Cyclonic Wind Speed Retrieval From SWIM Wave Spectrum Based on Machine Learning
Weizeng Shao, Meng Wei, Ying Xu, Xingwei Jiang · IEEE Geoscience and Remote Sensing Letters · 2024
In our study, machine learning is applied for wind speed retrieval in tropical cyclones (TCs) utilizing the wave spectrum measured by Surface Wave Investigation and Monitoring (SWIM) onboard the Chinese–French Oceanography SATellite (CFOSAT). These measured waves with a spatial resolution of 18 km are collocated with wind products of 0.25° spatial resolution derived from a Soil Moisture Active Passive (SMAP) microwave radiometer in the western Pacific Ocean from 2019–2021. Through our abundant dataset, we find that wind speeds up to 45 m/s are linearly correlated with significant wave height (SWH) with a 0.8 correlation (COR) and cross-zero mean wave period (MWP) with a 0.56 COR. Based on this finding, a machine learning method, denoted as Adaptive Boosting (AdaBoost), is applied to relate wind speed with two parameters (i.e., SWH and MWP). The wind speeds retrieved from SWIM-measured wave spectra are compared with the wind products obtained from SMAP radiometers in the China Seas during the TC season of 2021: we obtain a 2.78 m/s root mean square error (RMSE), a 0.85 COR, and a 0.21 scatter index (SI). These results are better than those obtained using parametric formulas among the wind-wave triplets, i.e., an RMSE > 4 m/s of wind speed, a COR0.25. We conclude that cyclonic winds and waves can be synchronously measured by SWIM without any prior information.