Fig. 14. The charts are prepared one to three times a week and are distributed primarily by USCG radiofacsimile broadcast. Fishermen use these charts to save time in searching for productive fishing areas associated with frontal features (Short, 1979; Breaker, 1981). On the east coast, charts depicting oceanographic features and sea surface temperatures derived from satellite infrared thermal imagery and ship reports are available by mail to interested fishermen through the NMFS Atlantic Environmental Group (AEG) in Narragansett, Rhode Island (Chamberlain, 1981). These products are produced from interpretations and detailed environmental analyses done at AEG on the Oceanographic Analysis Charts which are produced routinely as a joint effort by the National Weather Service and National Environmental Satellite, Data, and Information Service (NESDIS). High-resolution infrared images from NOAA GOES satellite and ship reports are used in the preparation of the charts for waters off the Atlantic coast (see Fig. 15). Of particular interest to fishermen, these charts show (1) the offshorelimit of shelf-watermass, in which most of the fishery resource species reside, and (2)the numbers, measurements,and persistence of warm-core Gulf Stream rings and the influence of these dynamic deep-ocean features on the relatively shallow waters of the fishing grounds. NESDIS field stations in Miami, Florida, and Washington, D.C., prepare regionally oriented flow charts of the Gulf Stream from Florida to Maine three times weekly during October through May from GOES infrared images. In addition, a chart is prepared depicting the path of the Loop Current in the Gulf of Mexico from the Mississippi Delta to the east coast of Florida. The charts are distributed in cooperation with Sea Grant marine advisory serivce agents along the Atlantic coast and Gulf of Mexico by mail and telephone telecopiers and in some cases a narrative version is prepared for voice broadcast by local radio stations (Lowry and Leaky, 1982 and Flimlin, 1982). The charts have been particularly useful to lobster fishermen in reducing gear loss due to strong currents of Gulf Stream warm-core eddies, and to swordfish and recreational fishermen (Lowry and Leaky, 1982).

FIG.14. Ocean frontal analysis charts for waters off the Pacific west coast.

446

R. MICHAEL LAURS AND JOHN T. BRUCKS

UATIONALWEATHER SERVICE JATIONAL EARTH SATELLITESERVICE

. . .

I SYMBOLLEGEND GULf STREAM WARM EDDY COLD EDDY SHELF WITER SLOPE WATER SARGASSO WATER FRONTAL LOCATION (0-3 days o l d ) FRONTAL LOCATION ( 4 - 1 drys o l d ) FRONTAL LOCATION ( 0 1 t i mated) DIRECTION OF FLOW h o t a x i s ) SEA SURFACE TEl RATURE

GS IWE

119.

I

'F

lii $:

I

'C

'F 15 59.0 1 4 - 572 13 55.4

.

~

11

. 51.8

FIG.15. Ocean frontal analysis chart for waters off the U.S. northeast coast.

11. LIVING MARINE RESOURCES APPLICATIONS

447

3.2. Sea-Ice Forecast Charts

Sea-ice forecast charts derived from Nimbus-7 SMMR and polar-orbiting satellite infrared imagery are prepared by the NOAA Alaska Ocean Services Unit and transmitted by USCG radiofacsimile to fishermen and other marine users. Sea ice is an important seasonal feature in the Alaskan environment that can be monitored by satellite measurements (Weeks, 1981; McNutt, 1981). Sea ice affects commercial fishing activities by threatening vessel safety, limiting navigation and access, and damaging fishing gear. However, sometimes waters near the edge of pack ice can offer shelter or rich fishing grounds. 3.3. NASAIJPL Satellite Data Distribution System and Fisheries Demonstration Program to US.West Coast Fisheries

The prime motivation leading to the expanded use of satellite observations in fisheries-aids products has been provided by the Seasat Commercial Demonstration Program (SCDP) sponsored by NASA/JPL.' The SCDP offered a unique opportunity to evaluate the use of oceanographic satellite observations for support of commercial fisheries. The program led to the development of an operational Satellite Data Distribution System (SDDS) used to distribute oceanographic products to various components of the oceanographic user community (Montgomery, 1981). The distribution of products to fishermen included transmission of fishery advisory charts via radiofacsimile to tuna purse seiners operating in the eastern tropical Pacific, albacore tuna and salmon fishingvessels working along the Pacific west coast, and king crab vessels fishing in the Bering Sea. The SDDS was planned and initially designed to distribute to marine users oceanographic products derived from measurements made by Seasat. When the Seasat prematurely failed, an alternative plan was devised to distribute subsets of the basic marine numerical analyses prepared at Navy Fleet Numerical Oceanographic Center (FNOC) in Monterey, California. The SDDS serves as a pilot demonstration from which general system requirements are being developed for future operational ocean-oriented satellite programs. The system also provides support for limited real-time experiments that test the utility of satellite observations in various maritime applications, including fisheries. The SDDS became operational in 1979 with products Capt. Paul M. Wolff (USN Retired) has played a key role in the development of operational techniques to integrate satellite observations of the ocean with conventional data to produce unique environmental charts for use by commercial fishermen.

448

R. MICHAEL LAURS AND JOHN T. BRUCKS

originating at FNOC. For fisheries users, the products consisted of numerically analyzed fields of winds, waves, and sea surface temperature. Although these products were operationally useful, they contained few or no satellite measurements and they were not tailored toward specific fisheries applications. Nevertheless, fishermen who utilized the products and kept evaluation logs estimated a l0-15% savings in search time as the result of using the products (Hubert, 1981). In 1981, a plan was adopted to distribute new fisheries support products which built upon the existing SDDS system. The new products were tailored toward specific fisheries applications. They were prepared using a man/ computer mix wherein additional knowledge and information, including satellite measurement, were incorporated. The program involved cooperative facilities and expertise in government, university, and commercial organizations. A schematic drawing showing the data sources and routing of the experimental fisheries-aids products, taken from Montgomery (198I), is given in Fig. 16. Beginning in 1983,the responsibility for preparation of most of the marine advisory products was transferred to the NOAA National Ocean Service. Charts continue to be distributed to fishermen at sea by the radiofacsimile facilities shown in Fig. 15. However, except for the thermal boundary charts (Section 3.1), the products are now in a format for general use by marine users. 3.3.1. Ocean Color-Boundary Charts. Experimental ocean-color boundary charts based on Nimbus-7 CZCS imagery are distributed to U.S. West Coast fishermen under the auspices of the NASA/JPL Fisheries Demonstration Program (Montgomery, 1981). These charts (see example in Fig. 17) delineate strong gradients in the blue/green color ratio (channel 1/channel 3 radiances). They are produced at almost weekly intervals depending on cloud conditions, and cover coastal areas up to 700,000 km2 between Guadalupe Island and Vancouver Island. Nimbus-7 CZCS passes along the Pacific coast are collected at the Scripps Institution of Oceanography (SIO) Satellite Oceanography Facility, processed in near-real time at the SIO Visibility Laboratory and transmitted by radiofacsimile the following day to fishing boats at sea from radio station WWD in La Jolla, California. Color photographs of the satellite images are also distributed by express mail to various fishing ports and to Sea Grant marine advisors in daily contact with fishermen. The color boundary charts and photographs are used primarily by commercial albacore and salmon fishermen, and recreational fishermen in southern California. Fishermen use the color boundary charts to locate color gradients or “breaks” which are important in determining potentially productive fishing areas.

CHARTS PRODUCTS (ICE BOUNDARIES. WINDS, STORM TRACKS, SEVERE WEATHER, SST.

(FAIRBANKS, A K )

CHART PRODUCTS (WINDS, STORM

PRESSURE )

PRESSURE)

NUMERICAL PRODUCTS (WINDS. WAVES. SST BOUNDARIES) FORECASTER

EARTH STATION (FNOC-MONTEREV)

SEA SURFACE TEMPERATURE CHARTS

-

1-1

I COLOR

1BOUNDARY 1CHARTS

TELECOPIER L I N K

90-VISIBI L l T Y LABORATORY

TELEPHONE L I N K

(SAN DIEGO)

NIMBUS-7

EARTH STATION ( S O - L A JOLLA)

FIG.16. Data sources and monitoring of experimental fisheries-aids products for the NASA/JPL Fisheries Demonstration Program to the U.S.West Coast fisheries. (From Montgomery, 1981.)

450

R. MICHAEL LAURS AND JOHN T. BRUCKS

VALID: JUNE 10

400-

OCEAN-COLOR CHART 1 . GREEN COASTAL WATER

FIG. 17. Nimbus-7 CZCS-derived ocean-color chart tailored for commercial fishing applications. (From Montgomery, 1981.)

REFERENCES Breaker, L. C. (1981). The application of satellite remote sensing to west coast fisheries. J . Mar. TPC~. SOC.15.32-40, Brucks, J. T.,Butler, J. A., Faller, K. H., Holley, H. J., Kemmerer, A. J., Leming, T.D., Savastano, K. J., and Vanselous, T.M. (1977). LANDSAT menhaden and thread herring resources investigation, final report. NOAA, Nat. Mar. Fish. Seru., Southeast Fish. Center Contr. No. 77-16F. Brucks, J. T.,Leming, T. D., and Burkett, S. B., Jr. (1984). A model investigation using high resolution SASS wind stress measurements to derive wnd driven surface layer transport properties in the Gulf of Mexico. NOAA Tech. Rep. In press. Carey, F. G., and Robinson, B. H.(1981). Daily patterns in the activities of swordfish, Ziphias gladuis. observed by acoustic telemetry. Fish. Bull., US.79,277-292. Chamberlain, J. L. (1981). Application of satellite infrared data to analysis of ocen frontal movements and water mass interactions off the northeast coast. NW Atlantic Fish. Organ. NAFO Scr. Doc. 81/1X1123, Ser. No. 429. Clark, D. K. (1981). Phytoplankton pigment algorithms for the NIMBUS-7 CZCS. In “Oceanography from Space” (J. F. R. Gower, ed.), pp. 227-238. Plenum Press, New York. Cram, D. L. (1979). A role for the NIMBUS-6 Coastal Zone Color Scanner in the management of a pelagic fishery. Fish. Bull. S. Afr. 11, 1-9. Ekman, V. W. (1905). On the Influence of the earth’s rotation on ocean currents. Ark. Mat. Astron. Fys. 2 . 1-53.

11. LIVING MARINE RESOURCES APPLICATIONS

45 1

Fiedler, P. C. (1983). Satellite remote sensing of the habitat of spawning anchovy in the Southern California Bight. CalCOFI Rep. 24,202-209. Fiedler, P. C. (1984a). Some effects of El Niiio 1983 on the northern anchovy. CalCOFl Rep. 25,53-58.

Fiedler, P. C. (1984b). Satellite observations of El Niiio along the US.Pacific coast. Science 224,1251-1254.

Fiedler, P.C., Smith, G. B., and Laurs, R. M. (1984). Fisheries applications of satellite data in the eastern North Pacific. Mar. Fish. Rev. 46(3), 1-13. Flimlin, G. (1982). A call saves time and fuel. Sea Grant Toaby 12.6. Hela, L., and Laevastu, T. (1961). Fisheries hydrography. Fish. News (London). Hela, L., and Laevastu, T. (1970). Fisheries oceanography. Fish. News (London). Hubert, W.(1981). An evaluation of the utility of Seasat data to ocean industries. Final Report, Seasat Commercial Demonstration Program. Jet Propulsion Laboratory Document No. 622-225. Kemmerer, A. J., Benigno, J. A., Reese, G. B., and Minkler, F. C. (1974). Summary of selected early results from ERTS-I Menhadden experiment. Fish. Bull. 72,375-389. Lasker, R. (1978). The relation between oceanographic conditions and larval anchovy food in the California Current: Identification of factors contributing to recruitment failure. Rapp. P. u. Re’un. Cons. lnt. Explor. Mer. 173, 212-230. Lasker, R. (1981). Factors contributing to variable recruitment of the northern anchovy (Engraulis mordax) in the California Current: Contrasting years, 1975-1978. Rapp. P. u. Re’un Cons. Int. Mer. 179, 375-388. Lasker, R., Pelaez, J., and Laurs, R. M. (1981). The use of satellite infrared imagery for describing ocean processes in relation to spawning of the northern anchovy (Engraulis mordax). Remote Sens. Enuiron. 11,439-453. Laurs, R. M. (1971). Fishery-advisory information available to tropical Pacific tuna fleet via radio facsimile broadcast. Comm. Fish. Rev. 33,40-42. Laurs, R. M., and Austin, R. (1984). Small-scalemovements of albacore tuna in relation to oceanic frontal features observed in satellite. In preparation. Laurs, R. M., Yuen, H. S. H., and Johnson, J. H. (1977). Small-scale movements of albacore, Thunnus alalunga, in relation to ocean features as indicated by ultrasonic tracking and oceanographic sampling. Fish. Bull. U.S. 73, 347-355. Laurs, R. M., Lynn, R. J., Nishimoto, R., and Dotson, R. (1981). Albacore trolling and longline exploration in eastern North Pacific waters during mid-winter 1981. NOAA Tech. Memo. NMFS, NOAA-TM-NMFS-SWFC-IO. Laurs, R. M., Fiedler, P. C., and Montgomery, D. C. (1984). Albacore tuna catch distributions relative to environmental features observed from satellite. Deep-sea Res. 31(6). Leming, T. D. (1981). Ocean pelagics remote sensing applications. Interim. Rep. NMFS Intern. Document. Lowry, B., and Leaky, T. (1982). Cooperation produces a flow of Gulf Stream information. Sea Grant Today 12.3-5. McNutt, S. L. (1981). Remotesensing analysis of icegrowth and distribution in theeastern Bering Sea. In “The Eastern Bering Sea Shelf Oceanography and Resources (D. W.Hood and J. A. Calder, eds.), Vol. pp. 1, 141-165. U.S. Govt. Printing Office, Washington, D. C. Maul, G. A., Williams, F., Roeffer, M., and Sousa, F. (1984). Remotely sensed patterns and variability of bluefin tuna catch in the Gulf of Mexico. Oceanol. Acta 7(4), 469-480. Montgomery, D. R. (1981). Commercial applications of satellite oceanography. Oceanus 24,5665.

