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High-Resolution Hurricane Vector Winds from C-Band Dual-Polarization SAR Observations [Journal of Atmospheric and Oceanic Technology](Journal of Atmospheric and Oceanic Technology Via Acquire Media NewsEdge) ABSTRACT This study presents a new approach for retrieving hurricane surface wind vectors utilizing C-band dual-polarization (VV, VH) synthetic aperture radar (SAR) observations. The copolarized geophysical model function [C-band model 5.N (CMOD5.N)] and a new cross-polarized wind speed retrieval model for dual polarization [C-band cross-polarized ocean surface wind retrieval model for dual-polarization SAR (C-2POD)] are employed to construct a cost function. Minimization of the cost function allows optimum estimates for the wind speeds and directions. The wind direction ambiguities are removed using a parametric two-dimensional sea surface inflow angle model. To evaluate the accuracy of the proposed method, two RADARSAT-2 SAR images of Hurricanes Bill and Bertha are analyzed. The retrieved wind speeds and directions are compared with collocated Quick Scatterometer (QuikSCAT) winds, showing good consistency. Results suggest that the proposed method has good potential to retrieve hurricane surface wind vectors from dual-polarization SAR observations. (ProQuest: ... denotes formulae omitted.) 1. Introduction Large tropical storms form as typhoons in the Pacific Ocean and hurricanes in the Atlantic Ocean. Recent hurricanes such as "Superstorm'' Sandy (2012) caused significant damage with intense rainfall and storm surges, as well as serious flooding. At least 286 people were killed along Sandy's path in seven countries. Therefore, it is important to accurately forecast and monitor hurricane tracks and intensities, particularly for the protection of coastal residents and infrastructure. Better under- standing of the hurricane surface wind fields is also essential. The first applications of microwave remote sensing technology to hurricanes involved an airborne active scatterometer and passive radiometer during Hurri- cane Allen, demonstrating their potential for reliable measurements (Jones et al. 1981). Although scatter- ometers and radiometers can also observe the ocean surface by day or night, they can only provide coarse- resolution measurements, and thus their observations are not very suitable to probe high-resolution wind gra- dient variations between the hurricane eye and eyewall. Yueh et al. (2003) examined the collocated Quick Scat- terometer (QuikSCAT) winds and Special Sensor Mi- crowave Imager (SSM/I) rain rates, and showed that the regions of high rain rate (.20 mm h21)havealower normalized radar cross section (NRCS) than the neigh- boring areas, indicative of the attenuation effects of rain. Compared to these sensors, C-band synthetic ap- erture radar (SAR) is not only able to contribute fine- resolution sea surface wind field information but also is much less influenced by rain perturbations resulting from volume scattering and attenuation by raindrops in the atmosphere than the Ku-band sensors men- tioned above (Tournadre and Quilfen 2003; Weissman and Bourassa 2011). Although SAR images of typhoons and hurricanes occasionally show dark spiral-shaped features due to extreme precipitation events (Reppucci et al. 2008, 2010), the advantages of C-band SAR make it a potentially excellent instrument to investigate meso- scale hurricane intensity and structure. Great efforts have been devoted to extract hurri- cane surface wind fields using spaceborne SAR mea- surements. Horstmann et al. (2005) used a C-band RADARSAT-1 HH-polarized SAR image of Hurri- cane Ivan to investigate the feasibility of hurricane sur- face wind retrieval using conventional VV-polarized geophysical model functions (GMFs), such as C-band models 4 and 5 (CMOD4 and CMOD5, respectively). However, studies have shown that CMOD4 is inaccurate for high wind speed retrieval under extreme weather conditions (Migliaccio et al. 2003; Horstmann et al. 2005; Signell et al. 2010). In any case, whether CMOD4 or CMOD5 is used, ultimately, wind direction has to be determined prior to wind speed retrieval from SAR. In Seasat imagery, linear features were first observed to be aligned with the direction of the wind on the scale of a few kilometers (Gerling 1986). Moreover, marine atmosphere boundary layer (MABL) rolls are frequently observed in SAR images as linear streaks (Alpers and Brummer 1994). These steaks are caused by the convergence or divergence of MABL rolls in the planetary boundary (Brown 2000). Therefore, wind direction can be estimated using the linear features associated with wind streaks, in combination with spec- tral analysis of the SAR image through a discrete Fourier transform (Vachon and Dobson 1996), or measurement of SAR image streaks via wavelet transform methods (Du et al. 2002), or by acquisition of the orthogonal of the most frequent gradient direction using the local gradient method (Koch 2004). However, these methods all produce wind direction with inherent 1808 ambigu- ities, which can be resolved using the two-dimensional continuous wavelet transform technique to isolate wind- related cells and features, and to extract wind direction without the 1808 ambiguity (Zecchetto and De Biasio 2002, 2008). There can be alias features, such as oceanic or atmo- spheric internal waves, that produce similar linear fea- tures on the same spatial scales as wind rows. These nonwind streak features are not generally aligned with the local wind vectors and can therefore contaminate wind direction retrievals inferred directly from the SAR image. In hurricane wind regimes, although the wind direction dependence of the NRCS for CMOD5 is much weaker than that of CMOD4 (Horstmann et al. 2005), inaccurate wind directions are still able to cause serious errors in wind speed retrievals. To avoid the influence of inaccurate wind directions on wind speed retrieval, Shen et al. (2006) proposed an approach to determine hurricane wind vectors, assuming constant wind speeds and equal-distant wind directions in three neighboring subimage blocks of any specific concentric circle around the hurricane eye. However, this method needs to be modified in some circumstances, for ex- ample, for degraded hurricanes, which tend to gradu- ally lose their circular structure, and for SAR images not containing the hurricane eye. Saturation of the VV-polarized NRCS can be a prob- lem for high wind retrieval from SAR. As wind speed increases, VV-polarized NRCS values also increase, even- tually reaching a maximum and then starting to decrease for higher values of wind speeds. Thus, the VV-polarized NRCS experiences saturation under high wind condi- tions, which