Cascade Deep Learning With Physics Guidance for Typhoon Intensity Prediction

Abstract

Typhoons pose a significant threat to both human safety and economic stability. As a crucial metric for assessing their destructive potential, typhoon intensity (TI) prediction has become an important research focus, with numerous methods developed. However, effectively combining two‐dimensional typhoon structure domain‐expert knowledge (2D‐TSDK) with three‐dimensional typhoon structure data‐driven knowledge (3D‐TSDK) for accurate TI prediction remains a challenge. In this paper, we propose a novel cascaded deep learning framework that combines domain expertise with data‐driven refinement for typhoon intensity prediction. The two‐stage architecture first generates initial intensity estimates by fusing 2D‐TSDK features with established physical wind‐pressure relationships via a specialised loss function. These estimates are subsequently refined by a vision transformer that extract fine‐grained spatial patterns from 3D wind field reanalysis data. Both stages incorporate temporal attention mechanisms with dual (absolute and relative) position encoding, providing comprehensive modelling of typhoon evolution dynamics. Extensive experiments on CMA–BST and ERA‐interim datasets demonstrate consistent performance improvements over baseline methods, confirming the framework's effectiveness for TI prediction.