Google introduces GenCast, an AI model for weather forecasting | Infinium-tech

AI is Google’s new favorite hammer and the next nail in its path is weather forecasting. The company is introducing GenCast, a “high resolution AI ensemble model”, detailed below Paper published in Nature,

Accurate weather forecasting is important for anything from your daily life to disaster preparedness and even renewable energy. And Gencast outperforms existing top systems, ECMWFof ENS, in forecasts up to 25 days in advance.

Gencast is a diffusion model, similar to what you may have seen in AI image generators. However, it is specifically tuned to Earth’s geometry. It was trained on four decades of historical data from ECMWF’s archives.

To test this, Google trained Gencast on historical weather data dating back to 2018 and ran 1,320 different forecasts for 2019 and compared its output to ENS and actual weather. Gencast was more accurate than ENS in 97.2% of the cases, up to 99.8% for forecasts 36 hours ahead or longer.

Here is a demo. Google tasks Gencast with predicting its path storm hagibisWhich collided with Japan in 2019. In red you can see the path taken by the storm, in blue are the possible paths predicted by Google’s AI model. Over 7 days, they have spread out significantly, but as the storm approaches landfall, they narrow along the actual path.

Gencast is predicting the path of Typhoon Hagibis

Giving local officials more time to prepare for severe weather is one use case. Gencast can also predict wind speed near wind farms, weather at solar farms, etc.

Gencast is an “ensemble model”, meaning it makes 50+ predictions with different probabilities. Google says that one such forecast with a 15-day forecast can be generated in 8 minutes on Google Cloud TPU v5. Multiple predictions can be made in parallel. Meanwhile, a traditional weather forecast model takes hours on a supercomputer.

Google is releasing Zencast as an open model and sharing its code and weights. The company plans to continue collaborating with weather forecasting agencies and scientists to further improve future forecasts.

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