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DeepMind opens its cyclone forecaster: three-day tracks as good as yesterday's two-day ones

Google DeepMind has published WeatherNext Cyclones in Nature and released the model weights on GitHub, alongside the broader WeatherNext 2 system. The headline claim is a full day of lead time: three-day forecasts of a storm's track and intensity now reach the accuracy that previous models achieved only at two days. The model was trained on roughly 20 terabytes of atmospheric data and about 5,000 historical storms, and it forecasts by ensemble — DeepMind scaled the run from 50 scenarios to 1,000, which is what allows a forecaster to read the spread as a probability rather than a single line on a map. A 15-day forecast at 28 by 28 kilometre resolution runs in under a minute on a single TPU. For a catalogue of AI models this is a useful reference point on what open weights now cover. Weather prediction has been a showcase for machine learning for several years, but the operational systems behind national forecasts have stayed closed. Releasing the weights moves a model of this class into the hands of meteorological services that cannot afford to train one.