JPL PUBLICATION 84-57

Science Opportunities Using the

NASA Scatterometer on N-ROSS

M.H. Freilich

February 1, 1985

NASA

National Aeronautics and

Space Administration

Jet Propulsion Laboratory

California Institute of Technology

Pasadena, California

(597-200)
JPL PUBLICATION 84-5? NASA S & T Library

Washington, DC 20546

Science Opportunities Using the

NASA Scatterometer on N-ROSS

M.H. Freilich

February 1, 1985

NASA

National Aeronautics and

Space Administration

Jet Propulsion Laboratory

California Institute of Technology

Pasadena, California

(597-200)
The research described in this publication was carried out by the Jet Propulsion

Laboratory, California Institute of Technology, under a contract with the National

Aeronautics and Space Administration

Reference herein to any specific commercial product, process, or service by trade

name, trademark, manufacturer, or otherwise, does not constitute or imply its

endorsement by the United States Government or the Jet Propulsion Laboratory,

California Institute of Technology
NASA

National Aeronautics and

Space Administration

Washington, D.C.

20546

JAN 4 1385

EE

Dear Colleague:

Oceanography is a relatively new venture for NASA, with the first oceanic

microwave sensor having flown aboard Skylab in 1972. Since that time,

ocean-related sensors have flown aboard several satellites, most recently

Seasat and Nimbus-7, both launched in 1978.

In the coming decade, NASA plans a series of spaceflight activities

designed to further our understanding of ocean circulation, air-sea

interactions, and the biological processes taking place in the upper ocean.

This series—the NASA Scatteroneter (NSCAT) to fly aboard the Navy Remote

Ocean Sensing System (N-ROSS) satellite, the Ocean Topography Experiment

(TOPEX, a dedicated altimeter mission), and a color scanner for a platform

of opportunity—will also contribute to the World Climate Research Program,

Global Habitability Program, and the International Geosphere/Biosphere

Program.

I would like to take this opportunity to inform you that funding has been

approved for NSCAT, the first elanent in NASA's planned series. Vector

wind data from NSCAT has the potential for benefiting oceanography and

meteorology, as well as making fundamental contributions to earth science.

In addition, the understanding gained from the flight of NSCAT on N-ROSS

will assist in the design of future operationally oriented observing

systems for both the oceans and the atmosphere.

The enclosed Science Opportunities Document describes the capabilities of

the NSCAT instrument and outlines various types of experiments that could

be performed utilizing NSCAT data. The studies are designed to take

advantage of the unique resolution, accuracy, and coverage of NSCAT vector

wind data in order to allow models of oceanographic and meteorological

processes to be developed and tested. I hope that this docxment will aid

you and your colleagues in preparing to exploit the potential offered by

NSCAT, and I invite you to share in the scientific planning and execution

of this exciting project.

Sincerely,

Shelby G. Tilford, Director

Earth Science and Applications Division

Office of Space Science and Applications
Abstract

A scatterometer (The National Aeronautics and Space Administration [NASA]

Scatterometer [NSCAT]) will be flown as part of the Navy Remote Ocean Sensing

System (N-ROSS) scheduled for launch in 1989. The NSCAT will provide frequent

accurate and high-resolution measurements of vector winds over the global oceans.

NSCAT data will be applicable to a wide range of studies in oceanography, meteorology,

and instrument science. This document outlines the N-ROSS mission, describes the capabilities of the NSCAT flight instrument and an associated NASA research ground dataprocessing and distribution system, and surveys representative oceanographic, meteorological, and instrument science studies that may benefit from NSCAT data.

IV
Acknowledgments

This document has benefited greatly from the contributions and suggestions by members of the Satellite Surface Stress Working Group, chaired by Professor J. J. O'Brien.

Their magnum opus, Scientific Opportunities Using Satellite Wind Stress Measurements

Over the Ocean (O'Brien, 1982), has served as a reference and guide during the preparation of this opportunities summary. Special thanks are due to D. Chelton, E. Harrison,

K. Katsaros, L. McGoldnck, J. O'Brien, W. Pierson, P. Woiceshyn, M. Wurtele, and the

members of the Jet Propulsion Laboratory NASA Scatterometer Project team.
Executive Summary

The atmosphere and the oceans interact in many complex ways. On the one hand,

winds blowing across the ocean's surface generate surface waves, mix the waters in the

upper ocean, and help to maintain the major current systems and general circulation of

the ocean On the other hand, changing sea-surface temperature patterns, which often

emerge in response to shifting winds, cause variations in both near-surface winds and the

general circulation of the atmosphere. The oceans, then, with their vast supplies of heat

and large heat constants, act as the flywheel of the coupled ocean-atmosphere system to

determine regional and global climate.

Satellite observation systems are necessary to monitor and study large-scale motions in

the atmosphere and the oceans. Past observational and modeling efforts, using data

obtained by conventional means, have been severely hampered by a lack of accurate wind

measurements with high resolution yet global coverage and a lack of frequent sampling

coupled with records having long duration. The scatterometer flown on Seasat in 1978

clearly demonstrated that spaceborne microwave scatterometers were capable of obtaining the needed measurements of near-surface Winds over the oceans.

The National Aeronautics and Space Administration (NASA) will provide a scatterometer as part of the Navy Remote Ocean Sensing System (N-ROSS) scheduled for launch in

1989. Although based on the Seasat design, the NASA Scatterometer will represent a significant advance in satellite microwave scatterometer technology. Specifically, an onboard digital doppler processor will allow colocation of backscatter cells from all antenna

beams throughout the planned orbit, thus increasing both the accuracy and the coverage

of the measurements. An additional antenna beam on each side of the subsatellite track

will greatly increase the instrument's ability to determine unique wind directions. The

planned three-year lifetime of the mission will allow investigations of longer term phenomena than was possible with the short Seasat mission.

In addition to its scatterometer flight instrument, NASA will provide a research

ground data-processing and distribution system. This system will process the satellite data

and produce several levels of geophysical wind products that will be archived and made

available for use by science investigators.

The NASA Scatterometer data will be useful for a variety of studies in oceanography,

meteorology, and air-sea interaction. Oceanographic studies include regional and global

surface wave prediction, mixed layer modeling, and investigations of the dynamics of

equatorial and midlatitude current systems. Meteorological and air-sea interaction studies

involve the development of more accurate numerical weather-prediction and analysis

models and include investigations of the mechanisms of tropical and extratropical cyclogenesis, of the response of the global atmospheric circulation to changes in the tropical

oceans, and of the role of the atmosphere in the angular momentum balance of the earth.

VI
Satellite Surface Stress (S3

) Working Group

James J. O'Brien,

Chairman

Janet Witte,

Executive Assistant

Robert Atlas

Emedio Bracalante

Otis Brown

Robert Haney

D. Edmunds Harrison

Capt. David Honhart

Harley Hurlburt

Richard Johnson

W. Lmwood Jones

Kristina Katsaros

Roger Lambertson

Lawrence McGoldrick

Richard Moore

Steven Peteherych

Willard Pierson

James Price

Duncan Ross

Robert Stewart

Frank Wentz

Peter Woiceshyn

Morton Wurtele

The Florida State University

Nova University

Goddard Space Flight Center

NASA/Langley Research Center

University of Miami

U.S. Naval Postgraduate School

Massachusetts Institute of Technology

U.S. Navy, Chief of Naval Operations Staff

Naval Oceanographic Research and Development Authority

Ocean Routes, Inc.

Satellite Television Corp.

University of Washington

Control Data Corporation

Applied Physics Laboratory/Johns Hopkins University

University of Kansas

Atmospheric Environment Service, Canada

The City College of the City University of New York

Woods Hole Oceanographic Institution

NOAA-Atlantic Oceanographic and Meteorological Laboratory

Jet Propulsion Laboratory and Scripps Institution of Oceanography

Remote Sensing Systems

Jet Propulsion Laboratory

University of California, Los Angeles

VII
Acronyms

AAFE RADSCAT

ACC

DMS

DMSP

ENSO

FNOC

GDR

GOASEX

ITCZ

JASIN

JPL

JSC

NASA

NCAR

NDBC

NOAA

NRCS

NRL

N-ROSS

NSCAT

NWP

PEL

PODS

S

3

SASS

SDR

SMMR

SNR

SSM/I

SST

TOGA

TOPEX

TWTA

WOCE

Advanced Aerospace Flight Experiment Radiometer/Scatterometer

Antarctic Circumpolar Current

Data management system

Defense Meteorological Satellite Program

El Nino - Southern Oscillation

Fleet Numerical Oceanography Center

Geophysical data record

Gulf of Alaska Experiment

Intertropical convergence zone

Joint Air-Sea Interaction Experiment

Jet Propulsion Laboratory

Johnson Space Center

National Aeronautics and Space Administration

National Center for Atmospheric Research

National Data Buoy Center

National Oceanic and Atmospheric Administration

Normalized radar cross section

Naval Research Laboratory

Navy Remote Ocean Sensing System

NASA Scatterometer

Numerical weather prediction

Planetary boundary layer

Pilot Ocean Data System

Satellite Surface Stress

Seasat-A Satellite Scatterometer

Sensor data record

Scanning Multichannel Microwave Radiometer

Signal-to-noise ratio

Special Sensor Microwave/Imager

Sea-surface temperature

Tropical Ocean/Global Atmosphere Experiment

Ocean Topography Experiment (proposed altimetric satellite)

Traveling wave tube amplifier

World Ocean Circulation Experiment

VIII
Contents

I. Introduction 1

A. The Need for Satellite Scatterometry 1

B. History of Wind Measurement by Scatterometers 1

II. N-ROSS Mission Description 7

III. NASA Scatterometer System 9

A. Requirements and Capabilities 9

B. Instrument Description 9

C. NSCAT Data-Processing System 12

D. NSCAT Verification 14

IV. Science Opportunities 17

A. Oceanographic Applications 17

B. Meteorological Applications 21

C. Scatterometer Instrument Science Studies 23

Appendix A. Summary of SASS Results 25

Appendix B. Principles of Scatterometry 27

References 31

Figures

1. Locations of all ship observations of surface meteorological

variables for the period 1 July 1978 to 9 October 1978 2

2. Scattering cross section per unit area for 13.96 GHz

vertical polarization 3

3. The O-Q dependence on azimuth angle and wind speed 3

4. The analysis and map of global oceanic winds from

SASS data for 14-15 September 1978 5

5. Schematic configuration of the N-ROSS spacecraft 7

6. Plan view of N-ROSS measurement swaths on the

earth's surface ... 8

7. Plan view showing the 16 CTO cells used to construct

a 50 km x 50 km wind vector resolution cell 9

IX
8. NSCAT coverage over time 10

9. Schematic plan view of the illumination patterns of the

antennas on the earth . ... 10

10. Schematic block diagram of the NSCAT flight instrument . . 11

11 Timing diagram 11

12. Schematic diagram of the NSCAT data-processing

system 13

13. Frequency-wavenumber region for winds of interest for

oceanographic applications 17

14 Vector- and time-averaged wind field from Seasat SASS for

the period 6-7 September 1978 26

15. Definitional sketch of the scattering geometry 27

16. Geometry of doppler filtering for along-beam resolution 28

17. Simulated speed vs direction solution curves for noise-free

data from NSCAT 29

Table

1. Data level definitions 12
Section I

Introduction

Satellite scatterometers can provide frequent, accurate,

high-resolution measurements of surface vector winds over the

global oceans. Such scatterometer data will play an increasingly

large role in a variety of oceanographic and meteorological

studies ranging from weather and wave prediction to investigations of the long-term variability of climate and ocean-current

systems. The global nature of these scatterometer wind measurements will make them especially valuable to planned

large-scale experiments such as the World Ocean Circulation

Experiment (WOCE) and Tropical Ocean/Global Atmosphere

(TOGA).

