TCIE-Net: Physics-Guided Neural Networks for Tropical Cyclone Intensity Estimation

Abstract

Accurate tropical cyclone (TC) intensity estimation is crucial for preventing and mitigating TC-related disasters. Despite recent advances in TC intensity estimation using convolutional neural networks (CNNs), existing techniques fail to adequately incorporate the priori knowledge of TCs. Therefore, information strongly correlated with TC intensity can be obscured by irrelevant data, limiting model performance. To address this challenge, we introduce the Convective-Stratiform Separation Technique, which acts as a physical constraint on the model, to extract pivotal features from the convective core in satellite infrared imagery. Concurrently, we propose a new dual-branch TC intensity estimation model, comprising a "Satellite Imagery Analysis Branch" to extract overall features from satellite imagery and a "Physics-Guided Branch" to analyze the identified convective cores. We further improve the estimation accuracy by incorporating key physical and environmental factors that are often overlooked by the model. We train the model on 1285 TC cases globally during 2003-2016 and evaluate the performance of best-optimized model using an independent test dataset of 95 TC cases globally from 2017. The results show that the root mean square error (RMSE) of TC intensity estimation is 8.13 kt, demonstrating superior performance compared to existing deep learning models.