GraphCast: Data-Driven Weather Forecasting
GraphCast predicts atmospheric state autoregressively on an icosahedral mesh using a graph neural network, trained on decades of reanalysis data (ERA5).
Reported results: Better than the operational deterministic baseline on the large majority of verified variables and lead times; a 10-day forecast completes in under a minute on a single accelerator, versus hours on a supercomputer.
Industrial relevance: The clearest evidence that learned surrogates can complement - and in some regimes beat - first-principles numerical solvers at a fraction of the compute cost.
Tags: WeatherGraph Neural NetworkForecastingNWPLarge Scale
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