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.

Global medium-range weather forecasting is critical to decision-making across many social and economic domains. Traditional numerical weather prediction uses increased compute resources to improve forecast accuracy, but cannot directly use historical weather data to improve the underlying model. We introduce a machine learning-based method called "GraphCast", which can be trained directly from reanalysis data. It predicts hundreds of weather variables, over 10 days at 0.25 degree resolution globally, in under one minute. We show that GraphCast significantly outperforms the most accurate operational deterministic systems on 90% of 1380 verification targets, and its forecasts support better severe event prediction, including tropical cyclones, atmospheric rivers, and extreme temperatures. GraphCast is a key advance in accurate and efficient weather forecasting, and helps realize the promise of machine learning for modeling complex dynamical systems.

Effective high-dimensional representation learning necessitates properly exploiting the geometry of data [Bronstein et al., 2021]--otherwise, it is a cursed estimation problem. Indeed, early success stories of deep learning relied on imposing strong geometric assumptions, primarily that the data lives on a grid domain; either spatial or temporal. In these two respective settings, convolutional neural networks (CNNs) [LeCun et al., 1998] and recurrent neural networks (RNNs) [Hochreiter and Schmidhuber, 1997] have traditionally dominated. While both CNNs and RNNs are demonstrably powerful models, with many applications of high interest, it can be recognised that most data coming from nature cannot be natively represented on a grid. Recent years are marked with a gradual shift of attention towards models that admit a more generic class of geometric structures [Masci et al., 2015, Veličković et al., 2017, Cohen et al., 2018, Battaglia et al., 2018, de Haan et al., 2020, Satorras et al., 2021].