#cyclone

www.unite.ai

Google's WeatherNext 2 Gains a Full Day of Cyclone Warning, Goes Open Source

Google has published a peer-reviewed result for its WeatherNext AI weather model, claiming state-of-the-art accuracy in predicting tropical cyclones and a lead-time gain the company describes as roughly a decade of meteorological progress compressed into a single system. The paper appeared in Nature on August 6, 2026, and Google is open-sourcing the models alongside it. The headline number is lead time. On cyclones from 2023 through 2025, WeatherNext’s track, intensity, and wind-structure predictions carry an average of a day or more of advantage over leading operational models — meaning its three-day forecast matches what prior systems delivered at two days. In cyclone forecasting, where evacuation and staging decisions hinge on hours, that margin is the metric that matters. The work was a collaboration between Google DeepMind and Google Research researchers and operational forecasters at the National Hurricane Center, the Cooperative Institute for Research in the Atmosphere, and the UK Met Office, several of whom are co-authors on the paper. What the Nature paper reports The paper introduces WeatherNext Cyclones, an AI model that produces ensemble forecasts of a tropical cyclone’s track, intensity, and size up to 15 days out. Evaluated against leading operational systems on storms from 2023–2025, it gained...

www.newscientist.com

DeepMind AI gives an extra day of warning ahead of deadly cyclones

DeepMind has developed an AI model that can predict deadly cyclones one day further out than existing methods, potentially saving lives by allowing more accurate and timely decisions about evacuations. Many existing weather forecasts are based on physics simulations run on powerful supercomputers that model and extrapolate weather patterns as accurately as possible. AI models may represent a faster and less costly method. WeatherNext is able to generate a 15-day forecast in less than a minute on one of Google’s custom Tensor Processing Unit chips. Meanwhile, physics-based models can consume days of high-powered computer time. The new model works on a simulation of the atmosphere where the smallest cell is 28 square kilometres, making it a hundred times less granular than traditional models. It was trained on nearly 20 terabytes of global atmospheric data, as well as data on 5000 historical storms, which Ferran Alet at DeepMind says was crucial to improving WeatherNext’s accuracy at predicting the behaviour of new cyclones. The DeepMind team compared its WeatherNext AI model with Google’s GenCast model, the European Centre for Medium-Range Weather Forecasts’s ENS model and the National Oceanic and Atmospheric Administration’s Hurricane Analysis and Forecast System. It found that WeatherNext was able...

www.nature.com

Operational Tropical Cyclone Forecasting with AI

Tropical cyclones are among the most dangerous and costly weather phenomena, yet forecasting them remains a profound scientific challenge. Here, we introduce WeatherNext Cyclones (WN-C), an AI operational weather model producing state-of-the-art ensemble forecasts for track, intensity, and size of tropical cyclones worldwide. Trained on a combination of global analysis data1 and a global database of historical tropical cyclones2,3, WN-C generates large ensembles of possible global weather and cyclone scenarios extending 15 days into the future. Evaluated on tropical cyclones from 2023–2025, the track, intensity and wind radii predictions from WN-C offer an average of a day or more of lead time advantage over leading operational models, an improvement in accuracy comparable to the progress seen over the last decade of operational development. We achieved these results using inputs 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 this coarser atmospheric data contains more intensity signal than previously recognised. Including predictions from WN-C in a weighted-average consensus ensemble substantially improves its skill. The scalability of WN-C enables up to 1,000-member ensembles which better capture rare events over conventional 50-member ensembles. By providing state-of-the-art operational ensemble guidance to...