creates a wind speed ambiguity problem (multiple solutions) for SAR wind speed retrieval (Shen et al. 2009). Investigations from global positioning system (GPS) dropwindsonde observations and labo- ratory experiments, as well as airborne scatterometer measurements, suggest that sea surface roughness, and the air-water drag coefficient, approaches limiting values, and also suggest that the NRCS acquired at VV polari- zation does not continue to increase as winds increase beyond marginal hurricane strength (Powell et al. 2003; Donelan et al. 2004; Fernandez et al. 2006; Black et al. 2007). Bubble annulus experiments suggest that the bubble layer in saltwater impedes the transfer of mo- mentum from the wind (Lundquist 1999). Altimeter observations also showed that the reflectivity is strongly impacted by foam layer and whitecap coverage (Quilfen et al. 2010; Toffoli et al. 2011). In addition to the pos- sible effects of foam, sea spray is hypothesized to sig- nificantly influence the transfer of momentum, heat, and moisture in tropical cyclones (Kepert et al. 1999; Perrie et al. 2004a,b). A significant weakness of the wind speed retrieval using VV-polarized SAR images of hurricanes or typhoons is the CMOD5 saturation under high wind conditions. Shen et al. (2009) developed a technique to remove the wind speed ambiguity based on the dominant hurricane wind structure. Reppucci et al. (2010) proposed a method to overcome the prob- lem of saturation in the tropical cyclone wind regime, basing the technique on the combined use of SAR mea- surements for areas of wind speed of 20 m s21 or less, and a parametric hurricane profile model (Holland 1980). However, hurricane vector wind retrievals from single-polarization SAR data are still a challenge, with- out assumptions on hurricane wind structure or external auxiliary wind information. C-band cross-polarized ocean backscatter from RADARSAT-2 fine quad-polarization SAR data has been documented as being insensitive to the wind di- rection or the radar incidence angle, and quite linear with respect to the wind speed (8-26 m s21), and thus can be used to directly retrieve wind speeds (Vachon and Wolfe 2011; Zhang et al. 2011). The dependence of cross-polarization returns on high wind measurements suggests that it may be ideal for hurricane surface wind retrievals (Hwang et al. 2010a,b). Recently, Zhang and Perrie (2012) proposed an empirical C-band cross- polarization ocean backscatter model (C-2PO) to re- trieve high wind speeds, based on quad-polarization SAR measurements and collocated buoy observations. Although the C-2PO model is established from fine quad-polarization mode SAR data with a low noise floor, it may possibly apply to other imaging modes, for example, ScanSAR Wide. However, the user should exercise care when using this model to retrieve high winds from dual-polarization SAR imagery, because the noise floors for dual- and quad-polarization data are different. It is therefore necessary to develop a wind speed retrieval model based on dual-polarization SAR observations. For hurricanes, wind speed and wind direction are of special interest to marine weather forecasters. Scatter- ometers can illuminate the sea surface at three differ- ent azimuth angles via three antennas, and thus the unique surface wind vector field can be obtained through multiangle observations. However, SAR has one an- tenna that only provides observations at one azimuth angle. It is difficult to acquire wind speed and direction without ambiguities, by means of individual angle mea- surements from SAR. Although fully polarimetric SAR measurements provide complementary directional infor- mation for the ocean surface wind fields (Zhang et al. 2012), the observed swaths are very small (25 km 3 25 km or 50 km 3 50 km), which are not suitable for moni- toring hurricanes from space. In VV polarization, the SAR-measured NRCS is a nonlinear cosine function with respect to wind speed and direction. For a given radar incidence angle and wind speed, one NRCS is associated with four rela- tive wind directions over the range (08;3608). In princi- ple, wind directions can be acquired from the wind streaks and standard image processing techniques (Vachon and Dobson 1996; Du et al. 2002; Koch 2004; Zecchetto and De Biasio 2002, 2008). Though often successful, there are some errors that exist in wind di- rections from these methods. Moreover, the linear fea- tures from which wind directions are inferred from the SAR images are not always present, and they can some- times be confused with oceanographic features on the same spatial scales (Monaldo et al. 2004). Scatterometers can provide wind direction observations over the open ocean but are contaminated by land signals near coastal regions (Yang et al. 2011b). Meteorological forecast models tend to produce physically reasonable wind direction estimates, but the disadvantage is that their spatial resolutions are generally limited to be far less than the SAR resolution. Moreover, global weather models do not necessarily include sufficient aspects of marine atmospheric boundary layer physics to resolve the finescale features observed by SAR (Li et al. 2011). Compared to single-polarization SAR, dual-polarization SAR can measure ocean backscatters with different scattering characteristics, which provides an opportunity to investigate hurricane wind vector retrievals. There- fore, the motivation of this study is to seek a feasible methodology to simultaneously acquire wind speed and direction using dual-polarization SAR observations. The retrieved wind fields need to be evaluated using available data, from buoys, scatterometer measurements, and airborne stepped-frequency microwave radiometer (SFMR) data (Uhlhorn and Black 2003; Uhlhorn et al. 2007), and compared to winds from the National Oceanic and Atmospheric Administration's (NOAA's) Hurri- cane Wind Analysis System (H*Wind) data (Powell et al. 1998). In this study, we aim to 1) present a new cross-polarized wind speed retrieval model based on observations, in- cluding SFMR and H*Wind data; 2) develop an ap- proach to simultaneously retrieve wind speed and wind direction from two dual-polarization SAR images of Hurricanes Bill and Bertha; 3) remove wind direction ambiguities utilizing a parametric sea surface inflow angle model; and 4) assess the accuracy of the proposed hurricane wind vector retrieval method with collocated QuikSCAT winds. 