Marine wind speed retrieval from RADARSAT-2 dual-polarization imagery

Transcription

1 Can. J. Remote Sensing, Vol. 37, No. 5, pp , 2011 Marine wind speed retrieval from RADARSAT-2 dual-polarization imagery Sergey Komarov, Alexander Komarov, and Vladimir Zabeline Abstract. New regression models for the retrieval of marine wind speeds from RADARSAT-2 dual-polarization images and results of the comparative analysis of these models with the C-band geophysical model function CMOD_IFR2 are presented. In the two new models, synthetic aperture radar (SAR) co-polarization (VV) and cross-polarization (VH) radar cross-sections, instrument noise floor, and antenna beam incidence angle were used as independent variables. In addition, one of the models also included wind direction input. To build and test the empirical relationships between wind speed and RADARSAT-2 parameters, we created a database containing 570 samples of ocean buoy wind speed observations collocated and coincident with the information obtained from SAR images. The models were tested on independent wind speed data reaching up to 40 knots. The CMOD_IFR2 was also tested on the same data. Analysis of the test results proved a higher accuracy of the new regression models, including the model without wind direction, as compared with CMOD_IFR2, which uses wind direction information. This means that the degraded accuracy of the SAR wind retrieval model without input of the wind direction can be compensated for by using cross-polarization backscatter information. The models were validated through current buoy and image data, provided by the quasi-operational Wind Information Processing System (WIPS). The developed models have been integrated into and are currently functioning within WIPS (at the Meteorological Service of Canada). Résumé. Cet article présente des nouveaux modèles de régression pour l extraction des vitesses du vent en mer à partir des images à double polarisation de RADARSAT-2, ainsi que les résultats de l analyse comparative de ceux-ci avec le modèle géophysique de bande C, CMOD_IFR2. Dans ces deux nouveaux modèles, les surfaces équivalentes radar de copolarisation (VV) et de polarisation croisée (VH) du radar à synthèse d ouverture (RSO), le plancher de bruit de l instrument et l angle d incidence du faisceau de l antenne ont été utilisés comme variables indépendantes. De plus, l un des modèles comportait également l apport de la direction du vent. Afin d élaborer et de tester les relations empiriques entre la vitesse du vent et les paramètres de RADARSAT-2, nous avons créé une base de données de 570 échantillons d observations tirées de bouées océaniques, ces observations étant cosituées et coïncidentes avec l information obtenue par les images du RSO. Ces modèles ont été testés sur des données indépendantes de la vitesse du vent pouvant atteindre jusqu à 40 næuds. Le modèle CMOD_IFR2 a également été testé sur ces mêmes données. L analyse des résultats de ces tests a révélé une plus grande exactitude des nouveaux modèles de régression, y compris pour le modèle sans la direction du vent et ce, comparativement au CMOD_IFR2 qui, quant à lui, utilise l information liée à la direction du vent. Cela signifie que l exactitude dégradée des modèles d extraction du vent du RSO (sans l apport de la direction du vent) peut être compensée en utilisant l information de la rétrodiffusion en polarisation croisée. Ces modèles ont été validés par des mesures récemment obtenues aux bouées, ainsi que par les données des images du Système de traitement de l information du vent. Les modèles développés ont été intégrés à ce système et fonctionnent présentement au sein de celui-ci (au Service météorologique du Canada). Introduction Synthetic aperture radar (SAR) imagery is extensively used for the retrieval of wind speed at the ocean s surface. Although the physical theory of electromagnetic wave scattering from rough surfaces is already well developed (Ulaby et al., 1982; Fung 1994; Voronovich, 1994), it is rarely employed for wind speed retrieval from SAR images, because the physical scattering model requires accurate input information on ocean surface roughness and significant computational resources. Thus, relatively simple empirical geophysical functions are proposed. For instance, widely popular C-band geophysical model functions (CMODs) (Quilfen and Cavanie, 