Neill, W. H. (1976). Mechanisms of behavioral thermoregulation in fishes. Report of Workshop on the Impact of Thermal Power Plant Cooling Systems in Aquatic Environments. Electric Power Res. lnst. Spec. Rep. 38, 156-169.

452

R. MICHAEL LAURS AND JOHN T. BRUCKS

Parrish, R. H., and MacCall, A. D. (1978). Climatic variation and exploitation in the Pacific mackerel fishery. Calif:Dept. Fish. Game, Fish. Bull. 167, 1-1 10. Pearcy, W. G. (1973). Albacore oceanography off Oregon-1970. Fish. Bull. 71,489-504. Roffer, M., Carl, M., and Williams, F. (1982). Atlantic bluefin tuna-oceanography-remote sensing. Proc. Annu. Tuna Cons., 32nd, Inter-Am. Trop. Tuna Comm., La Jolla, Ca. Savastano, K. J., Pastula, E. J., Jr., Woods, E. G., and Faller, K. J. (1974). Preliminary results of fisheries investigation associated with Skylab-3. Int. Symp. Remote Sens. Environ., 9th. Environ. Res. Inst. Mich. (unpublished report). Sette, 0. E. (1961). Problems in fish population fluctuations. CalCOFZ Rep. 8,21-24. Shannon, L. V., Mostert, S. A., Walters, N. M., and Anderson, F. P. (1983). Chlorophyll concentrations in the southern Benguela current regions as determined by satellite (NIMBUS-7 Coastal zone colour scanner). J . Plankton Res. 5,565-583. Short, K. (1979). How satellites can help you catch more fish and cut costs. Natl. Fisherman 60, 38-39. Smith, R. C., and Baker, K. S. (1982). Oceanic chlorophyll concentration as determined by satellite (NIMBUS-7 Coastal Zone Color Scanner). Mar. Bid. 66,269-279. Smith, R. C., and Wilson, W. H. (1981). Ship and satellite bio-optical research in the California Bight. In “Oceanography from Space” (J. F. R. Gower, ed.), pp. 281-294. Plenum, New York. Squire, J. L., Jr. (1961). Aerial fish spottingin the United States commercial fisheries. Comrn. Tech. Rev. 23, 1-7. Squire, J. L., Jr. (1972). Apparent abundances of some pelagic marine fishes off the southern and central California coast as surveyed by an airbornemonitoring program. Tech. Bull. U.S. 70, 1005-1019. Squire, J. L., Jr. (1982). Catch temperature for some important marine species off California: NOAA Tech. Rep. NMFS SSRF-759. Thomas, J. P. (1981). Assessment of superflux relative to fisheries research and monitoring. In “Chesapeake Bay Plume Study” (J. W. Campbell and J. P. Thomas, eds.), NASA Con$ Publ. 2188,503-509. Weeks, W. F. (1981). Sea ice: The potential of remote sensing. Oceanus 24,39-48.

APPENDIX A. INSTRUMENTS Introduction . . . . . . . . . . . . . . . . . . . . . A.2. Radar Altimeter. . . . . . . . . . . . . . . . . . . . A.3. Seasat Scatterometer System . . . . . . . . . . . . . . . A.4. Scanning Multichannel Microwave Radiometer . . . . . . . . . AS. Synthetic Aperture Radar . . . . . . . . . . . . . . . . A.6. Coastal Zone Color Scanner . . . . . . . . . . . . . . . A.l.

. . .

453

. . . $453 . . . 453 . . . 451

. . . . . .

457 451

A. 1. INTRODUCTION Details are provided here on the coverage and characteristics of the five principal oceanic sensors carried on Seasat and Nimbus-7, the Seasat radar altimeter (ALT), scatterometer (SASS), Scanning Multichannel Microwave Radiometer (SMMR), and Synthetic Aperture Radar (SAR), and the Nimbus-7 SMMR (identical to Seasat) and Coastal Zone Color Scanner (CZCS). The footprint comparison of all Seasat sensors is given in Fig. A-1. A.2. RADARALTIMETER

The altimeter was a nadir-viewing, short-pulse, 3-nsec radar operating at 13.5 GHz. This instrument measured the vertical distance from the spacecraft to the ocean surface along the subsatellite track with an accuracy of f 10 cm RMS. Global coverage required 152 days. The data obtained provide the sea surface geoid and allow mapping of prominent surface depressions, such as deep-ocean trenches. Elevations resulting from seamounts, plateaus and ridges, and heights associated with geostrophic currents are also detected. Figure A-2 gives the instrument specifics. A.3. SEASAT SCATTEROMETER SYSTEM

The scatterometer was a dual-polarized system operating at 14.59 GHz. The antenna radiated four fan beams (two orthogonal pairs) which point +45" and f 135" relative to the direction of flight. These beams illuminated the ocean surface for a distance of 1000 km on either side of the subsatellite track. Spatial resolution elements, 50 x 50 km, were produced by range gating and the use of 15 Doppler filters along each fan beam as shown in Fig. A-3. The resolution cells from the fore and aft beams produced nearly overlapping orthogonal pairs with incident angles almost equal. 453 ADVANCES IN GEOPHYSICS, VOLUME 21

Copyright @ 1985 by Academic Press, Inc. All rights of reproduction in any form reserved. ISBN 0-12-018827-9

t

'\

INSTRUMENT POINTING (DEGREES)

CONE

/

ALT SAR SASS I

I 12 km 1 I

I

1

1

-L

.

I &

..

::

;

1

SMMR

; \

1ooh 650 km

1556 TO1974 km

I

1-

I

4

VlRR I

I :

;

2246 km

FIG.A-1, Seasat-A instrument coverage.

0 20.5

0-7, 19.5-55

42 51 -38

CLOCK 0

90 4s

135 225 315 133-183 90,270

F OV 1.5 CIRCULAR d 3 CONE f 0.25 CROSS CONE

i 2 CONE f 0.15 CROSS CONE

PERFORMANCE 0

68% (lo1 OF 1-sec ALTITUDE MEASUREMENTS TO LIE WITHIN ? l o cm OF THE FITTED MEAN REAL-TIME 1-sec SIGNIFICANT WAVE HEIGHT (H,,,) MEASUREMENT ACCURACY AT LEAST 210% OR 0.5 rn, WHICHEVER IS GREATER FOR H, FROM 1 TO 20 rn BACKSLATTER MEASUREMENT ACCURACY WITHIN 51 .O dE

,

COVERAGE

, I

I

-

, 1 I

ALTIMETER FOOTPRINT COVERS 6.62 km in 1 sec MEASUREMENT TIME

TECHNICAL CHARACTER ISTICS FREQUENCY-13.49932 GHz i160 MHz BANDWIDTH-320 MHr TRANSMIT TlMElTOTAL TIME-3.3 x PULSE WIDTH-3.2 rsec CHIRP RATE-100 MHzlrsec PULSE COMPRESSION-1000 TIME BANDWIDTH PRODUCT-1000 EFFECTIVE PULSE WIDTH-3.2 nsec PEAK TRANSMITTED POWER-2.0 kW PRF-1030 PULSElsec AVERAGE TRANSMllTED POWER425 W 0 AVERAGE POWER INPUT-150 W

*

ACQUISITION SIGNAL.3.2usec PULSE MODULATED BY CW SIGNAL SYSTEM NOISE TEMP FIGURE-11 dB RECEIVER GAIN-95 d8 GAIN-AUTOMATIC, DIGITAL 0 AVERAGE BACKSCATTER-6 d6 0 RECEIVER DYNAMIC RANGE-63 dB RECEIVER POWER RANGEANTENNA PEAK GAIN40 dB 0 ANTENNA POLARIZATION-LINEAR 0

0

DATA RATE8.5 kbps TOTAL ENG DATA RATE-[ WEIGHT-72 kq

FIG.A-2. Altimeter technical summary.

PERFORMANCE 0 OCEAN SURFACE WIND DIRECTION-O-360 +20° MEASUREMENT INTEGRATION-1.89 sec SWATH-AS SHOWN I N COVERAGE FIGURE MAXIMUM BIAS ERROR C t 2 dB 0 CELL RESOLUTION-50 km 0 SYSTEM CALIBRATION C 2 dB 0 OCEAN SURFACE WIND SPEED.4 TO > 28 m/sec 0 CELL GRID SPACING-50 km x 50 km i 2 mlsec OR 10%. WHICHEVER IS GREATER 0

0

TECHN IC A L CHARACTER ISTI CS

DOPPLER CELLS EARTH COVERAGE DOPPLER CELLS (TYPICAL EACH ANTENNA FOOTPRINT) SATELLITE GROUND TRACK AFT BEAM

BACKSCATTER1 RECEIVED SIGNAL: BACKSCATTER, dB RECEIVED SIGNAL, dBm

0

*EXTRAPOLATED FROM i o w E R WIND SPEED DATA

FREQUENCY-14,59927 GHz BANDWIDTH-+500 kHr 0 TRANSMIT TlMElTOTAL TIME-0.2 0 PULSE WIDTH-4.8 mtec 0 PEAK TRANSMITTED POWER-110 W 0 PRF-34PULSESltec 0 AVERAGE TRANSMITTED POWER-20 W 0 AVERAGE RAW POWER-80 W REGULATED -85 W UNREGULATED 0 RECEIVER NOISE TEMP 1250 K 0 GAIN CONTROL-AUTOMATIC 0 ANTENNA PEAK GAIN-32.5 dB 0 ANTENNA POLARIZATION-HORIZONTAL/VERTlCAL DATA RATE-540 bps 0 0

750 km NEAR NADIR SURFACE MEASUREMENT SWATH

FIG.A-3. SASS technical summary.

APPENDIX A. INSTRUMENTS

457

A.4. SCANNING MULTICHANNEL MICROWAVE RADIOMETER The Scanning Multichannel Microwave Radiometer was a dual-polarized radiometer that measured microwave radiation at five frequencies:6.63,10.69, 18,21, and 37 GHz. This instrument generated a conical scan to the right of the subsatellite track at an incident angle of about 50". It recorded data in a swath 659 km wide for a series of elliptically shaped cells of varying sizes, depending on frequency as shown in Fig. A-1. The major axis of these elliptical cells varied from 121 km at 6.63 GHz, to 21 km at 37 GHz. The use of five frequencies permitted the radiometer to serve as an intermediate-to-high-speed wind-field anemometer (no wind direction), to measure sea surface temperature, to estimate corrections for atmospheric water vapor and liquid water, and to monitor sea-ice conditions. Additional characteristics are given in Fig. A-4. AS. SYNTHETIC APERTURE RADAR The synthetic aperture radar was an L-band 1.27-GHz radar with excellent cloud- and rain-penetration capability. The antenna illuminated a swath 100 km wide to the right of the flight track. The design provided spatial resolution of 25 m in both range and azimuth. Figure A-5 shows the principles of surface-element resolution. Range resolution was determined by the effective radar pulse length; azimuthal or cross-range resolution was determined by the antenna beamwidth. The image was formed from two bands of information. Range information was derived from the roundtrip travel time of radar echoes from the surface target. Azimuth information was composed of the Doppler shift history in the reflected signal from the surface in the direction of spacecraft motion. This information formed lines of constant Doppler shift in the azimuth or crossrange direction. The intersection of the two bands of information yielded the surface elements shown. Prime applications of the data are for wave directional spectra,coastal wave refraction analysis, and sea- and lake-ice dynamics. Because of the very high data rate (120 x lo6 b sec-'), there was no on-board recording of data. As a consequence, earth coverage was limited to swaths approximately 4000 km long in regions adjacent to the five ground receiving stations. A.6. COASTAL ZONECOLORSCANNER

The CZCS is an image scanner with six coregistered bands spectrally centered at 443, 520, 550,670, 750, and 1150 nm (Fig. A-6). The instrument utilizes a fully rotating scanner which scans across track at a rate of

