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GraphCast

GNN weather model that beat the gold standard on 90% of metrics.

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About

GraphCast, published by Google DeepMind in Science on November 14, 2023, was the first AI weather model to beat conventional forecasting in a peer-reviewed head-to-head: it outperformed ECMWF's HRES on 90% of 1,380 tested metrics including wind speed, temperature, and air pressure. The graph neural network runs on a multi-mesh icosahedral representation of the globe, producing precise 10-day forecasts in under a minute on a single machine — versus hours on a supercomputer for physics-based systems. It famously predicted Hurricane Lee's Nova Scotia landfall nine days ahead.

Limitations: deterministic rather than probabilistic, so it doesn't quantify forecast uncertainty the way GenCast's ensembles do. DeepMind positioned it as a complement to traditional methods rather than a replacement, and operational use still leans on physics-based systems for initialization data.

Specs

Context window

Pricing

Free quota

API access

Weights

Open weights

Released

November 14, 2023

Benchmarks

Provider-reported unless a source says otherwise. Benchmarks are marketing-adjacent by nature — treat them as directional.

Use cases

weather-forecastingclimaterenewable-energygraph-neural-networks

Trust score — 7/10

/10 trustMetrics-checked

Scored 7/10 (metrics tier, metrics-v1). Evidence: Verified via vendor site/docs/GitHub and reputable coverage, wave 63; Trending score 70/100 from researcher assessment.

Sources