1991; Hoffman, 1993; Stoffelen and Anderson, 1997; Hersbach et al., 2007; Hersbach, 2010) link the C-band normalized radar cross section (NRCS) with wind speed at the ocean surface, antenna incidence angle, and wind direction. Several Received 31 March Accepted 24 November Published on the Web at on 13 March Sergey Komarov, 1 Vladimir Zabeline. Meteorological Service of Canada, Environment Canada, 373 Sussex Dr. Ottawa, ON Canada K1A 0H3. Alexander Komarov. Department of Electrical and Computer Engineering, University of Manitoba and Centre for Earth Observation Science, University of Manitoba, 467 Wallace Building, 125 Dysart Rd., Winnipeg, MB Canada R3T 2N2. 1 Corresponding author ( komarov@ieee.org). 520

2 Canadian Journal of Remote Sensing / Journal canadien de télédétection generations of CMOD models have been routinely applied to wind speed retrieval based on images from the European remote sensing satellites ERS-1 and ERS-2, RADARSAT-1 and RADARSAT-2, and the Advanced Synthetic Aperture Radar (ASAR) onboard Environmental Satellite (ENVI- SAT) (e.g., Horstmann et al., 2000; Beal et al., 2005). The Wind Information Processing System (WIPS) for the routine retrieval of surface wind speed over Canada s coastal waters is currently being implemented at the Meteorological Service of Canada, Environment Canada, as part of a 2 year pilot project. This quasi-operational system processes RADARSAT-1 and 2 as well as ENVISAT satellite images and provides wind speed maps at 500 m and 1000 m resolutions. Initially, WIPS was only able to process co-polarization images using the CMOD_IFR2 algorithm, which was developed for wind speed retrieval from vertical VV polarization (Quilfen and Bentamy, 1994; Quilfen et al., 1998), and later modified for horizontal HH polarization channels using the Kirchhoff approximation (Vachon and Dobson 2000). It is known that the NRCS for co-polarized VV and HH signals tends to reach saturation at high wind speeds (e.g., Shen et al., 2009). This leads to lower accuracy in retrieving high wind speeds. In addition, the CMOD-type models require wind direction input data typically provided by various numerical weather prediction (NWP) models. Initial attempts at using C-band cross-polarization for wind speed retrieval were recently undertaken by Hwang et al. (2010) and Vachon and Wolfe (2011) based on RADARSAT-2 data. In this paper, we explore a possibility of improving accuracy of wind speed retrieval by using cross-polarization channels and present two new regression models, based on the RADARSAT-2 ScanSAR mode, that are suitable for meteorological applications in terms of scene size and resolution. We collected and studied a significant number of ocean buoy measurements coincident and collocated with SAR observations over the Canadian west and east coasts. The time difference between SAR images and buoy data does not exceed 30 minutes. As a result of this study, two empirical models for marine wind speed retrieval based on RADARSAT-2 ScanSAR Wide (SCW) and ScanSAR Narrow (SCN) dual-polarization images have been developed and implemented for operational use. The first model employs co- (VV) and cross-polarization (VH) NRCS, noise-equivalent sigma zero (NESZ), and incidence angle as independent input variables (without wind direction). The second model includes the same predictors plus wind direction provided by the NWP global environmental multiscale regional (GEM REG) model (Côté et al., 1998). We demonstrated that the developed models integrating co- and cross-polarization channels perform better than the CMOD_IFR2 model. These results have been verified through independent subsets of data. Analysis of collected SAR and buoy data Over 300 RADARSAT-2 ScanSAR VV and VH images processed by WIPS were analyzed. The WIPS output product, a wind speed map, has 500 m or 1000 m resolution. The system s output consists of retrieved wind speed (CMOD_IFR2), C-band co- and cross-polarization NRCS, incidence angles, NESZ, and NWP-model wind direction. In this study, we analyzed RADARSAT-2 ScanSAR VV and VH images. To establish the empirical relationship between wind speed at the ocean surface and RADARSAT-2 parameters, ocean buoys were employed. We used observations derived from 17 Canadian west coast buoys and 9 Canadian east coast buoys and developed an algorithm for the automatic extraction of