458

APPENDIX A. INSTRUMENTS

PERFORMANCE OCEAN SURFACE WIND SPEED FROM 7 TO 5 0 * 2 m/SeC OR *lo%, WHICHEVER IS GREATER 0

OCEAN SURFACE TEMPERATURE TO WITHIN t2"C ABSOLUTE AND tO.5"C RELATIVE WIND AND TEMPERATURE RESOLUTION-121 km ICE FIELD MAPS. RESOLUTION-21 k m MEASUREMENT OF INTEGRATED ATMOSPHERIC WATER VAPOR AND LIQUID MATTER I N A COLUMN ALONG THE SIGNAL VECTOR MEASUREMENT OF RAIN DROP SIZE AND DISTRIBUTION IN A COLUMN ALONG THE SIGNAL VECTOR

TECHNICAL CHARACTER ISTICS CLOCK INPUTS-1 Hz, 10 kHz, 1.6 MHz, SATELLITE TIME ENG AND SCI DATA RATE-2 kbps WEIGHT-53 kg PRIME POWER-61 W (AVERAGE) FREQUENCY, GHr 0 ANTENNA DIAMETER, rn ANTENNA BEAMWIDTH. HALF-POWER, deg POLARIZATION FOOTPRINT MAJOR AXIS) ,km DIMENSIONS MINOR AXIS F U L L SWATH ANGLE, deg SWATH ARC WIDTH, km INCIDENCE ANGLE OF BEAM CENTER AT SURFACE, deg ORBITAL ALTITUDE, k m RF BANDWIDTH. MHz 0 DISSIPATIVE LOSSES: ORTHOMODE TRANSDUCER dB WAVEGUIDES SWITCHES AND ISOLATOR TOTAL DISSIPATIVE LOSSES, dB NOISE FIGURE (MIXER + IF AMPI, DSB.dB REFERRED TO PORT DsB' K OF MODULATOR PREDETECTION BANDWIDTH, MHz INTEGRATION TIME CONSTANT, milliseconds TEMPERATURE RESOLUTION, K ( l o ) (300 K TARGET) ABSOLUTE TEMPERATURE ACCURACY, K ( l o ) DYNAMIC TEMPERATURE RANGE,K 0

(

),

6.63

-

10.69

18 21 0.79 2.6 1.6 1.4 D U A L LINEAR 74 44 38 49 29 25 50

-

4

4.2

121

79

-~

~

V

"

"

37

0.8

21 14

* -

-

48.8-

+

794250-

0.55 0.25 0.6 1.4 4

0.37 0.34 0.6 1.21 4

0.52 0.25 0.6 1.37 5

1.03 0.2 0.7 1.93 5

0.3 0.2 0.7 1.2 5

490

490

692

703

728

100 62 0.89

62 1.01

-

t

--

126 0.51 2-

62 0.72

FIG.A-4. SMMR technical summary.

10-330

30 1.23

APPENDIX A. INSTRUMENTS

PERFORMANCE RADAR IMAGES AND INTENSITY SPECTRA OF WAVES I N DEEP OCEAN AND NEAR COASTS RADAR IMAGES OF SEA ICE AND FRESHWATER ICE 0 RADAR IMAGES OF LAND AND SNOW COVER 100-krn SWATH WIDTH, 4000km SWATH LENGTH I N l b r n i n PASS FOUR INDEPENDENT CELL MEASUREMENTS (4 LOOKS1 0 25 x 25 m CELL RESOLUTION (4 LOOKS) 0 . 5 . ~INTEGRATION ~ TIME PER CELL MEASUREMENT (PER LOOK) CELL SNR OF > dB OVER 100-km SWATH (4 LOOKS)

RESOLUTION CELLS FROM NADIR

SAT FLIGHT

230 km

-

1

330krn

I

1

1

j

I I

I

+1OOkm SWATH

d/

25 rn x 25 m R ESOLUTlON CELLS

I I

!

23 deg 4000 km 17 deg PER FROM NADIR 1Min PASS

TECH NIC A L CHAR A C T E R IST ICS CENTER FREQUENCY-1274.8 GHZ BANDWIDTH-19 MHz TRANSMIT TlMElTOTAL TIME = 0.35 PULSE WIDTH-33.8 psec CHIRP RATE-0.562 MHz/wec PULSE COMPRESSION RATIO (TIME BANDWIDTH PRODUCTM42 EFFECTIVE PULSE WIDTH-53 nsec PEAK TRANSMITTED POWER-1125 W NOM 0 PRF's-1464, 1540, 1580, 1647 Pulses/sec AVERAGE TRANSMITTED POWER-55 W

FIG.A-5. SAR technical summary.

459

I

I

Performance Parameters

Center Wavelength ( h Nanometera) Spectral Bandwidth ( A h Nanometers)

Channe 1s

1

2

3

4

5

6

443 (blue)

520 (green)

550 (yellow)

670 (red)

750 ( f a r red)

1150 (infrared)

433

- 453

510

- 530

Instantaneous Field of View (IFOV)

I

- 560

660

- 680

700

- 800

1050

- 1250

0.865 x 0.865 Milliradians (0.825 x 0.825 km a t sea l e v e l )

I

Coregistration a t NADIR

540

0.15 Milliradians

Accuracy of Viewing Posit ion Informat ion a t NADIR

2.0 Milliradians

~~

Signal- to-Noise Ratio (min) a t Radiance Input -2 N (mW c m Ster

m)

I

Consecutive Scan Overlap

I

Modulation Transfer Function (MTF)

> 150

> 140

> 125

> 100

at 5.41

at 3.50

at 2.86

at 1.34

100 at 10.8

25%

1 a t 150 km t a r g e t s i z e , 0.35 min. a t 0.825 km t a r g e t s i z e

FIG.A-6. CZCS technical summary.

NETD of 0.220 K at 270 K

APPENDIX A. INSTRUMENTS

46 1

8.0808 rev sec-'. The instantaneous field of view (IFOV) is 0.05",equating to a sea-level square of 825 m on a side from the nominal orbital altitude of 955 km (Fig. A-7). The active portion of the scan is 78.7", which produces a cross-track swath of 1659 km. The scan rate and IFOV size are such that each swath overlaps the preceding swath by about 25%. The scanner mirror is capable of being tilted forward or backward k20" line of sight about the spacecraft pitch axis in 2.0" increments. This movement is commandable and is used to avoid sun glint while taking advantage of maximum solar elevation angles.

0 = 1.374

rad (78.72')

a = 0 . 8 6 5 ~ 1 0 - ' rad ( 0.0496') d = 0.825 km orbital altitude = 955 km

D = 1659 km FIG.A-7. CZCS geometry.

This Page Intentionally Left Blank

APPENDIX B. SEASAT VALIDATION PROGRAM JOHNC. WILKERSON NOA A / NESSIOceanic Sciences Branch Washington, D.C.

B.l. B.2. B.3. 8.4. B.5. B.6. B.I. B.8.

Introduction . . . . . . . WeatherConditions . . . . Plan of Operations (General) . OSSOceanographer . . . . Ocean Weather Station PAPA. NOAADataBuoys . . . . Aircraft . . . . . . . . . Total Data Sets . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

463 466 467 469 414 414 411 411

B. 1. INTRODUCTION An initial and critical part of the Seasat proof-of-concept mission was a major NASA/NOAA field experiment in the Gulf of Alaska. The experiment was designed to collect in situ physical oceanographic and meteorological data for verification and evaluation of Seasat sensor performance. The Gulf of Alaska Experiment was conducted during the period of August 28 to September 26,1978, and was carried out from ships, aircraft, and buoys. The experiment was conducted under the control of a coordinator based at an experiment control center at the NOAA Pacific Marine Environmental Laboratory (PMEL), Seattle, Washington. After September 14, the lead ship, the NOAA Survey Vessel OSS Oceanographer, was joined by four research aircraft (two NASA, one Navy, and one Canadian), which made underflights coinciding with the passage of the satelliteover the ship. The aircraft collected remotely sensed data simultaneously from airborne sensors corresponding to those carried aboard the satellite. Nine NOAA data buoys moored in the Gulf of Alaska also supported the experiment, as did two Canadian weather ships, Quadra and Vancouver, stationed alternately at Ocean Station PAPA. Participating times of ships, buoys, and aircraft used in this experiment are given in Fig. B-1. Key personnel are identified in Table B-I. 463 ADVANCES IN GEOPHYSICS,VOLUME 21 ISBN 012418827-9

1

1

1

I

I

1

I

1

1

-

1

1

1

I

I ~

G W A 3 NVIOVNV3 V t d U AAVN

OWA3 VSVN

M L I N VSVM

EMJI

DaoJI 1w9*

m*

cm*

MMS*

EOOS? LWSb IWWSAOflE VdVd N O I l V l S NV330 Y3RdVYOONV330

52

97.

8%

011 692 11

DZ

12

CZ

1% 192 OM

bS2 CM

992 592

192

I2

02

91

01

652 157.

Ll

LFZ 952

FI

91

DI

Cl

I

TABLE B-I. KEYEXPERIMENT PERSONNEL Experiment Control Center Experiment Coordinator Aircraft Coordinator PMEL Coordinators

J. Wilkerson J. Blue M. Bvrne F. Gonzalez L. McNutt R. Anderson R. Ankney

NOAAINESS JPL NOAA/PMEL NOAA~PMEL NOAA/PMEL NOAA/NWS NOAA/PMC

R. Reed P. De Leonibus R. Berles M. Grigsby R. Lindsey

NOAA/PMEL NOAA/NESS NOAA/AOML NOAAjPMEL Contract employee

NASA NC-130B Mission Manager Chief Scientist

J. Lindeman L. Jones

NASA/JSC NASA/LRC

NASA CV-990A Mission Manager Chief Scientist

E. Peterson T. Wilheit

NASAIARC NASA/GSFC

Staff Meteorologist Communications Specialist OSS Oceanographer & Chief Scientist u n Senior Oceanographer Electrical Engineer Physical Scientist Physical Science Technician

Navy RP-3A Mission Manager Chief Scientist

J. Hollinger J. Hollinger

NRL NRL

Canadian CV-580 Mission Manager

R. Lowry

Chief Scientist

J. Gower

INTERA Ottawa, Ontario DFE Victoria, B.C.

Ocean Station PAPA Seasat Liaison Officer CCGC Quadra CCGC Vancouver National Data Buoy Office Seasat Coordinator Buoy Data Processing

A. Gibb J. Scarlett R. Weber

AES, Vancouver, B.C. CCG, Victoria, B.C. CCG, Victoria, B.C.

E. Kerut R. Erickson A. Johnson

NOAA/NDBO NOAA/NDBO NOAA/NDBO

466

JOHN C. WILKERSON

B .2. WEATHER CONDITIONS Weather conditions during the experiment period, August 29-September 9, were characterized by generally good weather. Winds were from calm to moderate and did not exceed 20 knots for sustained periods. Seas were light to moderate, with very few prolonged periods of overcast. Much stronger winds from the southeast or west were encountered from September 10 to September 26. These winds were accompanied by heavy seas and swell. The most severe weather was from September 10 to 14, with winds approaching 40knots and seas 15 to 20 feet. Conditions moderated somewhat from September 15 to 18, but from September 18 to 21, they worsened again, and winds frequently exceeded 30 knots. From September 22 to 26, conditions

WIND SPfED,

kmtr

I1

5 1-

3 2 -

--

I

0 5 4 -

- - L f 29

31

I

4UtUIl

I

30

2

1

4

5

6

7

8

?

I0

I!

I1

I1

I4

15

16

I7

I8

IV

20

21

SIPIfHlfR

FIG.B-2. Time histories of observations from OSS Oceanographer.

-

22

23

24

15

26

27

APPENDIX B. SEASAT VALIDATION PROGRAM

467

W I N D SEED. k M n I 5

f-

DEW MINI.

Y

I5

r

10

-

S-

W I N D WAVES,

rn

SWELL. m

FIG.B-3. Time histories of observations from weather ships at Ocean Station PAPA.

were appreciably less severe. During this period, winds were light to moderate and seas were low. Figures B-2 and B-3 show the time histories of weather observations from the Oceanographer and the weather ships at Ocean Station PAPA taken daily at 00:00Z, 06:OOZ, 12:00Z, and 18:00Z, and illustrate general weather conditions.

B.3. PLAN OF OPERATIONS (GENERAL) The Gulf of Alaska Experiment consisted of three phases. During phase 1, the Oceanographer proceeded to Ocean Station PAPA where an intership calibration of instruments was carried out with the Vancouver. During phase

-@- O C E A N S T A T I O N PAPA 0

P::

?

. .

S E C T I O N 1\

\

0

I

‘‘a/

1W T ~ \

._.

DATABUOY

I I I

SEPT

I

\

I

\ \ \

I

\+*--: I--______ 5

31 AUG

I

+r

SITE B 8 SEPT

0

*-zz=-s--

SITE A 9 SEPT

SITE C\

- 26 SEPT 0

FIG.B-4. Operating area of the Gulf of Alaska Experiment.

