DPCD-Net: Data-Physics Co-Driven Network for Tropical Cyclone Trajectory and Intensity Prediction

Abstract

Tropical cyclones (TCs) are among the most destructive weather systems in the geophysical environment. Accurate and efficient prediction of tropical cyclone trajectories and intensities is critical for disaster mitigation, including the prevention of property damage and reduction of casualties. While deep learning has advanced TC prediction, existing models still struggle to represent the coupled dynamics among environmental fields and the internal variability of key meteorological factors. In this study, we propose a Data-Physics Co-Driven Network (DPCD-Net) based on a generative adversarial framework for TC trajectory and intensity prediction. To model interactions between environmental fields, we design a Generative Adversarial Sequence Network integrating wind and geopotential height fields. For intra-field characteristics, a Wind Field Feature Cross-Fusion Module is introduced to capture dynamic wind-field correlations, and a Geopotential Height Spatiotemporal Feature Extraction Module is developed for evolutionary pattern analysis. Furthermore, a physics-informed loss grounded in fluid dynamics principles is incorporated to better characterize wind-field spatiotemporal variations. Experiments on the China Meteorological Administration Best Track Dataset (CMA-BST) demonstrate that the proposed method achieves superior trajectory and intensity prediction performance, particularly at 12 h and 24 h lead times. These results highlight the potential of integrating environmental-field fusion and physics-aware learning to advance next-generation TC prediction.