Combined Active and Passive Microwave Sensing of Ocean Surface

Wind Vector from TRMM

Seubson Soisuvarn, W. Linwood Jones, and Takis Kasparis

Central Florida Remote Sensing Laboratory

University of Central Florida

School of Electrical Engineering and Computer Science

PO Box 162450

Orlando, FL 32816-2450, USA

Voice/fax (407) 275-4390

Seubson Soisuvarn <se867731@ucf.edu>

Abstract- This paper presents a new ocean wind vector

measurement technique that uses the combined passive and

active microwave measurements respectively from the

Tropical Rainfall Measuring Mission (TRMM) Microwave

Imager (TMI) and the Precipitation Radar (PR). The wind

speed is inferred by TMI over a wide swath that includes the

narrower PR swath. The PR scans cross-track ± 18°; and near

the swath edges, where the radar backscatter responds to both

the magnitude and direction of the surface wind, we use the

microwave radiometer estimate of wind speed and the

measured sigma-0 at incidence angles greater than 15 degrees

to derive wind direction. Because the PR provides only a single

azimuth look, multiple possible wind direction solutions exist.

The ability to select the proper (single) direction is beyond the

scope of this paper; but comparisons are presented between

the “closest” retrieved TRMM wind vectors and near-

simultaneous wind vectors measured by the QuikSCAT

satellite scatterometer to demonstrate the potential for

measuring ocean surface vector winds.

I INTRODUCTION

Based upon over two decades of research, the measurement

of ocean surface wind vector using satellite microwave

scatterometers is well established for wind speeds up to ~

20 m/s [1, 2]. In this technique, several (typically 3 or 4)

radar backscatter measurements are obtained for an ocean

location at different azimuth “looks”. These data and the

corresponding measurement information (e.g., incidence

angle, azimuth, and polarization) are used in a geophysical

wind vector retrieval algorithm to estimate both the speed

and the direction of the surface wind averaged over the

antenna footprint. The wind retrieval process is based on a

statistical relation between wind-induced sea surface

roughness and the corresponding ocean microwave

reflectivity (normalized radar cross section, σo). This

mostly-empirical relation, denoted as geophysical model

function (GMF), is derived from collocated sets of radar σo

measurements and independent “surface truth” wind vector

observations. Unfortunately, during the retrieval process

that uses a collocated set of multi-azimuth scatterometer

measurements, the second-harmonic nature of the σo

anisotropy with wind direction results in multiple

“possible” wind direction solutions (called aliases). Both σo

measurement noise and a weak GMF wind direction

signature contribute to ambiguity in the retrieved wind

direction. An additional algorithm is employed using

median filtering of ranked aliases to select the correct wind

direction with high skill (typically > 90%). Never the less,

in most wind retrieval algorithms, this ambiguity in

direction remains a major component of the direction error.

The passive microwave remote sensing technique for ocean

surface wind speed is also well established, and ocean wind

speeds are measured operationally using the Special Sensor

Microwave Imager (SSMI) on the Defense Meteorological

Support Program satellites and using the Tropical Rainfall

Measuring Mission (TRMM) Microwave Imager (TMI).

The wind speed is retrieved using a non-linear algorithm

that retrieves simultaneously a number of atmospheric and

ocean parameters from the multi-frequency, dual-polarized

brightness temperatures measured by these conical scanning

imagers [3 - 5].

Both the active and passive remote sensing techniques

compare well with surface truth wind speeds from

numerical weather models and/or in situ buoys (typical

differences being ~ 1 – 2 m/s). Moreover, when compared

to each other, the agreement is even better (typical

differences of 0.5 – 1 m/s). This high spatial correlation

between the active and passive measurements allows us to

combine them in a common retrieval algorithm.

II TROPICAL RAINFALL MEASURING MISSION

In addition to the prime rain measuring mission, the TRMM

sensors (TMI and PR) have also been used to measure

ocean surface wind speed but not direction [6, 7]. The TMI

is a conically canning sensor with a 780 km swath that has

two wind speed products (10.7 GHz winds and 37 GHz

winds) that are earth gridded on a 0.25° lat/lng grid. The

Precipitation Radar (PR) is a Ku-band pulse radar operating

at 13.8 GHz that makes backscatter measurements in the

atmosphere and from the surface. The sensor antenna is an

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electronic scanning phased array that steps normal to the

flight direction (cross-track) through the nadir with

measurements at 49 beam positions. The antenna

beamwidth of 0.7° results in a footprint horizontal

resolution at the surface of 4 km with an associated swath

width of 220 km. The incidence angle varies symmetrically

by ± 18° with beam position about the nadir. For incidence

angles < ~ 12° (beam positions 10 – 40), the ocean σo

measurements are independent of the surface wind

direction; however, for incidences > 12°, the ocean σo

increases with wind speed and is anisotropic with wind

direction. This σo signature makes wind direction

measurements possible for beam positions 1 – 9 and 41 –

49; however for this paper only the outer three beam

positions are considered as shown in fig. 1.

