GenCast predicts weather and the risks of extreme conditions with state-of-the-art accuracy

New AI model advances the prediction of weather uncertainties and risks, delivering faster, more accurate forecasts up to 15 days ahead

Weather impacts all of us â shaping our decisions, our safety, and our way of life. As climate change drives more extreme weather events, accurate and trustworthy forecasts are more essential than ever. Yet, weather cannot be predicted perfectly, and forecasts are especially uncertain beyond a few days.

Because a perfect weather forecast is not possible, scientists and weather agencies use probabilistic ensemble forecasts, where the model predicts a range of likely weather scenarios. Such ensemble forecasts are more useful than relying on a single forecast, as they provide decision makers with a fuller picture of possible weather conditions in the coming days and weeks and how likely each scenario is.

Today, in a paper published in Nature, we present GenCast, our new high resolution (0.25°) AI ensemble model. GenCast provides better forecasts of both day-to-day weather and extreme events than the top operational system, the European Centre for Medium-Range Weather Forecastsâ (ECMWF) ENS, up to 15 days in advance. Weâll be releasing our modelâs code, weights, and forecasts, to support the wider weather forecasting community.

The evolution of AI weather models

GenCast marks a critical advance in AI-based weather prediction that builds on our previous weather model, which was deterministic, and provided a single, best estimate of future weather. By contrast, a GenCast forecast comprises an ensemble of 50 or more predictions, each representing a possible weather trajectory.

GenCast is a diffusion model, the type of generative AI model that underpins the recent, rapid advances in image, video and music generation. However, GenCast differs from these, in that itâs adapted to the spherical geometry of the Earth, and learns to accurately generate the complex probability distribution of future weather scenarios when given the most recent state of the weather as input.

To train GenCast, we provided it with four decades of historical weather data from ECMWFâs ERA5 archive. This data includes variables such as temperature, wind speed, and pressure at various altitudes. The model learned global weather patterns, at 0.25° resolution, directly from this processed weather data.

Setting a new standard for weather forecasting

To rigorously evaluate GenCast's performance, we trained it on historical weather data up to 2018, and tested it on data from 2019. GenCast showed better forecasting skill than ECMWFâs ENS, the top operational ensemble forecasting system that many national and local decisions depend upon every day.

We comprehensively tested both systems, looking at forecasts of different variables at different lead times â 1320 combinations in total. GenCast was more accurate than ENS on 97.2% of these targets, and on 99.8% at lead times greater than 36 hours.