Rapid Weakening Tropical Cyclone Intensity Estimation Based on Deep Learning Using Infrared Satellite Images and Reanalysis Data

Tropical cyclone (TC) is a major devastating natural disaster that leads to property destruction worth billions of dollars and threatens millions of lives. However, rapid changes in TC are the main source of the current TC forecast error. This study proposes a model for estimating the rapid weakening of TC intensity based on deep learning using infrared satellite images and sea surface temperature (DEEP_RW_TCIE). This model is in two parts: a many-to-many TC intensity estimation network, composed of spatiotemporal code and decode; and a network with multi-layer perception as the core for constraining TC intensity estimation sequence, based on sea surface temperature (SST) and intensity change rate (ICR). We investigated the effects of different time series lengths, different ranges of SST, and different feature vector composition methods on the effect of the rapid weakening (RW) of the TC intensity estimation model. Moreover, we verified the rationality and feasibility of the proposed method through the analysis of experimental methods. The results show that the TC intensity at several moments before rapid TC weakening is of great significance for estimating a current rapid TC weakening, SST, and ICR. Our method greatly reduces the estimating error of the rapid TC weakening intensity. The mean of absolute errors (MAE) and the root mean square error (RMSE) are 6.68 kt and 8.68 kt, respectively, which decrease by over 10%, compared to the benchmark convolutional neural network CNN-TC model.