This document describes the capabilities of a scatterometer

(the National Aeronautics and Space Administration [NASA]

Scatterometer [NSCAT]) to be flown as part of the Navy

Remote Ocean Sensing System (N-ROSS) as well as an associated NASA ground data-processing and distribution system. A

representative survey of possible oceanographic and meterological studies using scatterometer data forms the latter half of

this document. Many of these studies were identified in the

report of the Satellite Surface Stress (S3) Working Group

(O'Brien, 1982), which served as a guide for the preparation of

this document. Potential investigators are urged to evaluate the

possible utility of NSCAT data for their specific oceanographic

and meteorological studies.

A. The Need for Satellite Scatterometry

The temporal and spatial distributions of fluxes of radiation, kinetic energy, heat, and momentum between the atmosphere and the oceans greatly influence near-surface oceanic

and atmospheric motions. The vertical flux of horizontal

momentum due to winds blowing over the oceans ("wind

stress") is the largest single input of momentum to the oceans.

Winds thus play a crucial role in generating surface and internal waves, in mixing the upper ocean, and in establishing and

maintaining currents and large-scale oceanic circulation. Winds

over the oceans also strongly influence and modulate the transfers of kinetic energy, heat, and moisture between the air and

the sea.

Just as winds are directly or indirectly responsible for a

wide range of oceanic phenomena, so the properties of the

upper ocean, notably sea-surface temperature, strongly influence atmospheric motions. Meteorological interest in oceanic

winds stems in large part from the need to understand the

many ocean-atmosphere interrelationships which define

regional and global climate. In addition, a major source of

error in predicting atmospheric motions and weather is the

incomplete knowledge of the present state of the atmosphere.

Oceans account for 70% of the earth's surface; therefore,

accurate knowledge of the winds over the oceans is crucial

to our ability to define the state of the atmosphere in a global

sense.

Deficiencies in existing large-scale wind data sets have

severely hampered past studies of both the ocean's response to

wind forcing and the interactions between the ocean and the

atmosphere. While these studies require nearly simultaneous,

high-resolution wind data over large areas and for long periods

of time, conventional wind measurements from ships, buoys,

and islands are sparsely and irregularly distributed over the

global oceans (Fig. 1). Measurements from merchant ships are

notoriously inaccurate, while data obtained from islands and

coastal stations may not be representative of open ocean

conditions due to orographic contamination.

Instruments mounted on polar-orbiting satellites can provide data with the coverage and spatial resolution required for

the study of many oceanographic and atmospheric phenomena.

Moore and Pierson (1966) first suggested the use of a satelliteborne active microwave scatterometer for the global measurement of oceanic winds. Although satellite-borne active and

passive systems (altimeters, radiometers, and scatterometers)

have subsequently been used to measure wind speed, scatterometers have demonstrated clear superiority because they

can measure vector winds (both speed and direction).

B. History of Wind Measurement

by Scatterometers

The major efforts leading to the current design of the

NSCAT system are summarized below. A more complete

review of the historical development of scatterometer systems

to measure surface winds over the ocean can be found in

Moore and Fung (1979).

The use of active microwave systems for measuring oceanic

winds resulted from the development in World War II of radar

systems for air defense: the causes of "sea clutter" were

intensely researched in the U.S. and Britain following the end

of World Wai II. Experimental studies (Wiltse et at., 1957;

Grant and Yaplee, 1957; Campbell, 1959) in the 1950s and

early 1960s established an apparent correlation between nearsurface wind speed and the normalized radar cross section (a0)

of the ocean at moderate incidence angles (the vertical angle

between the normal to the surface and the illuminating

radiation).

Extensive series of airborne measurements by the Naval

Research Laboratory (NRL), utilizing a four-frequency pencilbeam scatterometer, and by NASA/Johnson Space Center
HI
(JSC), utilizing an airborne fan-beam scaUerometer at Ku-band

frequency, established an empirical power-law dependence of

a0 on wind speed (Valenzuela et al., 1971; Newton and Rouse,

1972). However, instrument calibration uncertainties prevented comparing data from different experiments, and the

data were incorrectly interpreted to suggest that OQ tended to

saturate at high wind speeds.

In the early 1970s NASA developed an improved pencilbeam aircraft scatterometer, the Advanced Aerospace Flight

Experiment Radiometer/Scatterometer (AAFE RADSCAT);it

was stable and demonstrated good absolute calibration (Jones

et al., 1977). Field experiments were conducted that refined

the power-law relationship between a0 and wind speed at incidence angles from 20° to 70° (Fig. 2). In addition, data

20

10

z

o

o -10

Oat

U

-20

-30

-40

INCIDENCE

ANGLE

UPWIND

_L

10

WIND SPEED, m/s

100

Fig. 2. Scattering cross section per unit area (ITO) for 13.96 GHz

vertical polarization, as a function of incidence angle and wind

speed (adapted from Jones, Werrtz, and Scnroeder, 1978)

acquired while the aircraft flew in circular patterns allowed the

examination of the relationship between a0 and azimuth angle

X. the horizontal angle between the radar illumination and the

wind direction (Jones and Grantham, 1975, Jones et al.. 1977,

Jones and Schroeder, 1978). It was found that o0 (in dB)

varied approximately as cos(2x) (where the quantity x = 0 [TT]

corresponds to upwind [downwind]) and that the amplitudes

of the modulations varied with incidence angle, wind speed,

and polarization (Fig. 3). U is the nearly harmonic variation of

o0 with azimuth angle that allows satellite scatterometers to

determine wind speed and direction by measuring a0 from a

given area of the sea surface at multiple azimuth angles.

10

5

o

-5

-'0

-

-25

-30

-35

-40

i i i i r I I I I

I I I I I I I I I I I I I I I I I

40 80 120 160 200 240 280 320 360

X (WIND RELATIVE DIRECTION), Aeg

Fig. 3. The crg dependence on azimuth angle and wind speed (from

the SASS-I geophysical model function, horizontal polarization, 30°

incidence angle; adapted from Boggs, 1982)

The first spaceborne radiometer/scatterometer was flown

aboard the Skylab missions SL2, SL3, and SL4m 1973-1974

(Young and Moore, 1977). Many measurements of a0 were

obtained at various incidence angles for a wide tange of wind

conditions. Since the scatterometer employed a single pencil

beam, only a single measurement of a0 at one (unknown)

value of the azimuth angle was obtained from each area on the

sea surface. Because a0 varies with azimuth angle, independent

knowledge of the wind direction was necessary to infer wind

speed from the measured a0. Although the Skylab experiment

demonstrated that spaceborne scatterometers were indeed

feasible, the lack of high-quality surface wind measurements

for comparison with Skylab measurements greatly detracted

from the scientific quality of the scatterometer data.

Additional experiments with the airborne AAFE

RADSCAT in the mid 1970s led to a further refinement of the

empirically based geophysical model function used to relate a0

to surface wind speed as a function of incidence angle and azimuth angle. Ross and Jones (1978) showed that scatterometer

estimates of wind speed were relatively insensitive to surface

gravity wave conditions, while Jones and Schroeder (1978)

suggested that a0 might be more highly correlated with surface

stress (expressed as friction velocity) than with actual wind

velocity at a fixed anemometer height.

The Seasat-A Satellite Scatterometer (SASS), a fan-beam,

dual-polarized, Ku-band scatterometer, was flown aboard the

Seasat satellite from June to October, 1978. The four antennas

(two on each side of the spacecraft) allowed measurements of
o0 at two azimuth angles (one from the fore antenna, one

from the aft) with spatial resolution of approximately 50 km,

in two swaths approximately 600-km wide on each side of the

subsatelhte track. The o0 measurements from the fore and aft

beams were combined to provide estimates of wind speed and

up to four possible directions. A multifrequency radiometer

(the Scanning Multichannel Microwave Radiometer [SMMR])

was used to correct some measurements for the small effects

of liquid atmospheric water The world's ice-free oceans were

sampled frequently during the three-month lifetime of Seasat.

Some results of studies based on SASS data are described in

detail in Appendix A; below are listed only major

accomplishments.

(1) SASS demonstrated that accurate (±2 m/s) measurements of wind speed and direction (up to four possible

directions, one of which was correct to within ±20°)

could be obtained from satellite scatterometer data

alone.

(2) The geophysical model function was further refined by

comparing SASS data and high-quality m situ data

obtained in the Joint Air-Sea Interaction and the Gulf

of Alaska Experiments (JASIN and GOASEX,

respectively)

(3) SASS data were used to construct the first global,

nearly synoptic maps of surface-wind speed and direction (Fig. 4).

The NASA Scatterometer, NSCAT, while similar to SASS,

will have enhanced capabilities:

(1) An additional antenna on each side of the subsatelhte

track will reduce the number of directions associated

with nearly every wind speed to 2, nearly 180° apart.

(2) An onboard digital doppler processor will allow coregistration of o0 measurements from different antennas

over the entire swath width.

(3) The quantity a0 will be measured with a spatial resolution of approximately 25 km.

(4) A dedicated NASA ground data-processing and distribution system will provide research quality data to the

science community in a timely fashion

(5) The expected 3-year lifetime of NSCAT will allow

for some studies of seasonal and interannual variations.

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Section II

N-ROSS Mission Description

N-ROSS is a satellite system designed to provide frequent,

accurate, high-resolution measurements of near-surface wind,

ocean topography, wave height, sea-surface temperature, and

atmospheric water content over the global oceans. The design,

implementation, launch, and operation of the N-ROSS mission

will involve interagency collaboration between the Navy,

NASA, the National Oceanic and Atmospheric Administration

(NOAA), and the Air Force. Drawing on experience gained in

the design and implementation of SASS, NASA will provide

the scatterometer flight instrument as well as a research

ground data-processing and distribution system. In all, N-ROSS

will have four microwave instruments mounted on a single

satellite in a near-polar orbit:

(1) A scatterometer (described more fully in Section III).

(2) A microwave altimeter, similar to that flown on Seasat,

to measure ocean topography and significant wave

height.

(3) A Special Sensor Microwave/Imager (SSM/I), a fourfrequency (19, 22, 37, and 85 GHz) scanning microwave radiometer, that will be used to acquire data on

sea-surface temperature, scalar wind speed, atmospheric

water content, and ice characteristics.

(4) A low-frequency (5.2 and 10.4 GHz) scanning microwave radiometer that, in conjunction with SSM/I data,

will be used to measure sea-surface temperature--with a

resolution of 25 km.

A schematic diagram of the proposed locations and orientations of the four instruments is shown in Fig. 5. Figure 6 illustrates the measurement swaths of the instruments.

Although final mission parameters have not yet been fully

established, present plans call for N-ROSS to be launched

aboard a Titan-II rocket in mid-1989. N-ROSS will be placed

in a sun-synchronous orbit at an altitude of approximately 830

km and an inclination angle of 98.7°. The designed mission

duration is three years.

