MAPPING OF MESOSCALE AND SUBMESOSCALE WIND FIELDS USING SYNTHETIC APERTURE RADAR

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1 MAPPING OF MESOSCALE AND SUBMESOSCALE WIND FIELDS USING SYNTHETIC APERTURE RADAR Donald R. Thompson, Francis M. Monaldo, Robert C. Beal Johns Hopkins University/APL Johns Hopkins Road Laurel, MD Tel: ; Fax: ; INTRODUCTION The long-term goal of the research effort to be described in this paper is to investigate the possibility of obtaining quantitative information about the near-surface wind field and perhaps other parameters that characterize the Marine Atmospheric Boundary Layer (MABL) from an analysis of Synthetic Aperture Radar (SAR) imagery. Because of its potential for obtaining such information, this application of SAR, especially for use in coastal waters, would represent a significant advance over most scatterometer and passive microwave sensors that yield only coarse-resolution estimates of the wind field. Moreover, the extended swath width of the ScanSar beam modes, already available on RADARSAT and planned for ENVISAT, can significantly extend the coverage necessary for practical applications. Based on the results of investigators from both Europe and the US over the past several years, we now believe that the possibility of SAR wind mapping has been demonstrated [1-3]. In fact, our wind-map generation procedure has recently been automated so that high-resolution surface-wind estimates can be obtained within about 30 minutes after the SAR image file becomes available to us. Wind maps collected over the past several seasons along the US East Coast and in the Gulf of Alaska may be accessed from our web site at: click "RADARSAT Coverage. Although the results of these investigations have been quite promising, much work remains to be done. To obtain accurate wind maps, precise calibration of the SAR sensor is, of course, an absolute necessity. Because of their broad swath however, proper calibration of ScanSar beams is extremely difficult. Calibration issues concerning the RADARSAT ScanSar beams are still being investigated [1-3] nearly five years after the satellite s launch. Even when these calibration issues are solved, important questions concerning the relationship between radar cross section (RCS) and the surface wind vector (i.e. the scatterometer algorithm) still remain. This is especially true for the RADARSAT SAR and the proposed ENVISAT ASAR operating in the HH-pol mode since a reliable HH-pol algorithm comparable to the widely-used VV-pol Cmod4 [4] is until now not readily available. In most of the SAR wind conversion research to date, a simplified form for the C-band HH/VV ratio is chosen [5] and the HH-pol cross section is assumed to be given by this ratio times the RCS from Cmod4. We plan to utilize the ENVISAT ASAR in its alternating polarization mode to more accurately determine the polarization ratio. The availability of virtually simultaneous dual-polarization images covering a wide range of incidence angles, when coupled with the proper environmental characterization, should offer a unique opportunity to improve our high-resolution wind inversion technique. BACKGROUND In this section, we will describe briefly our present inversion procedure, and discuss some of the associated issues that need further attention. For illustration, we show in Fig.1 a wind map of the Gulf of Alaska produced as part of the Stormwatch/Alaska SAR Demonstration project [6] sponsored by the US National Oceanic and Atmospheric Administration (NOAA). The wind-speed values are referenced to the color bar near the bottom of the figure. "!# $#! % #& ' ( ) +*, ' ' *' ) -. / " ) ' : ' ; 2 2* < ) - 6 =' 2/ 6 = > *? ) 6 A 5 ( B 2' ' *'@ 6 6> 2 ( ' ' " " C >.2 D * E F B G2' This work was supported by a grants from the US Office of Naval Research and the US National Oceanic and Atmospheric Administration.

Fig.1. High-resolution SAR wind map of the Gulf of Alaska near Cook Inlet. The map was derived from a RADARSAT wide ScanSar SAR scene collected on 24 December 1999 1t 03:44:08 UT.

