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arXiv Machine Learning By Nathan Chalumeau, Xavier Renard, Remi Meynadier, Hugo Rakotoarimanga, Marcin Detyniecki, Emmanuel de Bezenac

Can AI Forecast the Extremes? Evaluating Probabilistic Weather Models on Tropical Cyclones

Read the original on arXiv Machine Learning →

The study evaluates probabilistic AI weather models—GenCast, FourCastNet 3, and FGN—against the ECMWF IFS-ENS ensemble for tropical cyclone forecasting. Using new probabilistic metrics, the authors find that AI models, particularly FGN, outperform IFS-ENS in cyclone tracking, achieving better location scores for 90% of storms in 2025. The analysis also identifies intensity and spread limitations, model‑specific failure modes, and the influence of pretraining data, highlighting opportunities for targeted improvement.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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