2. Dataset RADARSAT-2 SAR has capability of provide single- (HH or VVor HV orVH),dual-(VV,VHor HH, HV), and quad-polarization (HH, HV, VH, VV) imaging modes with different coverages. In this study, we focus on measurements from the dual-polarization (VV, VH) ScanSAR narrow and wide modes, because they provide wide swath (300 or 500 km) images, and are suitable for hurricane monitoring with large area coverage from space. The entire range of incidence angles for dual- polarization ScanSAR mode data is between 208 and 498. For ScanSAR narrow mode, the pixel spacing is 25m 3 25m, the resolution is 79.9;37.7m 3 60 m (range by azimuth), while for ScanSAR wide mode, the pixel spacing is 50 m 3 50 m, the resolution is 160;72.1 m 3 100 m (range by azimuth). The noise floor of the two modes is about 28 6 2 dB (Slade 2009). We remove the speckle noise in the SAR image using a polarimetric SAR speckle filtering approach (Lee et al. 1999) with a nonoverlapping 7 pixel 3 7 pixel moving window. This approach emphasizes the preserving of polarimetric properties, and statistical correlation be- tween channels, not introducing cross talk, and not degrading the image quality. We collected 648 RADARSAT-2 dual-polarization (VV, VH) SAR images for the time interval November 2008-March 2011, with 39 in situ National Data Buoy Center (NDBC) buoy observations in the Gulf of Alaska, and off the East and West Coasts of the United States. These buoys measured wind speed and direction, aver- aged over 8-min periods, and reported hourly wave pa- rameters (significant wave height, dominant wave period and direction) and surface meteorological parameters (air temperature, sea surface temperature, dewpoint temperature, sea surface pressure). All buoy wind speeds measured at different anemometer heights were ad- justed to a reference level of 10 m following Atlas et al. (2011). The spatial and temporal windows for the col- locations are required to be less than 10 km and 30 min, respectively. Buoys can measure ocean surface winds under low to moderate sea states, but they lose observational capa- bilities when wind speed approaches hurricane force. Hurricane surface winds have been estimated remotely using SFMR on board the NOAA hurricane research aircraft (Uhlhorn and Black 2003; Uhlhorn et al. 2007). The NOAA Hurricane Research Division's H*Wind (Powell et al. 1998) produces a gridded analysis by in- terpolating and smoothing wind speed observations from a vast array of marine, land, aircraft, and satellite platforms. H*Wind assimilates all of the available wind observations from a specific time period into a moving "storm relative'' coordinate system that allows for the production of an objectively blended wind field. In addition to collocations between SAR images and buoy observations mentioned above, we also col- lected SFMR and H*Wind data, which were collocated with SAR images of Hurricanes Earl and Ike ac- quired at 2259 UTC 2 September 2010 and 2356 UTC 10 September 2008, respectively. SFMR measurements over Hurricane Earl were obtained during 2230; 2330 UTC 2 September 2010. SFMR can potentially pro- vide along-track mapping of surface wind speeds at rela- tively high spatial (1.5 km) resolution. For Hurricane Ike, H*Winddatawereacquiredat0130UTC11September 2008. The spatial resolution of H*Wind data is 6 km. In total, the dataset consists of 1845 collocated pairs (buoy: 884, SFMR: 348, H*Wind: 613) of NRCS in VH polari- zation and wind speeds, which were used to develop the wind speed retrieval model for dual-polarization SAR. Moreover, we collocated two RADARSAT-2 dual- polarization SAR images of Hurricanes Bill (2226 UTC 22 August 2009) and Bertha (1014 UTC 12 July 2008), with QuikSCAT wind data of these two hurricanes, acquired at 2254 UTC 22 August 2009 and 0942 UTC 12 July 2008, respectively. For Hurricanes Bill and Bertha, the time difference between SAR observations and QuikSCAT data are only 28 and 32 min, respec- tively. The collocation is based on the nearest distance criteria. We only choose those points where the dis- tances between SAR retrievals and QuikSCAT mea- surements are smaller than 0.18 to validate the proposed dual-polarization SAR hurricane wind vector retrieval methodology. In this study, QuikSCAT surface wind fields are ob- tained from the Remote Sensing Systems (RSS) website (ftp://ftp.ssmi.com/qscat/). QuikSCAT level 3 (L3) data consists of global grid values of meridional and zonal components of winds, measured twice a day in an ap- proximately 0.25830.258 resolution, which is suitable for scientific applications (Dunbar and Perry 2001; Callahan 2006). QuikSCAT level 2B (L2B) provides 12.5-km-swath winds. This kind of data is possibly asso- ciated with potential applications in coastal regions. The mission requirements for QuikSCAT have an accuracy of 2 m s21 rms in wind speed for the range 3-20 m s21, 10% rms for the range 20-30 m s21,and208 rms in wind direction for wind speeds ranging from 3 to 30 m s21 (Dunbar et al. 2006). The original Ku-band GMF, Ku-2001, was developed for QuikSCAT wind vector retrieval (Wentz et al. 2001); validation data for winds higher than 20 m s21 were extremely limited. Analyses showed that high winds derived using the Ku-2001 GMF were significantly overestimated (Smith and Wentz 2003). Therefore, QuikSCAT ocean wind vectors were completely reprocessed based on the new GMF referred to as Ku-2011, which improves wind speed retrievals at high wind speeds (Ricciardulli and Wentz 2011). The Ku-2011 GMF was developed using wind speed data from the "WindSat'' sensor with cross-calibrated multiplatform data for wind direction (Atlas et al. 2011). WindSat is designed to demonstrate the capability of polarimetric microwave radiometry to observe ocean surface wind speeds. Meissner and Wentz (2009) developed a new algorithm for the WindSat winds and demonstrated that it has validity even in rain and storm conditions. The WindSat retrieval algorithms were trained with NOAA Hurricane Research Division (HRD) data, which are used as the optimum calibration for QuikSCAT wind speeds up to about 35 m s21 (Ricciardulli and Wentz 2011). The Physical Oceanography Distributed Active Archive Center (PO.DAAC) provides both QuikSCAT L3 and L2B wind products, but these two kinds of data are processed with the Ku-2001 GMF. Although RSS only provides QuikSCAT L3 data, they are produced using the new Ku-2011 GMF. They are therefore more suitable for our study on high winds than those from PO.DAAC. Thus, we use QuikSCAT L3 winds instead of L2B winds to validate the proposed dual-polarization SAR vector wind retrieval algorithm. 