Data from the N-ROSS mission will be transmitted to the

Fleet Numerical Oceanography Center (FNOC) in Monterey,

California. Raw NSCAT data, orbit and attitude data, and

earth-located SSM/I brightness temperatures (corrected for

antenna pattern biases) will be provided to the NSCAT

research ground data-processing system by the Navy. As

described in Section III.C, NASA will process all usable

scatterometer data collected over the ocean and will distribute

scatterometer data (at various levels of processing) and some

SSM/I data to scientific users in a timely fashion. Reduction,

analysis, and dissemination of other N-ROSS data (altimeter

and radiometer data) will not be carried out under the auspices

of the NSCAT project.

- LOW-FREQUENCY

RADIOMETER

SCATTEROMETER ANTENNAS

SCATTEROMETER ANTENNAS

ALTIMETER

Fig. 5. Schematic configuration of the N-ROSS spacecraft, showing the locations of the scatterometer, SSM/I,

low-frequency radiometer, and altimeter antennas
1400 km

r- S5M/I SWATH -7

175km

\

ALTIMETER

SWATH

-WIND VECTOR ZONEFig. 6. Plan view of N-ROSS measurement swaths on the earth's

surface. Swath widths are drawn to scale. Details of the measurement swath for the high-resolution radiometer are not yet known.
Section III

NASA Scatterometer System

The baseline design for the NASA scatterometer system is

described below. Although the detailed information presented

in the following subsections is current as of the printing of this

document, minor changes may be made in the final design and

implementation phases of the instrument, research ground

data-processing system, and validation activities.

A. Requirements and Capabilities

Performance requirements for the NSCAT system were

established by the S3 Working Group (O'Brien, 1982) and the

N-ROSS Project (Jet Propulsion Laboratory, 1983). During the

definition study (Phase A) conducted at the Jet Propulsion

Laboratory (JPL) in 1983, the requirements were further

refined and reviewed by the S3 Working Group and the

N-ROSS Project. The following summarizes the performance

requirements for the NSCAT flight instrument and its associated research ground data-processing system.

1. Wind Speed Accuracy

The rms accuracy will be better than the greater of 2 m/s or

10% of the true wind speed in the range from 3 to 30 m/s. If

the geophysical model function remains valid for winds greater

than 30 m/s, the system will be capable of measuring winds up

to 100 m/s.

2. Wind Direction Accuracy

At least 90% of the wind vectors retrieved will have no

more than two directional ambiguities which subtend angles of

150° to 210°. The wind direction rms accuracy will be 20° or

better for the one ambiguity closest to the true wind. The

research ground data-processing system will employ an objective algorithm to choose a unique wind direction associated

with each speed solution

3. Rain Flag

Based on SSM/I data, a flag will be present on a0 and

vector-wind cells where the presence of liquid atmospheric

water would degrade the vector-wind measurement such that

wind speed and direction accuracy requirements would not be

met (see subsections III.A 1 and III.A.2). Cells for which there

are no usable SSM/I data will be identified by a separate flag.

4. Spatial Resolution

The a0 measurements will be obtained with a resolution of

25 km (the distance between centers of adjacent cells along an

antenna beam). For NASA users, winds will be processed with

a resolution of 50 km (each retrieved wind vector will characterize the winds within a square, 50 km on each side; see

Fig 7).

50 kn

Fig. 7. Plan view showing the 16 <r0 cells used to construct a

50 km x 50 km wind vector resolution cell

5. Wind Vector Cell Location Accuracy

Cell centers of 50-km wind cells will have an absolute location accuracy of better than 50 km (rms). The relative location

of any two adjacent wind-cell centers will be better than

10 km (rms).

6. Coverage

Vector winds over at least 90% of the global ice-free oceans

will be obtained at least once in every two-day period.

7. Data Reduction and Distribution

All wind data collected over the ocean will be processed

within two weeks after timely (seven days from satellite acquisition) reception from the N-ROSS Project. Raw data will be

processed to vector winds, and the vector winds will be combined into wind-field maps. All raw data, as well as selected

levels of processed data, will be archived for the duration of

the mission. All ocean vector-wind data and wind-field maps

will be made available to users via an on-line data-management

system.

B. Instrument Description

The NSCAT flight instrument utilizes design concepts similar to those used for SASS. Pulses of radiation at a microwave
frequency of 13.995 GHz are emitted, and then the normalized radar cross section (a0) of the ocean is determined by

accurately measuring the backscattered power. The a0 measurement, in turn, can be related to the local winds. Measurements of a0 are obtained in swaths approximately 600-km

wide on each side of the subsatellite track. The swath coverage

will allow winds over ~90% of the ice-free oceans to be

observed in each two-day period (see Fig. 8[a]; Fig. 8[b]

provided for comparison). A more complete description of the

principles of scatterometry can be found in Appendix B.

Fig. 8. NSCAT coverage over time: (a) after 2 days. Gray areas are

regions in which vector wind measurements have been made at

least once during the 2-day period. White areas denote regions that

have not been covered by the NSCAT wind measurement swath.

Approximately 90% of the oceans are covered at least once every 2

days; (b) after 6 orbits. Gray and white areas represent regions in

which wind vector measurements have taken place. Note that some

high-latitude regions are sampled more than once.

1. Physical Subassemblies

The flight instrument is composed of three major subassemblies: an RF subassembly, the antennas, and a digital subassembly. The RF subassembly contains the transmitter and

receiver subsystems. The antennas generate a fan-like beam

and produce the ground-illumination geometry shown in

Fig. 9. The digital subassembly contains both the dopplerprocessing subsystem used to achieve along-beam (incidence

angle) resolution and the control and logic subsystems for the

instrument.

2. Functional Subsystems

Figure 10 shows a schematic block diagram of the NSCAT

flight instrument. The information below describes several of

the major subsystems.

a. Transmitter. The transmitter subsystem employs a

traveling wave tube amplifier (TWTA) to generate radar pulses

at 13.995 GHz. The pulses are ~5 ms in duration. A single

antenna cycle consists of a series of 25 pulses (each followed

by an approximately 16-ms receiving interval), followed by a

64.6-ms noise measurement interval (Fig. 11). This pattern is

repeated cyclically for each antenna beam. The peak pulse

power is approximately 100 W.

b. Receiver. A portion of the backscattered signal is captured by the antennas during the 16-ms receiving interval following each transmitted pulse and is then amplified by a lownoise amplifier. The amplified signal is made up of the desired

backscattered signal as well as "noise" due to both the intrinsic system noise and the natural microwave emissivity of the

ANTENNA 6

-45°

ANTENNA 5

ANTENNA 4

SUBSATELLITE

TRACK

RIGHT WIND

VECTOR SWATH

ANTENNA 1

BEAM IV

ANTENNA 2

BEAMS 2H, 2V

ANTENNA 3

BEAM 3V

600km175km 175km-600km

Fig. 9. Schematic plan view of the illumination patterns of the

antennas on the earth

10
STAIO/

FREQUENCY

SYNTHESIZER

CALIBRATION NOISE SOURCES

6 ANTENNAS

8 ANTENNA

BEAMS

fig. 10. Schematic block diagram of the NSCAT flight Instrument

(a)

(b)

TRANSMIT

PULSE

/ ECHO

h- H0.79ms

1 MEASUREMENT INTERVAL

4 NOISE

MEASUREMENT

25 PULSES

64.6ms

TIME PER ANTENNA 0.468 t

TIME PER ANTENNA CYCLE 3.746 t

Fig. 11. Timing diagram: (a) Transmit and receive timing; and (b) 25

transmit/receive cycles followed by a 64.6-ms receive-only period

comprising a full cycle on a single antenna

ocean. In order to extract the desired backscatter signal from

the amplified signal, the unwanted noise power must be

estimated and properly subtracted. This is accomplished in the

64.6-ms "receive-only" period following each cycle of 25

transmitted pulses.

c. Antenna. The antenna subsystem is made up of 6 antennas, shown schematically in Figs. 5 and 9. Each of the physical

antennas is approximately 3-m long, and each generates a fanlike beam that is narrow (~0.5°) in the across-beam dimension

and wide (~25°) in the along-beam dimension. (Due to the

curvature of the earth, the incidence angle range sampled by

the fan-beam geometry is from approximately 10° to 60°.) On

deployment, the antennas are tilted and rotated relative to the

spacecraft to produce the ground-illumination geometry

shown in Fig. 9. In the baseline design, each fore and aft

antenna on either side of the spacecraft (labeled 1,3,4, and 6

in Fig. 9) generates a single, vertically polarized beam. Each

middle antenna (labeled 2 and 5) generates two beams, one

vertically polarized, and one horizontally polarized.

d. Digital processor. The received signals are processed

by an onboard digital processor in order to achieve alongbeam resolution. The basis of the processor's operation is that

the satellite's motion and the earth's rotation cause a doppler

shift in the received signal. This shift is a function of alongbeam distance. Thus, the backscattered power from a selected

along-beam area can be obtained by filtering the signal within

a chosen band of doppler-shift frequencies. The dopplerprocessing subsystem uses adjustable center frequencies in

order to allow colocation of cells from all antennas (fore,

11
middle, and aft) everywhere in the spacecraft's orbit This digital doppler-processing system on NSC AT represents a significant improvement over the SASS fixed analog filter system.

The adjustable center frequencies of the NSCAT digital processor will allow vector-wind measurements to be made over the

entire swath everywhere in the planned NSCAT orbit, thus

increasing the coverage over that achieved by SASS (Due to

the effects of the earth's rotation, the fixed filter system on

SASS achieved full swath coverage only at the extreme highlatitude portions of the orbit) In addition, more accurate

colocation of OQ cells from different antenna beams on NSCAT

will greatly simplify the ground processing used to retrieve

vector winds as well as provide for more accurate wind measurements in regions with large gradients in wind speed and

direction.

C. NSCAT Data-Processing System

As part of the NSCAT program, JPL will implement and

operate a research ground data-processing and distribution

system for NASA research users. This system will produce

several levels of wind products and will distribute data to users

in a timely manner.

The NSCAT data-processing system will provide dedicated,

nonreal-time processing of scatterometer data for research

users. The system will be designed to process all ocean data

with no backlog and will be tested and ready for use at launch.

Management and distribution of the data will be provided by

an on-line archiving system such as the Pilot Ocean Data

System (PODS).

1. Data Processing and Products

It is assumed that the Navy will provide the data-processing

system with all available time-ordered frame-synchronized raw

scatterometer data, N-ROSS orbit and attitude data, and

ground-located SSM/I-brightness temperatures within three

days of data acquisition. This integrated data package will be

all that will be necessary to produce the desired wind products. After an initial six-month shakedown period following

launch, data will be available to users two weeks after JPL

receives the raw data

Data processing will proceed through several levels (see

Fig. 12). The detailed definitions of the various data levels are

shown in Table 1 Raw data are referred to as Level 0 After

data cleanup, conversion from binary to engineering units,

earth location, and backscatter computation, the normalized

radar backscatter cross section that is referred to as Level 1 5

will be obtained These data are the primary physical measurements of the scatterometer system and constitute the

sensor data record (SDR). All usable data received from FNOC

will be processed to Level 1.5. After earth location, the data

will be flagged as to whether the cell is ocean or nonocean

(land or ice).

