Cyclonic wind speed retrieval based on Bayesian regularized neural network using CYGNSS data

Abstract

The destruction created by cyclones depends upon their intensity. Ocean wind is the crucial parameter to understand and forecast the intensity of cyclones. During cyclones, due to dynamic ocean conditions and the limited data availability, a neural network with the regularization approach is used to retrieve cyclonic wind (CW) speed. Several optimization algorithms are compared to get a robust neural network. The Bayesian regularization with the Levenberg–Marquardt optimized network (BNN) is found suitable to develop the geophysical model to retrieve CW speed using Cyclone Global Navigation Satellite System (CYGNSS) measurements. To select the suitable observables for BNN, sensitivity analysis of CYGNSS observables is carried out. The root-mean-square difference between retrieved CW speed and the airborne radiometer data is found to be 4.35 ms − 1, which is smaller than the value quoted in the literature for CYGNSS CW speed. Further, a rigorous analysis is also done to find out the effect of the rain on the retrieved wind speed. Independent validation of our approach is carried out using Soil Moisture Active Passive (SMAP) radiometer high-wind data with a special case of category-5 cyclone Lorenzo. The results support that the proposed algorithm agrees well with the SMAP CW data.