3. Methodology, retrieved winds, and validation a. Dual-polarization SAR wind vector retrieval Figure 1 shows the relation between RADARSAT-2- measured VH-polarized NRCS (s0VH) and collocated observed 10-m neural wind speed (U10), from buoys, SFMR measurements, and H*Winds data. We find that s0VH is not sensitive to the radar incidence angle or wind direction, but it is dependent on wind speed. This characteristic is very similar to that of the C-2PO model derived from fine quad-polarization SAR data (see Fig. 2, following Zhang and Perrie 2012). The inter- cepts of solid lines in Figs. 1 and 2 are different, which reveal that s0VH from dual- and quad-polarization data have different relationships with U10. Previous studies have shown that the wind direction dependence on NRCSinVVpolarization(s0VV) under high winds be- came weaker than the dependence in moderate winds, but not for radar incidence angles (Horstmann et al. 2005). Moreover, s0VH levels off as the wind speeds in- crease above hurricane force as shown in Fig. 1, which is possibly associated with s0VH attenuation due to intense rainfall and related high sea-state processes. It should be noted that the C-2PO model is based on a dataset that includes only a few wind speeds above 20 m s21,with a maximum wind of 26 m s21. Although C-2PO has the potential for retrieval of high wind speeds, it needs fur- ther improvement by investigation of higher wind speed observations. Airborne C-band dual-polarization (HH, VV) SAR observations over the ocean under moderate and high winds have exhibited the essential backscatter features of NRCS in copolarizations (Mouche et al. 2005; Sapp et al. 2013). However, the behavior of cross-polarized NRCS from spaceborne dual-polarization (VV, VH) SAR was not reported. Thus, we need to study cross- polarized SAR ocean surface backscatter character- istics especially under extreme weather conditions and to develop a wind vector retrieval algorithm for dual- polarization SAR. In the present study, we incorporate SFMR-measured surface winds and H*Wind data. In total, there are 633 wind speeds above 20 m s21, and the highest wind speed is 39.7 m s21 as shown in Fig. 1, providing an opportunity to develop a high wind speed retrieval model for dual- polarization SAR observations. We use nonlinear least squares to derive a model relating s0VH to U10 as follows: ... (1) In Eq. (1), the units of s0VH and U10 are decibels and meters per second, respectively. The correlation co- efficient between observed s0VH and simulated s0VH with Eq. (1) is 0.93, and the relation is independent of ex- ternal wind direction or radar incidence angles. There- fore, we have developed a C-band cross-polarized ocean surface wind retrieval model for dual-polarization SAR (C-2POD). The wind speed range of the fit for C-2POD is 3.7- 39.7ms21, whereas for C-2PO, the range is 2.0-26.0ms21. In addition to differences in the intercepts, between C-2POD and C-2PO models, as addressed previously, the slopes (angular coefficients) for the two models are different. They are 0.332 and 0.580, respectively. For example, if we assume the VH-polarized NRCS from dual-polarization SAR observation is 224 dB, and in- put this value into both the C-2POD and C-2PO models, the resulting wind speeds are 18.5 and 20.1 m s21,re- spectively. It is evident that the C-2PO model over- estimates wind speed. Thus, it is important to use C-2POD, instead of C-2PO, to retrieve wind speeds from dual-polarization SAR images. Because dual-polarization SAR provides simulta- neous co- and cross-polarized ocean backscatter with different scattering characteristics, we can potentially retrieve wind vectors. Therefore, to simultaneously retrieve wind speed and direction, we construct a cost function involving both the copolarized geophysical model function [C-band model 5.N (CMOD5.N)] and C-2POD. The cost function is ... (2) where sVmV and sVmH are simulated and soVV and soVH are observed NRCS in VV and VH polarizations, respec- tively. Here, i and j are the line and column pixel loca- tions, respectively, in the SAR image. For each pixel, we estimate sVmV and sVmH with the observed radar in- cidence angle and assumed wind direction and speed. Using a nonlinear least squares technique, along with simulated and observed NRCS in VV and VH polari- zations, we minimize the cost function at each pixel by requiring that the partial derivatives with respect to wind speed and wind direction be zero. We thus ob- tain optimum estimates for wind speed, and direc- tion, with associated ambiguities (multisolutions; e.g., u, p 2 u, p 1 u,2p 2 u). b. Wind direction ambiguity removal Because of the friction between hurricanes and the ocean surface, the winds inside the hurricane rotate counterclockwise and the inflow angle is toward the storm's center in the Northern Hemisphere. The aver- age inflow angle is approximately 208 (Powell et al. 1996). Recent investigation by Zhang and Uhlhorn (2012) has shown that the mean inflow angle in hurricanes is in the range 222:6862:28 with 95% confidence. This result is obtained from analysis of near-surface (10 m) inflow angles using wind vector data from over 1600 quality- controlled global positioning system dropwindsondes deployed by aircraft on 187 flights in 18 hurricanes. They proposed an analytical parametric model for the surface inflow angle aSR, which requires, as inputs, the storm motion speed, maximum wind speed, and radius of maximum wind, according to the relationship ... (3) where Vmax and Vs are maximum wind speed and storm motion speed, respectively. The normalized radial dis- tance r* has a radius of maximum wind speed (r* 5 r/Rmax); « is model error; and Aa0, Aa1,andPa1 are functions that are described by Zhang and Uhlhorn (2012). In this study, the model-estimated inflow angle can be used to select the "optimum wind direction'' from the multiple of possible wind direction solutions. At each pixel location, the wind alias nearest to the counter- clockwise tangential direction, less than the estimated inflow angle (toward the storm center), is chosen as the correct wind direction. To obtain the tangential di- rection at each pixel location, we use the approach of Du and Vachon (2003) to determine the location of the center of the hurricane eye. The maximum wind speed (Vmax) is directly obtained from the C-2POD model. The maximum wind speed Vmax can also be es- timated via fitting of a parametric model for the trop- ical cyclone wind speed (Holland 1980) to the SAR measurements in the lower wind speed regime (Reppucci et al. 2010). Thus, the radius of maximum wind can be obtained by estimating the distance between the hurricane eye center and the maximum wind speed location. The storm motion speed can be acquired from National Hur- ricane Center (NHC) 6-hourly best-track data. Therefore, the inflow angle is easily calculated using hurricane in- tensity and the motion parameters, using the parametric inflow angle model. SAR wind direction determination, among the possible aliases, is therefore easily accom- plished, making use of the inflow angle at each pixel location. c. Retrieved winds Figures 3a and 3b