A fast physics-based perturbation generator of machine learning weather model for efficient ensemble forecasts of tropical cyclone track

Traditional ensemble forecasting based on numerical weather prediction (NWP) models, is constrained by the need for massive computational resources, resulting in limited ensemble sizes. Although emerging artificial intelligence (AI)-based weather models offer high forecast accuracy and improved computational efficiency, they still face considerable challenges in ensemble forecasting applications, due to the unclear error growth dynamic and the lack of suitable ensemble methods in AI-based models. In this study, we propose a fast, physics-constrained perturbation scheme through the self-evolution dynamics of an AI-based weather model for ensemble forecasting of tropical cyclones (TCs). These initial perturbations are conditioned on specific amplitude and spatial characteristics, exhibiting physically reasonable dynamical growth and spatial covariance. Based on this perturbation scheme, the TC track ensemble forecasts within the AI-based model significantly outperform those from the European Centre for Medium-Range Weather Forecasts (ECMWF) for both deterministic and probabilistic metrics. Notably, we conduct TC track forecasts with 2000 members for the first time, achieving further enhanced forecast skills in probability distribution and extreme scenarios of TC movement.