CANADA

APPENDIX B. SEASAT VALIDATION PROGRAM

469

2, the Oceanographer took two conductivity/temperature/depth (CTD) sections to define the general circulation in the Gulf of Alaska while collecting the appropriate in situ oceanographic and meteorological data for satellite sensor validation at relevant overpass times. During phase 3, the Oceanographer occupied sites near 49"N which marked the daily intersections of satellite orbits during the 3-day repeat cycle. At these sites, the Oceanographer took in situ data coincident with the overpass of the satellite for that day. During this phase, the aircraft also conducted overflights of the ship simultaneously with the passage of the satellite over these sites. Figure B-4 shows the general plan of operations.

B.4. OSS OCEANOGRAPHER A partial listing of in situ data taken from the OSS Oceanographer during phase 2 of the experiment is presented in Tables B-I1 and B-111. The locations are shown in Figs. B-5and B-6. The types of observation taken during other phases of the experiment are listed in Table B-IV.

TABLE B-11. CTD LOCATIONS:SECTION 1

Statioil

Latitude ("N)

Longitude

Depth (m)

54"13' 54"33'

150"18' 150"53' 151"28' 152"OO' 152"13' 152"24 152"36 152"41' 152"W 152"57' 152"os 153"ll' 153"16 153"22' 153"29 153"36 153"42' 153"48'

1501 1497 1500 1502 1500 1490 1502 1502 1504 1500 1497 1497 1502 95 1 355 319 214 79

1 2 3 4 5 6 7 8 9 10 11 12

55" 13' 55"19' 55"25' W32' 55"38' 55"43'

13

55"54

14 15 16 17 18

56"W 56"W 56"08' 56"1@ 56"18'

54"W

55"W

55"48' 55"52'

("w

TABLE 9-111. CTD LOCATIONS: SECTION 2 Latitude

Longitude

Station

(ON)

("W

19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36

5448 55"28' 55"58' 56" 13' 56"32' 56"51' 57"07' 57"28' 57"34' 5T40 57"45'

147"ll' 145"W 144"33'

57"53'

58"W 58"05' 58"Il' 58"14' 58"20' 56"17'

1WW 143"13' 142"32' 141"47' 141"W 140"43' 140"29' 14W16 1WOI' 13Y44' 139"28' 139"lI' 13YW 138"54' 139"18'

Depth (m)

Station

1504

31 38 39 40 41 42a 43 44 45 46 41 48 49 50 51 52 53 54

1496 1487 1484 1502 1500 1494 1499 1498 I500 1500 1499 1498 1498 1500 219 104

1502

Latitude ("N)

Longitude

Depth

("W)

(m)

56"OI'

140"02' 140"W 141"48' 142"35' 143"OI' 141"41' 141"44 141"43' 141"44' I41"42' 14l"42' 141"43' 141"42' 141"44' 141"44' 141'38' 141"W 141"44'

1497

55"37' 55"16 54"59 54"w

48"42' 48"45' 48"36 48-36' 48"42' 48"41' 48"W 48"42' 48"43' 48"34 48"42' 48"41' 48"42'

~~

Stations 42-54 were taken during the occupation of site 9. I so

I 50"

IS0

I SO0

FIG.9-5. CTD Station: section 1.

470

1501

1501 1501

1502 1500 1500 1500 1500

1500 1500 1500 1500 1500 1500 1500

1500 1500

150"

145'

1 40°

150"

145'

140'

1 35O

135" _.

FIG.B-6. CTD Station: section 2.

TABLE B-IV. OBSERVAT-ION PLANFOR OSS OCEANOGRAPHER Data type

Measurement accuracy

Temperature, salinity, and depth Expandable bathythermograph

*0.002"c, *O.Ol%

Sea surface temperature

f0.2"C

Sea surface salinity

f0.02%

*4 m

fO.TC, k 5 m

Wind speed and direction Wind-velocity GATE boom Air-temperature GATE boom Wet-bulb-temperature GATE boom Air-pressureGATE boom

+O.l"C k0.2 mbar

Recorded output 1-m averages of T, S,E, and D Temperature vs depth at almost any interval chosen Temperature vs time; various averaging schemes possible Salinity vs time; various averaging schemes possible Speed and direction vs time/2-min or 10-min averages spectra U and V means every 1.07 min Temperature mean every 1.07 min Temperature mean every 1.07 min Pressure mean every 1.07 min

Processing schedule

Responsible individual

1 January

R. Reed

Available

M. Byrne

PMEL PMEL Available

M. Byrne PMEL

Available

M. Byrne PMEL

15 March

M. Byrne PMEL

1 May

M. Reynolds

1 May 1 May

1 May

PMEL M. Reynolds PMEL M. Reynolds PMEL M. Reynolds PMEL

Sea surface temperature and salinity (bucket) Sea surface temperature (infrared) Rainfall amount rain gage

5 W

+0.2"C f0.02% +O.O8"C

f 10%

Insolation

*2%

Positions

kO.1 km

Drift buoys-Nimbus tracked Upper-air temperature and humidity Radiosondes Surface wave height and direction/wave-rider and pitch and roll buoy Radarscope photograph

+ 1 km

+ 1°C f 10% + 20 mbar 3-5% H,,3 3-5 direction

List of values for each overpass List of values for each overpass Amounts for various intervals usually weekly, but could be daily Daily totals Insolation vs time average possible List of positions a t various sites Daily speed and direction Pa,T,q with height

Available

Autospectra directional

Available

Available Available

R. Reed PMEL R. Reed PM EL R. Reed PMEL

Available

R. Reed PMEL

Available

Operations Officer Oceanographer R. Reed PMEL M. Reynolds PMEL

Available Available

D. Ross AOML

Photo print

Available

M. Byrne PMEL

474

JOHN C. WILKERSON

B.5. OCEAN WEATHER STATION PAPA Ocean weather station ships Vancouver and Quadra, of the Canadian Coast Guard, occupied Ocean Station PAPA (50"N,145"W) during the experiment period. In addition to the special weather observations, both ships monitored a wave-rider buoy, anchored at 50"N,144"58'W, and took continuous 20-min wind records with strip chart recorders at satellite overpass times. The ship's schedule was as follows:

Ship

On-station period

Vancouver Quadra

30 July to 10 Sept 10 Sept to 22 Oct

8.6. NOAA DATABUOYS The NOAA Data Buoys in operation in the Gulf of Alaska during AugustSeptember 1978 are shown in Table B-V. Locations of National Data Buoy Office (NDBO) buoys in the Gulf of Alaska are given in Fig. B-7. From August 28 to September 1, all buoys with the exception of Buoy 46003 were on a 3-hr synoptic reporting period. On September 1, NDBO switched all buoys from 3-hr to hourly reporting for the duration of the experiment. Typical measurement capabilities of the buoys are listed in Table B-VI.

TABLEB-V. NOAA DATABUOYS Buoy designation

Latitude ("N)

Longitude

("W

TYPe

46001 46002 46003" 46004 46005 46006 46007 46008 46009

5600' 42"30

148'90' 13000' 156"00' 136"00' 131"00' I38"oo' 152"42' 151"42' 146"48'

10-m discus 10-m discus 10-m discus 12-m discus 12-m discus 10-m discus 6-m boat 6-m boat 6-m boat

a

52"W 51"00'

46"w 41"OO'

5Y12 57"W W12'

This buoy deployed September 18, 1978.

4

TABLEB-VI. MEASUREMENT CAPABILITIES OF 10-m Drscus BUOYS" Parameter

Sensor type

Wind speedb Wind directionb

Vane-directed impeller Vane and gimbaled digital magnetic compass Platinum resistance vane, oriented Variable capacitance

Air temperature Barometric pressure Significant wave height Wave period Wave spectra

Surface water

Level (m)

Sampling interval

Range

Averaging period (min)

Measurement accuracy

+ 0.4 m sec-

0 to 65 m sec-' 0 to 360"

1/8sec 1/8sec

8.5 8.5

9

- 15 to

1/8 sec

8.5

- 0.2"C

9

900 to 1050 mbar

1/8sec

8.5

k0.6 mbar

Accelerometer

0

Oto 100m

Continuous

32

+0.3 m

Accelerometer Accelerometer (12-channel WSA)

0 0

4 to 25 sec 0.05 to 0.33 Hz 0.05-0.1 Af = 0.01 0.1-0.16 Af = 0.02 0.16-0.28 Af = 0.03 0.28-0.33 Af = 0.05 - 5 to 35°C

Continuous Continuous

32 32

f0.5 sec

1/8 sec

8.5

Platinum resistance

10 10

-1

40°C

+lo

+

d

+0.2"C

Buoys 46001,46002,46003,and 46006.

* Instantaneous wind direction and speed are translated into components, north and east. speed and direction. Total network system accuracy. *15%.

The averaged components are transformed into

477

APPENDIX B. SEASAT VALIDATION PROGRAM

B.7. AIRCRAFT Four aircraft participated in the Gulf of Alaska Experiment: the NASA NC-130B and CV-990A, the Navy RP-3A, and the Canadian CV-580. Plots of typical flights are shown in Figs. B-8-B-10.

DATASETS B.8. TOTAL The total number of data sets collected from ships and buoys during the GOASEX period are listed in Table B-VII. TABLE B-VII. TOTALBUOY DATASETSDURlNG GOASEX" Buoy no./ship

SAR

SASS

SMMR

ALTb

46001 46002 46003 46004 46005 46006 46007

1 20

3 20 4 9 20 20

0 0 0

46008

6

46009 R/V Oceanographer CCGC Quadra' CCGC Vancouvelf

9

27

5 40 16 15 40 29 79 39 58 51 31 27

180

430

Totals

4

3 10 8 10

51 31

25

9 18 51 31

0

0 0 0

0

27

0 0 0 0

231

0

~

Aircraft data sets not included. the subsatellite track failed to pass directly over buoys during the frozen 3-day repeat cycle of GOASEX, some passes were within 80 km of the buoys. These, together with other passes occurring outside the GOASEX period, were used in the validation of significant wave-height measurements from the altimeter. ' I n addition, the Canadian coast Guard cutters Quadra and Vancouver took special Seasat data sets twice each day during the periods July 17-August 17 and Sept 27-October 9, while on station at 0s PAPA.

* Although

FIG.B-8. Data flight of NC-130B on September 17, 1978, for orbit 1183. Time 15:17-19:38 GMT.

FIG.B-9. Data flight of CV-580 on September 22,1978,for orbit 1255. Time 17:24-21:57 GMT.

10'

10'

IP"

P 00 0

10

180

30'

30' 117'

30'

I1ba

30

12.5"

30

114'

30'

113'

FIG.B-10.Data flight of CV-580on September 23, 1978, for orbit 1269. Time 16:24-20:16 GMT.

30'

APPENDIX C. DATA AVAILABILITY BRUCEH. NEEDHAM NOAAI NESDlS~N~DC~SatelliIe Data Services Division Washington. D.C.

C.l. Seasat Data Archive and Distribution . . . . . . . . . . . . . . . . 481 C.l.l. General Remarks . . . . . . . . . . . . . . . . . . . . . 481 C.1.2. Chronology of Receipt of Data . . . . . . . . . . . . . . . . 482 C.1.3. Seasat Data Utilization . . . . . . . . . . . . . . . . . . . 487 C.2. Nimbus-7 Coastal Zone Color Scanner (CZCS) . . . . . . . . . . . . . 487 C.2.1. General Remarks and Background . . . . . . . . . . . . . . . 487 C.2.2. Contents of Data Archive . . . . . . . . . . . . . . . . . . 491 C.3. OrderingData . . . . . . . . . . . . . . . . . . . . . . . . 493

c.1. SEASAT DATAARCHIVEAND DISTRIBUTION C . l . l . General Remarks and Background

The Satellite Data Services Division (SDSD) of the National Climatic Data Center (NCDC), part of NOAA's National Environmental Satellite, Data, and Information Service (NESDIS), has been closely involved in the planning for the acquisition, archival, and distribution of Seasat data since the early stages of the project. SDSD has provided input to and maintained close liaison with the producers of the data at the Jet Propulsion Laboratory, and has worked with other members of the project during the Announcement of Opportunity process and internal NOAA investigations. Such'inputs have included suggestions on data formats, estimations of quantities of data requested by users (and associated costs for such data), reports to the project and JPL on users' interest'in data, definition of the zones for the Interim Geophysical Data Record (IGDR) products, identification of highest priority areas and dates to be processed, and many others. Services provided by the SDSD for the project have included the safe archival of all Seasat film and digital tapes; the production of inventory lists of data on archive;illustrations of geographic coverage by each sensor; provision of applicabledocumentation, user guides, and other publications to users; and development of software to extract specific geographic areas from the global archive tapes. SDSD personnel within the Data Services Branch have worked closely with users to help define the best data to suit their needs and to help identify the optimal numbers and least costly products to fill their 48 1 ADVANCES IN GEOPHYSICS,VOLUME 21 ISBN 0-12-018827-9

