Toward the GMF for Wind Speed and Surface Stress Retrieval in Hurricanes Based on the Collocated GPS-Dropsonde and Remote Sensing Data

Abstract

This article describes the first step toward 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 ten major hurricanes. Based on the processing of the measurements in the hurricanes Irma 2017/09/07 and 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 on the wind speed and wind friction velocity were obtained and used for constructing the GMFs.