buoy data from the Environment Canada Thetis database. Buoy observations for the period from October 2009 to January 2011 were collected. The time difference between SAR images and buoy data did not exceed 30 minutes. The SAR image and the buoy observation with the shortest time gap were analyzed. To identify the spatial locations of the buoys inside a SAR image, we applied the gradient search method (Khlopenkov and Trishchenko, 2008). This approach was very convenient for the conversion of a buoy position given in geographical coordinates (latitude and longitude) to the image rectangular grid (pixel and line). Based on the buoy s relative location on the image, it was possible to extract the co- and cross-polarization NRCS r 0 VV, and r 0 VH, as well as the incidence angle u, NESZ r 0 NE, and NWP wind speed direction over the buoy s position. As a result, a database containing 570 buoy measurements collocated and coincident with SAR data was created. In our database ( ), wind speed did not exceed 20.4 m/s (about 40 knots). Initially, we analyzed correlations of NRCS values for VV and VH channels, and wind speed derived from in situ buoy observations. Figure 1 shows the dependency of co- and crosspolarization NRCS r 0 VV (db) and r0 VH (db) on buoy wind speed for the incidence angle range of 21 0 BuB28 0.From Figure 1a one may observe that the co-polarized signal tends to saturate with an increase in wind speed. The crosspolarization NRCS is an increasing nonsaturated function on wind speed (Figure 1b), but is typically much lower than the co-polarization NRCS. At low and moderate wind speeds, the cross-polarization NRCS value is close to the corresponding NESZ that interferes with the signal. Thus, the noise level should be taken into account when the cross-polarization channel is used. The analysis of hundreds of SAR images allowed us to conclude that the noise pattern was stable for different images. The behaviour of the NESZ across the satellite track came 521

3 Vol. 37, No. 5, October/octobre 2011 Figure 1. Dependencies of the co-polarization r 0 VV (db) (a), and the cross-polarization r0 VH (db) (b) on buoy wind speed for 136 samples. The incidence angle range is 21 0 BuB28 0. from the irregular antenna elevation pattern. The typical NESZ level for SCN and SCW SAR ocean images varies from 30.3 to 27.2 db. In situations where buoy wind speeds are 46 m/s, the probability of the VH signal being greater than the corresponding noise level is approximately 90%. The signal-to-noise ratio is about 34 db for wind speeds 1012 m/s and increases to 7.5 db at 20 m/s. For further study, we considered the cross-polarization NRCS and the NESZ expressed in linear units: r 0 VH lin and r 0 NE lin. Figure 2 shows a monotonic relationship between the VH cross-polarization NRCS r 0 VH lin and wind speed for different incidence angles. NESZ values r 0 NE lin (black diamonds) are distributed mainly within the horizontal stripe Br 0 NE lin B For low wind speeds, the NRCS is close to the NESZ. Figure 3 demonstrates angular dependences of r 0 VH lin and r 0 NE lin. The cross-polarization signal for wind speeds greater than 12 m/s (red circles) was significantly higher than the NESZ. Points corresponding to the wind speeds lower than 6 m/s (black circles), and some points from the 612 m/s range, formed a distinctive pattern in the vicinity of the NESZ. The comparison of this pattern with the NESZ angular dependence showed that, for the low wind speeds, the correlation between noise and cross-polarization signal was very pronounced. This indicates that the noise signal may control the VH signal. Figure 2. Dependence of the NRCS r 0 VH lin and the NESZ r0 NE lin on buoy wind speed for three ranges of incidence angles u; 570 samples. Figure 3. Dependence of the NRCS r 0 VH lin and the NESZ r0 NE lin on incidence angle for three ranges of wind speeds U (m/s); 570 samples. 522

