Introducing WeatherNext 3️⃣— our most advanced global weather AI model yet from @GoogleDeepmind and @GoogleResearch
With prediction capabilities that are up to 5x sharper than WeatherNext 2, the model generates a forecast with high spatial resolution in order to catch fast-evolving rainstorms, map local temperature shifts, and even help wind farms predict their power output.
So, how does it do that?
While traditional weather models rely on massive, physics-based supercomputer simulations that can carry a 6-hour forecast lag, WeatherNext 3 leverages live geostationary satellite observations as inputs and trains directly on real-world surface and atmospheric observations.
By pulling this raw satellite data, it’s able to update the global forecast every single hour. And because weather develops at lightning speed, these quick, detailed insights can help bring more localized forecasting to billions of people and local businesses, especially in regions that are historically underserved due to the high costs of traditional weather forecasting models.
A learned model now ingests raw satellite observations directly, dropping the six-hour lag of physics-based forecasting to hourly global updates. A concrete case of an observation-trained model displacing a simulation pipeline.
