Combined active and passive microwave sensing

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 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 nearsimultaneous 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

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“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.

θi 25 km

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°.

220 km

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:

σ o = GMF(U , χ , θ , p) ,

(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°.

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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 σo were 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 σo measurement 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

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V RESULTS 25 km grid by beam

Collocate by beam

Retrieve Wind Dir Aliases

PR Sig-0 2A21 Products

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°.

TMI Wind Speed

Select Closest Wind Dir Aliases

Compare & Stats

QuikSCAT Wind Field L2B Products 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.

Fig. 5 Ordinate is QuikSCAT wind direction and abscissa is PR closest wind direction for beam pairs 1&49 – 17330 points.

GMF( TMI Wind Speed, 18° )

PR sig-0 dB


Relative Wind Dir, deg Fig. 4 Example of the PR wind direction retrieval. Wind direction aliases occur at ± 20° and ± 140°.

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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

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

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[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 special sensor microwave / imager," Journal of Geophysical Research, vol. 102, pp. 8703-8718, 1997 [4] C. A. Mears, D. K. Smith, and F. J. Wentz, "Comparison of SSM/I and bouy-measured wind speeds from 1987 - 1997," Journal of Geophysical Research, vol. 106, pp. 11719-11729, 2001. [5] T. Meissner, D. Smith, and F. Wentz, "A 10-year intercomparison between collocated SSM/I oceanic surface wind speed retrievals and global analyses," Journal of Geophysical Research, vol. 106, pp. 1173111742, 2001. [6] Connor, L. and P. Chang, Ocean Surface Wind retrievals using the TRMM Microwave Imager, IEEE Trans. Geosci. Rem. Sens., 38, 2009–2016, 2000 [7] Li, L., Im, E., Connor, L. N., and P. Chang, Detecting Ocean Surface Winds using TRMM Precipitation Radar, Proc. IGARSS’02, Ontario, Canadia, Jun. 2002 [8] Wentz, F. J. and D. Smith, “A Model Function for Ocean Normalized Radar Cross Section at 14 GHz Derived from NSCAT Observations,” J. Geophys. Res.- Oceans, Feb. 1999.

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