Fig. 1 Precipitation Radar wind direction measurement swath at the outer

three beam positions (cross-hatched).

III GEOPHYSICAL MODEL FUNCTION (GMF)

The radar backscatter is proportional to the amplitude and

density of ocean waves of centimeter to decimeter lengths,

and these waves are in near equilibrium with the local

frictional wind at the sea surface. For a given operating

frequency radar, the GMF defines σo as a function of the

geophysical variables and sensor parameters:

),,,(GMF pUo θχσ = , (1)

where U is wind speed, χ is normalized wind direction

(antenna azimuth angle subtracted from the wind direction),

θ denotes measurement incidence angle, and p is radar

antenna beam polarization.

Traditionally, scatterometer GMFs are empirical functions

that relate ocean σo with independent “surface truth” wind

vector data from numerical weather models, buoys, and

research vessels. An example is SASS2, which was

developed using the NASA’s Seasat-A Satellite

Scatterometer (SASS). The wind speed used for this

correlation corresponds to the neutral stability wind at a 10

m altitude. For SASS, the GMF was defined for an

operating frequency of 14.6 GHz, vertical and horizontal

polarizations, and incidence angles between 16° and 66°.

For PR, the operating frequency is slightly different than

SASS (13.8 GHz versus 14.6 GHz) and the range of

incidence angles just overlaps at the lowest SASS angle.

A. Precipitation Radar GMF

To develop this GMF, a data set of 96 orbits of PR σo

collocated with ocean surface wind vectors was produced,

which was sufficient to create a stable relationship over the

range of 4 – 8 m/s. For the surface wind vector, TMI

retrieved wind speeds (10.7 GHz) and collocated wind

direction retrievals from the SeaWinds Scatterometer on the

QuikSCAT satellite were used. TRMM revs were

collocated within a ± 1 hr window about the QuikScat

observations. To generate the GMF, the data were sorted,

first by beam position (incidence angle) and then by wind

speeds between 4 and 8 m/s. For a constant wind speed, PR

σo averages were performed over the normalized wind

direction range of 0° to 180° in 5° bins. The mean σo for

each wind speed were remove from SASS GMF and

replaced with the dc bias calcuated from PR. An example of

the resulting GMF is given in Fig. 2 for beam 1 and wind

speeds of 4, 6 & 8 m/s. This procedure was repeated for the

outer 3 beam positions that corresponded to PR incidence

angles between 16° and 18°.

Fig. 2 Comparison of PR and SASS GMF’s for wind speeds 4, 6 and 8

m/s (bottom to top).

IV WIND DIRECTION RETRIEVAL

The wind direction retrieval algorithm block diagram is

given in Fig. 3. The inputs were the PR surface echo ( σo)

time series and the gridded TMI wind speeds. Because the

PR and TMI were on the same satellite, they were precisely

collocated in space and time. The PR σowere separated by

beam (incidence angle) and averaged along track within a 25

km grid cell. This smoothing matches the spatial scale of the

TMI wind speed and reduces the mean σomeasurement

uncertainty (standard deviation). The retrieve wind direction

was calculated from the value of χ that minimized the

difference between the measured PR σo and the modeled

25 km

220 km

θi

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25 km grid by beam

PR Sig-0 2A21 Products

Collocateby beam

TMI Wind Speed

Retrieve Wind Dir Aliases

SelectClosestWind Dir Aliases

QuikSCAT Wind Field L2B Products

Compare & Stats

Fig. 3 PR wind direction retrieval (vertical boxes) and validation

procedure (horizontal boxes).

value (GMF). This procedure is illustrated in Fig. 4, where

the GMF curve was defined by the incidence angle of the

beam and the corresponding TMI wind speed. Intersections

of the GMF curve with the measured PR σo yielded

“possible” wind direction solutions (known as aliases).

