Parametric Representation of Tropical Cyclone Outer Radical Wind Profile Using Microwave Radiometer Data

This is the authors' abstract. We don't add key points for this paper.

The Soil Moisture Active Passive (SMAP) satellite can measure sea surface winds under tropical cyclone (TC) conditions with its L-band microwave radiometer, without being affected by rainfall or signal saturation. Through the statistical analysis of SMAP data, this study aims to develop radial wind profile models for the TC outer area whose distance from TC center is larger than the radius of maximum wind (Rm). A total of 196 TC cases observed by SMAP were collected between 2015 and 2020, and their intensities range from tropical storm to category 5. Based on the wind and radius data, the key model parameters α and β were fitted through the Rankine vortex model and the tangential wind profile (TWP) Gaussian model, respectively. α and β control the rate of change of the tangential wind speed with radius. Subsequently, for the parametric representation of α and β, we extracted some TC wind filed parameters, such as maximum wind speed (Um), Rm, the average wind speed at Rm (Uma), and the average radius of 17 m/s (R17) and examined the relationship between Uma and Um, the relationship between Rm and R17, the relationship between α, Um and Rm, and the relationship between β, Um and Rm. According to the results, the new radial wind profile models were proposed, i.e., SMAP Rankine Model-4 (SRM-4), SMAP Rankine Model-5 (SRM-5), and SMAP Gaussian Model-1 (SGM-1). A significant advantage of these models is that they can simulate average wind distribution through the conversion from Um to Uma. Finally, comparisons were made between the new models and existing SRM-1, SRM-2, and SRM-3, according to the Advanced Microwave Scanning Radiometer 2 (AMSR-2) measurements of 126 TC cases. The results demonstrate that the SRM-4 simulated the radial wind profile best overall, with the lowest root mean-square error (RMSE) of 5.57 m/s, due to replacing the parameter Um with Uma, using Rankine vortex for α parameterization and modeling with adequate data. Moreover, the models outperform in the Atlantic Ocean, with a RMSE of 5.37 m/s. The new models have the potential to make a contribution to the study of ocean surface dynamics and be used for forcing numerical models under TC conditions.