show a RADARSAT-2 SAR im- age of Hurricane Bill in VV and VH polarizations, respectively. The cost function uses co- and cross- polarized wind speed retrieval models (CMOD5.N and C-2POD), as well as NRCS in VV and VH polariza- tions and radar incidence angles, to obtain the wind speed and direction, with ambiguities. Figure 3c shows the optimum wind speeds associated with the mini- mum cost function. Figure 4 shows wind speeds from the CMOD5.N model with overlaid interpolated wind directions from QuikSCAT. The wind speed resolutions for Figs. 3c and 4 are 1 km. Compared to scatterometer winds, the advantage of high-resolution SAR is that it can image super hurricanes with very small eyes (;10 km) and analyze the wind gradient variations be- tween the eye and eyewall regions. Moreover, the Ad- vanced Scatterometer (ASCAT) on the Meteorological Operation-A (MetOp-A) satellite is able to provide ocean surface winds with an enhanced resolution up to 12.5 km, which is particularly suitable for observing large storm systems with fast-moving propagation speeds. Figure 3b clearly shows that the smaller values for s0VH are associated with lower wind speed-for example, in the hurricane eye area-while larger s0VH values are related to higher wind speeds in the eyewall region. This characteristic demonstrates that the cross-polarized SAR observations are not dependent on wind direction or radar incidence angle, but qualitatively vary linearly with wind speeds. It should be noted that, as shown in Fig. 1, s0VH is not saturated when wind speeds are smaller than 30 m s21 but levels off as the wind speeds increase above that value, above hurricane force winds, which is possibly caused by intense rainfall contamination and additional effects associated with severe sea states. The resultant retrieved winds allow us to easily derive the hurricane intensity (maximum wind speed, Vmax) and radius of maximum wind (Rmax). They are 34.8 m s21 and 40.1 km for Hurricane Bill, and 31.8 m s21 and 47.5 km for Hurricane Bertha, respectively. The propa- gation motion speeds (Vs ) of Hurricanes Bill and Ber- tha, as given by the NHC 6-hourly best-track data, are about 9.4 and 2.5 m s21, respectively. To remove the ambiguities in the retrieved wind directions, the tan- gential direction and inflow angle at each pixel location have to be ascertained. The VH-polarized SAR image is used to determine the hurricane eye center (HEC) following Du and Vachon (2003). The HEC longitude and latitude are 6282201100 W and 378310300N for Hurri- cane Bill, and 628310 2800 W and 298430 4900 N for Hurricane Bertha. Thus, the tangential direction can be deter- mined using a line between HEC and the pixel location. The locations of HEC and Vmax for Hurricane Bill are indicated in Fig. 3c. The storm-relative inflow angles are calculated using the hurricane intensity, radius of max- imum wind, and storm motion speed, using the para- metric model of Zhang and Uhlhorn (2012). Figure 5 shows the estimated inflow angles. The mean value is 222.58. As mentioned previously, the wind alias closest to the counterclockwise tangential direction, less than the estimated inflow angle (toward the storm center), is se- lected as the optimum wind direction. Thus, we remove the ambiguity in the solutions and obtain the final unique wind directions, which are shown in Fig. 3d. Figures 6a and 6b show a corresponding RADARSAT-2 SAR image of Hurricane Bertha in VV and VH polari- zations, respectively. Figure 6c shows the optimum wind speeds associated with the minimum cost function, and the locations of HEC and Vmax.WealsousetheCMOD5.N model and QuikSCAT-measured wind directions to ob- tain wind speeds, which are shown in Fig. 7. Again, to remove wind direction ambiguities, the inflow angles are calculated using Vmax and Rmax and the storm motion speed. Figure 8 shows the estimated inflow angles. The mean value is 221.88. Figure 6d shows the wind di- rections without ambiguities. Since no available SFMR and H*Wind data can be collocated with SAR images of Hurricanes Bill and Bertha at 2226 UTC 22 August 2009 and 1014 UTC 12 July 2008, respectively, we use collocated SAR wind retrievals and QuikSCAT measurements to evaluate the accuracy of the proposed hurricane wind vector method. For Hurricane Bill, we compare the wind speeds from C-2POD and QuikSCAT in Fig. 9a. They show good consistency even for high wind speeds (.20 m s21). Figure 9b gives the wind speeds from C-2POD versus those from CMOD5.N. It is shown that CMOD5.N wind speeds are underestimated, compared to C-2POD values, which is possibly caused by saturation of the NRCS in VV polarization, under high wind conditions and inaccuracy in the external wind direction input. Figure 9c shows the SAR-retrieved wind directions versus QuikSCAT measurements. Rain has a signifi- cant impact on QuikSCAT wind speeds, but not on wind directions, except for very high rain rates (Ricciardulli and Wentz 2011). We quantitatively estimate the bias, and centered root-mean-square error (RMSE), based on the formula proposed by Koh et al. (2012). Compared to conventional RMSE, the centered RMSE is not dependent on the bias. We calculate the wind direction correlation following the vector correlation (ry2)ap- proach (Crosby et al. 1993), where ry2 5 2andry2 5 0 are related to perfect correlation and zero correlation, respectively. Figure 10 illustrates the comparisons for Hurricane Bertha. Overall, the results show that the SAR-retrieved wind vectors are comparable to the QuikSCAT winds. Since wind comparisons are made for SAR and QuikSCAT operating at different frequencies (C band and Ku band), apparent differences between SAR re- trievals and QuikSCAT measurements can be found at individual points. The proposed dual-polarization SAR wind vector retrieval method still needs to be vali- dated using additional SFMR measurements, H*Wind data, and reliable hurricane model results. Although numerical simulations have shown that rain attenuation and scattering at the C band are one order of magnitude lower than at the Ku band (Tournadre and Quilfen 2003), we need to consider the modification of theNRCSbyrain,forhighrainratesandhighwinds. It was shown that heavy rainfall (35 mm h21) could induce serious wind speed retrieval bias (18.4 m s21 for CMOD5.N and 11.7 m s21 forC-2PO)betweenSAR and SFMR (Zhang and Perrie 2012). Objectively, nei- ther C-2POD nor CMOD5.N involve any rain param- eter because of the lack of collocated high-resolution rain-rate data, and thus wind speed retrieval accuracies of the two models are affected by high rain rates. Future investigations will focus on correction of the NRCS in VV and VH polarizations for intense rainfall regions, which can potentially improve dual-polarization SAR wind vector retrievals in extreme wind conditions. 