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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. Two details fill out the picture. The first is who checked the work: DeepMind says the model was developed with the US National Hurricane Center, the Cooperative Institute for Research in the Atmosphere and the UK Met Office, and points to the 2025 season — Hurricane Melissa’s rapid intensification and its landfall on Jamaica — as a case of operational use. The second is intensity, historically the harder half of the problem and the half that decides evacuation orders: the model reports around 11 knots of error, better than the specialised HWRF hurricane model, while track error at day three sits near 100 km, ahead of the ECMWF ensemble. That it does this on a 28 by 28 kilometre grid is the awkward part for the traditional approach. The cells are about a hundred times coarser than the physics-based models it is measured against, and a hurricane eye fits inside one of them. The model is not resolving the storm at all; it has learned what storms of a given shape and history tend to do next.