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The authors report that FengWu had the lowest errors, followed by FuXi and GraphCast, with ECMWF's operational IFS in the middle and Pangu-Weather last. For the 11 typhoons of that period, FengWu forecast tracks best but had the largest intensity errors. For Typhoon Haikui, the IFS had the largest average track error, above 1000 km at 96 hours, against 41 km for FengWu. The journal npj Climate and Atmospheric Science published the study on 28 September 2024. The IFS started from its real-time analysis, which raised its early errors, so the authors weigh error trends. 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FengWu, FuXi and GraphCast had lower errors than ECMWF's IFS, and FengWu tracked Typhoon Haikui best.","account":"Scientists at Taiwan's Central Weather Administration, with National Taiwan University and the University of Colorado Colorado Springs, ran five AI weather models themselves. They started FengWu, FuXi, GraphCast, FourCastNet v2 and Pangu-Weather from the same ERA5 reanalysis. They checked forecasts over East Asia and the western North Pacific from June to November 2023. The authors report that FengWu had the lowest errors, followed by FuXi and GraphCast, with ECMWF's operational IFS in the middle and Pangu-Weather last. For the 11 typhoons of that period, FengWu forecast tracks best but had the largest intensity errors. For Typhoon Haikui, the IFS had the largest average track error, above 1000 km at 96 hours, against 41 km for FengWu. The journal npj Climate and Atmospheric Science published the study on 28 September 2024. The IFS started from its real-time analysis, which raised its early errors, so the authors weigh error trends. All forecasts were scored against ERA5 after the season."}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["summary","account"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"systems[].autonomy_permissions":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"systems[].component_role":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"systems[].deployment_configuration":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"systems[].inputs_outputs":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"systems[].model_name":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"systems[].model_version":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"systems[].product_name":{"state":"value_present","value":{"kind":"record_fields","fields":{"system":"FengWu, FuXi, GraphCast, FourCastNet v2 (FCN2) and Pangu-Weather"}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["system"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"systems[].provider":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"taxonomy_crosswalks[]":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"title":{"state":"value_present","value":{"kind":"record_fields","fields":{"title":"Taiwan's Central Weather Administration finds that FengWu, FuXi and GraphCast forecast East Asian weather in 2023 with lower errors than ECMWF's IFS","headline":"*FengWu*, *FuXi* and *GraphCast* beat ECMWF's model in Taiwan's test"}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["title","headline"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"version":{"state":"value_present","value":{"kind":"identifiers","identifiers":["38ab0538cc08addaabf1277e7e26199a6199b2a121a9a099bc5367d0ea067214"],"role":"SHA-256 of the complete public record and its assessments"},"provenance":{"kind":"curatorial","basis":"SHA-256 of the public record, its assessments, and its displayed answers and notes, excluding its own identifier and the derived coverage summary.","record_fields":[],"source_refs":[]},"review_state":"record_mapped","coverage":"complete"},"workflow":{"state":"value_present","value":{"kind":"record_fields","fields":{"account":"Scientists at Taiwan's Central Weather Administration, with National Taiwan University and the University of Colorado Colorado Springs, ran five AI weather models themselves. They started FengWu, FuXi, GraphCast, FourCastNet v2 and Pangu-Weather from the same ERA5 reanalysis. They checked forecasts over East Asia and the western North Pacific from June to November 2023. The authors report that FengWu had the lowest errors, followed by FuXi and GraphCast, with ECMWF's operational IFS in the middle and Pangu-Weather last. For the 11 typhoons of that period, FengWu forecast tracks best but had the largest intensity errors. For Typhoon Haikui, the IFS had the largest average track error, above 1000 km at 96 hours, against 41 km for FengWu. The journal npj Climate and Atmospheric Science published the study on 28 September 2024. The IFS started from its real-time analysis, which raised its early errors, so the authors weigh error trends. All forecasts were scored against ERA5 after the season.","replication":{"produced_by":["The authors ran all five models from identical ERA5 initial conditions with 13 levels and a 6-hour step.","Forecasts ran to 168 hours and were checked against ERA5 and the agency's best tracks."],"must_hold":["Starting conditions must come from a reanalysis or analysis of good quality.","Typhoon intensity needs other guidance, since all five models showed weak intensity biases."],"echoes":[]}}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["account","replication"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"}},"summary":{"total":91,"addressed":91,"known":28,"reviewed":2,"reviewed_complete":2,"reviewed_partial":0,"record_mapped":26,"review_not_assessed":63,"not_assessed":0,"complete":false,"by_state":{"value_present":28,"not_reported":0,"unresolved":63,"not_assessed":0,"not_applicable":0,"withheld":0,"source_unavailable":0}}},"presentation_version":"9ec327b2d854d70c533bcc99d8d6964fcf17a0331a836c8d1c56432535159546"}