A Synergistic Approach: Dynamics-AI Ensemble in Tropical Cyclone Forecasting

This study addresses a critical challenge in AI-based weather forecasting by developing a physics-informed ensemble system (Orthogonal Conditional Nonlinear Optimal Perturbations, O-CNOPs) that bridges the gap between computational efficiency and physical consistency for tropical cyclone (TC) forecasting. Unlike conventional NWP ensembles constrained by computational costs or current AI ensembles limited by inadequate perturbation methods, O-CNOPs generates dynamically optimized perturbations that both capture fastest-growing errors of AI model and maintain physical plausibility. The key innovation lies in its ability to produce orthogonal perturbations that respect the nonlinear dynamics of AI model, yielding physically interpretable probability forecasting and structure of perturbations reflecting dominant dynamical controls. Demonstrating superior deterministic and probabilistic forecasting skills over operational Integrated Forecasting System ensemble prediction system, this work establishes a new paradigm for ensemble forecasting that combines AI's computational advantages with rigorous dynamical constraints. The success in TC track forecasting paves the way for reliable ensemble forecasting across other high-impact weather systems, marking a significant step toward operational AI-based ensemble forecasting system.