482

BRUCE H.NEEDHAM

requests; numerous hours have been spent laboring over a light table to find specific areas or features on SAR imagery to be sent to users. In this appendix the goal is first to summarize the chronology of the acquisition of all Seasat data currently on archive, with descriptions of the different types of data available, and, second, to provide a summary of how the data have been accessed and utilized by the end users. C.1.2. Chronology of Receipt of Data C.1.2.1. Interim Geophysical Data Records (IGDRs), SDSD received its first Seasat data product in late December 1978 with the altimeter IGDR “Starter Set” contained on one 9/800 bpi tape. Identified users were sent a letter in January 1979 informing them of the availability of this data set. Six users responded requesting copies of this data set. Additional IGDR data were received commencing in April 1979 and ending in May 1980. These included ALT, SASS, and SMMR IGDRs. A summary of these data is given in Table C-I. The IGDR data sets described above and in Table C-I were preliminary data sets meant for initial testing by Seasat experimenters. The geophysical parameters were not accurate, as compared to the Geophysical Data Records (GDRs). All users should thus order Seasat data from the GDRs and not the IGDRs. C.1.2.2. Sensor Data Records (SDRs). Commencing in May 1979, JPL proceeded to send the entire set of ALT SDRs to SDSD. The entire set of 1124 ALT SDRs are currently on archive. C.1.2.3. GeophysicalData Record (GDRs). Altimeter (ALT). Commencing in June 1979,SDSD started to receive the ALT GDR data tapes. The final set of ALT GDR tapes was received in December 1980. The ALT GDR data set comprises two types of data. One set contains the GEOPHYSICAL FILE ONLY and has 6 days of continuous, global ALT data on each tape. A total of 14 computer-compatible tapes (CCTs) are archived. The second set of ALT GDRs contains the SENSOR FILE ONLY and has 3 days of continuous, global ALT data on each tape. A total of 26 tapes are archived. A more complex description of these “Files” for the ALT and other sensors is shown in Table C-I1 and a summary of all the GDR tapes is shown in Table C-111, Scatterometer (SASS). SASS GDRs started arriving at SDSD commencing in May 1979 and ended in June 1981. The SASS GDR data set consists of three types: 1. Geophysical and sensor files: 6 hr/tape, total of 381 tapes.

IGDR TABLE C-I. SEASAT Data-set name

Dates of data (1978)

A L T 26 day A L T 12-day global A L T global weather A L T global SDR Zone 11

9/13-1019 7128-818 9/15-9116 7117- 10110 9/13-1019

Philippines Sicilian

9/25-9130 9128-29,1013-4

AND

SDR DATAON ARCHIVE AT SDSD Geographic area

Zones 1-6,8,9, and 12 Global (land and water) Global Global Zone 11 (10"-20'",

110"-130"E)

(30"-45"N, 10"-20"E)

Hurricane Fico

I / 12-7/20

Zones 1 and 9 extended

9/ 13- 1019

Hurricane Fico, Pacific Ocean Zones 1 and 9 extended

JASIN

7/15-9115

(53"-65"N, 5"-20"W)

SASS 21 day SASS 2-day global

9/13-1019 7/ 16-71 I 7

SASS 9-day global Zones 8,9, and 10 SASS 1.9, and extensions SASS Sicily SMMR miniworkshop

917- 15 9113- 1019 8119-916 8116-916 7110- 1019

Zones 1-5 Global (land data excluded) Global (land and water) Zones 8,9, and 10 Zones 1.9, and extensions Sicilian area 31 Revs, GOASEX, Grand Banks

Sensor ALT ALT ALT ALT ALT SASS ALT SASS ALT SASS ALT SASS ALT SASS ALT SASS

VlRR SASS SASS SASS SASS SASS SASS

SMMR

No. of CCTs 6 4 1 1124 1

3 1 1

1 1

1 1 1 7 2 4 (Imagery only) 21 8 36 14 4 2 1

TABLE C-11. SWAT FINALGEOPHYSICAL DATAREcoRo CONTENT DEFINITION Sensor File and record type

P

Geophysical Basic geophysical record

ALT One point Time/lat./long. Fully corrected h, and tidal height

Steric anomalies Corrected SS height above ellipsoid Atm. press. effect Ionosphere correction Wet dry tropo. corr. Surface pressure Supplemental geophysical record

SASS Mean tirne/lat./long. Wind-stress magnitude and u Wind-stress direction and u Mag/dir correlation 120 Wind-vector solutions

with aliases (Ambiguity in direction will not be removed)

Time/lat./long. Fully corrected backscatter coefficient (a") Individual SMMR channel temps. (int. over area for each of 15 cells/fan beam 1.89 sec

SMMR Mean time/lat./long SST, SSW, rain rate Atm. liq. water/ Water Vapor Path-length corr. 600 x 600-km area !90 sec of data

VIRR

Sensor Basic sensor record

Supplemental sensor record

One point Time/lat./long. Instrument corr. h Fully corr. IfLI3

Time Ah 1 min Calibration mode

Time/lat./long. Instr. corr. back. coeff. (a") Corner lat./long. S/N ratio a' corrections 1.89 sec Time 7.6-min. cal. sequence Noise Calibration mode

Mean time/lat./long. Individual channel brightness temps. and T , Same area as above

Start time of scan line Lat./long. of start-middle-end VIS brightness/IR temp. One scan line- 1.25 sec

Start/time/nadir lat./long. Cone-clock angle Indiv. chan. hot cold cal. mean and a Ant. temp. Footprint lat./long. for 30 footprint locations One 4.096-sec scan line Calibration data

Start time Nadir lat./long. Altitude IRjVIS HOT/COLD Cal. Calibration mode

486

BRUCE H. NEEDHAM TABLE C-111. INVENTORYOF SEASAT GDR DATAON ARCHIVE

Files

Sensor ALT

Geophysical

ALT

Sensor

SASS SASS SASS

Geophysical and sensor Geophysical Basic geophysical record only Geophysical and sensor Geophysical

SMMR SMMR

Dates of data

Storage type

717-7117,7124-8/28, 9/1,9/6,9/7,9/10, 9/13,9/15-10/10 7/7-71 17,7/24-8/28, 911,916,917,9110, 9/ 13,9/15-10/10 717-10/10 717-10/10 717-10/10

6 days global/tape

14

3 days global/tape

26

6 hr globalltape 24 hr global/tape 48 hr global/tape

381 96 48

717-10/10 717-10/10

6 hr global/tape 4 days globalltape

381 24

No. of CCTs

2. Geophysical file only: 24 hr/tape, total of 96 tapes. 3. Basic geophysical record only: 48 hrltape, total of 48 tapes. Microwave Radiometer (SMMR). SMMR GDRs started to arrive at SDSD in July 1981 and ended in September 1981. The SMMR GDR data set consists of two types of data: 1. Geophysical and sensor files: 6 hr/tape, total of 381 tapes. 2. Geophysical file only: 4 days/tape, total of 24 tapes.

Synthetic Aperture Radar ( S A R I . SAR optically processed 70-mm negative reels started to arrive at SDSD in February 1979 and continued through September 1981. A total of 518 reels have been received. The reels, each containing four swaths, vary in length from a minimum of 1 min to a maximum of 11 min, 45 sec. Most fall into the 2- to 5-min range, and all or portions of 353 of the 480 orbits have been processed (73.5%), including some from the United Kingdom and Canadian ground stations. A complete list of all 70-mm negatives on archive is available from SDSD. SAR digitally processed imagery in the form of 9/1600 bpi tapes and 4“ x 5“ or 8” x 8” negatives and prints started to arrive at SDSD in December 1979 and continue to be received up to the present. As of August 30,1982, a total of 332 scenes measuring approximately 100 x 100 km each have been archived. A complete inventory of all SAR digitally processed data on archive is available from SDSD. Visible and infrared radiometer (VIRR) photographic negatives are also archived at SDSD in limited numbers. These data are primarily over the JASIN experiment area and over the time periods from July 15 through August 27,1978.

APPENDIX C. DATA AVAILABILITY

487

C.1.3. Seasat Data Utilization

A study was conducted by SDSD to produce an itemized breakdown, by sensor type, describing how many orders were received for each sensor or data type, the products produced and delivered, and total costs recovered for each fiscal year, plus information showing which classes of users requested each data type. This itemized breakdown is summarized in Tables C-IV and C-V. In summary, as of August 31, 1982, SDSD received 267 orders, provided 3191 photographic products, and recovered $122,522,for a total of 750 orders, 4909 products, and $247,522. The majority of the requests came from U.S. universities (18%); the second highest category was the combination of foreign governments and foreign organizations (1573, the third highest category was consultants (1573, and the fourth highest was the U.S. Federal Government [NOAA, 10%; DOD, 2%; other Federal Government, 11% (see Table C-VI)]. The largest number of orders was for SAR film products, resulting in 63% of the total orders and nearly half the total dollar amount recovered ($122,522,or 49%). The second largest volume data type requested was the SASS, accounting for 11% of the orders and $62,800(25% of dollar amount). SAR CCTs were a close third with 11% of the orders and $13,534 (5% of the revenue), and the ALT CCTs were fourth with 10% of the orders and $15,084 (6% of the revenue). SMMR CCTs were lowest in number of requests with only 4% of the total orders, but produced $34,008 in revenue (nearly 14%). There were 82 orders placed in fiscal year 1979 (FY79), resulting in 507 products and revenue of $36,741. In FY80,196 orders were placed, resulting in 1005 products and revenue of $52,110. In FY81, 277 orders were placed, resulting in 1725 products and revenue of $79,426. In FY82 (as of August 1982), 195 orders were placed, resulting in 1672 products and revenue of $79,245. Of notable interest is the response for data from foreign (non-U.S.) concerns, amounting to 15% of the total orders. The largest in this category is Canada, with Japan second. c.2. NIMBUS-7 COASTAL ZONECOLOR SCANNER ( c z c s ) C.2.1. General Remarks and Background

The SDSD archives data only from the Coastal Zone Color Scanner (CZCS) from Nimbus-7. SDSD started to receive the CZCS data in late 1980 and continues to receive large volumes daily from NASA’s Goddard Space Flight Center.

TABLE C-IV. SEASAT DATAUTILIZATION SUMMARY FY79, FY80, FY8 1, FY82" Sensor

Product

Format

ALT ALT ALT SASS SMMR SAR SAR

IGDR SDR GDR GDR GDR Image Image

Mag tape Mag tape Mag tape Mag tape Mag tape Mag tape Photos

No. of orders

7

Total

User class breakdown

01 Individual 02 Attorney 03 Insurance adj. 04 Univ.Research 05 Non-univ. research 06 Engineer/contractor 07 Consulting meteorologst 08 Other consultants

ALT

SASS

SMMR

SS recovered

No. of tapes/film

6 61 81 21 85 483

11 tapes I tapes 169 tapes 914 tapes 451 tapes 160 tapes 3191 images

750

4909 products

1152 432 13,500 62,800 34,008 13,534 122,096

$241,522

SAR film

Total orders

2

20

22

3

129 53 25

18 8 4 1 14

20 6

I

11

7

9 6

86 31 19

1

3

8

I

14

12

101

1

% of total

SARCCT

1

09 Manufacturers 10 Utilities 11 Transportation 12 Agriculture 13 Other business 14 NOAA 15 DOD 16 Member of congress 17 Other fed. govt. 18 State & local govt. 19 Foreign govt. 20 Foreign organization 21 Newsmedia 22 Education Total

11 2

9

1

5

16

1

10 11

17 15

3

73

79

6

8

33

41

6

1 4 9 1

6 45 37 9

7 49 70 12

1 7 10 2

10 1 5

42 4 3 32

75 5 42 65

11 1 6 9

1

81

441

1 700

1 100

Tape summary-267 tape orders, 1718 tapes produced, $94,826 recovered. Imagery summary-483 images produced, $122,522 recovered. Totals-750 orders, 4909 products, $247,522 recovered.

imagery orders, 3,191

490

BRUCE H.NEEDHAM TABLE c-V. DATAUTILIZATION BY FY

Sensor

Product

Format

No. of orders

No. of products

%% recovered

FY19

ALT ALT ALT SASS SMMR SAR SAR

IGDR GDR SDR GDR GDR Image Image

Mag tape Mag tape Mag tape Mag tape Mag tape Mag tape Photos

ALT ALT ALT SASS SMMR SAR SAR

IGDR GDR SDR GDR GDR Image Image

Mag tape Mag tape Mag tape Mag tape Mag tape Mag tape Photos

2 10 1 11 0 0 58 82

2 32 2 119 0 0 352 507

120 1,950 120 5,400 0 0 29,151 $36,741

4 25 4 165 2 36 769 1005

240 1,530 240 9,560 120 2,153 38,267 $52,110

FY80 4 13 4 22 2 19 132 196 FY81

ALT ALT ALT SASS SMMR SAR SAR

IGDR GDR SDR GDR GDR Image Image

Mag tape Mag tape Mag tape Mag tape Mag tape Mag tape Photos

1 19 1 34 4 45 173 277

11

50 1 506 1 89 1067 1725

792 4,504 72 36,992 60 8,807 28,199 $79,426

FY82 ALT ALT ALT SASS SMMR SAR SA R

IGDR GDR SDR GDR GDR Image Image

Mag tape Mag tape Mag tape Mag tape Mag tape Mag tape Photos

0 19 0 14 21 21 120 195

0 124 448 35 1003 1672

0 5,5 16 0 10,848 33,828 2,574 26,479 $19,245

Totals (FY79-82)

7 50

4909

$247,522

0

62

49 1

APPENDIX C. DATA AVAILABILITY TABLEC-VI. DATAUTILIZATION BY USERCLASSEACH FY-NUMBER OF ORDERS User class breakdown

FY79

01 Individual 02 Attorney 03 Insurance adj. 04 Univ. research 05 Non-univ. research 06 Engineer/contractor 07 Consulting meteorologist 08 Other consultants 09 Manufacturers 10 Utilities 11 Transportation 12 Agriculture 13 Other business 14 NOAA 15 DOD 16 Member of congress 17 Other fed. govt. 18 State& local govt. 19 Foreign govt. 20 Foreign organization 21 News media 22 Education Total

1

6

9

19 7 2 1 13 2

44

40

18

10 2 19 8

19 13 0 37 18

19 3

1 22 8

5 17 23

7 0 0 8

16 0 10 12

82

176

FYSO

FY81

FY82

Totalno.