Because the GMF was an even function of the normalized

wind direction χ , ± wind direction aliases were produced,

and the wind direction was found by adding the radar

azimuth to χ . The true wind direction was one of these

solutions; but “the selection of which” is beyond the scope

of this paper. For this work, each alias was compared to the

corresponding QuikSCAT direction for the same grid point

and the closest selected. Differences between the selected

PR direction and QuikSCAT were produced and the mean

and standard deviation were calculated. Using this

procedure results in an optimistic estimate of the PR wind

direction retrieval error (assumes 100% alias selection

efficiency). It is assumed that the PR and QuikSCAT errors

are gaussian and that the variance of their difference is the

root-sum-squared of the respective variances.

PRsig-0dB

Relative Wind Dir, deg

GMF( TMI Wind Speed, 18° )

Fig. 4 Example of the PR wind direction retrieval. Wind direction aliases

occur at ± 20° and ± 140°.

V RESULTS

Results of PR and QuikSCAT comparisons, for a 207

revolution set are presented for 3 outer beams in Fig. 5 - 7.

As discussed previously, the PR measurements are obtained

in a time window that is ± 1 hr with the QuikSCAT wind

field. Scatter diagrams are presented for the selected

“closest PR wind direction” alias versus the corresponding

QuikSCAT directions (0° – 360°) . Figures are separate for

beams pairs 1 & 49, 2 & 48 and 3 & 47 for wind speed >

4m/s. The colorbar represent density of the wind directions,

the majority of which, occur in the vicinity of the trade

winds. When combined, there are 54,171 points that have a

mean difference (QuikSCAT minus PR) of 1.1° and an rms

value of 21.8°. The histogram of the wind direction

diference is given in Fig. 8. A normal distribution curve

(shown in red) is fit well to the histogram for wind direction

difference standard deviation of ~ 20°.

Fig. 5 Ordinate is QuikSCAT wind direction and abscissa is PR closest

wind direction for beam pairs 1&49 – 17330 points.

Fig. 6 Ordinate is QuikSCAT wind direction and abscissa is PR closest

wind direction for beam pairs 2&48 – 17988 points.

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Fig. 7 Ordinate is QuikSCAT wind direction and abscissa is PR closest wind direction for beam pairs 3&47 – 18853 points.

Fig. 8 Histogram of QuikSCAT – PR closest wind direction with mean 1.1° and standard deviation 21.8° – 54171 points. The red curve is the

normal distribution fit with standard deviation = 20°.

VI SUMMARY

This paper presents a novel ocean wind vector measurement

technique that uses the combined passive and active

microwave measurements respectively from the Tropical

Rainfall Measuring Mission (TRMM) Microwave Imager

(TMI) and the Precipitation Radar (PR). A PR geophysical

model function (GMF) is developed from collocated and

near-simultaneous PR, TMI and QuikSCAT scatterometer

observations. This PR GMF was derived from the SASS2

GMF with proper bias adjustment This gives credibility that

the PR can provide accurate ocean normalized cross section

measurements.

A new wind direction retrieval algorithm is presented that

uses wind speeds inferred by TMI and the PR normalized

cross section measurements to infer wind direction at the

swath edges. Because the PR provides only a single

azimuth look, multiple possible wind direction solutions

exist. The ability to select the proper (single) direction is

beyond the scope of this paper; and comparisons are

presented between the “closest” retrieved wind direction

and near-simultaneous wind vectors derived from the

QuikSCAT. Wind direction comparisons for 207

revolutions (54,171 points) are very encouraging with the

mean difference being 1.1° and the rms being 21.8° for a

wind speed range > 4 m/s. Assuming that the QuikSCAT

and PR wind measurement errors are independent gaussian

processes, we estimate that the wind direction accuracy for

PR is < 20° rms. These preliminary results demonstrate the

potential for measuring ocean surface vector winds with

combined TMI and PR.

Future research will be focused at expanding the wind

speed range and at developing the PR GMF over more

beam positions (incidence angles). Also future efforts will

be to develop an alias selection algorithm that does not

depend upon QuikSCAT. Finally, independent wind

direction validation will be conducted using buoys and

NOAA NCEP surface wind products.

VII ACKNOWLEDGMENTS

This work was sponsored under a grant from the Tropical

Rainfall Measuring Mission (TRMM) Project at the NASA

Goddard Space Flight Center.

VIII REFERENCES

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