ECMWF makes a machine-learned forecast model operational
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The European Centre for Medium-Range Weather Forecasts brought its Artificial Intelligence Forecasting System into operational service, reporting gains of up to 20% over the physics-based system on several measures including tropical cyclone track error. An intergovernmental agency whose forecasts are used for aviation, shipping and emergency planning thereby accepted operational accountability for learned-model output. It did not switch the physics-based system off: the numerical model still produces the reanalysis the learned model is trained on, so the two are complements and the learned model cannot outrun its own training source.
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- Story
- The European Centre for Medium-Range Weather Forecasts brought its Artificial Intelligence Forecasting System into operational service, reporting gains of up to 20% over the physics-based system on several measures including tropical cyclone track error. An intergovernmental agency whose forecasts are used for aviation, shipping and emergency planning thereby accepted operational accountability for learned-model output. It did not switch the physics-based system off: the numerical model still produces the reanalysis the learned model is trained on, so the two are complements and the learned model cannot outrun its own training source.
- Knowledge
- Machine-learned weather forecasting
- European Centre for Medium-Range Weather Forecasts
- ECMWF makes a machine-learned forecast model operational
- Connections
- European Centre for Medium-Range Weather Forecasts
- Machine-learned weather forecasting
- Machine-learned weather forecasting
- ECMWF makes a machine-learned forecast model operational
- Deep learning
- European Centre for Medium-Range Weather Forecasts
- GenCast is published with a like-for-like comparison against an operational ensemble
- ECMWF makes a machine-learned forecast model operational
- Evidence
- ECMWF's Artificial Intelligence Forecasting System became operational on 25 February 2025, outperforming the physics-based system on many measures including tropical cyclone tracks with gains of up to 20%; GenCast, published in Nature in December 2024, is reported to have beaten the ECMWF ensemble on 97.2% of 1,320 evaluation targets and to produce a fifteen-day global forecast in about eight minutes on a single accelerator. V55 verification basis: neither ecmwf.int nor nature.com was fetched. The operational date and the up-to-20% figure were carried in search results attributing them to ECMWF's own news item; the 97.2%, 1,320-target and eight-minute figures were carried with attribution to the Nature paper and to reporting of it.
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