Can AI Forecast the Extremes? Evaluating Probabilistic Weather Models on Tropical Cyclones
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.
ProofPaper ↗
Key points
- 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.
- 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.
Sources (1)
- [1]Can AI Forecast the Extremes? Evaluating Probabilistic Weather Models on Tropical CyclonesarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 03:35 PM
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.
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.
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