The data-processing system will receive earth-located

SSM/I brightness temperatures (Level 1.5) from FNOC. These

data, where available, will be used to produce a rain flag for o0

cells Because the SSM/I swath does not completely cover

the scatterometer swath, some a0 cells cannot be flagged. A

"no information" flag will be used for these cells

As shown in Fig. 7, sixteen a0 measurements (each with

25-km resolution) will be combined into 50-km squares for

wind retrieval. Each cluster of four a0 cells in Fig. 7 represents

colocated a0 measurements from each antenna beam (vertical

polarization from the fore, middle, and aft antennas; horizontal polarization from the middle antenna only).

Extensive simulation studies have shown that multiple

vector wind solutions are obtained when the sixteen a0

measurements are processed. These solutions are found to have

similar speeds but varied directions. In contrast to the SASS

case, the polarization mix and antenna beam angles on NSCAT

will allow the multiple vector wind solutions to be ordered by

the relative likelihood of their being the correct solution. This

ordering does not depend in any way on additional in situ

Table 1. Data level definitions

Level Definition

0 Raw data, frame synchronized, gap filled,

time ordered and nonredundant (obtained

from the Navy)

1 0 Decommutated data in engineering units

1 5 Sensor data record (SDR)' Data in sensor

units (earth-located on).

2 0 Data in geophysical units (vector wind speed

and direction) with 6 or fewer solutions at

each location Two solutions, at least 150°

apart, will have probabilities greater than

90% of being the correct solution.

2 5 Geophysical data record (GDR) As 2 0 but

with a single wind speed and direction at each

location. Wind solutions are tagged with algorithm, time, location, and quality flags,

3 0 Wind map, 1° X 1° earth-fixed grid map of

vector-averaged winds Vector averages are

over all wind vector solutions falling within

each spatial grid during the preceding twoday period

12
SCAH

FNOC

GROUP,

MERGE,

AND

RAIN

FLAG

ARCHIVE

ARCHIVE

ARCHIVE

Q

USER PORTS

Fig. 12. Schematic diagram of the NSCAT data-processing system. Numbers (e.g. 1.5,2.0) represent levels of processing as discussed in

Subsection HI.C.1. "PS" stands for "Performance Summary"; "C" stands for "Catalog entry."

information. The wind data with multiple solutions are referred to as Level 2.0.

The multiple wind solutions with their associated relative

likelihoods will be used as input to an iterative objective

scheme for selecting unique wind directions. The relative

likelihoods of the solutions in a cell and the directions selected

for its neighbors will be used to select a unique direction for

each wind retrieval cell. The resulting unique vector winds at

50-km resolution will be referred to as Level 2.5 data. Simulations indicate that the highest likelihood direction points in

the direction of the true wind at least 70% of the time. It is

expected that the ambiguity removal scheme will select the

vector wind solution that points closest to the direction of the

true wind more than 90% of the time. The ambiguity removal

algorithm is currently under development at JPL. Exact

specifications of the algorithm performance will be available in

early 1985.

To provide easy access to the wind data, a wind-field map

product will be produced. This product will consist of daily

maps with 1° X 1° resolution in longitude and latitude. Each

map will be produced from the previous two days' data. Every

1° X 1° cell will contain the vector average of all wind vectors

falling in the cell over the two-day period. These maps are not

intended for direct scientific analysis but rather for aiding data

selection. Areas in which there are no data during the two-day

period will be indicated. All usuable ocean data will be processed to maps. The mapping algorithm is currently under

development at JPL, and the requirements of NASA research

users will be evaluated in defining the final form of the windfield maps.

In addition to processed data, summaries of system performance will be produced. These will allow the checking of

instrument health, data system throughput, and gross statistics

of the wind data.

13
2. Data Management and Distribution

Data will be available to users through a data management

system (DMS) such as the PODS (Brown et al., 1983). The

DMS will support on-line access to selected scatterometer data

and will allow the specification of longitude, latitude, and time

boundaries of desired data. The requested data will be transmitted to the user either electronically or on magnetic tape.

The data available on-line will be as follows:

(1) All wind-field maps

(2) All Level 2.5 data

(3) All Level 2.0 data

(4) A selected 5% of Level 1.5 data for the most recent

12 months

The science investigators and project personnel will determine which regions are included in the 5% of Level 1.5 data

that are stored on-line.

In addition to the scatterometer data, the DMS will host in

situ data for comparison purposes. It may be possible to host

certain user data sets that meet format and validation criteria

established by the DMS. These in situ data will also be available through the on-line selection procedures used for the

scatterometer data.

The DMS will have a catalog(s) of available data organized

in convenient ways. It will also maintain an on-line bibliography of relevant project and external literature on scatterometry and related subjects.

The DMS will not have the capability for extensive user

computing or analysis. The graphics tools available will be

mainly for data selection and perusal, not for producing scientific products. Also, there is no plan to use multiple or alternative algorithms in the wind-retrieval processing. Investigators

wishing to perform such processing may obtain the Level 1.5

data for their work.

The DMS will archive data (off-line) at several levels. The

plan is to archive all of the following:

(1) Data received from the Navy

(2) Level 1.5 data

(3) Level 2.0 data

(4) Level 2.5 data

(5) Wind-field maps

(6) In situ data

Since much of the archived data will be stored off-line, an

operations staff will be available to extract and distribute

selected portions of the archived data in response to users'

requests.

D. NSCAT Verification

The wind data from the NSCAT system will be verified in

the first year following launch. The goal of the verification will

be to quantitatively assess the overall accuracy of the data in

terms of location, o0, wind speed and direction, and the presence or absence of the rain flag. The sensor (location and a0)

and geophysical (wind vector and rain flag) verification efforts

currently planned for NSCAT are briefly described below.

Although the studies described are limited in temporal and

geographical extent, similar comparisons are expected to be

made between the scatterometer data and the high-quality

in situ data collected by oceanographic and meteorological

field programs such as WOCE and TOGA.

1. Instrument Verification

The a0 measurements and location accuracies will be verified using up to three mobile ground monitoring stations,

onboard calibration sources, data obtained from a0 monitoring

cells, isotropic scatterers, and airborne scatterometer measurements. The ground monitoring stations will measure the transmitted power as received on the surface (over land) as the

satellite passes overhead. Some additional information on the

antenna gain pattern will be obtained from these data. Onboard calibration sources will be used to monitor gain characteristics of the instrument. As shown in Fig. 2, a0 cells at an

incidence angle of approximately 10° are fairly insensitive to

wind speed and direction. Data obtained from cells at this

incidence angle will be used to monitor and calibrate a0 measurements over the ocean. The backscatter from isotropic

scatterers, such as large regions of rain forest, can be used to

identify and correct biases in the gain pattern for any individual antenna (Bracalante et al., 1980). Finally, comparisons

between measured a0 from the NSCAT over the ocean and

suitably averaged aircraft underflight measurements of a0 will

provide direct, if limited, verification of a0 accuracy over the

ocean under realistic conditions.

A quantitative assessment of o0 accuracy will be made

within the first six months after launch. However, data for

monitoring purposes (ground stations, calibration sources, and

isotropic scatterers) will be collected and routinely examined

throughout the mission. Level 1.5 data processed in the first

six months of the mission will be called "interim" data because the o0 validation will not have been completed.

14
2. Geophysical Verification

To evaluate the accuracy of the geophysical measurements

(wind vector and rain flag) the system will be verified by comparing NSCAT winds with winds measured or inferred by conventional, non-NSCAT means. Present plans call for acquiring

in situ conventional wind data from two experiments conducted at different geographical regions, each lasting approximately two months. The regions will be chosen to encompass a

wide range of oceanic and atmospheric conditions. Direct measurements of surface winds will be obtained from buoys (both

National Data Buoy Center [NDBC] type moored buoys and

drifting buoys), aircraft (such as the National Center for

Atmospheric Research [NCAR] Electra or the NOAA P3), and

possibly oceanographic ships and instrumented platforms. In

addition to direct comparisons between in situ and scatterometer winds, plans call for preparing high-quality wind-field

maps for the validation regions based on the conventional measurements as well as all other available meteorological information. These maps, which represent an interpolation and

smoothing of the wind field, will provide vector wind estimates that can be compared with scatterometer measurements.

Although the direct comparisons discussed above provide

an estimate of the intrinsic accuracy of the scatterometer

system, many oceanographic and meteorological uses of the

data will involve both spatial and temporal averaging of

measured winds. As the scatterometer data are irregularly

distributed in both space and time, it is not clear how errors

in individual measurements will affect averages constructed

from many scatterometer measurements. The errors in

averaged winds are much smaller than the random errors in the

individually measured winds. However, systematic biases, if

any, remain in the averaged products. The two-month duration

of the system verification experiments, as well as their regional

extent (as opposed to isolated point-comparison data sets),

will allow estimates of the accuracy of averaged NSCAT data.

The quantitative accuracy of the NSCAT geophysical measurements will be summarized by the end of the first year of

the mission. At that time, the NSCAT project, in conjunction

with the science investigation team, will determine the necessity for and the nature of any changes to the geophysical

model function used to reduce a0 measurements to wind vectors. All Level 2.0 and higher level data processed during the

first 12 months of the mission will be labeled "interim" data

to denote that the geophysical wind products will not have

been validated during this period.

15
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Section IV

Science Opportunities

A number of oceanographic, meteorological, and scatterometer instrument science studies that could benefit from

NSCAT data are identified in this section The specific studies

described below are in no way intended to form a complete or

comprehensive list, they merely illustrate the range of potential scientific applications of NSCAT data

Although each opportunity is listed under a single heading

(oceanographic, meteorological, or scatterometer instrument

science), it is clear that many of the studies transcend the

boundaries of such an arbitrary classification The ocean

responds in many different ways to atmospheric forcing, and

the atmosphere in turn responds to changing sea-surface

temperature fields. The study of ocean-atmosphere interactions on various scales falls in the provinces of both meteorologists and oceanographers.

Direct measurements of fluxes of momentum (wind stress),

heat, and water vapor across the air-sea interface have rarely

been made, and no data sets containing directly measured

fluxes exist for the large space and time scales in which many

oceanographers and meteorologists are interested. Instead,

fluxes are usually calculated, using a parameterization scheme,

from bulk measurements of wind velocity and other quantities such as humidity and potential temperature (see, for

instance, Large and Pond, 1981) The accuracies of the resulting fluxes depend on both the accuracy of the input data (such

as wind velocity) and the validity of the parameterization

scheme Details of the formulations of the parametenzations

continue to be debated. However, measurements of wind

velocity over the oceans have historically been so sparse that

calculations of fluxes have been inaccurate principally due to

the paucity of bulk wind measurements. Thus, significant

scientific progress can be achieved in the study of oceanographic and meteorological problems using flux quantities

crudely derived (using parameterization schemes) from the

extensive wind velocity data to be collected by NSCAT. Many

of the scatterometer instrument science studies are aimed at

refining the interpretation of scatterometer measurements in

light of the important geophysical quantities of interest to

oceanographers, meteorologists, and those studying small-scale

air-sea interactions.