4. Discussion Conventional C-band geophysical model functions (GMFs) for wind speed retrieval are generally derived from VV-polarized scatterometer measurements. Al- though wind speeds and directions are unknown and both are involved in the GMF, they can be derived from scatterometer observations at different azimuth angles. Compared to scatterometers, SAR only observes the ocean surface in one azimuth angle. Thus, it is difficult to simultaneously derive wind speeds and directions, combining SAR observations with GMFs, without an- cillary or external wind information. Saturation of the NRCS in VV polarization, under high winds, can cause wind speed ambiguities (multi- solutions). As shown in Figs. 4 and 7, low wind speeds appear to occur in parts of the hurricane eyewall regions, which are caused by NRCS saturation. The comparisons between SAR retrievals and QuikSCAT measurements showed that high winds derived using CMOD5.N were underestimated (Figs. 9b and 10b). Moreover, the CMOD5.N model estimates include both wind speed and direction, and thus one can obtain wind direction before deriving wind speed (from the SAR imagery). Wind di- rection can also be obtained from the SAR image itself, scatterometer measurements, numerical weather predic- tion models, and buoy observations, but inaccurate wind direction from these sources inevitably yield wind speed retrieval errors from SAR images. Dual-polarization (VV, VH) SAR can simultaneously measure ocean surface backscatter with different scat- tering characteristics, compared to single polarized SAR images (HH or VV or HV or VH). The NRCS in VV polarization is dependent on radar incidence angle and wind direction as well as wind speed, whereas NRCS in VH polarization is only dependent on wind speed. We can use NRCS in VH polarization to first retrieve wind speed. The resulting wind speed and the radar incidence angle and NRCS in VV polarization are utilized to esti- mate wind direction. As a result, dual-polarization SAR observations provide a good opportunity to obtain wind vector measurements from space. There are several important factors that affect the dual-polarization SAR wind vector retrieval accuracy. The NRCS calibration error is one of them. For high winds over 20m s21, the 0.5-1.0-dB NRCS calibration error will cause 3-8 m s21 errors in wind speed (Yang et al. 2011a). Moreover, there is potential interchannel cross talk, which cannot be corrected, for dual-polarization data (Touzi et al. 2010). Investigation has demonstrated that the highest wind regions within hurricanes are usu- ally accompanied by significant rain (Weissman and Bourassa 2011). Heavy rain contamination and addi- tional effects associated with severe sea states can strongly dampen the co- (HH or VV) and cross-polarized (HV or VH) NRCS. Intense rainfall in high wind condi- tions can lead to NRCS attenuation (Powell 1990), and thus underestimation of high wind speeds (Tournadre and Quilfen 2003). Studies using scatterometer data have shown that for wind speeds above 30 m s21 and rain rates exceeding 15 mm h21, the error in the winds can be more than 10 m s21 (Yang et al. 2004). Thus, the accurate retrieval of high wind speeds in intense rain areas has the potential to be improved only if the NRCS could be modified in these regions. 5. Conclusions We have presented a method to simultaneously de- termine hurricane wind speed and direction with C-band RADARSAT-2 dual-polarization SAR observations. Thus, a new cross-polarized wind speed retrieval model for dual polarization (C-2POD) is first developed by us- ing SAR-observed NRCS in VH polarization, with col- located wind speeds from in situ buoys, airborne SFMR data, and H*Winds. This model and the VV-polarized geophysical model function CMOD5.N are used to construct a cost function. Optimum wind speeds and directions can be estimated when the cost function achieves its minimum. The hurricane inflow angles are further used to remove wind direction ambiguities. They are estimated utilizing a parametric model with inputs of maximum wind speed (Vmax), storm motion speed (Vs ), and radius of maximum wind (Rmax). Here, Vmax and Rmax are derived from the retrieved wind speeds, and Vs is from the NHC best-track data. Two dual-polarization SAR images of Hurricanes Bill and Bertha are used to evaluate the proposed approach. The retrieved winds are shown to be comparable to the collocated QuikSCAT data. Compared to the CMOD5.N model (as shown in Figs. 9a and 10a), the proposed dual-polarization SAR wind vector retrieval algorithm not only improves the wind speed retrieval accuracy but also derives wind di- rection without ambiguity. Although we use reprocessed QuikSCAT data from the Ku-2011 GMF to validate our method, there are still some inherent wind speed errors present in the data, especially when wind speeds are above 35 m s21. Moreover, studies have shown that rain is one of the main sources of error in scatterometer winds (Draper and Long 2004; Nie and Long 2008; Allen and Long 2005; Weissman et al. 2013). The proposed method is, therefore, in need of additional validation, using more SFMR hurricane surface wind measurement and other high-quality observations. In this study, our estimate for the storm translational speed is based on the best-track data; we cannot de- termine storm motion from the SAR image itself. In the future, the Canadian RADARSAT Constellation Mission (RCM) will consist of three satellites in a low- Earth orbit, and thus provide observations of a given location in rapid succession. This will be very helpful for monitoring hurricane speed; two consecutive SAR im- ages of hurricane locations in a couple of hours can be used to determine the hurricane eye centers and thus estimate the storm motion. Additionally, the hurricane intensity Vmax and structure parameter Rmax can also be calculated. These parameters and high-resolution sur- face wind field from dual-polarization SAR images are important for investigations of hurricane dynamics. Acknowledgments. The authors thank the Canadian Space Agency (CSA) for providing RADARSAT-2 dual- polarization SAR hurricane imagery, and NOAA HRD and NDBC for supplying SFMR and buoy observations, respectively. This work was supported by Chinese Na- tional High Technology Research and Development (863) Program Grant 2013AA09A505, Natural Science Youth Foundation of Jiangsu Province Grant BK2012467, Natural Science State Key Foundation of Jiangsu Prov- ince Grant BK2011008, National Natural Science Youth Foundation of China Grant 41206171, National Natural Science Foundation of China Grant 41176160, State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences