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• W4312226181 abstract "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." @default.
• W4312226181 created "2023-01-04" @default.
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• W4312226181 date "2022-12-24" @default.
• W4312226181 modified "2023-10-17" @default.
• W4312226181 title "GraphCast: Learning skillful medium-range global weather forecasting" @default.
• W4312226181 doi "https://doi.org/10.48550/arxiv.2212.12794" @default.
• W4312226181 hasPublicationYear "2022" @default.
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