Retrieving Tropical Cyclone Wind Speed with Random Forest Using RADARSAT and Sentinel-1A/B SAR Images

This study proposes a deep learning (DL) approach for retrieving high wind speeds during tropical cyclones using a random forest algorithm applied to RADARSAT and Sentinel-1A/B Synthetic Aperture radar (SAR) images. The effectiveness of the proposed DL-based model is then demonstrated through a comprehensive validation of the results. Statistical analysis of the results showed that the proposed method performs well, with a low root-mean-square error, mean bias, and high correlation coefficient when compared to SFMR (Stepped-Frequency Microwave Radiometer) winds. The reconstructed wind speeds and inner-core structures were found to be in good agreement with surface wind measurements from SFMR. These findings could have significant implications for improving our understanding and prediction of tropical cyclone dynamics, as well as for operational forecasting and disaster management.