2 Fig.1. High-resolution SAR wind map of the Gulf of Alaska near Cook Inlet. The map was derived from a RADARSAT wide ScanSar SAR scene collected on 24 December t 03:44:08 UT. The arrows show the predicted wind field from the NOGAPS Model. Global Atmospheric Prediction Model (NOGAPS) developed at the Fleet Numerical Meteorological and Oceanographic Center (FNMOC) for 00:00 UT (on 24 December, 1999). The wind direction from these estimates, available from NOGAPS on a 1 1 grid, are interpolated to the 600 m pixels spacing of the map in Fig.1 for use in the wind inversion algorithm. The colors associated with the arrows in Fig.1 are also referenced to the color bar, and indicate the wind speed predicted by the model at each grid point. Since the SAR-derived wind speeds are independent of the model-predicted speeds, these quantities may be compared as a partial validation of the inversion technique. We will return to this point in the next section of the paper. C > 1 illustrates the utility of high-resolution wind maps in regions where the wind field is strongly influenced by the rapid changes in the local topography. One can see that the wind speed in the center of Cook Inlet (at about -151ºE, 58º30 N) is nearly 20m/s, but in the sheltered region just to the north it is only 10m/s or even less. Even narrower jet-like structures in the wind field, strongly correlated with rapid changes in elevation of the local terrain, are seen at several other locations in the image. Because of their coarse resolution, conventional scatterometers or passivemicrowave wind sensors are unable to resolve most of these structures. One can also see, by comparing the color of the arrows with the surrounding region of the wind map, that the agreement between the model and SAR-derived wind speeds is generally good.

3 ASSESSMENT OF WIND RETRIEVAL ALGORITHM As discussed previously in [1, 5], our proposed HH-pol scatterometer function is based on the well-established Cmod4 V function [4] that predicts the VV-pol cross section σ ( U, 0 θ, φ ). It takes the form 2 ( 1+ αtan θ ) 2 ( 1+ 2tan θ ) (, θ φ) H V σ σ, U 2 =, (1) where U is the wind speed, θ is the incidence angle, φ is the azimuth angle of the radar with respect to the wind direction, and σ H 0 is the resulting HH-pol cross section. The simple (empirical) polarization ratio used in (1) yields good agreement with measured C-band HH-pol cross sections for incidence angles between about 20 to 50 [7], when the value for the parameter α is chosen to be 0.6. (For further details, see the discussion in [1] and [5].) In order to assess the algorithm and ultimately the quality of the wind retrieval, we have recently compared the SARderived winds with other independent estimates of the surface wind speed. One such comparison is with the wind speed measured by the National Data Buoy Center (NDBC) buoys located along the eastern US coast. From imagery collected over the past 3 winter seasons, we have accumulated more than 325 cases where comparisons with buoy wind-speed measurements can be made. Furthermore, the NDBC buoys also measure air and sea temperature so that the measured wind can be converted to an equivalent 10 m neutral-stability wind speed as required by our inversion algorithm. We have performed this correction using the TOGA-COARE bulk flux algorithm [8]. (For a small portion of the buoy data where air or sea-surface temperature was not available, we have assumed neutral stability at a temperature equal to the available temperature. When neither the air or the sea-surface temperature was available, we assumed 15º C for both.) A scatter plot of the equivalent 10 m neutral-stability buoy wind speed versus the SAR-derived wind speed averaged over a 5 km radius about the buoy location is shown in Fig.2. The SAR-derived speeds in the left-hand panel were H I J2K L M N O(L P Q R S = T U V(W XY T Z[\ W ] ^ = 0.6 was used for those in the right-hand panel. One can see from Fig.2 that = _ ` a2b c d e f g h i j h k k h c'l m c h h n"h o kpq k r2k r h?j f d s"n"h l i f c h n"h o k i ` t o"u l g k v q k l b b h l c ik r l k l"i w q m r k w s(i n"l w w h c x l w f hpd f w e provide even closer agreement (similar to our findings in the model comparison to be discussed below). We have recently noticed that the SAR-derived wind speeds at incidence angles less than about 25º or so seem to be biased low. At present, we believe this bias may be the result of calibration errors. When the bias is removed using an adhoc correction procedure [9], the resulting mean difference between the buoy wind speeds and the (un-biased) SAR wind speeds is lowered to about 0.5 m/s. 25 Wind Speed Scatter Plot Mean Difference: 2.40 m/s 25 Wind Speed Scatter Plot Mean Difference: 1.02 m/s 20 Standard Deviation: 1.75 m/s alpha = Standard Deviation: 1.82 m/s alpha = 0.6 SAR SAR Buoy Buoy Fig.2. y z { } ~ ' ƒ 2 ˆ Š" } } Š } ~?y Œ: :Ž Š } ~ ˆ } Š ˆ Š2 } } Š ƒ ~ 2 } ƒ Ž { Š { } { Š 2 ~ ˆ Ž hand panel). The buoy winds have been corrected to10 m neutral stability.

