{"data":{"event":{"id":"82594965-b45e-4a2d-a994-75ae6e84a536","slug":"graphcast-medium-range-weather","title":"GraphCast outperforms ECMWF HRES on most medium-range weather targets","short_summary":"DeepMind’s GraphCast, published in Science on 14 November 2023, produced 10-day 0.25° global forecasts in under a minute and beat HRES on about 90 percent of evaluated targets.","full_description":"GraphCast is a graph neural network trained on ERA5 reanalysis that predicts hundreds of atmospheric variables at six-hour steps. The Science paper reported better deterministic skill than ECMWF’s high-resolution forecast on 90 percent of 1,380 verification targets and improved cyclone tracking and extreme-temperature metrics in the authors’ tests. Code and weights were released. The result is a research and later operational-influencing model, not a replacement of national weather services on its own.","ledger_type":"benefit","primary_domain_id":"99fd8a40-a540-41d3-af98-57c1709ee422","event_status":"verified","event_date":"2023-11-14T00:00:00.000Z","discovery_date":"2023-11-14T00:00:00.000Z","first_published_date":"2023-11-14T00:00:00.000Z","last_reviewed_date":"2023-11-14T00:00:00.000Z","geographic_scope":"Global atmosphere","affected_population":"Weather-forecasting agencies and researchers comparing MLWP to NWP","base_impact_tier":3,"base_score":"10.00","attribution_multiplier":"0.9000","evidence_multiplier":"0.7000","realization_multiplier":"0.7000","durability_multiplier":"0.6000","current_event_score":"2.646000","confidence_level":"high","score_explanation":"Major operational-skill result (tier 3). Peer-reviewed comparison to HRES. Realization includes open weights and later agency experiments, not universal operational replacement. Durability medium because successors (GenCast, AIFS, Aurora) arrived quickly.","methodology_version_id":"d7881163-fb23-4e71-8625-bac1a8662c0f","original_methodology_version_id":"d7881163-fb23-4e71-8625-bac1a8662c0f","published_at":"2023-11-14T00:00:00.000Z","created_at":"2026-09-13T20:47:20.075Z","updated_at":"2026-09-13T20:47:20.075Z","flags":[],"domain_name":"Climate & Environment","domain_slug":"climate-environment","methodology_version":"0.1"},"contributions":[{"id":"60f5e8f2-b074-4772-9a11-8d82ba8eaa01","event_id":"82594965-b45e-4a2d-a994-75ae6e84a536","model_id":"98db8c72-e960-4ae0-adfb-c75bdeae3f21","role_description":"Learned medium-range global weather model evaluated against HRES.","attribution_multiplier":"0.9000","credit_share":"1.0000","contribution_score":"2.646000","attribution_rationale":"The published model is the forecast system.","attribution_confidence":"medium","first_used_date":"2023-11-14T00:00:00.000Z","model_version_if_known":null,"review_status":"approved","created_at":"2026-09-13T20:47:20.075Z","model_slug":"graphcast","model_name":"GraphCast","identity_class":"named_release","is_internal":false,"family_name":"GraphCast","family_slug":"google-deepmind-graphcast","organization_name":"Google DeepMind","organization_slug":"google-deepmind"}],"sources":[{"id":"b0216cae-5583-4fc2-aaf3-b03ab9ec335b","event_id":"82594965-b45e-4a2d-a994-75ae6e84a536","url":"https://www.science.org/doi/10.1126/science.adi2336","canonical_url":"https://www.science.org/doi/10.1126/science.adi2336","source_type":"peer_reviewed","publisher":"Science","author":null,"publication_date":"2023-11-14T00:00:00.000Z","retrieved_at":"2026-09-13T20:47:20.075Z","title":"Learning skillful medium-range global weather forecasting","excerpt":null,"content_hash":null,"source_reliability_class":"high","is_primary_source":true,"is_independent":true,"is_peer_reviewed":true,"archived_url":null,"created_at":"2026-09-13T20:47:20.075Z"},{"id":"1d05b416-a1a8-4d40-a4d5-44c9fb6af06d","event_id":"82594965-b45e-4a2d-a994-75ae6e84a536","url":"https://deepmind.google/discover/blog/graphcast-ai-model-for-faster-and-more-accurate-global-weather-forecasting/","canonical_url":"https://deepmind.google/discover/blog/graphcast-ai-model-for-faster-and-more-accurate-global-weather-forecasting/","source_type":"developer_announcement","publisher":"Google DeepMind","author":null,"publication_date":"2023-11-14T00:00:00.000Z","retrieved_at":"2026-09-13T20:47:20.075Z","title":"GraphCast: AI model for faster and more accurate global weather forecasting","excerpt":null,"content_hash":null,"source_reliability_class":"low","is_primary_source":true,"is_independent":false,"is_peer_reviewed":false,"archived_url":null,"created_at":"2026-09-13T20:47:20.075Z"}],"claims":[{"id":"fdca52a5-7ae7-4f44-a833-b1f606a9214f","event_id":"82594965-b45e-4a2d-a994-75ae6e84a536","claim_text":"GraphCast beat HRES on about 90 percent of the paper’s verification targets.","claim_type":"outcome","claim_status":"supported","confidence_score":null,"created_at":"2026-09-13T20:47:20.075Z","updated_at":"2026-09-13T20:47:20.075Z"},{"id":"5b12eb6a-1b8a-4105-a64f-2df055563efa","event_id":"82594965-b45e-4a2d-a994-75ae6e84a536","claim_text":"GraphCast replaced ECMWF operations worldwide in 2023.","claim_type":"outcome","claim_status":"disputed","confidence_score":null,"created_at":"2026-09-13T20:47:20.075Z","updated_at":"2026-09-13T20:47:20.075Z"}],"revisions":[{"id":"215d3afc-9cce-4c29-975c-2d5d16218ab3","event_id":"82594965-b45e-4a2d-a994-75ae6e84a536","model_id":null,"previous_score":"0.000000","new_score":"2.646000","previous_factors":{},"new_factors":{"evidence":0.7,"base_score":10,"durability":0.6,"attribution":0.9,"realization":0.7},"change_reason":"Initial adjudicated seed score under methodology 0.1.","trigger_type":"seed","trigger_source_ids":null,"reviewer_id":null,"review_status":"published","created_at":"2026-09-13T20:47:20.075Z"}],"secondary":[{"name":"Physics","slug":"physics"},{"name":"Engineering","slug":"engineering"}]},"methodology_version":"0.1"}