Grant LTO1206, Scientific Research Foundation of Nanjing University of Information Science and Tech- nology and Grant S8111003001, a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), and Canadian Space Agency GRIP funding. We thank three anony- mous reviewers for their constructive comments, which greatly helped to improve the manuscript. REFERENCES Allen, J. R., and D. G. Long, 2005: An analysis of SeaWinds-based rain retrieval in severe weather events. IEEE Trans. Geosci. Remote Sens., 43, 2870-2878. Alpers, W., and B. Brueurommer, 1994: Atmospheric boundary layer rolls observed by the synthetic aperture radar aboard the ERS-1 satellite. J. Geophys. Res., 99 (C6), 12613-12621. Atlas, R., R. N. Hoffman, J. Ardizzone, S. M. Leidner, J. C. Jusem, D. K. Smith, and D. Gombos, 2011: A cross-calibrated, mul- tiplatform ocean surface wind velocity product for meteoro- logical and oceanographic applications. Bull. Amer. Meteor. Soc., 92, 157-174. Black, P. G., and Coauthors, 2007: Air-sea exchange in hurricanes: Synthesis of observations from the coupled boundary layer air-sea transfer experiment. Bull. Amer. Meteor. Soc., 88, 357- 374. Brown, R. A., 2000: Serendipity in the use of satellite scatter- ometer, SAR and other sensor data. Johns Hopkins APL Tech. Dig., 21, 21-26. Callahan, P. S., 2006: QuikSCAT science data product user's manual: Overview and geophysical data products, version 3.0. JPL D-18053-RevA, 85 pp. Crosby, D. S., L. C. Breaker, and W. H. Gemmill, 1993: A proposed definition for vector correlation in geophysics: Theory and application. J. Atmos. Oceanic Technol., 10, 355-367. Donelan, M. A., B. K. Haus, N. Reul, W. J. Plant, M. Stiassnie, H. C. Graber, O. B. Brown, and E. S. Saltzman, 2004: On the limiting aerodynamic roughness of the ocean in very strong winds. Geophys. Res. Lett., 31, L18306, doi:10.1029/ 2004GL019460. Draper, D. W., and D. G. Long, 2004: Evaluating the effect of rain on SeaWinds scatterometer measurements. J. Geophys. Res., 109, C02005, doi:10.1029/2002JC001741. Du,Y.,andP.W.Vachon,2003:Characterizationof hurricaneeyes in RADARSAT-1 images with wavelet analysis. Can. J. Re- mote Sens., 29, 491-498. _____, _____, and J. Wolfe, 2002: Wind direction estimation from SAR images of the ocean using wavelet analysis. Can. J. Re- mote Sens., 28, 498-509. Dunbar, R. S., and K. L. Perry, 2001: SeaWinds on QuikSCAT level 3 daily, gridded ocean wind vectors. JPL SeaWinds Project, Guide Doc., Version 1.1, JPL Doc. D-20335, 39 pp. [Available online at ftp://podaac-ftp.jpl.nasa.gov/allData/ quikscat/L3/jpl/docs/qscat_L3.pdf.] Fernandez, D. E., J. R. Carswell, S. Frasier, P. S. Chang, P. G. Black, and F. D. Marks, 2006: Dual-polarized C- and Ku-band ocean backscatter response tohurricane-force winds. J.Geophys. Res., 111, C08013, doi:10.1029/2005JC003048. Gerling, T. W., 1986: Structure of the surface wind field from the Seasat SAR. J. Geophys. Res., 91 (C2), 2308-2320. Holland, G. J., 1980: An analytical model of the wind and pressure profiles in hurricanes. Mon. Wea. Rev., 108, 1212-1218. Horstmann, J., D. R. Thompson, F. Monaldo, S. Iris, and H. C. Graber, 2005: Can synthetic aperture radars be used to esti- mate hurricane force winds? Geophys. Res. Lett., 32, L22801, doi:10.1029/2005GL023992. Hwang, P. A., B. Zhang, and W. Perrie, 2010a: Depolarized radar return for breaking wave measurement and hurricane wind retrieval. Geophys. Res. Lett., 37, L01604, doi:10.1029/ 2009GL041780. _____, _____, J. V. Toporkov, and W. Perrie, 2010b: Comparison of composite Bragg theory and quad-polarization radar back- scatter from RADARSAT-2: With applications to wave break- ing and high wind retrieval. J. Geophys. Res., 115, C08019, doi:10.1029/2009JC005995. Jones, W. L., P. G. Black, V. E. Delnore, and C. T. Swift, 1981: Airborne microwave remote-sensing measurements of Hur- ricane Allen. Science, 214, 274-280. Kepert, J. D., C. W. Fairall, and J.-W. Bao, 1999: Modelling the interaction between the atmospheric boundary layer and evap- orating seaspraydroplets.Air-SeaExchange:Physics,Chemistry and Dynamics, G. L. Geernaert, Ed., Atmospheric and Ocean- ographic Sciences Library, Vol. 20, Kluwer, 363-409. Koch, W., 2004: Directional analysis of SAR images aiming at wind direction. IEEE Trans. Geosci. Remote Sens., 42, 702-710. Koh, T.-Y., S. Wang, and B. C. Bhatt, 2012: A diagnostic suite to assess NWP performance. J. Geophys. Res., 117, D13109, doi:10.1029/2011JD017103. Lee, J. S., M. R. Grunes, and G. de Grandi, 1999: Polarimetric SAR speckle filtering and its implication for classification. IEEE Trans. Geosci. Remote Sens., 37, 2363-2373. Li, X., W. Zheng, X. Yang, Z. Li, and W. G. Pichel, 2011: Sea surface imprints of coastal mountain lee waves imaged by synthetic aperture radar. J. Geophys. Res., 116, C02014, doi:10.1029/2010JC006643. Lundquist, J., 1999: Surface drag and momentum exchange in hurricane conditions. Proc. Aquatic Sciences Meeting, Santa Fe, NM, American Society of Limnology and Oceanography, SS54WE1538S. [Available online at http://aslo.org/meetings/ santafe1999/abstracts/SS54WE1538S.html.] Meissner, T., and F. J. Wentz, 2009: Wind-vector retrievals under rain with passive satellite microwave radiometers. IEEE Trans. Geosci. Remote Sens., 47, 3065-3083. Migliaccio, M., P. Lecomte, G. De Chiara, and R. Crapolicchio, 2003: ERS C-band scatterometer and tropical cyclone obser- vation. Atti Fond. Giorgio Ronchi, 58, 143-151. Monaldo, F. M., D. R. Thompson, W. G. Pichel, and P. Clemente- Col^on, 2004: A systematic comparison of QuikSCAT and SAR ocean surface wind speeds. IEEE Trans. Geosci. Remote Sens., 39, 283-291. Mouche, A., D. Hauser, J. Daloze, and C. Guerin, 2005: Dual- polarization measurements at C-band over the ocean: Re- sults from airborne radar observations and comparison with ENVISAT ASAR data. IEEE Trans. Geosci. Remote Sens., 43, 753-769. Nie, C., and D. G. Long, 2008: A C-band scatterometer simulta- neous wind/rain retrieval method. IEEE Trans. Geosci. Re- mote Sens., 46, 3618-3632. Perrie, W., X. Ren, W. Zhang, and Z. Long, 2004a: Simulation of extratropical Hurricane Gustav using a coupled atmosphere- ocean-sea spray model. Geophys. Res. Lett., 31, L03110, doi:10.1029/2003GL018571. _____, W. Zhang, X. Ren, Z. Long, and J. Hare, 2004b: The role of midlatitude storms on air-sea exchange of CO2. Geophys. Res. Lett., 31, L09306, doi:10.1029/2003GL019212. Powell, M. D., 1990: Boundary layer structure and dynamics in outer hurricane rainbands. Part I: Mesoscale rainfall and ki- nematic structure. Mon. Wea. Rev., 118, 891-917. _____, S. H. Houston, and T. A. Reinhold, 1996: Hurricane Andrew's landfall in south Florida. Part I: Standardizing measurements for documentation of sea wind fields. Wea. Forecasting, 11, 