Global Tropical Cyclone Precipitation Estimation via a Multitask Convolutional Neural Network Based on HURSAT-B1 Data

Fast and accurate global tropical cyclone (TC) precipitation estimation from satellite observations is still a challenging issue. In this article, we propose an effective model based on a multitask convolutional neural network (CNN) to estimate near-real-time global TC precipitation from HURSAT-B1 data. Our network mainly consists of three modules: the feature extraction module, the wind grade classification module, and the precipitation estimation module. The first module aims at extracting the spatial features of satellite imageries, the second module focuses on classifying the wind grades of the satellite imageries into six categories that are used to assist in estimating TC precipitation, and the third module is to estimate TC precipitation. To evaluate the effectiveness of our proposed model, we compare it with multiple linear regression (MLR) and random forest (RF) models based on integrated multisatellite retrievals for the global precipitation measurement (GPM) mission (IMERG). Besides, four typical TC events are selected to specifically analyze the temporal and spatial distribution of TC precipitation estimation. Experimental results show that the probability of detection and accuracy achieved by our proposed model are 0.68 and 0.81, while the correlation coefficient (CC) and MSE are 0.61 and 7.80, respectively. In terms of the four TC events, our proposed model obtains a more consistent and continuous spatial distribution of precipitation than MLR and RF. More importantly, our proposed model can achieve high spatiotemporal results, which has the potential to serve as an operational algorithm for global TC precipitation estimation.