4 We have also compared our SAR-derived wind estimates with the NOGAPS model wind speed predictions at the 1º 1º grid points. As already described in the Background section, the wind directions predicted by NOGAPS at these grid points are used in our inversion procedure, but the wind-speed predictions are not. The predicted speeds may therefore be used for independent comparison and assessment of the inversion procedure. Even though the model predictions for a particular meteorological situation might be in error, we expect the model winds to be correct on average, and thus useful for the isolation systematic errors in the SAR wind retrieval. Given the radar cross section and the incidence š œ? œ ž Ÿ2 " œ š œ:ž 2 œ ž 2 œ? : œ «ª : : œ œ 2 œ œ ' : š œ? ± ' ² ³ µ (³ µ ž œ "œ? µ 2 " ¹ " Ÿ œž œ š2 œ œ œ 2 œ?œ ž Ÿ œ ² º Ÿ œ ª :» ( œ œ?? Ÿ comparison are averaged over an area comparable to the model grid spacing.) We then compare these averaged SAR ¼½ ¾ "À Á Â Â À ¼½ Ã Ä Ã Ä Â?Å"Æ Â ÇÀ Á Â Â À'È À'È?É Ê ¾ Ë Ã ½ Æ ¾2Æ É Ì ÍÆ Î ÂÃ Ä È ¾Ï Ð Ð Ð"À È Å2Á Ç Â?Á Æ ½ ¾ Ã À È Î ÂÊ À Â 2½ ¾ Ã Ä ½ À'Ë Æ Å2Á È Î ½ À Æ ¾Ì Ñ Ä ½ À Ò Â Á Â ¾ Â ¾ Ë Â:½ À ½ Ç Ç Ê À Ã Î È Ã Â ½ ¾ Ó½ Ô Ì Õ:Ö 2ÈË Æ Å2Á È Î ½ À Æ ¾2Æ ÉÁ Î Æ Ö È Ö ½ Ç ½ Ã 2 Â ¾ À ½ Ã É Ê ¾ Ë Ã ½ Æ ¾ ÀØ Á É À ÙÀ Ä Æ ¼½ ¾ Ô Ã Ä Â?Á Î Æ Ö È Ö ½ Ç ½ Ã of occurrence of particular wind-speed values. The thick lines represent the pdf of the model winds, while the thin lines Ú Û Ü Ý»Þ Û ß2à á:âã ä å å Ü æ'þ Û ß:ç è ä ç é ê Þ ß ä ë ê ì í ß î ï Û ßå ç æ Ú Þ ê è ä Ú ß é Ü è ä ð"ü ð"ß è Þ ÚÜ å'þ Û ßð"Ü ä ß ì ê è äà á:âýç è ä Úê Ú'Ýß ì ì ê Ú'Þ Û ß é Ü æ æ ß ì ê Þ ç Ü èé Ü ß å å ç é ç ß è Þ'å Ü æß ê é Û ë ê ì í ß2ê æ ß"ê ì Ú Ü(ì ç Ú Þ ß äç èß ê é Ûã ê è ß ì îá«ë ç Ú í ê ìß ñ ê ð2ç è ê Þ ç Ü èü å:òç ó î ôú í ó ó ß Ú Þ Ú:Þ Û ê ÞÞ Û ß à á:â:õê è ä ð"ü ä ß ì õ ä ß æ ç ë ß ä ã ä å Ú'ê æ ß?ð"Ü Ú Þ Ú ç ð"ç ì ê æ å Ü æ õ ë ê ì í ß Ú ö ß Þ Ýß ß è î ø2ê è ä2 î ù î áì Ú Ü ú Þ Û ß:ð"ß ê è"ä ç å å ß æ ß è é ß?ö ß Þ Ýß ß è2þ Û ß ð"ü ä ß ì:ê è äþ Û ßà á:âûß Ú Þ ç ð"ê Þ ß Ú2ê æ ßÚ ð"ê ì ì ß Ú Þ:å Ü æþ Û ç Úæ ê è ó ß(Ü å ë ê ì í ß Ú î'á?þ?þ Û ß(ß ñ Þ æ ß ð"ß Ú ú = üý þ ÿ apparent from Fig.2 that there are significant differences between the model- and SAR-derived pdfs. Model Winds and SAR Wind PDF (Alpha= 0.0) SAR mean: 9.44 m/s SAR SD: 5.69 m/s RMS Diff: 4.86 m/s Correlation: Model Winds and SAR Wind PDF (Alpha= 0.2) SAR mean: 8.35 m/s SAR SD: 4.99 m/s RMS Diff: 4.26 m/s Correlation: Model Winds and SAR Wind PDF (Alpha= 0.4) SAR mean: 7.45 m/s SAR SD: 4.42 m/s RMS Diff: 3.82 m/s Correlation: Model Winds and SAR Wind PDF (Alpha= 0.6) SAR mean: 6.69 m/s SAR SD: 3.95 m/s RMS Diff: 3.49 m/s Correlation: Model Winds and SAR Wind PDF (Alpha= 0.8) SAR mean: 6.05 m/s SAR SD: 3.55 m/s RMS Diff: 3.25 m/s Correlation: Model Winds and SAR Wind PDF (Alpha= 1.0) SAR mean: 5.51 m/s SAR SD: 3.20 m/s RMS Diff: 3.07 m/s Correlation: Fig3. Comparisons of the wind speed pdf computed from the NOGAPS model (dark line) and from the corresponding! " $# " %$&! $! '$ '(! & )

