Evaluating Shanghai Typhoon Model against State-of-the-Art Machine-Learning Weather Prediction Model: A Case Study for Typhoon Danas (2025)

This study traces the development of the Shanghai Typhoon Model (SHTM) from a traditional physics-based regional model toward a data-driven, machine-learning typhoon forecasting system.After upgrading its initial and boundary conditions, SHTM now leverages large-scale constraints from machine-learning weather prediction (MLWP) models, resulting in an ML-physics hybrid framework. During Typhoon Danas (2025), the hybrid SHTM achieves substantially lower track errors than both the advanced ECMWF Integrated Forecasting System (IFS) and leading MLWP models such as PanGu and FuXi. Furthermore, the hybrid SHTM consistently maintains mean track errors below 200 km up to 108 hour forecast lead time, representing a significant advancement in forecast accuracy. In addition, this study highlights the technical roadmap for transitioning from a physics-based typhoon model to a fully data-driven ML typhoon forecast system. It also emphasizes that advances in the physical modeling framework provide a critical foundation for further improving the performance of future data-driven ML typhoon models.