4 Canadian Journal of Remote Sensing / Journal canadien de télédétection Figure 4. Dependence of the cross-polarization NRCS and NESZ difference r 0 VH lin r0 NE lin on incidence angle for three ranges of wind speeds U (m/s); 570 samples. The proposed threshold is o To reduce such undesirable impacts, we introduced a difference between the cross-polarization NRCS and the NESZ in linear units: r 0 VH lin r0 NE lin. The dependence of this variable on the incidence angle is shown in Figure 4. The impact of noise on this variable decreased about twofold compared with r 0 VH lin (Figure 3). For additional noise impact reduction, we introduced a threshold o that cut out the cross-polarization signal, which was lower or closer to the noise in accordance with the condition r 0 VH lin r0 NE lin e (1) The database samples that satisfy the criterion r 0 VH lin r0 NE lin > e (2) are considered sufficiently informative for wind speed retrieval. Thus, to utilize the cross-polarization signal in wind speed retrieval algorithms, we proposed a new crosspolarization variable, h VH, as follows: ( r0 10 log g VH ¼ VH lin r0 NE lin r 0 e VH lin r0 NE lin > e 0 r 0 VH lin r0 NE lin e (3) Figure 5 shows the dependence of this variable on wind speed. Selection of the threshold o level was based on several practical considerations. Excessively high o values reduced wind speed retrieval accuracy. On the other hand, low values could produce noise stripes on the wind speed image. The acceptable threshold value for wind speed retrieval from dual-polarization SCW and SCN modes (VV, VH) was established at o Figure 5. Behaviour of h VH versus wind speed for threshold o Cross-polarization signal and noise for 264 samples from 570 fulfil the condition r 0 VH lin r0 NE lin > e and describe this dependence. Wind speed retrieval models using crosspolarization For building new wind speed retrieval models, the regression technique was employed. For the purposes of regression analysis, we divided the initial dataset of 570 samples into two groups: a training subset of 313 samples and a testing subset of 257 samples. Initially, wind speed was calculated according to the CMOD_IFR2 algorithm. Figure 6 demonstrates a comparison of the wind speed provided by the CMOD_IFR2 and buoy data for both subsets. The root mean square errors (RMSE) were equal to m/s and m/s, respectively. Independent input variables for regression models are presented in Table 1. The relative wind direction 8 is defined as 8(8 t 90)8 W (deg), where 8 t is the satellite track angle and 8 W is the angle of the wind direction counting clockwise from the North Pole. For regression models we proposed quadratic polynomial functions between the buoy wind speed as a dependent variable and the other independent variables shown in Table 1. The two developed models (with and without input of wind direction) are presented below. VV VH model without wind direction The model predictors are co- and cross-polarization variables and incidence angle. The relationships for wind speed U are defined as follows regression functions: 523

5 Vol. 37, No. 5, October/octobre 2011 ( U ¼ c 0 þ c 1 r0 VV þ c 2 h þ c 3 g VH þ c 4 ðr0 VV Þ2 þ c 5 h 2 þ c 6 r 0 VV h þ c 7 ðg VH Þ2 þ c 8 r 0 VV g VH r 0 VH lin r0 NE lin > e p 0 þ p 1 r 0 VV þ p 2 h þ p 3 ðr0 VV Þ2 þ p 4 h 2 þ p 5 r 0 VV h r0 VH lin r0 NE lin e (4) Figure 6. Retrieved wind speed from the CMOD_IFR2 algorithm versus buoy data (VV polarization). Left: training subset, 313 samples; right: testing subset, 257 samples. Buoy observations wind directions were used for the training subset; GEM REG wind direction model was used for the testing subset. Table 1. Independent variables of regression models. Co-polarization variable Cross-polarization variable Incidence angle Wind direction r 0 VV (db) h VH (db) u (deg) f (deg) Linear regression algorithm estimates coefficients c i and p i (Equation (4)) using the training subset. Figure 7 compares wind speeds computed by the VVVH regression model (Equation (4)) for training (dependent) and testing (independent) subsets with buoy data. A comparison of Figures 6 and 7 shows that the proposed regression model performed better than the conventional CMOD_IFR2 model. For example, for the testing data subset, the regression model provided RMSE m/s and R compared with RMSE m/s and R for the CMOD_IFR2. Also note, that the scale of scattering of the graph points with high wind speeds derived from our regression model was lower than the scattering of points with high wind speeds derived from the CMOD_IFR2. This can be explained by the strong positive influence of cross-polarization signals in situations of higher wind speeds. Furthermore, the model comparison allowed us to infer that the loss of accuracy due to a lack of information on wind direction could be compensated for by