%of total

6

22

3

26 9 0 0 33 12

149 53 25 3 102 40

18 8 4 1

1

8 18 13

7 66 22 13

1 9 7 2

26 4 13 33

26 1 18 16

75 5 41 64

11

257

1 188

703

1

15

6

1

6 10 1 100%

C.2.2. Contents of Data Archive

The SDSD CZCS archive consists of photographic negatives (25 x 25 cm) and digital tapes (9/1600) for both the “Level I” (calibrated radiances) and “Level 11” (derived geophysical parameters) products. These are described in more detail as follows: Level I. (1) Photographic product-Six full swath images approximately 35 x 70 mm, each containing 2 min of data on a single 25 x 25-cm archived negative. Black and white transparency, negative, or print. Band 1 through 5 Rayleigh corrected. Band 6 equivalent blackbody temperature (normally not available in NH winter months). (2) Magnetic-tape product-(calibrated radiance tape) (CRT); nine-track, 1600 bpi CCT, 6 min of data maximum (three 2-min scenes). All data on photo product, no Rayleigh correction.

Level 11. (1) Photographic product-2 min of data containing four full swath images twice the size of Level I imagery; reproduced as two 25 x 25-cm archived negatives. Pigment concentration; diffuse attenuation coefficient; upwelled radiance, 443 nm; aerosol path radiance, 670 nm. (2) Magnetic tape product-CRCST; nine-track, 1600bpi CCT, 2 min of ~, L W ~ ,Pigment ~. concentration; diffuse attenuation data per CCT. LwdA3.L W , ~ LW,,~, coefficient; temperature.

120

120

El-W

120

60

W O E

60

120

E k s o W

120

120

60

W O E

60

120

El-W

120

FIG.C-1. Sample page from NASA CZCS monthly catalog, December 30,1980.

APPENDIX C. DATA AVAILABILITY

493

The SDSD maintains a computerized listing of all CZCS data on archive (Level I and Level I1 tapes and imagery), and will provide specific searches to users free of charge for small geographic areas. In addition, the SDSD archives and will distribute free of charge to users copies of the NASA CZCS monthly catalog (see Fig. C-1), which depicts the passes scheduled for processing. As of August 30, 1982, the SDSD had on archive a total of over 14,000 Level I scenes on tape and film, and nearly 250 Level 11 scenes. C.3. ORDERING DATA

Seasat and Nimbus-7 CZCS catalogs may be requested and data may be ordered by contacting: NOAA/NESDIS/NCDC Satellite Data Services Division Room 100, World Weather Building Washington, D.C. 20233 Telephone:Commercial-(301) 763-81 1 1 FTS-(301) 763-8111

This Page Intentionally Left Blank

APPENDIX D. GLOSSARY OF ACRONYMS WILBER B. HUSTON NANCY J. HOOPER 0.40 Corporafion Greenbelt, Maryland

Metrics, Incorporated Atlanta, Georgia

GLOSSARY OF ACRONYMS AFWTR AGC AGU ALT ASA AOML APT ARC ATS AVHRR AXBT BESEX CCT CNES CRCST CRT

czcs

DC DFT Duck-X EM ERL ESA ESMR ETG FGGE FFT FNOC

Air Force Western Test Range Automatic gain control American Geophysical Union (Seasat) a1timeter Applied Science Associates Atlantic Oceanographic and Meteorological Laboratory Automatic Picture Transmission (system) Ames Research Center Applications Technology Satellite Advanced Very High Resolution Radiometer Airborne expendable bathythermograph Bering Sea Experiment Computer-compatible tape Centre Nationale d’ Etudes Spatiale CZCS tape designator Calibrated radiance tape Coastal Zone Color Scanner Digital count Digital Fourier transform Seasat correlative data experiment conducted near Duck, North Carolina Electromagnetic bias Environmental Research Laboratories European Space Agency Electrically Scanning Microwave Radiometer Evaluation task group First GARP Global Experiment Fast Fourier transform Fleet Numerical Oceanographic Central (formerly FNWC, Fleet Numerical Weather Central) 495

ADVANCES IN GEOPHYSICS.VOLUME 21

Copyright @ 1985 by Academic Press, Inc. All rights of reproduction in any form reserved. ISBN 0-12-018827-9

496

WILBER B. HUSTON AND NANCY J. HOOPER

FOV FY GARP GDR GEM GEOS GMT GOASEX GOES GSFC HF HIRS HRIR IGDR INS IR ITOS JASIN JONSWAP JPL JTWC LMST MANICE MCSST MY NASA NCDC NDBO NESDIS

Field of view First year (sea ice) Global Atmospheric Research Project Geophysical data record Goddard earth model Geodynamic Experimental Ocean Satellite Greenwich mean time Gulf of Alaska Seasat Experiment Geostationary Operational Environmental Satellite Goddard Space Flight Center High frequency High-Resolution Infrared Spectrometer High-Resolution Infrared Radiometer Interim geophysical data record Inertial navigation system Infrared Improved TIROS Operational Satellite Joint Air-Sea Interaction experiment Joint North Sea Wave Analysis Project Jet Propulsion Laboratory Joint Typhoon Warning Center Local mean solar time Manual of Sea Ice Reporting Multichannel Sea Surface Temperature Multiyear (sea ice) National Aeronautics and Space Administration National Climatic Data Center National Data Buoy Office National Environmental Satellite, Data, and Information Service National Earth Satellite Service Nimbus Experiment Team National Hurricane Center National Hurricane Research Laboratory National Meteorological Center National Marine Fisheries Service National Oceanic and Atmospheric Administration Naval Ocean Research and Development Agency Designation of a numerical forecast experiment in which SASS-derived winds were excluded for comparison with forecasts in which they were used Naval Research Laboratory

NESS NET NHL NHRL NMC NMFS NOAA NORDA NOSASS NRL

APPENDIX D. GLOSSARY OF ACRONYMS

OFT PBL PGS-S4 PMEL PPI RA RFI RMS RTE SAR SASS

SCM SDSD SEMS SLAR SMMR

SMO SMS SR SSH SST SWH THIR TIREX TIROS TOT0 VIRR VIS WPL XBT

Optical Fourier transform Planetary boundary layer Preliminary gravity solution-Seasat version 4 Pacific Marine Environmental Laboratory (Radar) plan position indicator Radar altimeter Radiofrequency interference Root mean square Radiative transfer equation Synthetic Aperture Radar Seasat A Scatterometer System Successive correction method Satellite Data Services Division Severe extratropical massive storms Side-Looking Airborne Radar Scanning Multichannel Microwave Radiometer Surface meteorological observations Synchronous Meteorological Satellite Scanning Radiometer Sea surface height Sea surface temperature Significant wave height Temperature-Humidity Infrared Radiometer TIROS Ice Experiment Television and Infrared Observation Satellite Tongue of the Ocean Visible and Infrared Radiometer Visible Wave Propagation Laboratory Expendable bathythermograph

497

This Page Intentionally Left Blank

APPENDIX E. GLOSSARY OF SYMBOLS WILBER B. HUSTON 'OAO Corporalion Greenbelt, Maryland

GLOSSARY OF SYMBOLS Radius of earth; absorption coefficient Fraction of storm covered by the kth temperature Component of long ocean wave orbital acceleration along the radial direction Maximum radial acceleration due to the long wave Area of a storm as defined by the -20°C isotherm Backscattering coefficient Empirical weighting function of the (4)pixel Empirical weighting coefficient corresponding to the kth temperature Planck blackbody radiation function Azimuthal radar bandwidth Radar system bandwidth Velocity of light; phase speed of the short waves Actual group speed, nonlinear waves Small-amplitude, linear internal wave group speed Shallow-water phase (and group) velocity Cross-correlation function Ice concentration; pigment concentration; wave velocity (C = w/k = velocity bunching parameter Drag coefficient Specific heat of air at constant pressure Water depth Decibel Interpacket distance; difference Mean of a set of differences Absolute value of a difference Rainfall rate in the (ij) pixel of a grid Total energy of a wave field Mean of differences Mean of absolute value of differences Systematic component of radial ephemeris error Electromagnetic bias Coriolis parameter (f = - 2Rsin A) at latitude A Spectral peak frequency Fraction of ice which is multiyear; earth crust flexural rigidity Solar irradiance at wavelength 1, Acceleration due to gravity

a):

499 ADVANCES IN GEOPHYSICS,VOLUME 21

Copyright @ 1985 by Academic Press, Inc. All rights of reproduction in any form reserved. ISBN 0-12-018827-9

500

WILBER B. HUSTON Area (km') of the (ij)pixel of a grid Two-way antenna gain pattein, normalized so that it is unity at its maximum (accounts for pointing error)

B(J5m)

Antenna pattern RMS wave height; altitude; satellite elevation above the sea surface; radial distance of the satellite from the instantaneous sea surface N + (height of surface above ellipsoid) Satellite height; heat flux H(l + H/a), "extended" satellite height H/(l + H/a), reduced satellite height Significant wave height Height of a rain cell Hertz Point target intensity response along slant range direction Point target intensity response along flight direction Image intensity caused by moving ocean waves Zero-order modified Bessel function of the first kind with argument z Downward component of radiation Rainfall rate for tropical storms (set equal to 1.2 mm hr-') Index for each temperature increment in the IR data equal to or greater than -20°C; ocean wavenumber (2nlA)); diffuse attenuation coefficient (CZCS) Wavenumber of the long ocean waves Vector wavenumber = (k,,k,) Radar wavenumber (241) Linear wavenumber (2n/A) One-way signal attenuation rate due to rain Absorption coefficient Kilometer Kelvin; Karman constant; diffuse attenuation coefficient Upwelled spectral radiance Diffuse attenuation coefficient for pure sea water Corrected radiance; leading crest length of an internal wave Radiance contribution due to aerosol scattering Radiance contribution due to Rayleigh scattering Radiance received at sensor Water-leaving radiance In a surface wave field, wavelengths of the short and long waves Argument of the complete elliptic integral of the first kind; modulation level Number of points Number of pairs Geoid elevation relative to the ellipsoid; number of multiple looks; gate numbers; Brunt-Vaisala frequency; geoid height Pre-leading-edge average noise level Degree and order of spherical harmonic expansion Pressure Denotes surface Joint height-slope probability density function of surface height (positive upward) of the waves above a mean local surface, and wave s l o p corresponding to specular angle 8. Legendre polynomial of the first kind

APPENDIX E. GLOSSARY OF SYMBOLS

PCA r

501

One-way range resolution Effective pulse shape at receiver output, normalized to unity at its maximum, vs spatial propagation distance, x = 4 2 Point of closest approach Geocentric satellite position; linear-regression coefficient; correlation coefficient; radar slant range Mean earth equatorial radius Geocentric radius of reference ellipsoid, rainfall rate, reflectance Equatorial radius of earth Volumetric output of rain Fresnel reflection coefficient of sea surface at normal incidence Fresnel reflection coefficient for smooth sea water Standard error of estimate Total ocean wave slope RMS ocean wave slopes along any two orthogonal axes tangent to the mean sphere Cross spectrum One-dimensional ocean wave-height spectrum Sea surface height Significant wave height Epoch time; echo time; earth crust thickness Temperature; coherent integration time; period (2xlw); period (T = l/w) Brightness temperature Downward component of radiation Physical temperature of ice Transmitted pulse width Swell period (sec) Physical temperature of surface Integration time Ocean temperature field (scalar) Wind speed; a variable of integration a24'/2H" Horizontal component of surface water velocity Row Ocean current field (vector) Wind speed at reference height of 10 m above sea level Ocean current vector in the direction of the wind Wind-friction velocity Unit-step function Radar platform velocity; velocity Complex surface current vector Eckman transport vector Group velocity ( s ; f m f o r deep water) Meteorologically measured wind Radar-measured wind Synoptic wind Earth gravitational potential Vertical-wind velocity Vertical velocity; white-noise power Wind speed at 10 m height Vertical-structure function Distance traversed by radar pulse; fetch Radar wind direction

502

WILBER B. HUSTON X.