A. Oceanographic Applications

The ocean is a forced system, and winds are the direct or indirect forcing for many of the most important oceanographic

motions. Unfortunately, wind forcing data have never been

available in sufficient number or with the accuracy necessary

to allow the determination of the detailed dynamic balances

governing these motions. Instead, oceanographers have tried to

explain observed ocean response in terms of simple hypothetical forcing patterns or have made ad hoc adjustments to

known forcing data to bring forcing, an ocean model, and

ocean data into rough agreement (O'Brien, 1982). Figure 13

illustrates the frequency-wavenumber region of interest to

oceanographers and compares the frequency-wavenumber

coverage of NSCAT to that of recent studies. Complete and

accurate forcing data will make it possible, for the first time,

to test the physical ocean models that have been developed.

This capability will be greatly enhanced by ocean field programs concurrent with the flight of N-ROSS.

10 yr 5 yr 1 day

10'

GOLDENBURG AND

O'BRIEN (1980)

O

I

10 10"

10

FREQUENCY, cpd

10

Fig. 13. Frequency-wavenumber region for winds of interest for

oceanographic applications, showing coverage of recent studies.

Note that the NSCAT data will be useful for characterizing the highfrequency, high-wavenumber portion of the spectrum.

Presented below, in approximate order of ascending temporal and spatial scales, are studies of specific oceanic motions

for which NSCAT could provide essential atmospheric forcing data.

1. Mixed Layer Response to Wind Forcing

The surface mixed layer is the link between atmospheric

forcing and deeper oceanic motions. In turn, the sea-surface

temperature (SST), which is determined by the mixing dynamics of the upper ocean, plays an important role in the exchange

of energy between the ocean and the atmosphere. Before the

coupled air-sea interaction problem can be tackled, the physics of the ocean mixed layer must be adequately understood

and described.

While one-dimensional models (Niiler, 1977) predict that

the depth of the mixed layer is governed by wind stirring at

the surface and by shearing across the base of the mixed layer

17
(primarily due to inertial currents), two-dimensional models

(deSzoeke, 1980) have shown that Ekman pumping can be as

important as turbulent entrainment in determining the mixed

layer depth. In addition, wind-driven advection can be important for establishing and intensifying horizontal gradients of

SST. It thus appears that three-dimensional models are required to describe the important dynamics governing the

mixed layer.

Our ability to construct and test three-dimensional models

of the surface mixed layer is severely limited by the lack of

adequate wind stress measurements with high spatial resolution. To adequately model the three-dimensional response of

the mixed layer beneath a mesoscale atmospheric frontal system, wind observations on a spatial scale of 20 to 50 km are

needed. The existence of such data coupled with high-resolution SST and other in situ measurements of mixed layer depth

and currents should allow critical testing of models and should

greatly enhance model development.

Stochastic mixed layer models consider the horizontal

variability of SST to be a natural consequence of the stochastic forcing of the ocean by the winds (Frankignoul and Hasselmann, 1977; Frankignoul and Reynolds, 1983). From these

models, the spectrum of SST can be predicted from the

spectrum of the wind stress. The evaluation of stochastic

mixed layer models requires knowledge of the frequencywavenumber spectrum of wind stress for wavelengths greater

than 50 km and for periods greater than about one day.

Existing spectral estimates from quasi-geostrophic winds

(Willebrand, 1978) and ship winds (Goldenburg and O'Brien,

1980; Gallegos-Garcia et al., 1981) do not have adequate

spatial or temporal resolution. Thus, verifying the stochastic

models using NSCAT wind data will improve climate modeling

of air-sea interactions.

2. Surface Gravity Wave Prediction

Global forecasting of ocean gravity wave directional energy

spectra from satellite derived wind fields is a potentially valuable, but little exploited, application of scatterometery. Knowledge of the full two-dimensional ocean wave spectrum is

essential in a variety of civil and military applications, such as

the prediction of severe, storm-generated waves, the design of

offshore structures, the study of beach erosion, and the optimal routing of ships. Although wave-prediction models have

evolved considerably since the mid 1950s, none has yet been

fully verified with actual field data. The current Northern

Hemisphere spectral ocean-wave model in use by the Navy at

FNOC does not include swell propagated from the Southern

Hemisphere, and indeed, the winds in the Southern Hemisphere are not well enough known to permit either wave

forecasts or hindcasts.

The wave field in any ocean region is a function of many

variables, the most important of which are as follows:

(1) The recent history of local winds

(2) The winds at earlier times and more distant locations

(those that generate swell that may propagate into

the region of interest)

(3) Local bathymetry and currents

While the effects of bathymetry and currents appear most

important in coastal areas (the Antarctic Circumpolar Current

may be an important exception) and are, in any case, reasonably well understood, the largest errors in present global waveprediction models are attributable to errors in the specification of the wind field, both local and remote (Cane and

Cardone, 1981). More accurate predictions of storm location

and intensity will lead to more accurate predictions of the

wind wave spectrum. The propagation of swell generated by

such distant storms can be modeled adequately with present

techniques; even more accurate swell predictions could be

obtained if the history of winds over the path of the swell

were known.

In order to verify wave-prediction models, it is necessary

to have both wind-field and wave-field data. NSCAT will

allow the necessary wind-field data to be obtained while waveheight data may be obtainable from satellite altimeters such as

those on N-ROSS or the Ocean Topography Experiment

(TOPEX; a proposed mission dedicated to obtaining altimetric measurements of the ocean surface; the N-ROSS and

TOPEX operational lifetimes are planned to overlap significantly). An exciting possibility is the use of data from the

European Remote Sensing Satellite ERS-1 (in either the SAR

or the wave-spectrometer mode) to estimate two-dimensional

wave spectra in the open ocean. Such open ocean wave data

would allow wave-prediction models to be evaluated on a

global scale as opposed to only in coastal regions or near

shipping lanes where adequate conventional wave data are

currently available.

3. Dynamics of Mldlatttude Current Systems

As reviewed by Fofonoff (1981), our knowledge of the

Gulf Stream has been expanded during the last two decades

through the development and application of conventional

and remote sensing techniques, including instrumented buoys

and moorings (Schmitz, 1978) and infrared imaging to map

the thermal patterns of the ocean surface from satellites

18
(Legeckis, 1978). Despite these advances, the dynamic mechanisms by which the Gulf Stream and other major midlatitude

current systems form, develop, decay, and finally merge into

the large-scaJe circulation have not been satisfactorily understood. It has not been firmly established whether the path of

the Gulf Stream, including its separation from the coast at

Cape Hatteras, is controlled by bottom topography (Greenspan, 1963; Pedlosky, 1965; Veronis, 1973), by the distribution of mean wind stress (Strommel, 1948. Munk. 1950,

Veronis, 1981), or by mechanisms yet to be determined

Leetmaa and Bunker (1978) show that the mean curl of the

wind stress reverses sign near Cape Hatteras and is zero over a

path that surprisingly resembles the mean path of the Gulf

Stream across the western North Atlantic Fofonoff (1981)

suggests that the position of the line of zero wind-stress curl

may be a consequence, as well as a cause, of the observed Gulf

Stream location Harrison (1981) has argued that such a

relation between the Gulf Stream and the zero curl may have

important consequences for the circulation of the entire

subtropical gyre.

Low-frequency variations of the wind-stress curl can force

quasi-geostrophic wave motions and variations in the major

current systems, such as the Gulf Stream and the Kuroshio.

The relationship between the wind-stress curl variations and

the oceanic variations has scarcely begun to be examined

because only the most rudimentary information about the

forcing variations is presently available. Both theoretical and

observational (statistical) studies are needed for this entire

class of important fluctuations of the ocean circulation. These

and other important aspects of large-scale current systems and

their relation to the wind can be addressed only when adequate observations of the surface winds over the oceans are

available.

4. Midlatitude Midocean Response to Winds

Increased knowledge of wind stress and its curl will improve

our understanding of the whole range of oceanic variability

in the midlatitudes. Synoptic and mesoscale events in the

wind-stress field can force a variety of internal, surfacegravity, and gravity-inertial waves. Lower frequency variations

of stress appear to play an important role in the generation of

surface fronts.

Inertial-internal and quasi-geostrophic waves may be

generated by meso- and synoptic-scale atmospheric systems.

Inertial-internal waves generally have periods of slightly less

than the local inertial frequency, have horizontal wavelengths

of tens to hundreds of kilometers, can have small vertical

scales, and may be quite energetic. Because of their large

vertical shear, inertial-internal waves very likely contribute to

vertical mixing in the ocean (Munk, 1981). Quasi-geostrophic

waves have periods ranging from several tens to a few hundred

days and wavelengths from a hundred to several thousand

kilometers. They have large vertical scales and little vertical

shear, but because of their long time scales, quasi-geostrophic

waves cause large horizontal particle displacements and contribute substantially to horizontal mixing in the ocean (Rhines,

1977)

Inertial gravity wave generation has been studied for

individual forcing events, like the passage of an idealized storm

(e.g.. Price, 1983), but detailed comparisons of ocean observations with theoretical predictions have never been made,

primarily because of uncertainties about the storm-stress field.

Calculations of storm surges have rarely been compared in

detail to data.

Alternate approaches are stochastic studies, in which a

wavenumber-frequency spectrum of stress is used to predict

the statistical properties of the current field (e g, Kase and

Olbers, 1980). These types of studies have also been limited

by a lack of knowledge about the spectrum of stress.

Similarly, both stochastic (Muller and Frankignoul, 1981)

and deterministic (Willebrand et al.. 1980) models of forced

quasi-geostrophic motions have been made Since the curl

of the wind stress is the primary forcing for these motions,

details of the stress spectrum and forcing pattern can have

important effects on the predicted response. The present discrepancies between model results and observations may be

due to poor models or inadequate forcing information. Better

wind and wind-stress data will help to determine the sources of

the discrepancies, which cannot be differentiated at present.

5. Eastern Boundary Current Response

to Wind Forcing

Theoretical models of eastern boundary currents all emphasize the importance of wind forcing to the variability of the

currents (see Allen, 1980, for a review of wind-driven currents

on the shelf). The current variability at any location over the

continental shelf is driven not only by local winds but also by

winds at locations closer to the equator that force poleward

propagating coastal trapped waves. The time scales of wind

forcing that are important to currents on the shelf range from

the 2- to 10-day time scales that characterize the variability

of synoptic scale wind events to the annual scales associated

with seasonal variability At sufficiently low frequencies, the

current response to fluctuating coastal winds spreads offshore,

resulting in a complex system of poleward and equatorward

flows. At high frequencies, Rossby wave propagation is no

longer possible, and the response to along-shore wind stress

19
remains trapped at the coast. Although models of eastern

boundary current response to winds have advanced considerably in recent years, three-dimensional models capable of

examining oceanic response to realistic wind forcing have yet

to be developed.

Few quantitative tests of eastern boundary current models

have been possible due to a lack of wind data. Clarke (1977)

reviews the observational evidence for the existence of windgenerated motions on the continental shelf. While there is

some consistency between observations and theory, the model

tests are severely limited by the rather poor quality of the

observed winds (see also Wang and Mooers, 1977), and it is not

at all clear what aspects of the wind field are important to the

dynamics. While most three-dimensional models have examined the response to zonally uniform equatorward winds, it is

known that a near-shore positive wind-stress curl is associated

with these winds. However, neither the length scale nor the

temporal variability of this wind-stress curl is known with any

reliability, although this near-shore wind-stress curl may be

very important to the dynamics of eastern boundary currents

beyond the shelf break (Pedlosky, 1974).