using the variable h VH in the model (Equation (4)). We conducted the test significance of all possible terms in the quadratic form proposed for the regression model VV VH. The result suggested that the cross-term uh VH can be neglected. Withdrawing this term from the quadratic form led to an RMSE difference in the fourth decimal place. Coefficient values for the model (Equation (4)) are presented in Table 2. VV VH Dir model with wind direction As independent variables, the model contains co- and cross-polarization variables, incidence angle, and wind direction angle. The dependence of the buoy wind speed U on independent variables is defined using the following regression functions: For the model training, the angle 8 w was taken from buoy measurements; for testing, it was drawn from GEM REG 524

6 Canadian Journal of Remote Sensing / Journal canadien de télédétection 8 c 0 þ c 1 r 0 VV þ c 2 h þ c 3 cos u þ c 4 g VH þ c 5 ðr0 VV Þ2 þ c 6 h 2 þ c 7 cos 2 u >< þ c U ¼ 8 r 0 VV h þ c 9 h cos u þ c 10 ðg VH Þ2 þ c 11 r 0 VV g VH p 0 þ p 1 r 0 VV þ p 2 h þ p 3 cos u þ p 4 ðr0 VV Þ2 þ p 5 h 2 þ p 6 cos 2 u >: þ p 7 r 0 VV h þ p 8 h cos u r 0 VH lin r0 NE lin > e r 0 VH lin r0 NE lin e (5) Figure 7. Retrieved wind speed from the VV VH model (Equation (4)) without wind direction versus buoy data. Left: training subset, 313 samples; right: testing subset, 257 samples. model data. The regressions coefficients c i and p i in Equation (5) were found in a similar way as for the model in Equation (4). Figure 8 shows the wind speed calculated by the model VV VH Dir (Equation (5)) (where the wind speed direction variable cos 8 was added) versus buoy wind speed for both training and testing data subsets. The values of RMSE and R 2 were m/s and 0.884, respectively, for the testing subset. The incorporation of the wind direction angle 8 as an additional independent variable led to an insignificant increase of accuracy in comparison with our previous model, Equation (4). Consequently, the model of Table 2. Coefficient values for the VVVH model (Equation (4)). c i Value p i Value c p c p c p c p c p c p c c c Equation (4) without wind direction could be of interest for practical applications. We applied the statistical significance test to all coefficients in the quadratic form proposed for the regression model VV VH Dir. The result suggested that the crossterms h VH cos8, uh VH, and 0 VV cos8 could be neglected. Withdrawing these terms from the quadratic form led to an RMSE difference in the third decimal place. Coefficient values for the model in Equation (5) are presented in Table 3. The RMSE statistics for the CMOD_IFR2 and developed regression models for different ranges of wind speeds are presented in Figure 9. For the two regression models the RMSE values for wind speeds m/s were lower than for 612 m/s and 06 m/s ranges. The opposite effect was observed for the CMOD_IFR2. This means that the crosspolarization variable increased model accuracy for high wind speeds. Also, for the middle range of wind speeds (612 m/s), the inclusion of the wind direction in the regression model slightly reduced RMSE. Nevertheless, for the high wind speeds, the RMSE value for the VV VH Dir model was a little higher than for the VV VH model. Also, a similar small RMSE increment was observed for the low wind speeds (06 m/s) for the testing subset. We assume 525

7 Vol. 37, No. 5, October/octobre 2011 Figure 8. Retrieved wind speed from the VV VH Dir model (Equation (5)) with wind direction versus buoy data. Left: training subset, 313 samples; right: testing subset, 257 samples. GEM REG wind direction model was used for testing subset. that such an effect is explained by the small sample size of training and testing subsets (30 and 37 points, respectively), and by errors in wind directions, taken from buoy observations and the NWP model. Overall, our regression models (VV VH and VV VH Dir) provided a lower RMSE than CMOD_IFR2 for all three analyzed ranges of wind speeds. The assessment of the spatial distribution of wind speeds derived from our models is required to verify the consistency of the wind speed images. We compared the spatial distribution of wind speeds produced by the CMOD_IFR2 model against our regression models for images with different wind regimes. Analysis of about fifty wind fields produced by our models and the CMOD_IFR2 