Cyclonic Wind Speed Retrieval From SWIM Wave Spectrum Based on Machine Learning

• Published in: IEEE geoscience and remote sensing letters 2024, Vol.21, p.1-5
• Main Authors: Shao, Weizeng, Wei, Meng, Xu, Ying, Jiang, Xingwei
• Format: Article
• Language: English
• Subjects: Cyclonic winds; Geoscience and remote sensing; Machine learning; Microwave radiometry; Microwave theory and techniques; Oceanography; Sea measurements; tropical cyclone (TC); Wave spectra; wave spectrum; Wind; Wind speed; Winds
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2024.3403136

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 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 to 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 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 COR < 0.7, and an SI >0.25. We conclude that cyclonic winds and waves can be synchronously measured by SWIM without any prior information.