304-328. _____, _____, L. R. Amat, and N. Morisseau-Leroy, 1998: The HRD real-time hurricane wind analysis system. J. Wind Eng. Ind. Aerodyn., 77-78, 53-64. _____, P. J. Vickery, and T. A. Reinhold, 2003: Reduced drag co- efficient for high wind speeds in tropical cyclones. Nature, 422, 279-283. Quilfen, Y., B. Chapron, and J. Tournadre, 2010: Satellite micro- wave surface observations in tropical cyclones. Mon. Wea. Rev., 138, 421-437. Reppucci, A., S. Lehner, J. Schulz-Stellenfleth, and C. S. Yang, 2008: Extreme wind conditions observed by satellite synthetic aperture radar in the north west Pacific. Int. J. Remote Sens., 29, 6129-6144. _____, _____, _____, and S. Brusch, 2010: Tropical cyclone intensity estimated from wide-swath SAR image. IEEE Trans. Geosci. Remote Sens., 48, 1639-1649. Ricciardulli, L., and F. Wentz, 2011: Reprocessed QuikSCAT (V04) wind vectors with Ku-2011 geophysical model function. Remote Sensing Systems Tech. Rep. 043011, 8 pp. Sapp, J. W., S. J. Frasier, J. Dvorske, P. S. Chang, and Z. Jelenak, 2013: Airborne dual-polarization observations of the sea sur- face NRCS at C-band in high winds. IEEE Geosci. Remote Sens. Lett., 10, 726-730. Shen, H., W. Perrie, and Y. He, 2006: A new hurricane wind re- trieval algorithm for SAR images. Geophys. Res. Lett., 33, L21812, doi:10.1029/2006GL027087. _____, Y. He, and W. Perrie, 2009: Speed ambiguity in hurricane wind retrieval from SAR imagery. Int. J. Remote Sens., 30, 2827-2836. Signell, R. P., J. Chiggiato, J. Horstmann, J. D. Doyle, J. Pullen, and F. Askari, 2010: High-resolution mapping of Bora winds in the northern Adriatic Sea using synthetic aperture radar. J. Geophys. Res., 115, C04020, doi:10.1029/2009JC005524. Slade, B., 2009: RADARSAT-2 product description. MacDonald, Detwiler and Associates Ltd. Doc. RN-SP-52-1238, 46 pp. [Available at http://gs.mdacorporation.com/products/sensor/ radarsat2/RS2_Product_Description.pdf.] Smith, D. K., and F. J. Wentz, 2003: Results of QuikSCAT high wind data validation. Preprints, 12th Conf. on Satellite Mete- orology and Oceanography/12th Conf. on Interactions of the Sea and Atmosphere, Long Beach, CA, Amer. Meteor. Soc., JP4.9. [Available online at https://ams.confex.com/ams/ annual2003/techprogram/paper_56156.htm.] Toffoli, A., A. V. Babanin, M. A. Donelan, B. K. Haus, and D. Jeong, 2011: Estimating sea spray volume with a laser al- timeter. J. Atmos. Oceanic Technol., 28, 1177-1183. Tournadre, J., and Y. Quilfen, 2003: Impact of rain cell on scat- terometer data: 1. Theory and modeling. J. Geophys. Res., 108, 3225, doi:10.1029/2002JC001428. Touzi, R., P. W. Vachon, and J. Wolfe, 2010: Requirement on antenna cross-polarization isolation for the operational use of C-band SAR constellations in maritime surveillance. IEEE Trans. Geosci. Remote Sens., 7, 861-865. Uhlhorn, E. W., and P. G. Black, 2003: Verification of remotely sensed sea surface winds in hurricanes. J. Atmos. Oceanic Technol., 20, 99-116. _____, _____, J. L. Franklin, M. Goodberlet, J. Carswell, and A. S. Goldstein, 2007: Hurricane surface wind measurements from an operational stepped frequency microwave radiometer. Mon. Wea. Rev., 135, 3070-3085. Vachon, P. W., and F. W. Dobson, 1996: Validation of wind vector retrieval from ERS-1 SAR images over the ocean. Global Atmos. Ocean Syst., 5, 177-187. _____, and J. Wolfe, 2011: C-band cross-polarization wind speed retrieval. IEEE Geosci. Remote Sens. Lett., 8, 456-459. Weissman, D. E., and M. A. Bourassa, 2011: The influence of rainfall on scatterometer backscatter within tropical cy- clone environments-Implications on parameterization of sea-surface stress. IEEE Trans. Geosci. Remote Sens., 49, 4805-4814. _____, B. W. Stiles, S. M. Hristova-Veleva, D. G. Long, D. K. Smith, K. A. Hilburn, and W. L. Jones, 2013: Challenges to satellite sensors of ocean winds: Addressing precipitation effects. J. Atmos. Oceanic Technol., 29, 356-374. Wentz, F. J., D. K. Smith, C. A. Mears, and C. L. Gentemann, 2001: Advanced algorithms for QuikSCAT and SeaWinds/AMSR. Proceedings of the IEEE 2001 International Geoscience and Remote Sensing Symposium: Scanning the Present and Re- solving the Future, Vol. 3, IEEE, 1079-1081. Yang, J., and Coauthors, 2004: Effect of precipitation on ocean wind scatterometry. 2004 IEEE International Geoscience and Remote Sensing Symposium Proceedings: Science for Society; Exploring and Managing a Changing Planet, Vol. 7, IEEE, 4913-4916. Yang, X., X. Li, W. G. Pichel, and Z. Li, 2011a: Comparison of ocean surface winds from ENVISAT ASAR, MetOp ASCAT scatter, buoy measurements, and NOGAPS model. IEEE Trans. Geosci. Remote Sens., 49, 4743-4750. _____, _____, Q. Zhen, X. Gu, W. G. Pichel, and Z. Li, 2011b: Comparison of ocean-surface winds retrieved from QuikSCAT scatterometer and RADARSAT-1 SAR in offshore waters of the U.S. West Coast. IEEE Geosci. Remote Lett, 8, 163- 167. Yueh, S. H., B. W. Stiles, and W. T. Liu, 2003: QuikSCAT wind retrievals for tropical cyclones. IEEE Trans. Geosci. Remote Sens., 41, 2616-2628. Zecchetto, S., and F. De Biasio, 2002: On shape, orientation, and structure of atmospheric cells inside wind rolls in two SAR images. IEEE Trans. Geosci. Remote Sens., 40, 2257- 2262. _____, and _____, 2008: A wavelet-based technique for sea wind ex- traction from SAR images. IEEE Trans. Geosci. Remote Sens., 46, 2983-2989. Zhang, B., and W. Perrie, 2012: Cross-polarized synthetic aperture radar: A new potential technique for hurricanes. Bull. Amer. Meteor. Soc., 93, 531-541. _____, _____, and Y. He, 2011: Wind speed retrieval from RADARSAT-2 quad-polarization images using a new po- larization ratio model. J. Geophys. Res., 116, C08008, doi:10.1029/2010JC006522. _____, _____, P. W. Vachon, X. Li, W. G. Pichel, J. Guo, and Y. He, 2012: Ocean vector winds retrieval from C-band fully polari- metric SAR measurements. IEEE Trans. Geosci. Remote Sens., 50, 4252-4261. Zhang, J., and E. W. Uhlhorn, 2012: Hurricane sea surface inflow angle and observation-based parametric model. Mon. Wea. Rev., 140, 3587-3605. BIAO ZHANG School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China WILLIAM PERRIE Fisheries and Oceans Canada, Bedford Institute of Oceanography, Dartmouth, Nova Scotia, Canada JUN A. ZHANG Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, Florida ERIC W. UHLHORN NOAA/AOML/Hurricane Research Division, Miami, Florida YIJUN HE School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China (Manuscript received 21 December 2012, in final form 1 November 2013) Corresponding author address: Dr. William Perrie, Bedford In- stitute of Oceanography, 1 Challenger Drive, P.O. Box 1006, Dartmouth NS B2Y 4A2, Canada. E-mail: [email protected] DOI: 10.1175/JTECH-D-13-00006.1 (c) 2014 American Meteorological Society |