Google DeepMind isn’t the only big tech firm that is applying AI to weather forecasting. Nvidia released FourCastNet in 2022. And in 2023 Huawei developed its Pangu-Weather model, which trained on 39 years of data. It produces deterministic forecasts—those providing a single number rather than a range, like a prediction that tomorrow will have a temperature of 30 °F or 0.7 inches of rainfall.
GenCast differs from Pangu-Weather in that it produces probabilistic forecasts—likelihoods for various weather outcomes rather than precise predictions. For example, the forecast might be “There is a 40% chance of the temperature hitting a low of 30 °F” or “There is a 60% chance of 0.7 inches of rainfall tomorrow.” This type of analysis helps officials understand the likelihood of different weather events and plan accordingly.
These results don’t mean the end of conventional meteorology as a field. The model is trained on past weather conditions, and applying them to the far future may lead to inaccurate predictions for a changing and increasingly erratic climate.
GenCast is still reliant on a data set like ERA5, which is an hourly estimate of various atmospheric variables going back to 1940, says Aaron Hill, an assistant professor at the School of Meteorology at the University of Oklahoma, who was not involved in this research. “The backbone of ERA5 is a physics-based model,” he says.
In addition, there are many variables in our atmosphere that we don’t directly observe, so meteorologists use physics equations to figure out estimates. These estimates are combined with accessible observational data to feed into a model like GenCast, and new data will always be required. “A model that was trained up to 2018 will do worse in 2024 than a model trained up to 2023 will do in 2024,” says Ilan Price, researcher at DeepMind and one of the creators of GenCast.
In the future, DeepMind plans to test models directly using data such as wind or humidity readings to see how feasible it is to make predictions on observation data alone.