5 FUTURE PLANS *,+ -/ :3 ; 0 < -/ = - 3< 3 > 9 -? 0 4 = = - 4@5 7BA C D :E F G:5 7IH >,3 H =(J 5 = +:= + - measurements of [7]. In spite of this agreement with the buoy- and model-derived wind speeds, some questions remain. K LM@N O P O L Q R M R O N$S T N(O U M VR W O X Y T N Z Q V M L L X O Q M W [ \ ] ^ _ X ` M a O@S T b L c/q ` M Q$M@a M W b OS T N P W T Z OQ T/b L d Q e f d a O Z,g O Q Q O N(M f N O O V O L Q in their SAR wind-speed inversion. These authors do mention however, that calibration issues could be a major problem and may be partially to blame for the apparent discrepancy between our findings and theirs. In any case, it is clear that present knowledge of the C-band polarization ratio is inadequate. Even at moderate incidence angles, measured values for this ratio are larger than those predicted by the rough-surface scattering models commonly in use. At 45 incidence for example, the measured HH/VV ratio for a 10 m/s wind directed toward the radar is about -3.5dB at Ku-band and about -5.5dB or so at C-band [7]. Bragg-based scattering models predict the HH/VV ratio at 45 to be about -9.5dB, independent of radar frequency. Ratios predicted by composite-type scattering models that include the effects of longwave tilt and hydrodynamic modulation yield some improvement [11], but the predicted polarization ratios from these models generally remain too small. The ENVISAT ASAR in its alternating polarization mode has the capability to collect virtually simultaneous dualpolarization images covering a wide range of incidence angles. We believe that this imagery when coupled with the proper environmental characterization will offer a unique opportunity to improve our understanding of the polarizationratio discrepancy discussed above. By using descending overpasses along the eastern US coast roughly between the Grand Banks and Cape Hatteras, North Carolina, we can ensure that several NDBC data buoys are located within each SAR scene. When it becomes available, we plan to compare trends in the HH- and VV-pol imagery versus incidence angle with predictions from rough-surface scattering models as well as with commonly used C-band scatterometer wind algorithms (mostly VV-pol to date). Since both of these comparisons require measurements of the local wind vector (and air-sea temperature difference if available), the requirement that the NDBC buoys be located within the scenes is very important. Besides the relevance of dual-polarization ENVISAT imagery for the fundamental investigation of ocean backscatter physics, it is clear from the discussion earlier in this paper that the availability of such imagery has practical applications as well. As we have already seen, a better understanding of the behavior of HH-pol backscatter for practical applications such as SAR wind retrieval is critical. With this imagery as a guide, we should be able not only to advance our understanding of the fundamental physics that governs microwave backscatter from the ocean, but also to develop better empirical algorithms (especially at HH-polarization) for use in important practical applications such as operational SAR wind retrieval. REFERENCES [1] Thompson, Donald R. and Robert C. Beal, Mapping of Mesoscale and Submesoscale Wind Fields Using Synthetic Aperture Radar, APL Technical Digest, , [2] Horstmann, Jochen, Susanne Lehner, Wolfgang Koch, and Rasmus Tonboe, Computation of Wind vectors over the Ocean Using Spaceborne Synthetic Aperture Radar, APL Technical Digest, , [3] Vachon, P. W. and F. W. Dobson, Validation of Wind Vector retrieval from ERS-1 Images Over the Ocean, Global Atmos. Ocean Sys., 5, , [4] Stoffelen, A. and D. L. T. Anderson, Wind retrieval and ERS-1 scatterometer radar backscatter measurements, Adv. Space Res., 13, 53-60, [5] Thompson, D. R., T. M. Elfouhaily, and B. Chapron, Polarization Ratio for Microwave Backscattering from the Ocean surface at Low to Moderate Incidence Angles, 1998 IEEE International Geoscience and Remote Sensing Symposium Proceedings III, , [6] Monaldo, Frank, The Alaska SAR Demonstration and near-real-time Synthetic Aperture Radar Winds, APL Technical Digest, , [7] Unal, C. M. H., P. Snooji, P. J. F. Swart, The polarization-dependent relation between radar backscatter from the ocean surface and surface wind vectors at frequencies between 1 and 18 GHz, IEEE Trans. Geosci. Remote Sensing, 29, , [8] Fairall, C.W., E.F. Bradley, D. P. Rogers, J.B. Edson, and G. S. Young, Bulk parameterization of the air-sea fluxes for Tropical Ocean-Global Atmosphere Response Experiment. J. Geophys. Res., 101, , 1996.

