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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 <[email protected]>
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
[1] Freilich, M. H. and R. S. Dunbar, The Accuracy of the
NSCAT Vector Winds: Comparisons with National
Data Buoy Center Buoys, J. Geophys. Res., Vol. 104,
No. C5, May, 1999
[2] Naderi, F., M. H. Freilich, and D. G. Long,
Spaceborne Radar Measurement of Wind Velocity over
the Ocean - An Overview of the NSCAT System, Proc
IEEE, vol. 79, no. 6, pp. 850-866, (1991).
[3] F. J. Wentz, "A well calibrated ocean algorithm for
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[4] C. A. Mears, D. K. Smith, and F. J. Wentz,
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[5] T. Meissner, D. Smith, and F. Wentz, "A 10-year
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[6] Connor, L. and P. Chang, Ocean Surface Wind
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