This paper describes the first step towards the development of the geophysical model function (GMF) for the retrieval of wind speed and wind stress in hurricanes, based on developing a relation between the cross-polarized satellite SAR data from Sentinel-1 and winds/stress observed from collocated NOAA GPS-dropsondes data. Field measurements and remote sensing data for tropical cyclones in the Atlantic Ocean were analyzed. Using the data measured by GPS-dropsondes, average wind velocity profiles were obtained, while the parameters of the wind boundary layer (drag coefficient and friction velocity) were restored from the "wake" part of the velocity profiles using the self-similarity property. The self-similarity of the velocity profile "defect" in the boundary layer, known from the fluid dynamics, was used to retrieve the parameters of the atmospheric boundary layer (the surface wind velocity, drag coefficient and friction velocity) from the dropsonde wind velocity profiles in 10 major hurricanes. Based on the processing of the measurements in the hurricanes Irma 2017/09/07, Maria 2017/09/21 and 2017/09/23, at a time close to the time of acquisition of the Sentinel-1 images, the dependencies of the cross-polarized normalized radar crosssection (NRCS) on the wind speed and wind friction velocity were obtained and used for constructing the GMFs.

This paper describes the construction of geophysical model function (GMF) for wind speed and surface stress retrieval at high winds from cross‐polarized radar backscatter at the water surface. The starting point is the laboratory experiment designed for the study of X‐band backscattering from water surface. In particular, it was shown that cross‐polarized normalized radar cross section (NRCS) keeps sensitivity to wind friction velocity at high winds alternatively to co‐polarized radar return. Basing on the analysis of the Doppler spectra and simultaneous measurements of parameters of surface waves, we suggest a hypothesis that at high winds the cross‐polarized backscattered signal is formed mainly due to scattering from wave breakers. The hypothesis is supported by the experimentally detected proportionality between the power of the scattered signal at cross polarization and the area swept by wave breakers. These results obtained in the laboratory environment are applied for constructing the similar dependencies for field conditions. Using the phenomenological statistical physics approach, a parameterization of active whitecap coverage fraction dependence on wind friction velocity is suggested. With the use of this parameterization, the empirical function expressing the cross‐polarized NRCS by u* is derived. Using the surface drag parameterization applicable at strong winds, this dependence is verified on the base of available data sets containing collocated satellite measurements of cross‐polarized C‐band NRCS and ground measurements of wind speed. GMFs for u* and U10 retrieval are suggested. Analysis shows that taking into account the angular dependence of cross‐polarized radar backscattered power improves the accuracy of wind speed and wind friction velocity retrieval.

This study presents an approach for friction velocity and aerodynamic drag coefficient retrieval utilizing C-band VH SAR observations from Sentinel-1. The dataset contained 14 SAR images collected under six hurricane scenes co-analyzed with stepped frequency microwave radiometer (SFMR) measurements. The basis for creating this approach utilizes the results proposed earlier linking the parameters of the atmospheric boundary layer from GPS-dropsondes data to the ocean surface emissivity from SFMR measurements. The obtained dependencies of the ocean surface emissivity on surface friction velocity, aerodynamic drag coefficient, and surface wind speed are analyzed together with the collocated SAR data leading to the new GMF valid for the retrieval of friction velocities ranging from 0.55–1.56 m/s and drag coefficient values ranging from 0.00076–0.00232 for all sub swaths. Within the framework of the proposed approach, dependences of the normalized radar cross-section on the surface wind speed were also obtained and used for comparison with existing GMFs to show that the proposed approach is valid. A good consistency was obtained when comparing our results with H14E and MS1A. As an example the distributions of friction velocity, drag coefficient, and surface wind speed retrieved from the Hurricane Maria SAR image (23 September 2017) were considered.

A method has been developed for the retrieval of the atmospheric boundary layer parameters in tropical cyclones, namely the dynamic speed, the wind speed at a 10 m height, and the roughness parameter. For the analysis, the wind speed profiles were obtained from NOAA GPS-dropsondes and collocated with the data from the Stepped-Frequency Microwave Radiometer (SFMR). The parameters of the atmospheric boundary layer from the GPS-dropsonde data were obtained by taking into account the self-similarity of the velocity defect profile. The emissivity, determined from the radiometric measurement data, was calibrated to the field data from the GPS-dropsondes. Empirical relations between the wind speed, dynamic wind speed, and aerodynamic drag coefficient with the surface emissivity have been proposed. Based on a comparison of the measured dynamic parameters and the surface emissivity, empirical formulas have also been proposed. From an analysis of cross-polarized Sentinel-1 SAR images and collocated SFMR measurements for hurricanes Irma (2017/09/07) and Maria (2017/09/21 and 2017/09/23), we have obtained the dependences of the NRCS on the ocean surface emissivity, surface wind speed, and friction velocity. These results could potentially be used to improve the algorithm for the retrieval of boundary layer parameters in tropical cyclones from remote sensing data.

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