Learning skillful medium-range global weather forecasting Article Swipe
Rémi Lam
,
Álvaro Sánchez‐González
,
Matthew Willson
,
Peter Wirnsberger
,
Meire Fortunato
,
Ferran Alet
,
Suman Ravuri
,
Timo Ewalds
,
Zach Eaton-Rosen
,
Weihua Hu
,
Alexander Merose
,
Stephan Hoyer
,
George Holland
,
Oriol Vinyals
,
Jacklynn Stott
,
Alexander Pritzel
,
Shakir Mohamed
,
Peter Battaglia
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1126/science.adi2336
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1126/science.adi2336
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 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.
Related Topics
Concepts
Tropical cyclone forecast model
Computer science
Numerical weather prediction
Tropical cyclone
Meteorology
Weather forecasting
Range (aeronautics)
Weather prediction
Model output statistics
Extreme weather
Key (lock)
Global Forecast System
Machine learning
Artificial intelligence
Climate change
Geography
Ecology
Materials science
Computer security
Composite material
Biology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1126/science.adi2336
- OA Status
- hybrid
- Cited By
- 800
- References
- 54
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388654737
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388654737Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1126/science.adi2336Digital Object Identifier
- Title
-
Learning skillful medium-range global weather forecastingWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-11-14Full publication date if available
- Authors
-
Rémi Lam, Álvaro Sánchez‐González, Matthew Willson, Peter Wirnsberger, Meire Fortunato, Ferran Alet, Suman Ravuri, Timo Ewalds, Zach Eaton-Rosen, Weihua Hu, Alexander Merose, Stephan Hoyer, George Holland, Oriol Vinyals, Jacklynn Stott, Alexander Pritzel, Shakir Mohamed, Peter BattagliaList of authors in order
- Landing page
-
https://doi.org/10.1126/science.adi2336Publisher landing page
- Open access
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://doi.org/10.1126/science.adi2336Direct OA link when available
- Concepts
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Tropical cyclone forecast model, Computer science, Numerical weather prediction, Tropical cyclone, Meteorology, Weather forecasting, Range (aeronautics), Weather prediction, Model output statistics, Extreme weather, Key (lock), Global Forecast System, Machine learning, Artificial intelligence, Climate change, Geography, Ecology, Materials science, Computer security, Composite material, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
800Total citation count in OpenAlex
- Citations by year (recent)
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2025: 441, 2024: 331, 2023: 27Per-year citation counts (last 5 years)
- References (count)
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54Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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