We introduce a machine-learning (ML)-based weather simulator—called “GraphCast”—which outperforms the most accurate deterministic operational medium-range weather forecasting system in the world, as well as all previous ML baselines. GraphCast is an autoregressive model, based on graph neural networks and a novel high-resolution multi-scale mesh representation, which we trained on historical weather data from the European Centre for Medium-Range Weather Forecasts (ECMWF)’s ERA5 reanalysis archive. It can make 10-day forecasts, at 6-hour time intervals, of five surface variables and six atmospheric variables, each at 37 vertical pressure levels, on a 0.25° latitude-longitude grid, which corresponds to roughly 25 × 25 kilometer resolution at the equator. Our results show GraphCast is more accurate than ECMWF’s deterministic operational forecasting system, HRES, on 90 . 0 % of the 2760 variable and lead time combinations we evaluated. GraphCast also outperforms the most accurate previous ML-based weather forecasting model on 99 . 2 % of the 252 targets it reported. GraphCast can generate a 10-day forecast (35 gigabytes of data) in under 60 seconds on Cloud TPU v4 hardware. Unlike traditional forecasting methods, ML-based forecasting scales well with data: by training on bigger, higher quality, and more recent data, the skill of the forecasts can improve. Together these results represent a key step forward in complementing and improving weather modeling with ML, open new opportuni-ties for fast, accurate forecasting, and help realize the promise of ML-based simulation in the physical sciences.

2022: Rémi R. Lam, Alvaro Sanchez-Gonzalez, M. Willson, Peter Wirnsberger, Meire Fortunato, A. Pritzel, Suman V. Ravuri, Timo Ewalds, Ferran Alet, Z. Eaton-Rosen, Weihua Hu, Alexander Merose, Stephan Hoyer, George Holland, Jacklynn Stott, Oriol Vinyals, S. Mohamed, P. Battaglia

https://arxiv.org/pdf/2212.12794v2.pdf