Google DeepMind’s WeatherNext 3 Trains on Weather Station Observations to Deliver 5 km Global Forecasts, Refreshed Every Hour
AI weather models have spent three years closing the gap with physics-based forecasting, but two problems stayed open: resolution too coarse for local terrain, and initialization tied to numerical weather prediction (NWP) analysis that arrives about six hours late. WeatherNext 3, released by Google DeepMind and Google Research, attacks both. It takes a live global geostationary satellite mosaic as a direct model input, re-initializes every hour, and emits forecasts down to 0.05° (~5 km) while t
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WeatherNext 3 is an AI weather forecasting model that produces global weather predictions at 5 kilometer resolution and updates hourly using live satellite data, addressing two longstanding limitations in AI weather models: coarse resolution unsuitable for local terrain and forecasts delayed by analysis data that arrives six hours late. The model trains directly on raw weather station measurements rather than smoothed model data, which allows its temperature and humidity outputs to better match what instruments actually record. It shows significant improvements in precipitation forecasting accuracy compared to existing methods and includes specialized outputs for renewable energy forecasting, suggesting it is designed for operational use by grid operators. Forecast data is available through certain platforms after approval, but the model weights themselves have not been released as open source.
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