Google DeepMind launches WeatherNext 3 global weather AI model
The updated model moves from traditional physics simulations to training directly on real-time satellite data for hourly local forecasts.
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- Google DeepMind introduced WeatherNext 3, its most accurate global weather forecasting AI model to date, on September 3, 2026.
- The model moves away from numerical weather prediction to ingest real-time global geostationary satellite observations with hourly refreshes.
- WeatherNext 3 delivers 5-kilometer resolution surface forecasts and shows up to a 60% improvement in precipitation prediction accuracy.
Google DeepMind has introduced WeatherNext 3, its most advanced and accurate global weather artificial intelligence model, on September 3, 2026. According to the company's official announcement, the model represents a fundamental paradigm shift in meteorology by moving away from traditional physics-based numerical simulations toward a model that learns directly from global atmospheric observations.
Unlike older models that relied on numerical weather prediction simulations with a standard six-hour data processing lag, WeatherNext 3 directly ingests real-time, global geostationary satellite data. This enables the model to deliver dense, high-fidelity forecasting fields that are refreshed hourly. The model achieves a 5-kilometer localized resolution for key surface variables like temperature and moisture, making its predictions roughly five times sharper than previous deep-learning weather models.
By training on high-resolution precipitation reanalysis datasets, such as NASA's IMERG, WeatherNext 3 achieves up to a 60% improvement in precipitation forecasting accuracy. The architecture utilizes a single, flexible Functional Generative Network mesh transformer to generate station-level predictions. Google has already begun integrating the model to power weather forecasting in Google Search, Gemini, Google Maps, and Google Earth Engine.
WeatherNext 3 demonstrates how deep learning can bypass the massive supercomputing costs associated with traditional physics-based meteorology. By enabling high-fidelity, hourly localized forecasting on commodity cloud hardware, Google is democratizing advanced weather prediction for underserved regions like Africa and Latin America. Additionally, the model's high-resolution wind and cloud predictions provide clean energy grid operators with critical data to optimize renewable energy distribution.
Will national meteorological agencies formally replace their traditional physics-based supercomputing pipelines with deep-learning models like WeatherNext 3?
Still open. When the paper finds out, it will say so here and on the open questions page — including if it got this wrong.