ML-Physical Fusion Models Are Accelerating the Paradigm Shift in Operational Typhoon Forecasting

Abstract: In this study, we develop a hybrid operational typhoon forecasting model that integrates the FuXi machine-learning (ML) model with the physics-based Shanghai Typhoon Model (SHTM) into a dual physics-data-driven framework. By employing spectral nudging, the hybrid model named FuXi-SHTM leverages FuXi's robust large-scale forecasting capabilities alongside SHTM's mesoscale strengths, significantly enhancing track, intensity, and precipitation predictions for super typhoons Yagi (2024) and Krathon (2024). Besides, this study aims to identify the sensitive regions for the hybrid model by using Conditional Nonlinear Optimal Perturbation (CNOP) method. Despite being constrained by FuXi's large-scale forecast fields, the dense assimilation of satellite observations within these sensitive regions can further enhance typhoon forecasts. Besides, this study offers key insights into the emerging paradigms that are set to shape the future development of both machine learning and physics-based modeling approaches.