Wind speed estimation for tropical cyclone from combined active and passive measurements

Sea wind speed (SWS) plays a key role for understanding air-sea interactions and marine physical processes. Both microwave radiometer and scatterometer can provide SWS products. However, under high wind speeds (HWS), especially during tropical cyclones (TCs), these products have larger errors. This paper proposes a method of combining active and passive observations to invert the SWS based on random forest (RF) regression during the global TCs. The estimation results indicated the good accuracy of the RF-AP model with the root mean square error (RMSE) about 2.09m/s and coefficient of determination was 0.89 for validation dataset. In contrast, the estimation results of the RF-active regression model (RMSE-3.63m/s) and the RF-passive regression model (RMSE-2.38m/s) alone are not as good as the RF-AP model. Overall, the proposed method in this study shows the potential for application in SWS inversion, and the results will be helpful for the inversion of SWS under TCs.