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Keyword: tropical cyclone forecasting ×Clear all

5 claims from 2 papers

1. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   GraphCast: Learning skillful medium-range global weather forecasting

   Lam, Sánchez‐González, Willson et al. · arXiv (Cornell University) · 2022

   The paper introduces GraphCast, a machine-learning weather model trained on reanalysis data, and reports it beats the leading operational deterministic forecasts on most verification targets.

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   1. UncheckedGraphCast, a machine-learning model, produces 10-day global forecasts of hundreds of weather variables at 0.25 degree resolution in under a minute.“It predicts hundreds of weather variables, over 10 days at 0.25 degree resolution globally, in under one minute.”
   2. Unchecked“We show that GraphCast significantly outperforms the most accurate operational deterministic systems on 90% of 1380 verification targets, and its forecasts support better severe event prediction, including tropical cyclones, atmospheric rivers, and extreme t…

2. Earth and Planetary Sciences › Tropical and Extratropical Cyclones Research

   Operational tropical cyclone forecasting with AI

   Alet, Andersson, Price et al. · Nature · 2026

   The authors present WeatherNext Cyclones, an AI ensemble model forecasting tropical cyclone track, intensity and size up to 15 days ahead, and report it outperforms leading operational models on 2023–2025 storms.

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   1. UncheckedOn 2023–2025 cyclones, the AI model WN-C's forecasts gave on average a lead-time gain of a day or more over leading operational models.“When evaluated on tropical cyclones from 2023 to 2025, the track, intensity and wind-radius predictions from WN-C offer an average lead-time advantage of 1 day or more over leading operational models—an improvement in accuracy comparable to the progress seen in the last decade of operational develo…”
   2. Unchecked“We achieved these results using inputs that are orders of magnitude coarser than regional models, suggesting that high resolution is not a strict prerequisite for state-of-the-art intensity forecasting and that these coarse atmospheric data contain more inte…
   3. UncheckedThe WN-C AI model can run ensembles of up to 1,000 members, which the paper says capture rare events better than conventional 50-member ensembles.“The scalability of WN-C enables ensembles of up to 1,000 members, which are better at capturing rare events than conventional 50-member ensembles.”