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    AI for science· 2022

    Learning skillful medium-range global weather forecasting

    Remi Lam (first author) · 2022 · published 2023

    Read the original

    DOI 10.1126/science.adi2336arXiv 2212.12794OpenAlex W4388654737

    On the reading lists

    Why it matters

    Demonstrated operationally competitive global forecasting using graph neural networks at dramatically lower computational cost.

    Condensed from the expert lists' notes.

    The record

    First released
    2022 (from arXiv 2212.12794)
    Published
    2023
    Citations
    1,463 citations, as counted by OpenAlex (CC0)
    Named by
    both expert lists
    Also relevant to
    Graph learning
    Score
    0.553 · computed rank 188 · consensus 0.9, must-read 1, citations percentile 0.522 (2020s · science), awards 0, existing lists 0. Method

    Editor's notes

    • Readability check: pass: GraphCast: a clear case of ML beating a physical model. (AI draft (Claude), not yet reviewed, 2026-10-29)

    Data sources

    Catalogue data from OpenAlex (CC0), Crossref (CC0 metadata; no abstracts are shown), arXiv, and Papers With Code (CC BY-SA 4.0). Each count names its source. See the method.