What is needed, then, is an accurate characterization of the

time and space scales of wind forcing in eastern boundary

current regions. To answer all of the scientific questions of

interest, measurements of wind stress capable of resolving the

energetic 2- to 10-day scales of atmospheric forcing events

are required. Although the temporal resolution of the scatterometer measurements is not sufficient for the shorter

period synoptic scales, it may be possible to interpolate in

time between the scatterometer measurements using highresolution medium-range forecast models. With wind information available on these time scales, observational studies could

be conducted in conjunction with current-meter or sea-level

data to quantitatively test models of wind-generated coastaltrapped wave propagation The availability of wind-stress

measurements capable of resolving spatial scales on the order

of the shelf width (typically 50 km and greater in eastern

boundary current regions) would help to guide the development and testing of three-dimensional models of eastern

boundary current response to wind forcing

the southern ocean between latitudes 45° S and 65° S. In addition, the ACC differs from other major current systems in that

the transport of the ACC is highly variable in time, perhaps by

as much as 65% of the mean value (Wearn and Baker, 1980).

All analytical and numerical models of the ACC indicate

that the flow is primarily forced by winds (e.g., Gill and Bryan,

1971). In contrast to the midocean response to the winds,

the zonal wind stress is more important to the dynamics of

the ACC than is the wind-stress curl (Munk and Palmen,

1951; Clarke, 1982). A recent model (Clarke, 1982) indicates

that for wind forcing with periods less than a few years, the

response of the ACC is barotropic. Over these time scales

the transport is more strongly correlated with circumpolaraveraged zonal wind stress than with local winds, resulting in

fluctuations in the flow that are coherent around Antarctica

There have been few observational studies of the ACC. The

mean flow has been mapped from several decades of hydrographic data However, hydrographic data are not adequate to

define the seasonal fluctuations of the density field. Current

meter measurements at several locations have resolved the

temporal variability over time scales up to about a year.

The bottom-pressure measurements of Wearn and Baker

(1980) indicate that the flow of the ACC through the Drake

Passage may indeed be more coherent with circumpolaraveraged zonal wind stress than local stress, as predicted by

the model of Clarke (1982). However, the wind data used by

Wearn and Baker are both sparse and of questionable accuracy.

Gaps in our present knowledge of both oceanic and meteorological variability in and above the ACC are enormous We

need to learn the dominant time and space scales of the wind

forcing in this region and the zonal scales of the current

fluctuations. Scatterometer winds will, for the first time,

resolve both the temporal and spatial scales of the winds over

the ACC. These winds can be used to provide realistic forcing

for models. Together with in situ measurements from current

meters and bottom-pressure gauges, the NSCAT winds will

allow detailed statistical studies of the response of the ACC to

fluctuations in the wind stress.

6. Antarctic Circumpolar Current Response

to Wind Forcing

The Antarctic Circumpolar Current (ACC) is one of the

major current systems in the world oceans In contrast to

other major currents, the ACC is unbounded, the water flowing virtually unimpeded (except for the Drake Passage) around

A particularly exciting prospect is the possibility of combining NSCAT wind measurements with altimeter measurements of the sea-surface height from an altimeter mission such

as TOPEX Since both models and observations suggest that

the ACC response to wind forcing is nearly barotropic and of

large amplitude, the altimeter could provide reliable measurements of fluctuations in the transport of the ACC

20
7. Equatorial Ocean Response to Winds and

Resulting Climate Fluctuations

The very large changes that occur in the tropical circulations of the ocean and the atmosphere during El Nino-Southern

Oscillation (ENSO) events have received much attention in the

past decade Observational evidence (Wyrtki, 1975; Barnett,

1977), analytical studies (McCreary, 1976, Moore and Philander,

1977), and numerical simulations (Hurlburt et al., 1976;

Kindle, 1979; Busalacchi and O'Brien. 1981) all associate

El Nifio with low-frequency variations in the wind. In the

present models of El Nino, a large amplitude Kelvin wave in

the ocean is excited by a relaxation of the zonal component

of the surface winds along the equator, and the wave rapidly

propagates eastward until its impact with the boundary initiates coastal warming. Since the nature of the response of the

equatorial ocean depends critically on the spatial distribution

of the surface wind field, it is essential to monitor the detailed

structure of the wind field in this region on at least seasonal,

annual, and interannual time scales.

In the atmosphere, the Southern Oscillation consists of

global-scale fluctuations of atmospheric mass between the

Pacific and Indian Oceans. Accompanying these swings of mass

are significant changes in the tropical and subtropical wind

systems, shifts in atmospheric planetary wave systems, changes

in the position of the jet stream, large changes in the rainfall

patterns in monsoonal regions and much of the Pacific basin,

and remarkable changes in the equatorial current system and

the upper ocean heat content of the tropical Pacific. (For a

brief review and references, see National Research Council,

1983.)

Evidence suggests that these large-scale oscillations of the

atmosphere and the ocean, originating in the tropical regions,

are subsequently communicated throughout the global atmosphere, leading to significant interannual climatic fluctuations

However, many of the analytical and numerical studies that

have been carried out are based on wind fields analyzed from

extremely sparse and inadequate data.

The TOGA research program being planned as part of the

World Climate Research Program is designed to increase the

understanding of events in the oceans and atmosphere that

significantly influence the predictability of seasonal to interannual climatic variations over much of the globe. The observational effort is broken down into three components:

(1) Monitoring atmosphere-ocean interactions associated

with the ENSO phenomenon over a period of a decade.

(2) Providing a detailed description of a single El Nino

episode spanning 15-18 months.

(3) Providing detailed information concerning the processes that control the sea-surface temperature and the

fluxes at the atmosphere-ocean interface.

The oceanographic monitoring effort will encompass

fluctuations on time scales ranging from weeks to years and

will include as one of its objectives a more detailed determination of the climatological mean annual cycle. Major emphasis

will be on phenomena with large space scales; therefore, the

monitoring network will be sparse but broad enough to cover

the entire Pacific basin. The generation (from wind-velocity

observations) and analysis of wind-stress fields in the tropical

oceans (20° S to 20° N) is an important research activity in

conjunction with TOGA. These activities will be centered

primarily in the Pacific Ocean, but important activities will

be undertaken in the Atlantic and Indian Oceans as well.

Wind measurements from NSCAT could allow the windstress field in the entire tropical ocean region to be characterized on spatial and temporal scales far finer than would be

possible by conventional means. In addition to defining the

major forcing function for equatorial ocean dynamics, these

NSCAT measurements would allow examination of some of

the detailed mechanisms by which the atmosphere-ocean feedbacks take place in the tropics. It has been suggested that

changes in the sea-surface temperature and surface wind convergence are directly linked in some tropical regions. It is,

however, extremely difficult to evaluate the surface windconvergence field from conventional wind data, so the link

between the two quantities remains largely speculative. Adequate wind information would permit the study of large-scale

air-sea interactions in convergence zones on a variety of spatial

and temporal scales. Because the ocean influences the tropical

atmosphere largely through the ocean's effect on the location

and magnitude of the surface-wind convergence field, understanding better the connections between these two fields is

essential to an increased understanding of the coupled oceanatmosphere problem and to developing an improved predictive

capability for ENSO events.

B. Meteorological Applications

Wind measurements are used as both prognostic and diagnostic data in the atmospheric sciences. Although atmospheric

observational networks are well established in the Northern

Hemisphere over land, little is known about winds in the

Southern Hemisphere or over the oceans. The following

research topics illustrate some meterological applications of

high-resolution measurements of surface winds over the global

oceans.

21
1. Incorporation of Scatterometer Data

Into Forecast Models

Experience and analysis have shown that errors in initial

conditions can rapidly dominate the solutions and predictions

of dynamically based forecasting models. Although the improved accuracy, resolution, and coverage of scatterometers

and other remote-sensing instruments should improve weather

forecasts, Bretherton (1983; private communication) and

Baker et al. (1984) conclude that current forecast and general

circulation models are not adequate, in resolution and in

fundamental surface physics, to dynamically assimilate the

vast continuous input of scatterometer wind data. The prospect of operational surface wind data from scatterometers

provides significant motivation and opportunity for fundamental model development and optimization. Models must be

developed that (1) more realistically account for air-sea interaction and atmospheric boundary-layer processes; (2) assimilate the data in a time-continuous manner; and (3) utilize the

unprecedentedly high resolution of scatterometer observations.

On the other hand, mesoscale modeling is highly developed,

and some models are already designed to deal with data on

the scale of the scatterometer. Some comparative forecast

studies with positive results through the use of SASS data have

been reported (Guymer, 1983). A detailed case study by

DelTOsso (1983), using the European Center for Medium

Range Weather Forecasting fine-mesh model, concludes that

high-resolution modeling is capable of improving surface

forecasting. (DelTOsso used high-density Central European

surface data for his study and emphasized the general need for

high-density data to initialize such models.) NSCAT could

provide an extremely large data base with which to develop

and test such improved models.

2. Tropical Meteorology

The tropical atmosphere lies primarily over ocean and is

poorly represented in the conventional, operational observing

system. A number of special international field experiments

have been conducted over tropical waters and have contributed to our understanding of many dynamic processes,

notably the organization of convection and the relation of

smaller to larger scale circulation systems. However, such

experiments cannot provide data over large areas or long time

periods, and it is clear that routine observations of reasonable

resolution are needed to further our understanding.

Scatterometer data may be used to study the dynamics

of the planetary boundary layer (PBL) both in extratropical

and tropical regions. Information on the vertical structure

and momentum transport in the PBL (e.q., Estoque, 1971;

Brummer et al., 1974) may be obtained, especially in cloudy

tropical regions, by comparing cloud-top movements to

surface winds measured by the scatterometer. Two types of

approaches compete for modeling the PBL' the so-called

modified Ekman theories (e.g., Holton et al., 1971) and the

mixed layer theories based on Lilly's (1968) original PBL

model. High-quality wind data, to be supplied in part by

NSCAT, are needed in order to validate the predictions of

these dynamical models.

Though one of the most steady features in the global

circulation, the intertropical convergence zone (ITCZ) varies in

intensity in space and time, sometimes in a sufficiently regular

way so as to show sharp spectral peaks. However, the existing

data are not adequate for a complete analysis (Holton et al.,

1971), and the wind structure has never been observed over a

significant extent of the ITCZ and over an annual cycle or

longer.

Another characteristically tropical phenomenon, also on a

small scale and too intense for ship observations, is the tropical

cyclone. The wind field near the surface surrounding the

cyclone is particularly important in that most of the inflow

feeding the vertical buildup occurs in the lowest hundred

millibars (Shea and Gray, 1973). The great majority of tropical

cyclones penetrated by instrumented aircraft have reached

relatively northern latitudes. In any event, the lower levels of

such storms have never been penetrated by aircraft. Thus,

from the point of view of diagnostic studies and modeling,

data on the surface wind field for the life of the storm would

be of great value.

Finally, there is some indication that the onset of the

Indian Monsoon may be correlated with an earlier flow pattern

in the Indian Ocean. Some ship reports are usually available,

but the data quality and coverage are inadequate. The entire

flow pattern around the African continent and the Indian

subcontinent requires study and characterization for a succession of monsoon events.

