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

TL;DR

This paper introduces an innovative AI-driven ensemble forecasting system for tropical cyclones that combines dynamic consistency with computational efficiency using orthogonal perturbations.

Contribution

It develops O-CNOPs to generate dynamically optimized perturbations, improving tropical cyclone forecast accuracy and interpretability over traditional methods.

Findings

Outperforms existing ensemble systems in deterministic forecasts.

Provides more physically plausible probabilistic forecasts.

Enhances operational tropical cyclone prediction capabilities.

Abstract

This study addresses a critical challenge in AI-based weather forecasting by developing an AI-driven optimized ensemble forecast system using Orthogonal Conditional Nonlinear Optimal Perturbations (O-CNOPs). The system bridges the gap between computational efficiency and dynamic consistency in tropical cyclone (TC) forecasting. Unlike conventional ensembles limited by computational costs or AI ensembles constrained by inadequate perturbation methods, O-CNOPs generate dynamically optimized perturbations that capture fast-growing errors of FuXi model while maintaining plausibility. The key innovation lies in producing orthogonal perturbations that respect FuXi nonlinear dynamics, yielding structures reflecting dominant dynamical controls and physically interpretable probabilistic forecasts. Demonstrating superior deterministic and probabilistic skills over the operational Integrated Forecasting System Ensemble Prediction System, this work establishes a new paradigm combining AI computational advantages with rigorous dynamical constraints. Success in TC track forecasting paves the way for reliable ensemble forecasts of other high-impact weather systems, marking a major step toward operational AI-based ensemble forecasting.