“GenCast: Diffusion-Based Ensemble Forecasting for Medium-Range Weather”, 2023-12-25 ():
Probabilistic weather forecasting is critical for decision-making in high-impact domains such as flood forecasting, energy system planning or transportation routing, where quantifying the uncertainty of a forecast—including probabilities of extreme events—is essential to guide important cost-benefit trade-offs and mitigation measures. Traditional probabilistic approaches rely on producing ensembles from physics-based models, which sample from a joint distribution over spatio-temporally coherent weather trajectories, but are expensive to run.
An efficient alternative is to use a machine learning (ML) forecast model to generate the ensemble, however state-of-the-art ML forecast models for medium-range weather are largely trained to produce deterministic forecasts which minimize mean-squared-error. Despite improving skills scores, they lack physical consistency, a limitation that grows at longer lead times and impacts their ability to characterize the joint distribution.
We introduce GenCast, a ML-based generative model for ensemble weather forecasting, trained from reanalysis data. It forecasts ensembles of trajectories for 84 weather variables, for up to 15 days at 1° resolution globally, taking around a minute per ensemble member on a single Cloud TPU v4 device.
We show that GenCast is more skillful than ENS, a top operational ensemble forecast, for more than 96% of all 1320 verification targets on CRPS and Ensemble-Mean RMSE, while maintaining good reliability and physically consistent power spectra.
Together our results demonstrate that ML-based probabilistic weather forecasting can now outperform traditional ensemble systems at 1°, opening new doors to skillful, fast weather forecasts that are useful in key applications.