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 minimise 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 degree 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 degree, opening new doors to skillful, fast weather forecasts that
are useful in key applications.

基于机器学习的概率天气预测模型 GenCast 在 1 度分辨率下可以比传统的集合系统更准确地预测天气，并展示了良好的可靠性和物理一致性。