6 [9] Monaldo, Frank M., Robert C. Beal, Donald R. Thompson, William G. Pichel, and Pablo Clemente-Colón, Validation of Wind Retrievals from RADARSAT SAR, IGARSS 2000, IEEE CD-ROM 00CH37120C, Honolulu, Hawaii, July [10] Horstmann, Jochen, Wolfgang Koch, Susanne Lehner, and Rasmus Tonboe, Wind Retrieval Over the Ocean Using synthetic Aperture Radar with C-Band HH-Polarization, IEEE Transactions on Geoscience and Remote Sensing, 38, , [11] Romeiser, Roland, Werner Alpers, and Volkmar Wissmann, An Improved Composite Model for the Radar Backscattering Cross Section of the Ocean Surface; 1. Theory and Model Optimization/Validation by Scatterometer Data, J. Geophys. Res., 102, 25,237-25,250, 1997.

WIND FIELDS RETRIEVED FROM SAR IN COMPARISON TO NUMERICAL MODELS

WIND FIELDS RETRIEVED FROM SAR IN COMPARISON TO NUMERICAL MODELS Jochen Horstmann, Wolfgang Koch, and Susanne Lehner 2 GKSS Research Center, Max-Planck-Str., D-2 Geesthacht, Tel. +4 42 87 67, Fax: +4 42