Machine-Learning-Based Algorithm for Significant Wave Height Retrieval From Microwave Radiometer During Tropical Cyclones - Hu, Yuyi; Shao, Weizeng; Xu, Ying; Jiang, Xingwei (IEEE geoscience and remote sensing letters, 2025)
This study develops a machine-learning-based approach to retrieve significant wave height (SWH) from soil moisture active passive (SMAP) radiometer data under tropical cyclone (TC) conditions, accounting for limited fetch effects. Using over 1000 SMAP measurements within 400 km of the eye from 400 TCs in 2018-2024, we used the symmetric hurricane estimates for wind (SHEW) model to determine TC eye locations and maximum wind radii radius of maximum wind (RMW). The three machine learning methods [i.e., eXtreme Gradient Boosting (XGBoost), random forest (RF), and convolutional neural networks (CNNs)] were trained to establish the relationship between SMAP wind speeds and WAVEWATCH-III (WW3)-hindcasted SWH, incorporating key parameters including wind speed, distance to TC eye, RMW, and TC translation speed/direction. Validation against both WW3 hindcasts and Haiyang-2 (HY-2) altimeter measurements in 2023-2024 demonstrated the superior performance of the XGBoost model compared with empirical wave-growth, RF, and CNNs models, i.e., lower root mean square error (RMSE) of 0.50 m (WW3) and 0.71 m (HY-2), along with higher correlation (Cor) of 0.96 (WW3) and 0.92 (HY-2).