Synoptic wind directipn Three-dimensional directional wave spectrum Radar-reflectivity factor; altitude Slope of the water surface relative to the marine geoid; Phillips constant Speed-correction factor Attitude relative to nadir, pointing error Peak-enhancernent factor Delta function; relative phase of a point target Radial ephemeris error Spacing between observations Emissivity Nondimensional fetch parameter; height of the sea; wave surface height above a mean local surface (positive upward); total instantaneous sea surface height referenced to geoid; [, I, Wave amplitude Time-average sea surface Time-varying surface topography Three-dimensional spatial and temporal displacement wave field specifying the sea surface Long-wave amplitude, wavelength and direction referred to azimuth Wave amplitude, vertical displacement Angle of incidence (20" for Seasat SAR); latitude; wave direction (0 = arctan ky/k,); angle of radiation Local angle of incidence Wave-propagation direction relative to flight direction Nondimensional wind shear Dominant wave direction Partitioning wavenumber Ocean wavelength; elastic modulus Radar wavelength Wave-height skewness coefficient Skewness coefficient between wave height and wave slope squared Vertical diffusion coet€lcient; elastic modulus Relaxation-time constant Component of orbital velocity along the radial direction Nondimensional peak frequency; kinematic viscosity; frequency of radiation Random component of radial ephemeris error Density Theoretical azimuthal resolution for a stationary target Resolution of a point target degraded due to motion of the long ocean waves Slant range resolution Coherence power spectrum Correlation coefficient of wave slopes Atmospheric density as function of altitude Peak width Amplitude of the radar return; normalized radar backscatter coefficient; nadir backscattering cross section Average backscattering cross section per unit area at normal incidence Backscattering cross section per unit area of ocean surface at normal or vertical incidence

+

00

a"(0")

APPENDIX E. GLOSSARY OF SYMBOLS

503

The amount (in space) by which a pulse is stretched by scattering from waves of RMS height h Normalized radar cross section Density anomaly Average radar cross section for a short-pulse altimeter as function of time Total wind stress; atmospheric transmittance; temporal half-power width of a Gaussian pulse Complex surface wind-stress vector Zonal component of T Wave-induced Reynolds stress Angle at earth center from satellite to point [ on ocean surface; longitude Angle between direction of travel of ocean wave field and flight direction Nondimensional wind shear Error function of argument y Angle at antenna from nadir to point [ on ocean surface Two-dimensional wave-height spectrum as function of wavenumber Frequency (o= 1/T) Frequency of long ocean waves Dispersion relation Rotation speed of the earth Ensemble average

This Page Intentionally Left Blank

INDEX

A Absorption coefficient, 308 Acronyms, glossary of, 495-497 Advanced Very High Resolution Radiometer, 53,54,286 characteristics, 54-55,292 in fisheries research, 437-440 SST and, 287-294 tuna research, 424-427 Aerosol radiance, 49 Aircraft in Gulf of Alaska experiment, 478-480 Hurricane Greta data, 239-244 rainfall data, 209-211, 213 wind speed data, 216-217 Albacore tuna distribution, 424-427, 428, 437 ALT, see Seasat radar altimeter Altimeter echo waveform, see Echo model Altimetric biases, study of, using models, 73-87 Altitude, ocean, 57 Amplitude of internal waves, 189-191 time-of-flight estimation, 189- 190 variation-of-wavelength estimation, 190- 191 Antarctica, 372, 373-387 Antenna pointing error, 80-83 Arctic Basin, 371, 372 Atlantic Ocean, 427, 429 Atmosphere correction algorithm, 50, 53 Atmospheric water content, 58 AVHRR data, 55 SMMR data, 14, 31, 32, 33 ATS satellites, 4 Attenuation coefficient, 310 AVHRR, see Advanced Very High Resolution Radiometer

B Backscattering coefficient, 308 Barrick model, see Echo model Beaufort Sea, 337-367 Benguela Current, 440-442 505

Bio-optical algorithm. 310 application of, 317-322 development of, 313-317 Bluefin tuna distribution, 427 Bombs, 205, 206,249-257 Brightness temperature, 281,282,352-353 Brunt-VGsala frequency, 181, 182. 184 Buoy, NOAA, location and characteristics, 474-476 C

Chester algorithm, 209, 210, 211, 212 Chlorophyll pigment concentration, see Pigment concentration Circulation, ocean, 58 Clear water radiance, 312 Cloud-motion winds, 217, 230, 234-237, 244-246,259 Cloud pattern. cyclone, remote sensing of, 206-207 Cloud temperature, VIRR data, 34, 35 Cnoidal internal wave model, 187-188, 191-192 Coastal Zone Color Scanner, 48 applications to fisheries uses, 421-429, 436-442 atmospheric correction algorithm, 50, 53 calibration, 305-306 characteristics, 49, 303-306, 457, 460,461 data characteristics, 487, 491-493 optical system, 304 response to oceanic and atmospheric conditions, 306-312 Conductivitylternperaturddepth section, 469-470 Constituent concentrations, 308 Cross-sectional modulation artificial, 155-160 real, 160-165 CTD section, see Conductivity/temperature/&pth section Current interaction, SAR data, 40,43

506

INDEX

Cyclone, tropical, see also Hurricane characteristicsof, 198- 199 forecasting, 265-270 precipitation in, 393-417 CZCS. see Coastal Zone Color Scanner

Fisheries research, satellite applications to, 421-442 Forecast from SASS data, 132-135 of tropical storms, 200, 205, 265-270 Fresnel reflectance, 307, 310, 359

D Diffuse attenuation coefficient, 49, 50 remote sensing of, 322-331 Digital count, 396,403, 404 Directional wave-height spectmm, 142- 143 Drag coefficient, 107-108, 109 Duck-X experiment, 166 Duration parameter, nondimensional, 144

E Earth Radiation Budget radiometer, 48, 53 Echo analysis convolutional, see Echo model double-deconvolution algorithm, 93-96 Echo model convolutional form derivation, 62-65 model tits, 68-73 with Gaussian beadpulse shapes and GramCharlier surface probability density, 73-75 rain effects, 83-87 Echo plateau model, 80-83 Eddy structures, SAR data, 4 3 , 4 4 4 6 , 48 Electrically Scanning Microwave Radiometer, 206-207,336,368 Electromagnetic bias, 87-93 Emissivity, 280-281 Engruulis mordax, see Northern anchovy ERB radiometer, see Earth Radiation Budget radiometer ESMR, see Electrically Scanning Microwave Radiometer ESSA satellites, 4 Eucaryotes, 301

F Feature identification, VIRR data, 34, 35 Fedor algorithm results, 21, 22 Fetch parameter, nondimensional, 144 Fisheries-aids products, from satellite data, 443-450

G Geodesy, 57 Global data assimilation experiments, 121-137 assimilation system, 122- 127 Global Sea Surface Temperature Computation, 55 GOASEX, see Gulf of Alaska Seasat Experiment GOES satellites, 3, 5, 6,427,428 hurricane wind speed data, 230, 235-237, 244-246 rainfall data, 212, 214, 393-405 sea-ice observations, 339, 343 GOSSTCOMP, see Global Sea Surface Temperature Computation Gradient ratio, spectral, 370 Gram-Charlier surface probability density, 70-71,73-75,88-89 Greenland, 371, 377-389 Griffith-Woodley satellite rain estimation technique, 394-397 Gulf of Alaska Seasat Experiment, 15, 16, 17. 111-114, 149, 166, 168, 170, 172, 173, 283,463,467-469 Gulf of Mexico, 426427,428,429-436,437 Gulf Stream, SAR data, 40.43

H HCMM satellite, 6 High Resolution Infrared Sounder, 286, 287 High resolution InfraRed Spectrometer, 53, 54 HIRS, see High Resolution Infrared Sounder HIRS/Z, see High resolution InfraRed Spectrometer Hunicane, see also Cyclone, tropical defined, ly9 ocean wind measurements, 207-208 rainbands in, 201, 202 structure, 200-204 wind profiles in, 201-202,203 Hurricane Allen, 201-202.203 Hurricane Anita, 201 Hurricane Ava, 207

INDEX Hurricane boundary layer, 200 Hurricane Caroline, 208 Hurricane David, 202, 203 Hurricane Ella model wind fields, 248-249 SASS data, 229-237.260-26d SMMR and aircraft data, 208, 209, 210, 21 I , 212, 213, 237-238, 264-267 Hurricane Eloise, 270 Hurricane Fico, 483 ALT data, 68, 69. 71-73, 76-78, 80, 81, 96 SAR data, 36-39 SASS data, 25, 28. 218-227. 268-269 SMMR data. 208. 212, 214, 227-229 VlRR rainfall data, 401-413 Hurricane Gilma, 171 Hurricane Ginger, 203 Hurricane Gloria, 207 Hurricane Greta, 216, 225 aircraft data, 239-244 cloud-motion wind data, 244-246 SASS data, 238-246,259-264 Hurricane Keny, 203 Hydrodynamic modulation, 164- 165 1 Ice edge, Seasat observations, 365-366 Ice sheet, Seasat observations of, 377-389 Ice-sheet tracking algorithm, 379-387 Infrared interferometric spectrometer, 289-290 Intensity, 156-160 Internal waves, see Ocean internal waves IRIS, see Infrared interferometric spectrometer Irradiance reflectance, 307 ITOS satellites, 3, 5 , 12

J JASIN experiment. I 11, 114, 119- 120. 166 JONSWAP spectrum, 91-92,97-99

K K , see Diffuse attenuation coefficient K algorithm

application of, 327-331 development of, 324-327 Korteweg-deVries equation, 184

507

L Landsat satellites, 5, 6, 7 Larval transport mechanisms, modeling of, 429-435 Limb Infrared Monitoring of the Stratosphere sensor, 48 LlMS sensor, see Limb Infrared Monitoring of the Stratosphere sensor Loop Current, 426-427,428

M Marine surface boundary layer, 105- 109 Marine surface temperature, see Sea surface temperature Marine surface wind definition of, 117- 120 microwave remote sensing of, 207-208 Marine surface wind direction SASS data. 13, 22-29. 121-137 sources of error, 262-264 Marine surface wind field factors affecting. 105-109 SASS data, 23-25 Marine surface wind speed. 57 altimeter results, 21, 23 in Antarctic, 373-377 comparison data, 110- I15 measurement using microwave systems, 102- 105 RMS observational errors, 125 SASS data, 13, 22-23. 25, 26.27.31 assimilation experiments, 128- 132 forecast experiments, 132- 135 model function for, 109-121 preprxessing of, 127-128 SMMR data, 14,29, 31 sources of error, 258-262 stability effects, 108-109 time-averaging problem, 116- I17 in tropical storms, 215-264 aircraft data, 216-217 cloud-motion data, 217 SASS data, 217-264 MCSST; see Sea surface temperature, AVHRR data Menhaden, 436 Microwave polarization, 369 Microwave Sounding Unit, 53. 54 Microwave spectral gradient ratio, 369-370

508

INDEX

Midlatitude marine storm, 199, 204-205 forecasting, 270-273 microwave measurements in, 206-208 smcture, 249-257 MSU. see Microwave Sounding Unit Multiyear sea-ice fraction, 370, 372

N

Peak frequency, nondimensional, 144 Peak frequency, spectral, 144 Penetration depth, 310 Pheopigment, 50, 309 Phillips constant, 144 Photosynthesis, 299-301 Phytoplankton, see also Pigment concentration ocean color and, 297-303 Pigment concentration, 49, 50, 52-53, 309-310, 437-438, 440, see also Biooptical algorithm remote sensing of, 3.13-322 Planck function, 279, 280, 281 Polarization, 370 Polar regions, Seasat observations, 337-367, 373-389