showed that the spatial behaviour of wind speeds was generally similar. At the same time, for high wind speeds ( 15 m/s), Table 3. Coefficient values for the VVVHDir model (Equation (5)). c i Value p i Value c p c p c p c p c p c p c p c p c p c c c regression models systematically performed better than the CMOD_IFR2. One example of such a comparison for a SAR scene wherein wind speed is relatively high is presented in Figure 10, which shows the wind fields created by CMOD_IFR2 (Figure 10a) and by regression model VV VH without wind direction (Figure 10b). Six National Oceanic and Atmospheric Administration observed buoys with wind speeds ranging from 8.8 m/s to 17.1 m/s are presented in Figure 10. The maximum wind speed (17.1 m/s) was registered by buoy number For this observation, CMOD_IFR2 provided a significantly lower speed value (14.3 m/s), while the regression model without wind direction (VV VH) improved the accuracy (16.2 m/s). For the moderate wind speeds registered by other buoys, the errors of the CMOD_IFR2 and the VV VH model were close. Conclusions In this study, we presented two new regression models for marine wind speed retrieval from RADARSAT-2 ScanSAR Wide and Narrow (VV, VH) dual-polarization images. ScanSAR mode captures an extensive area of the ocean surface ( km for SCN and km for SCW), which is suitable for numerous applications. ScanSAR mode has a relatively high noise level, which complicates wind speed retrieval based on cross-polarization channels. We demonstrated that the typical noise floor within SCN and SCW SAR images taken over the ocean s surface varied from about 30.3 to 27.2 db. The VH signal was higher than the corresponding noise level in approximately 90% 526

8 Canadian Journal of Remote Sensing / Journal canadien de télédétection Figure 9. Comparison of the RMSE (m/s) in various ranges of wind speeds for different models. (a) Training subset: 06 m/s, 93 samples; 612 m/s, 190 samples; m/s, 30 samples. (b) Testing subset: 06 m/s, 97 samples; 612 m/s, 123 samples; m/s, 37 samples. Figure 10. Comparison of wind speeds retrieved by CMOD_IFR2 (a) with VV VH without wind direction model (b). 12 November 2009 at 22:37 UTC. Buoy winds reported on 12 November 2009 at 23:00 UTC. The color indicates the wind speed value (scale bar in m/s). The black arrows indicate NWP wind directions. of cases where buoy wind speeds were 46 m/s. The signalto-noise ratio was about 34 db for wind speeds 1012 m/s, and increased to 7.5 db at 20 m/s. We proposed a new cross-polarization variable to utilize the cross-polarization signal in the wind speed retrieval algorithms. It contains cross-polarization NRCS, NESZ, and a threshold level for the VH signal. In a situation where the difference between the cross-polarization NRCS and the NESZ taken in linear units is greater than a threshold value, the cross-polarization variable is included in the wind retrieval algorithm. The first developed model does not contain wind direction, and is based on three independent input variables: co-polarization backscatter, cross-polarization variable, and incidence angle. The second model contains the azimuthal wind direction angle as the fourth independent variable. To establish the empirical relationship between wind speed at the ocean surface and RADARSAT-2 images, observations of ocean buoys were used. Wind speeds derived from buoy data were extracted from the Environment Canada Thetis database for the period from October 2009 to January SAR data included RADARSAT-2 SCN and SCW VV and VH images, and wind speeds retrieved by the CMOD_IFR2 model. We collected 570 ocean buoy measurements coincident and collocated with SAR observations. We divided the available dataset of 570 samples into two groups: a training subset of 313 samples and a testing subset of 257 samples. The regression wind retrieval models were built for wind speeds from 0 to 20.4 m/s. A detailed comparative analysis of accuracy of regression models and the CMOD_IFR2 model was conducted. One of the major outcomes of the conducted study is the new wind 527

