Operational tropical cyclone forecasting with AI

Résumé fourni par la source

Abstract Tropical cyclones are among the most dangerous and costly weather phenomena, but forecasting them remains challenging. Here we introduce WeatherNext Cyclones (WN-C), an artificial intelligence (AI)-based operational weather model that produces state-of-the-art ensemble forecasts of the track, intensity and size of tropical cyclones worldwide. Trained on a combination of global analysis data 1 and a global database of historical tropical cyclones 2,3 , WN-C generates large ensembles of possible global weather and cyclone scenarios extending 15 days into the future. 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 development. 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 intensity signal than has been previously recognized. Including predictions from WN-C in a weighted-average consensus ensemble improves its skill substantially. 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. By providing advanced operational ensemble guidance to human forecasters, this work represents a step change towards more reliable and timely forecasts and warnings that can help to protect lives and to mitigate the devastating effects of tropical cyclones.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.