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 training against raw weather station measurements rather than reanalysis grids alone. According to Google AI, independent live evaluations from Brightband rank it as the most accurate global weather model to date.
Is it deployable? Partially. Forecast data is available now through BigQuery, Earth Engine and Cloud Storage after an allowlist request, but WeatherNext 3 weights are not open source and on-demand custom inference still runs WeatherNext 2.
Architecture and inputs
WeatherNext 3 is a Functional Generative Network (FGN) mesh transformer, the same probabilistic family introduced with WeatherNext 2, scaled to multi-resolution output. Inputs are a live geostationary satellite mosaic plus ECMWF HRES analysis. Training draws on ERA5/HRES-fc0, NASA’s IMERG, station observations and satellite mosaics.
Most AI forecasters learn from NWP reanalysis, which smooths away the local variation that coastlines, valleys and mountains actually produce. WeatherNext 3 trains dedicated observational heads directly on raw station measurements, so its 0.05° temperature and dew point outputs are calibrated to what instruments record rather than to a model’s representation of the atmosphere.
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