9 Vol. 37, No. 5, October/octobre 2011 speed retrieval model (VV VH), which is independent of wind direction. For the developed models with and without wind direction, RMSE values were m/s and m/s, respectively, compared with m/s for the CMOD_IFR2. We believe that the loss of accuracy due to a lack of information on wind direction is compensated for by the cross-polarization variable. From the operational point of view, the additional stream of data with NWP wind directions is no longer required for routine mapping of high-resolution wind speed. The developed models were integrated into the quasioperational WIPS to provide a supporting service to the Meteorological Service of Canada. Analysis of about fifty wind fields produced by our models and the CMOD_IFR2 suggested that proposed models systematically performed better than the CMOD_IFR2. Acknowledgements Our work was conducted at Environment Canada s Meteorological Service of Canada, as part of the Canadian National SAR Wind Project. The project was supported by the Canadian Space Agency through the Government Related Initiative Program (GRIP). We thank the team members of the National SAR Winds Project, including Shahid Khurshid, Werner Reiche, Laurie Neil, and Dr. Chris Fogarty for their helpful discussions. We also thank three anonymous reviewers for constructive comments that improved this paper. References Beal, B., Young, G., Monaldo, F., Thompson, D., Winstead, N., and Scott, C High Resolution Wind Monitoring with Wide Swath SAR: A User s Guide. U.S. Department of Commerce, NOAA, Washington, DC. Côté, J., Gravel S., Méthot A., Patoine A., Roch M., and Staniforth A The operational CMC-MRB Global Environmental Multiscale (GEM) model. Part I: Design considerations and formulation. Monthly Weather Review, Vol. 126, No. 6, pp doi: / (1998)126. Fung, A.K Microwave Scattering and Emission Models and Their Applications. Artech House, Boston, Massachusates, 573 p. Hersbach, H., Stoffelen, A., and de Haan, S An improved C-band scatterometer ocean geophysical model function: CMOD5. Journal of Geophysical Research, Vol. 112 (C03006), doi: /2006jc Hersbach, H Comparison of C-band scatterometer CMOD5.N equivalent neutral winds with ECMWF. Journal of Atmospheric and Oceanic Technology, Vol. 27, pp doi: /2009JTE- CHO Hoffman, R.N A preliminary study of the impact of the ERS-1 C-band scatterometer wind data on the European centre for mediumrange weather forecasts global data assimilation system. Journal of Geophysical Research, Vol. 98, No. C6, pp doi: / 93JC Horstmann, J., Koch, W., Lehner, S., and Tonboe, R Wind retrieval over the ocean using synthetic aperture radar with C-band HH polarization. IEEE Transactions on Geoscience and Remote Sensing, Vol. 38, No. 5, pp doi: / Hwang, P.A., Zhang, B., and Perrie, W Depolarized radar return for breaking wave measurement and hurricane wind retrieval. Geophysical Research Letters, Vol. 37, L01604, doi: /2009gl Khlopenkov, K., and Trishchenko, A Implementation and evaluation of concurrent gradient search method for reprojection of MODIS level 1B imagery. IEEE Transactions on Geoscience and Remote Sensing, Vol. 46, No. 7, pp doi: /TGRS Quilfen, Y., and Cavanie, A A high precision wind algorithm for the ERS-1 scatterometer and its validation. In IGARSS-91, Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 36 June 1991, Espoo, Finland, pp Quilfen, Y., and Bentamy, A Calibration/validation of ERS-1 scatterometer precision products. In IGARSS-94, Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 812 August 1994, Pasadena, Calif., pp Quilfen, Y., Chapron, B., Elfouhaily, T., Katsaros, K., and Tournadre, J Observation of tropical cyclones by high-resolution scatterometry. Journal of Geophysical Research, Vol. 103, No. C4, pp doi: /97JC Shen, H., He, Y., and Perrie, W Speed ambiguity in hurricane wind retrieval from SAR imagery. International Journal of Remote Sensing, Vol. 30, pp , doi: / Stoffelen, A., and Anderson, D Scatterometer data interpretation: estimation and validation of the transfer function CMOD4. Journal of Geophysical Research, Vol. 102, No. C3, pp doi: / 96JC Ulaby, F.T., Moore, R.K., and Fung, A.K Microwave Remote Sensing: Active and Passive. Vol. 2. Addison-Wesley, 608 p. Vachon, P.W., and Dobson, F Wind retrieval data from RADARSAT SAR images: selection of a suitable C-band HH polarization wind retrieval model. Canadian Journal of Remote Sensing, Vol. 26, No. 4, pp Vachon, P.W., and Wolfe, J C-Band cross-polarization wind speed retrieval. Geoscience and Remote Sensing Letters, Vol. 8, No. 3, pp doi: /LGRS Voronovich, A.G Wave Scattering from Rough Surfaces. 2nd ed. Springer-Verlag, Berlin, 228 p. 528