Machine-learned weather forecasting

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Global weather prediction by learned models trained on decades of reanalysis data, rather than by numerically integrating the equations of atmospheric motion — and the clearest case of a machine-learning method being taken into operational service by a public scientific institution. DeepMind's GenCast, published in Nature in December 2024, is reported to have beaten the European Centre for Medium-Range Weather Forecasts' ensemble on 97.2% of 1,320 evaluation targets while producing a fifteen-day global forecast in about eight minutes on a single accelerator, against hours on a supercomputer. The institutional step came on 25 February 2025, when ECMWF made its own Artificial Intelligence Forecasting System operational, reporting gains of up to 20% on measures including tropical cyclone track error. This is a genuinely different kind of evidence from a benchmark score: an intergovernmental forecasting agency accepted operational responsibility for the output, and it runs alongside — not instead of — the physics-based system that generates the data it was trained on.

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