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 "id": "shermansiu/dm_graphcast_datasets",
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 "name": "dm_graphcast_datasets",
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 "last_modified": "2023-12-29T02:01:03.000Z",
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 "citation": "@article{\ndoi:10.1126/science.adi2336,\nauthor = {Remi Lam  and Alvaro Sanchez-Gonzalez  and Matthew Willson  and Peter Wirnsberger  and Meire Fortunato  and Ferran Alet  and Suman Ravuri  and Timo Ewalds  and Zach Eaton-Rosen  and Weihua Hu  and Alexander Merose  and Stephan Hoyer  and George Holland  and Oriol Vinyals  and Jacklynn Stott  and Alexander Pritzel  and Shakir Mohamed  and Peter Battaglia },\ntitle = {Learning skillful medium-range global weather forecasting},\njournal = {Science},\nvolume = {382},\nnumber = {6677},\npages = {1416-1421},\nyear = {2023},\ndoi = {10.1126/science.adi2336},\nURL = {https://www.science.org/doi/abs/10.1126/science.adi2336},\neprint = {https://www.science.org/doi/pdf/10.1126/science.adi2336},\nabstract = {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 does not directly use historical weather data to improve the underlying model. Here, we introduce GraphCast, a machine learning–based method trained directly from reanalysis data. It predicts hundreds of weather variables for the next 10 days at 0.25° resolution globally in under 1 minute. 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 cyclone tracking, atmospheric rivers, and ",
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