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Google’s best weather model now skips physics simulations entirely and learns from live satellite…

Yethikrishna ROriginal on Threads

google’s best weather model now skips physics simulations entirely and learns from live satellite data, and weathernext 3 forecasts are 50% more accurate on precipitation. it runs hourly on a 5km grid and already powers search, maps and gemini.

the physics that took meteorology a century to build is now optional.

Context

Verified date: Google's blog post Introducing WeatherNext 3 is dated Sep 3, 2026. It says the model now uses real-time satellite data, refreshes hourly, runs at higher resolution, gives precise precipitation forecasts and is integrated across Search, Gemini, Maps, Google Maps Platform and Cloud. The post says it shows temperature and moisture at 5-kilometer resolution, other surface variables at 10 kilometers and atmospheric variables such as wind at 25 kilometers.

Google's developer research page says precipitation shows up to a 50% reduction in Brier score and CRPS compared to numerical weather prediction baselines, evaluated against IMERG observations.

How it compares

The Sep 3 launch, the hourly refresh, live satellite input and the Search, Maps and Gemini integration match Google's pages. The 50% is a reduction in Brier score and CRPS, stated as up to, against numerical weather prediction baselines, which is not the same as 50% more accurate. The 5 km grid applies to some surface variables, with others at 10 and 25 km. 'Physics is now optional' is the author's opinion, and the paper describes the model taking two analysis frames from physics-based analysis plus satellite frames as inputs.

Related work

Watch next

  • Read the WeatherNext 3 paper for what the model takes as input besides satellites.

Sources

  1. Google: introducing WeatherNext 3blog.google
  2. Google for Developers: WeatherNext research and benchmarksdevelopers.google.com
  3. DeepMind: WeatherNext 3 paperstorage.googleapis.com

Provenance

The note above is reproduced unedited from the original post, first published on Threads on 1 October 2026 at 12:14 IST. Sources are the papers and datasets the note draws on.

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