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Google DeepMind's WeatherNext 3 addresses critical limitations in AI weather forecasting by ingesting live satellite mosaics and training directly on raw station observations to achieve 5 km resolution. This approach bypasses the latency of traditional numerical weather prediction initialization, offering hourly global updates that are currently more accurate than previous models. However, the model remains partially closed-source with weights unavailable for public inspection, limiting immediate reproducibility despite its superior performance metrics.

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Google DeepMind’s WeatherNext 3 Trains on Weather Station Observations to Deliver 5 km Global Forecasts, Refreshed Every Hour

MarkTechPostSeptember 4, 20263 min read

WeatherNext 3 ingests live geostationary satellite mosaics, refreshes hourly, and outputs 5 km forecasts across Search, Gemini, Maps. The post Google DeepMind’s WeatherNext 3 Trains on Weather Station Observations to Deliver 5 km Global Forecasts, Refreshed Every Hour appeared first on MarkTechPost.

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,…

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