3. Global Circulation and Climatology

Climatology refers in part to statistics calculated in a

systematic way and designed to shed light on the dynamics of

atmospheric motions. Global statistics are of special interest

in leading to conclusions concerning the largest scale general

circulation of the atmosphere (Lorenz, 1967). Massive compilations of such statistics are contained in Oort and Rasmussen

(1971) for the global atmosphere, and in Newell et al. (1972)

for the tropical atmosphere. Large simulation models rely

critically on statistical compilations of observations, using

them to help define both long-term mean states and the char22
actenstics of shorter term variability (e.g., Manabe et al.,

1974). In return, model results are analyzed in order to determine those statistics that play the most crucial role in defining

and predicting atmospheric motions. Many compilations of

surface-wind or wind-stress statistics (e.g., Han and Lee, 1983)

have used decades of data of varying quality in order to

obtain the global distribution of mean values. However, large

errors are introduced by data errors and data gaps, and few

studies have attempted to obtain data for the Southern Hemisphere south of 20° latitude.

The data coverage and quality of NSCAT will begin to

permit the study of atmospheric variability on time and space

scales hitherto inaccessible by conventional observation systems (although statistically reliable climatologies will require

more than the planned three years of data from the N-ROSS

mission). These scales of motion will contribute to the statistics and allow quantitative assessments of their importance

for the definition and prediction of atmospheric dynamic

processes.

4. Case Studies

a. Analysis and prediction of special storms. Already the

"Queen Elizabeth II" storm may be the most studied in the

history of meteorology, partly owing to the availability of

SASS data. The investigation of the development and evolution of unusual storms may provide insights into the principal

dynamics for a more general class of atmospheric phenomena.

b. Regional climatology. Circulation patterns that extend

to considerable distances offshore vitally affect many areas,

notably those dependent on monsoonal rainfall. An accurate

climatology is a first step toward understanding the meteorology of the phenomena involved.

c. Southern Hemisphere features. The only meteorological

information that we have of vast areas of the Southern Hemisphere is derived from satellite cloud imagery and temperature

soundings. Based on SASS data, it appears that there are

interesting phenomenological differences between Southern

and Northern Hemisphere features. Routine NSCAT observations will provide valuable material for scientific study in this

area.

d. Comparisons between NSCAT and cloud-motion winds.

Since late 1966, large-scale winds have been inferred by tracking cloud motions based on pictures from geostationary

satellites. Although data sets based on these cloud-motion

winds thus can potentially span long periods of time and large

geographical areas with moderately good spatial resolution,

difficulties in determining the cloud height (Whitney, 1983)

have led to differences in inferred low-level winds when the

same cloud patterns are analyzed by different organizations.

Global comparisons between cloud-motion winds and nearsurface winds from NSCAT may allow a general quantitative

assessment of the accuracy of the historical cloud-motion

wind data set. Constructing long time and space series of lowlevel winds with known accuracy would then be possible for

some geographical regions.

e. Atmospheric circulation and the earth's variable rotation.

Regarding the earth and the atmosphere together as a system

that conserves angular momentum, scientists have accepted

that the atmospheric circulation contributes to variations in

the earth's rotation (Lambeck, 1980). When upper air networks made possible creditable estimates of total atmospheric

momentum, changes in the balance of momentum between

the atmosphere and the earth could be calculated and correlated with the appropriate parameters of the earth's rotation.

The most elaborate studies to date are those of Hide et al.

(1980) and Barnes et al. (1983), in which daily values of the

equatorial and axial momentum transfer were compared to

changes in the length of day and polar wobble for time spans

of up to 16 months. These works concluded that the correspondence is consistent with a causal relationship (see Rosen

and Salstein, 1983; Eubanks et al., 1985).

An alternate method of establishing the effects of the

atmosphere on the earth's rate of rotation is by directly

estimating the torque of the atmosphere on the earth, using

surface wind and pressure data. Although momentum transfers

have been calculated from surface torque (Newton, 1971a,b),

the problems caused by data gaps and the uncertainties of

boundary-stress physics have generally been thought to render

the budget approach more acceptable. Wahr (1982) suggests

calculating torque over oceans, combined with upper wind

data over land masses.

The NCSAT data will offer a unique opportunity to determine the stress of the atmosphere on the ocean surface with

high precision and fine temporal resolution. By combining NSCAT wind observations over the oceans with observations and calculations from the existing land-based observational network, it may be possible to compare calculations

from the two methods, as well as to compare both methods to

the rotational parameters, which themselves are now being

measured with increased accuracy.

C. Scatterometer Instrument

Science Studies

The studies presented in this section are concerned with

refining and quantifying the relationship between scatterom23
eter measurements and basic geophysical quantities of interest,

such as fluxes of momentum, heat, and water vapor across the

air-sea interface. Wind measurements over the ocean have been

so sparse that significant scientific progress can be achieved by

using flux quantities crudely derived from scatterometer

data as an input to oceanographic and meteorological models,

as discussed below. Inevitably, however, the models will

become more refined, and their sensitivity will make the

accuracy of the input an important issue Studies related to

the accuracies and interpretations of quantities derived from

scatterometer measurements should be pursued prior to and

following the N-ROSS mission. Directly comparing satellitederived quantities and the calculations based on more conventional measurements will help to characterize the relative (as

opposed to absolute) accuracies of the calculations and to

shed light on the applicability and validity of the physical

assumptions used to establish the basic parameterization

schemes.

1. Stress Derived from Scatterometer

Measurements

Horizontal wind stress is rarely measured directly in an

oceanic environment. Instead, the momentum flux from the

atmosphere to the ocean is usually calculated from a few bulk

measurements of surface wind velocity using a parameterization scheme. The accuracy of the resulting quantities depends

both on the accuracy of the input data (the wind observations)

and the validity of the parameterization scheme. The values

of the drag coefficient(s) and the particular parametenzations

have been (and continue to be) generated from temporal

averages of wind velocity measured at a point. Since scatterometer winds represent spatial averages (over a footprint) at

a given time and since little is known regarding the space-time

relationships (if any) governing atmospheric meseoscale

motions (c.f. Pierson, 1983a), how to use conventionally

generated parametenzations and drag coefficients in conjunction with scatterometer data must be studied in detail (see also

Pierson, 1983b)

2. Other Fluxes Using Scatterometer Data

In order to calculate sensible and latent heat fluxes, scatterometer winds must be merged with other satellite data or

with synoptic data obtained from ships and buoys. An evaluation of such merging schemes, as well as their accuracies under

varying synoptic conditions, will certainly be needed for

future oceanographic and meteorological work.

3. Scatterometer-Derived Quantities for Storms

Extratropical and tropical cyclones are regions of high wind

speed and are responsible for the events dominating the total

momentum transfer from the atmosphere to the upper ocean.

In cyclones, the sea state is often not in equilibrium with the

wind stress because of changing wind direction and because

of the generation of high seas that travel quickly away from

the region and become swell.

Several recent studies indicate that drag coefficients based

on data obtained in nonstorm conditions are inappropriate

for use in storms, but the results are not conclusive (e.g.,

Byrne, 1982, Geernaert, 1983, K.Davidson, 1983, personal

communication). A more comprehensive study is needed.

In high-wind regions, heavy precipitation may interfere

will: the scatterometer signal. As was described in subsection III.A.6, NSCAT data will be flagged, based on SSM/Iinferred measurements of atmospheric liquid water content,

when such atmospheric interference seriously degrades the

accuracy of the resulting OQ and vector wind measurements.

It may be possible for individual investigators to devise corrections for the effects of the liquid water by using simultaneous

observations of integrated liquid water derived from brightness temperatures measured by passive microwave sensors. A

thorough study of this possibility is in order, since these highwind regions represent some of the most important situations

in which a scatterometer can make significant contributions

both operationally and scientifically.

24
Appendix A

Summary of SASS Results

The Seasat-A Satellite Scatterometer (SASS) was one of

five sensors flown aboard the NASA Seasat satellite from

28 June 1978 until the catastrophic failure of the satellite on

10 October 1978. Several review papers have been written

addressing aspects of the flight hardware (Grantham et al,

1977, Johnson et al.. 1980), data processing (Bracalante et al.,

1980; Schroeder et al., 1982, Boggs, 1982), and calibration

and geophysical evaluation of the data (Jones et al., 1982;

JASIN and GOASEX reports; Brown, 1983). An overall review

of SASS can be found in Guymer (1983). In this appendix we

concentrate on summarizing the evaluation of SASS accuracy

and the scientific use of SASS data. Without a doubt, SASS

demonstrated that a satellite-borne, active microwave scatterometer could provide accurate, global, high-resolution measurements of surface winds over the ocean.

A. Evaluation of SASS Accuracy

At the time of its launch, the algorithm used to relate SASS

measurements of the normalized radar cross section, OQ, to

wind speed and direction was based on very limited aircraft and

satellite (Skylab) data (Moore and Fung, 1979). Two regional

field experiments (GOASEX and JASIN), as well as data from

operational buoys and weather ships, were used to provide in

situ measurements of winds for comparison with SASS measurements. Schroeder et al. (1982) describe the evolution and

tuning of the geophysical model function resulting in SASS-I,

the algorithm used to produce the "official" Geophysical Data

Record of SASS winds. As the SASS-I model was tuned to

most of the available high-quality in situ wind data, little truly

independent data exist with which to evaluate the final wind

product Nonetheless, comparisons of SASS winds with highquality JASIN wind measurements and analyzed fields show

that SASS winds were accurate to better than ±1.7 m/s and

±17° in direction in the wind speed range 4-16 m/s (Guymer,

1983).

The evaluation studies conducted for SASS graphically

illustrated the importance of obtaining high-quality, compatible in situ measurements for comparison with satellite

wind measurements Most direct in situ measurements were

temporal averages of point measurements, whereas scatterometer measurements were spatial averages at a single time. As

Pierson (1983a) points out, a significant fraction of the rms

differences between satellite and in situ measurements can be

accounted for by the mesoscale variability of the wind field

and by the fact that SASS footprints and in situ measurements were in general neither colocated nor simultaneous. In

Pierson's simplified model, the magnitude of the "mesoscale

error" is a function of synoptic scale conditions, the scatterometer footprint size, and the temporal averaging used in the

in situ measurement

Although SASS data has not (and cannot) add significantly

to our knowledge of mesoscale winds with spatial scales

smaller than approximately 100 km, the need to calibrate

satellite data with in situ conventional measurements has

focused scientific attention on the importance of mesoscale

variability and the proper means of removing its effects from

both conventional and satellite data.

B. Utilization of SASS Winds for

Numerical Weather Prediction

A number of case studies have been conducted to illustrate

the impact of SASS data on Numerical Weather Prediction

(NWP) forecasts and analyses (Gyakum, 1980; Cane et al.,

1981; Atlas et al., 1982, Baker et al., 1984). It was thought

initially that the global, relatively dense coverage of SASS data

would provide a much better initial state for NWP models than

is available from conventional sources, thus leading to improved forecasting skill. In addition, the ability of SASS data

to aid subjective analysis of the model products for short range

and regional forecasts (cf. Atlas et al., 1982) was considered to

be great.

