GenCast is trained on weather data from ECMWF’s ERA5 archive
The AI model can run on the Google Cloud TPU v5
GenCast can generate the complex probability distribution of future weather scenarios
Photo Credit: Google
Google introduced its weather predicting artificial intelligence (AI) model GenCast on Wednesday. The AI model was developed by the Mountain View-based tech giant's AI research division Google DeepMind. The company's researchers have also published a paper on the technology highlighting its capabilities in making medium-range weather forecasts. The company claims that the system was able to outperform existing state-of-the-art forecasting models in terms of resolution and accuracy. Notably, GenCast can make weather predictions for the next 15 days with a resolution of 0.25 degrees Celsius.
Google GenCast Features
In a blog post, Google DeepMind detailed the new high resolution AI ensemble model. Highlighting that GenCast can make predictions for day-to-day weather and extreme events, it claimed that it was able to perform better than the European Centre for Medium-Range Weather Forecasts' (ECMWF) Ensemble (ENS) system. The performance of the model is now published in the Nature journal.
Notably, instead of using the traditional deterministic approach to predict weather, GenCast uses the probabilistic approach. Weather prediction models based on the deterministic approach produce a single, specific forecast for a given set of initial conditions and rely on precise equations of atmospheric physics and chemistry.
On the other hand, models based on probabilistic approach generate multiple possible outcomes by simulating a range of initial conditions and model parameters. This is also called ensemble forecasting.
Google DeepMind highlighted that GenCast is a diffusion model that adapts to the spherical geometry of the Earth, and generates the complex probability distribution of future weather scenarios. To train the AI model, researchers provided four decades of historical weather data from ECMWF's ERA5 archive. With this, the model was taught global weather patterns at 0.25 degree Celsius resolution.
In the published research paper, Google evaluated GenCast's performance by training it on the historical data up to 2018 and then asked it to make predictions for 2019. A total of 1320 combinations across different variables at different lead times were used and the researchers found that GenCast was more accurate than ENS on 97.2 percent of these targets, and on 99.8 percent at lead times greater than 36 hours.
Notably, Google DeepMind announced that it will be releasing GenCast AI model's code, weights, and forecasts to support the weather forecasting community.
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