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 paper describes laboratory experiments (using a high‐speed wind‐wave flume) of the effects of water waves on heat and momentum exchange in the near‐water atmospheric boundary layer at high wind speeds. Different from previous experiments of this type, the parameters of waves were controlled by a net stretched along the entire channel to effectively decrease the fetch. This helped to achieve dependency of the transfer coefficients on two independent parameters, namely, the wind speed and fetch (expressed via variations of the wavefield). Another key to the experiment was using a stable temperature stratification of the air flow, with the temperature of the air entering the flume 15–25° higher than the water. The experiments showed a sharp increase in the heat exchange coefficient at winds exceeding 33–35 m/s, similar to that observed earlier in the high‐speed wind‐wave flume of Kyoto University with conditions of unstable temperature stratification of the air flow. The joint analysis of the data obtained in the high‐speed wind‐wave flumes of IAP RAS and Kyoto University yields the universal dependency of the exchange coefficients and the temperature roughness on the peak wave number of surface wave spectra. This is independent of the type of temperature stratification of the atmospheric boundary layer, either stable or unstable. The sharp increase in the heat exchange coefficient is shown to be associated with increased whitecapping.

This paper presents data of laboratory experiments on a high-speed wind-wave flume of the Institute of Applied Physics (Nizhny Novgorod, Russian Federation), which are devoted to the investigation of the X-band co-polarized and de-polarized radar return in a wide range of high wind speeds (from 8 to 40 m s^–1). Microwave measurements were accompanied by the measurements of airflow and wave field parameters. Experiments showed that alternatively to the co-polarized return, the dependency of the de-polarized return on the wind speed does not saturate, although the growth rate decreases at wind speed exceeding 30 m s^–1. Comparison of the experimental data with the composite-surface Bragg scattering model for the measured parameters of the wind and waves showed that the model is in agreement with measurements of microwave co-polarized return, but fails to describe the de-polarized radar return. The obtained dependency of de-polarized radar return was compared with the empirical geophysical model function based on collocated airborne and satellite data.

Verification of the C-band geophysical model functions (GMF) for the open ocean and the Gorky reservoir was carried out using Sentinel-1 IW-mode Synthetic aperture radar (SAR) images. CMOD5.N, CMOD7, GMF for the Caspian Sea, and CSARMOD2 were considered. The motivation for this study is concerned with the clarification of applying C-band GMFs for SAR images including for the conditions of inland water bodies, as well as with the study of the influence of various wind speed direction sources on the results of wind speed magnitude retrieval for ocean conditions. Comparison of wind speed from the CMOD5.N algorithm using wind direction data from NOAA NDBC oceanographic buoys together with the data provided by NCEP reanalysis data showed that regardless of the geographic location, the result does not depend significantly on the choice of the wind direction source. Novel results of CMOD5.N, CMOD7, GMF for the Caspian Sea, and CSARMOD2 applications to the conditions of the Gorky reservoir are presented. The comparison of these results with the meteorological station measurements showed the best agreement for CMOD5.N. The preliminary results on the construction of new C-band GMF adjusted to the Gorky Reservoir have shown statistical parameters better than for Caspian Sea GMF and CSARMOD2.

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 cross-section (NRCS) on the wind speed and wind friction velocity were obtained and used for constructing the GMFs.

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.