Ai models outperform traditional systems in tracking tropical cyclones
Can AI Forecast the Extremes? Evaluating Probabilistic Weather Models on Tropical Cyclones
Machine Learning
Summary
Predicting weather storms like tropical cyclones is difficult, especially knowing exactly where they will go. The authors compared new AI models with a traditional physics-based weather system to see which predicts storm paths better. They found AI models can track storms more accurately and with better uncertainty estimates than the traditional system, though there are still challenges predicting storm intensity. These results suggest AI can learn important physical patterns in extreme weather conditions, offering new ways to improve forecasts.
What this means in practice
- •For weather forecasting teams: Provide more accurate and uncertainty-aware tracking of tropical cyclones to improve early warning and preparation efforts.
- •For emergency management planners: Enhance situational awareness during cyclone events by integrating improved probabilistic forecasts into disaster response systems.
Authors
Nathan Chalumeau, Xavier Renard, Remi Meynadier, Hugo Rakotoarimanga, Marcin Detyniecki, Emmanuel de Bezenac
Abstract
Weather prediction is undergoing an important shift, with data-driven AI models outperforming leading numerical weather prediction systems on average at a fraction of the computational cost. Yet, whether this success extends to extreme events remains contested. Earlier evaluations have exposed important shortcomings, but have largely focused on deterministic models and metrics, leaving the capabilities of the latest probabilistic models less understood. We conduct an extensive benchmark of tropical cyclone forecasting, comparing GenCast, FourCastNet 3, and FGN with ECMWF's operational physics-based ensemble, IFS-ENS. We develop a suite of probabilistic metrics and diagnostic analyses to assess cyclone tracking, intensity biases, uncertainty, and the wind-pressure relationship that characterizes storm structure. We find that, despite remaining limitations in intensity and ensemble spread, leading AI models substantially outperform IFS-ENS in cyclone tracking: FGN achieves better probabilistic location scores for 90% of evaluated storms in 2025. Further analyses reveal model-specific failure modes and suggest a lingering influence of pretraining data, pointing to opportunities for targeted improvement. Nonetheless, these results suggest that AI models are capable of capturing physical relationships that remain predictive well beyond typical weather conditions, motivating further investigation into how these capabilities emerge and how to strengthen them.