Nimbus-7, 1,7, 11-12,487,491-492 results, 50-53 sea-ice observations, 367-373 sensors on, 7, 48-50 surface information, 50, 51 Nimbus satellites, 4, 7, 286, 289 NMC Global Data Assimilation System, 122- 127 NOAA satellites, 5,6,53-54,286,287,288,339, Q 343, see also GOES satellites Northern anchovy, spawning data, 421-424, Queen Elizabeth I1 storm, 249-257, 270 4394,441,442 North Pacific Ocean, 424-426.437-439 R NOSASS data, 121-122, 128-136 Radar altimeter, see Seasat radar altimeter Radiance 0 clear water, 312 Ocean color sensor, 306-312 causes of, 297-299 upwelled spectral, 313 measurements of, 299-333 water-leaving, 307-309, 312, 313, 315 use in fisheries research, 436-442 Radiative transfer equation, 279-280 Ocean color boundary charts, 448-450 Rainband, 201 Ocean frontal analysis charts, 443-446 Rainfall Ocean internal waves, 39,42, 176-191 effects on altimeter echo, 83-87 Ocean Station PAPA, 474 in hurricane, 203-204,393-417 Ocean surface, quantitative measures for, 1-2 in tropical cyclones Ocean surface waves, 142- 175, see also SignifiSASS data, 208-209 cant wave height; Wave direction SMMR data, 208-215 ALT data, 146-153 Rainfall pattern, cyclone, remote sensing of, one-dimensional temporal spectrum. 143- 144 206-207 physical characteristics, 142- 146 Rain rate estimate from VIRR SAR data, 153-175 limitations, 398-400 two-dimensional spatial spectrum, 143, 145 results, 400-415 wave-height directional spectrum, model for, technique, 394-397 97-99 Rayleigh-Jeans approximation, 281 Ocean wavelength, SAR data, 36-37,40, 143 Residual sea height, 20, 21 Oil slicks, SAR data, 47, 48 Roughness parameter, 107 OSS Oceanographer, 469-473 RTE, see Radiative transfer equation

P Peak-enhancement factor, 144

S S- 193 RADSCAT, 207

509

INDEX SAM I1 sensor, see Stratospheric Aerosol Measurement sensor S A R , see Synthetic Aperture Radar SASS, see Seasat-A ScatterometerSystem Satellite, civil first generation, 4 historical review of, 1-7 second generation, 5-6 third generation, 7 Satellite Data Distribution System, 447-448 Satellite infrared thermal imagery, use in fisheries research, 421-429 SBVlTOMS, see Solar and Backscatter ultraViolet/Total Ozone Mapping System SCAMS, see Scanning Microwave Sounder Scanning Microwave Sounder, 206 Scanning Multichannel Microwave Radiometer advantages of, 367 characteristics, 14, 102,292,352,457,458 data characteristics, 483, 484-485, 486 ice-edge observations, 365-366 QE I1 StOITTI, 255-256, 258 results, 29-33 sea-ice algorithm, 369-372 sea-ice observations, 339, 344, 345, 351-357.366-373 sea surface temperatures and, 264-265. 266, 267, 282-286, 292-294 tropical cyclone rainfall data, 208-215 wind friction velocity data, 115 SCDP, see Seasat Commercial Demonstration Program SCM, see Successive correction method SDDS, see Satellite Data Distribution System SDME,119- 120 SDMR, 119- 120 SDRE,119-120 Sea ice, observation of meteorological and surface, 339-351 by Nimbus-7, 367-373 by SAR, 37-39.41 by Seasat, 331-367, 377-389 Sea-ice algorithm for SMMR, 369-372 Sea-ice concentration, 370-3’12 Sea-ice forecast chart, 447 Sea-ice mapping, 14,29, 31, 32,58 Sea-ice temperature, 372 Seasat automatic gain control system, 66 chronology and applications, 1,7, 1I comparison to GOES data, 400-406

data archive and distribution, 481-487 data utilization, 487-491 extratropical cyclone forecasting, 270-273 observations in storms, objectives, 199 operating area and flight tracks, 15-17 polar observations, 337-367, 373-389 sensors on, 12-14 surface information, 14-15 tropical storm forecasting and, 265-270 Seasat-A Scatterometer System, see also specific hurricanes

advantages of, 367 characteristics, 13, 22, 24, 102, 105, 351, 453, 454,456 data characteristics, 22-29,482-486 larval transport study, 429-435 model function for deriving surface wind speed, 109-121 observation geometry, 24,454 QE I1 Storm, 249-258 sea-ice observations, 344, 345, 351-357 for storm forecasting, 132-135, 265-273 tropical cyclone rainfall data, 208-209 tropical storm wind data, 217-218, 249-258 wind speed data, 102, 105 accuracy of, 259-262 assimilation experiments, 128- 132 preprocessing of, 127-128 Seasat Commercial DemonstrationProgram, 447 Seasat model, semiempirical neglecting pointing error, 75-78 tracker-bias study, 78-80 Seasat radar altimeter, see a h Echo model characteristics, 13, 15, 18, 20, 102, 104-105, ,146,453,454,455 data biases, 73-93 data characteristics, 482-486 echo analysis, using convolutional form of signal, 62-68 using deconvolution algorithm, 93-96 electromagnetic bias, 87-93 ice-sheet observations, 377-389 observation geometry, 18, 63, 85,454 pointing error effects, 80-83 rain effects on echo of, 83-87 results, 15-21 sea-ice observations, 357-364 Seasat orbit 1453 data, 359-362 Seasat orbit 1482 data, 362-364 tracker bias, 78-80

5 10

INDEX

Seasat radar altimeter (continued) wave-height determination accuracy study, 146-153 in Antarctic, 373-377 wind speed data, 104-105, 373-377 Seasat satellites, 7 Seasat Validation Program, 15, 463-486 data collection, 469-480 personnel, 465 plan of operations, 467-469 ships, buoys, and aircraft in, 464 weather conditions, 466-467 Sea surface probability density Gram-Charlier correction, 70-71, 73-75, 88-89 Lipa-BarricK algorithms, 66-68.94-95 model fits of, 68-73 recovery from Seasat data, 65-68 Sea surface radiance, 49 Sea surface temperature, 57 AVHRR data (MCSST), 54-56, 288, 290-292 infrared sensing of, 286-292 microwave sensing of, 281-286 multichannel, 54-56, 288, 290-292 near tropical cyclones, 264-265, 266, 267 radiative transfer and, 279-280 remote sensing of, 279-281 RMS observational errors, 125 sensors of, 286 SMMR data, 14, 29, 30, 264-265, 266, 267 VIRR data, 34 Sea surface topography, based o n Seasat altimeter data, 19,20 Sea surface wind direction, see Marine surface wind direction Sea surface wind field, see Marine surface wind field Sea surface wind speed, see Marine surface wind speed Sea water, emissivity, 280-281 SEH, 144 SEM, see Solar Environmental Monitor SEMS, see Midlatitude marine storm Sensor radiance, 306-312 Shrimp, penaeid, 429-436 Side-looking airborne radar, 339 Sigma coordinate, 63-65, 123 Significant wave height, 57, 144 ALT data, 13, 18, 21, 22 accuracy study, 146-153

in Antarctic, 373-377 in Beaufort Sea, 357-364 buoy data, 147-153 comparison study, 146-153 SLAR, see Side-looking airborne radar SMDA, 118-120 SMMR, see Scanning Multichannel Microwave Radiometer SMS satellites, 3, 5 Solar and Backscatter ultraVioletlTotal Ozone Mapping System, 48, 53 Solar Environmental Monitor, 53, 54 Solar irradiance, 310 Southern California Bight, 421-424, 439-440, 441,442 Southern Ocean, 373-381 Spectral gradient ratio, 370 SST, see Sea surface temperature SSU, see Stratospheric Sounding Unit Stability effects, 108-109 Stratospheric Aerosol Measurement sensor, 48 Stratospheric Sounding Unit, 53, 54 Stress, 106-107 Successive correction method, 271, 272 Surface-layer transport, 429-436 Surface probability density, see Sea surface probability density Surface roughness, 107-108 Surface temperature, see Sea surface temperature Surface truth winds accuracy of, 258-259 application of, 218-264 derivation of, 217-218 Surface wind direction, see Marine surface wind direction Surface wind field, see Marine surface wind field Surface wind speed, see Marine surface wind speed SWH,see Significant wave height Swordfish, Atlantic, 427-429 Symbols, glossary of, 499-503 Synoptic scale, 120-121 Synthetic Aperture Kadar characteristics, 13-14, 105, 141-142, 153155, 156, 157, 176, 457. 459 data characteristics, 486 internal wave measurements, 176-191 observation geometry, 155-156,454 ocean internal wave data, 176-191

51 1

INDEX Synthetic Aperture Radar (conrinued) results, 36-48 sea-ice observations, 339-351, 366-367 superiority of, 366-367 surface wave measurements, 165-175 SWH data artificial cross-section modulation, 155I60 hydrodynamic modulation, 164-165 real cross-section modulation, 160-165 tilt modulation, 161-164 theory, 153-165 wave measurements, 165- 175

T Temperature, see Sea surface temperature Temperature-Humidity Infrared Radiometer, 48 Thermal boundary charts, 443-446 THIR, see Temperature-Humidity Infrared Radiometer Thunnus alalunga, see Albacore tuna Thunnus fhynnus thynnus, see Bluefin tuna Tilt modulation, 161-164 Time-averaging, 116-117 TIROS-N, 286, 287 chronology, 1, 7, 12 sensors on, 7, 53-54 TIROS satellites, 2-3, 4, 7 Tongue Of The Ocean, 43,45-46 Tracker bias, 78-80 Transmissivity, 280-281 Tropical storm, see also Cyclone, tropical; Hurricane definition, 199 Tropical storm Agnes, 410, 413-414 Tropical storm Christine, 207 ' h a , see Albacore tuna; Bluefin tuna Typhoon definition, 199 wind speed analyses, 246-248 Typhoon Carmen, 414-415 Typhoon Wendy, 413

U Upwelled spectral radiance, 313

V Velocity bunching, 158-160 Vertical Temperature Profile Radiometer, 287 VIRR, see Visible and InfraRed Radiometer Visible and InfraRed Radiometer characteristics, 13 data characteristics, 483, 485, 486 rainfall data, 33-35, 212, 214 Visible and Infrared Spin Scan Radiometer, rain fite estimates from, 394-415 VISSR. see Visible and Infrared Spin Scan Radiometer VTPR, see Vertical Temperature Profile Radiometer

W Water-leaving radiance, 307-309, 312, 313, 315 Wave direction, see also Ocean surface waves detectability, 169-172 dominant, 166-169 two-dimensional wave spectrum, 172-175 Wave height, see Significant wave height Wavelength (ocean surface wave), 36-37, 40, 143 detectability, 169-172 dominant, 165-169 Wavenumber, 142 Weighting coefficient (in rainfall calculation), 396-397 Wentz algorithm, 208-209, 210, 211, 212, 213, 214, 215, 264 Wind direction, see Marine surface wind direction Wind field, see Marine surface wind field Wind shear, nondimensional, 108 Wind speed, see Marine surface wind speed Wind-wave height, 375

51 1

INDEX Synthetic Aperture Radar (conrinued) This Page Intentionally Left Blank results, 36-48 sea-ice observations, 339-351, 366-367 superiority of, 366-367 surface wave measurements, 165-175 SWH data artificial cross-section modulation, 155I60 hydrodynamic modulation, 164-165 real cross-section modulation, 160-165 tilt modulation, 161-164 theory, 153-165 wave measurements, 165- 175

T Temperature, see Sea surface temperature Temperature-Humidity Infrared Radiometer, 48 Thermal boundary charts, 443-446 THIR, see Temperature-Humidity Infrared Radiometer Thunnus alalunga, see Albacore tuna Thunnus fhynnus thynnus, see Bluefin tuna Tilt modulation, 161-164 Time-averaging, 116-117 TIROS-N, 286, 287 chronology, 1, 7, 12 sensors on, 7, 53-54 TIROS satellites, 2-3, 4, 7 Tongue Of The Ocean, 43,45-46 Tracker bias, 78-80 Transmissivity, 280-281 Tropical storm, see also Cyclone, tropical; Hurricane definition, 199 Tropical storm Agnes, 410, 413-414 Tropical storm Christine, 207 ' h a , see Albacore tuna; Bluefin tuna Typhoon definition, 199 wind speed analyses, 246-248 Typhoon Carmen, 414-415 Typhoon Wendy, 413

U Upwelled spectral radiance, 313

V Velocity bunching, 158-160 Vertical Temperature Profile Radiometer, 287 VIRR, see Visible and InfraRed Radiometer Visible and InfraRed Radiometer characteristics, 13 data characteristics, 483, 485, 486 rainfall data, 33-35, 212, 214 Visible and Infrared Spin Scan Radiometer, rain fite estimates from, 394-415 VISSR. see Visible and Infrared Spin Scan Radiometer VTPR, see Vertical Temperature Profile Radiometer

W Water-leaving radiance, 307-309, 312, 313, 315 Wave direction, see also Ocean surface waves detectability, 169-172 dominant, 166-169 two-dimensional wave spectrum, 172-175 Wave height, see Significant wave height Wavelength (ocean surface wave), 36-37, 40, 143 detectability, 169-172 dominant, 165-169 Wavenumber, 142 Weighting coefficient (in rainfall calculation), 396-397 Wentz algorithm, 208-209, 210, 211, 212, 213, 214, 215, 264 Wind direction, see Marine surface wind direction Wind field, see Marine surface wind field Wind shear, nondimensional, 108 Wind speed, see Marine surface wind speed Wind-wave height, 375