In fact, the most extensive set of studies of the impact of

SASS data on global analysis/forecast models (Baker et al.,

1984) showed SASS data had small to negligible positive

impact on the accuracy of the forecasts, due mostly to the

following:

(1) The difficulty in assigning unique directions to SASS

winds (a problem that will be greatly alleviated by the

addition of a third antenna on each side for NSCAT).

(2) The inability of current NWP models to fully incorporate surface winds acquired continuously in time,

rather than upper-level wind data or surface pressure at

fixed synoptic times.

(3) The relative coarseness of the models (4° X 5°) compared with the approximately 0.5° X 0.5° resolution of

SASS winds.

All workers have agreed that improved models and dataassimilation techniques, coupled with unambiguous scatterometer vector winds, will lead to more accurate forecasts and

analyses.

C. Characterization of Storms and

High-Wind Conditions

Hawkins and Black (1983) review SASS measurements of

tropical cyclones and storms. Few conventional wind measure25
ments are obtained in gale conditions (wind speeds greater

than 17.5 m/s), and there is an almost total lack of data from

tropical cyclones. Comparisons between SASS measurements

of the extent of gale-force winds and operational advisory predictions revealed several flaws in the forecast advisory values.

For example, in 72% of the cases (68) studied, advisories were

found to be conservative, that is, the extent of SASS-derived

gale-force winds was less than the extent of advisory winds

SASS measurements also revealed significant asymmetries in

the distribution of gale-force winds about the storm centers,

asymmetries which, in general, were not properly accounted

for by advisory forecasts. Comparisons between wind measurements from reconnaisance aircraft penetrations of several

Atlantic storms indicated that, even m the presence of moderate ram rates, SASS winds had an accuracy of better than

±2 m/s (rms) in gale-force wind conditions. Other studies,

notably that by Gyakum (1980) of the "Queen Elizabeth II

storm," have demonstrated that SASS winds can be used to

accurately place storm centers and determine high wind speeds

in otherwise data-poor regions.

60

D. Oceanographic Applications

of SASS Data

The lack of use of SASS data for oceanographic studies

deserves specific mention. By far the greatest drawback of the

SASS data set is its short duration: most oceanographic

phenomena have time scales of weeks to years, and the 100-

day duration of Seasat is too short to resolve these phenomena adequately.

In addition, most oceanographic phenomena of interest are

sensitive to vector winds, not simply wind speed. Although

significant progress has been achieved by various groups (most

notably that of the group working with Woiceshyn, Wurtele,

and Peteherych) in attempting to assign unique wind directions to retrieved winds, a global, 15-day data set (produced by

Woiceshyn et al. group) remains the most that has been

achieved to this point, although regional sets of unambiguous

vector winds exist for isolated passes through the GOASEX

and JASIN areas. Examples of temporally averaged vectorwind maps are shown in Figs. 4 and 14.

-- ~,~ ^A?cm, \

•

.

:rf=rt

-60

180 210

LONGITUDE

240 270 300

Fig. 14. Vector- and time-averaged wind field from Seasat SASS for the period 6-7 September 1978. Vector wind measurements were

interpolated onto a 1° x 1° grid and time averaged (courtesy of P. M. Woiceshyn).

26
Appendix B

Principles of Scatterometry

A scatterometer is a device that determines the normalized

radar cross section (a0) of a surface by measuring the backscattered power received when the surface is illuminated by

electromagnetic radiation. Microwave scatterometers can be

used to measure surface wind speed and direction over the

ocean due to an empirically determined correlation between

the measured a0 of the sea surface and surface winds. This

appendix briefly describes the mechanism by which wind

speed and direction are determined by scatterometers. More

detailed reviews can be found in Moore and Fung (1979),

Schroeder et al. (1982), and Pierson (1983b).

A. Radar Scatter at Moderate

Incidence Angles

Much theoretical and experimental work since 1950 has

established that the dominant source of radar backscatter at

moderate incidence angles (25° < 9 < 70°) is Bragg scatter

from small-scale roughness elements on the sea surface (e.g.,

Wright, 1968, see Fig 15 for a definitional sketch). The wavenumber of the water waves responsible for the Bragg scattering

is given by

SOURCE/RECEIVER

WIND

kB = 2kR sin (0)

where kB = 2n/\B:kK is the wavenumber of the incident radiation, and 6 is the incidence angle (vertical angle between the

normals to the scattering plane and the illuminating radiation).

The amplitude of the backscatter is proportional to the spectral density of the Bragg scatterers (for the NSCAT, Bragg

wavelengths range from 2 to 5.5 cm, in the gravity-capillary

range), the incidence angle, and the polarization of the incident and scattered radiation. A strong correlation between the

spectral density of the short gravity-capillary waves and the

local surface winds provides the basis for the interpretation of

Fig. 15. Definitional sketch of the scattering geometry. Incidence

angle 6 is measured in the plane normal to the mean ocean surface.

Azimuth angle x is the angle (in the plane of the mean surface)

between the wind vector and the projection of the radar illumination

vector.

a0 in terms of winds.

Since the ocean surface is composed of waves with many

different wavelengths spanning a range from millimeters to

hundred of meters, an exact determination of the incidence

angle of the illuminating radiation is impossible without

knowledge of the detailed shape of the sea surface. However, a

two-scale hypothesis (Wright, 1968; Bass et al., 1968), in

which locally planar patches ("facets") of Brag roughness

elements are tilted relative to the time-averaged mean surface

by large-scale waves (with wavelengths much greater than

Bragg wavelengths), has been shown to yield accurate predictions of a0. When the illuminated area on the sea surface is

large compared with even the large-scale waves (as is the case

with satellite scatterometers), the average backscatter coefficient at a mean incidence angle (i.e., relative to a flat ocean

surface) can be related to the local tilts of the illuminated

facets through an average with respect to the slope distribution functions of the large-scale waves (Wright, 1968).

B. Scatterometer Measurement of o-0

A scatterometer transmits pulses of monochromatic electromagnetic radiation earthward in a fan-like pattern defined by

the antennas. Each pulse illuminates an area on the ocean surface and is scattered back to the antennas as described immediately above. Because the scatterometer is mounted on a

satellite moving with respect to the ocean surface, the returned

signals are shifted in frequency by the two-way doppler effect.

Doppler filtering of this backscattered radiation is used to

achieve along-beam resolution on the earth's surface, as

illustrated in Fig 16 The returned signals are down-converted,

amplified, bandpass filtered, and square-law detected. A gated

integrator is used to average the output of the square-law

detector over a number of returned pulses yielding a measurement Ps+n, where the subscript "s + n" refers to the contributions of both the returned signals and the system noise to the

measured power. Following this signal-plus-noise measurement, a noise-only measurement, Pn, is made by integrating

the square-law detector output in the absence of radar-return

27
SATELLITE TRACK

I

CONSTANT

DOPPLER LINE

409kHz

168kHz

ANTENNA ILLUMINATION

-168kHz

-409kHz

- CONSTANT

DOPPLER

LINE

Fig. 16. Geometry of doppter filtering for along-beam resolution.

Bandpass filtering selects scattered signals originating between

two Isodoppler lines on the surface (corresponding to the limiting

frequencies of the bandpass filter).

pulses. Pn represents an estimate of the average power due to

receiver and antenna noise as well as the natural emissivity of

the ocean. Pn can be subtracted directly from Ps+n to determine an estimate, Pr> of the average power in the radar-return

pulses; i.e.,

** >S -N

P = P -P

r ' s+n n

Once Pr has been determined, a0 can be calculated from the

radar equation:

ffo

 =

where

Pr = Power in returned pulse

Pt = Power in transmitted pulse

G = Antenna gain

X = Wavelength of transmitted signal

A = Effective illuminated area

a0 = Scattering coefficient

R = Slant range to illuminated area

L = System losses

The measurement accuracy of o0 is affected primarily by

communication noise, spaceciaft-attitude uncertainty, instrument processing errors (e.g., quantization errors and gain

uncertainty), and various bias errors. The bias errors are generally deterministic and are removable, assuming the existence of

adequate comparison data. The remaining errors are random in

nature and cannot be removed, thus limiting the ultimate measurement accuracy. Of these random errors, those due to

instrument processing are relatively minor. Communication

noise, a major source of error, is a function of the dopplerfilter bandwidth, signal-integration period, and signal-to-noise

ratio (SNR). The most significant remaining error source is due

to spacecraft-attitude pointing uncertainties. Included are roll,

pitch, yaw angle uncertainties due to mechanical and thermal

effects, and squint angle (the angle between the electrical and

mechanical boresight of the antenna-beam pattern) uncertainty. For high SNR (corresponding to relatively high wind

speeds and moderate incidence angles), attitude uncertainties

dominate the error. For low SNR (corresponding to low wind

speeds and high incidence angles), communication noise dominates. The normalized standard deviation of a o0 measurement

is the root-sum-square of all random sources of error.

C. Geophysical Model Function

The geophysical model function is an empirical relationship

between a0 and the wind vector at 19.5 m above the surface in

a neutrally stratified atmosphere. The geophysical model function (SASS-I) that resulted from the Seasat mission is specified

in the form of a table that gives the two coefficients G and//

in the equation

Iog(a0) = G(6.x,e) + #(0, x, e)log(£7)

where 8 is the radar incidence angle, \ is the horizontal angle

between the wind direction and the radar azimuth, e is either

h or v (polarization), and U is the neutral stability wind speed

at a height of 19.5 m. The quantity a0 is a function of the

incidence angle, azimuth angle, and polarization; therefore, the

G and H coefficients are tabulated separately for v and h

polarizations every 2° in incidence angle and 10° in azimuth.

These tables thus relate backscatter to wind velocity, given the

azimuth and incidence angles. The backscatter varies harmonically with azimuth, with maxima at upwind and downwind and minima at crosswind angles. Figure 3 shows that the

functional relationship between a0 and azimuth angle \ is

nearly 00 * cos (2x). Multiple measurements of o0 from

approximately the same area on the ocean surface but with

different aximuth angles \ can thus be used to determine U

and wind direction. The three antennas on each side of

NSCAT provide for four different views of the same ocean

area, separated by at most a few minutes (depending on

incidence angle).

28
Given the model function, the surface wind speed and

direction can be estimated using two or more of the noisy

measurements a0. Differing algorithms, based on differing

assumptions about the statistical properties of a0, are currently being investigated. A typical algorithm for estimating

wind speed U and wind direction \ (relative to satellite-fixed

coordinates) attempts to minimize the residual error between

o0 and the predicted cross sections for the given U and x As a

result of the harmonic dependence of a0 on x, the residual

error has several local minima. For the case of two noise-free

measurements 90° apart, between one and four local minima

occur. The set of candidate wind vectors obtained are called

"ambiguities." One ambiguity will correspond to the true wind

vector, and the others are false solutions. The solutions due

to four views from the NSCAT are shown schematically in

Fig. 17. In general, the NSCAT configuration allows determination of wind direction with a potential ambiguity of

180°.

20

15

10

I I I I I I I I I I I I I I I I

X = 240°

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 i i i r

X=260°

I I I I I I I I I I I I I I I I I

IIIIIIIIIIIIIITI I

X = 280°

I I I I I I I I I I I I I I I I I

0 40 80 120 160 200 240 280 320 360

X, dog

Fig. 17. Simulated speed vs direction solution curves for noise-free

data from NSCAT. The SASS-I model function was used in the

generation of the curves (after Pierson, 1983b).

29
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