{"id":"AIBD-0751","title":"The National Hurricane Center finds that Google DeepMind's ensemble gave the most accurate intensity guidance for Hurricane Erin from 24 to 120 hours","summary":"NHC found that Google DeepMind's ensemble mean had the lowest mean intensity errors of the guidance for Hurricane Erin from 24 to 120 hours. NHC's own errors ran above average.","date_observed":"2025-08-11","date_basis":"event","date_precision":"day","benefit_status":"reported","source_collection":null,"source_entry":null,"account":"Hurricane Erin became a tropical storm over the Cabo Verde Islands on 11 August 2025 and reached category 5 on 16 August. Its large wind field brought watches or warnings to the Leeward Islands, the Bahamas, the North Carolina and Virginia coasts and Bermuda. Erin caused 13 direct deaths, seven of them in flooding in Cabo Verde. NHC first published its tropical cyclone report on 16 January 2026 and corrected its intensity verification on 30 January 2026. NHC's official intensity errors were higher than its five-year averages at every lead time, and the rapid intensification of 16 August was not well predicted. In a homogeneous comparison, NHC found that GDMI had the lowest mean intensity errors from 24 to 120 hours. At 48 hours, GDMI erred by 9.7 knots on average, against 13.7 knots for the official forecast. For track, NHC found that the HCCA consensus and GDMI were the best performers. 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NHC gives no account of how forecasters used GDMI for Erin. Because NHC uses the model and helped Google DeepMind develop it, NHC is not fully outside the case."},"evidence":[{"kind":"report","citation":"Pasch, R. J., and A. D. Mahoney. Hurricane Erin (AL052025), 11–22 August 2025. National Hurricane Center Tropical Cyclone Report, 30 January 2026 (original report dated 16 January 2026), p. 6 and Table 6b.","title":"Tropical Cyclone Report: Hurricane Erin (AL052025)","url":"https://www.nhc.noaa.gov/data/tcr/AL052025_Erin.pdf","date":"2026-01-30","written_by":"deployer","quote":"A homogeneous comparison of the official intensity errors with selected guidance models is given in Table 6b. For the 24-h through 120-h forecast intervals, the GDMI had the lowest mean errors."},{"kind":"press release","citation":"National Weather Service. AI in Hurricane Forecasting at the National Hurricane Center: A Q&A with NHC's Science Operations Officer. 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Lowry publishes verification charts for Erin prepared by James Franklin, former branch chief at the National Hurricane Center.","title":"New AI Model Shines During Hurricane Erin","url":"https://michaelrlowry.substack.com/p/new-ai-model-shines-during-hurricane","date":"2025-08-26","written_by":"independent","quote":"Google's DeepMind (GDMI - red line) beat all other models for Erin's intensity through 3 days (72 hours) and was only bested by NOAA's next-generation hurricane model (the Hurricane Analysis and Forecast System or HAFS model) beyond 3 days out."}],"replication":{"produced_by":["NHC included GDMI among the guidance models in its homogeneous comparisons for Erin.","NHC measured each model's errors against Erin's best track and intensity."],"must_hold":["The comparison covers the forecasts for a single long-lived storm.","The official forecast remains NHC's own judgment drawn from many models."],"echoes":[]},"paired_incident":null,"headline":"*GDMI* gave the best intensity forecasts for category 5 Hurricane Erin","metric":"9.7 knots on average, against 13.7 knots for the official forecast","comparator":{"against":"NHC official intensity forecasts (OFCL) for Erin in the homogeneous comparison","before":"OFCL errors of 11.9, 13.2, 13.7, 14.5, 15.5, 15.7 and 14.2 kt at 24, 36, 48, 60, 72, 96 and 120 h","after":"GDMI errors of 10.4, 10.6, 9.7, 12.0, 12.5, 13.9 and 13.6 kt","basis":"measured","quote":"For the 24-h through 120-h forecast intervals, the GDMI had the lowest mean errors.","url":"https://www.nhc.noaa.gov/data/tcr/AL052025_Erin.pdf"},"later":null,"value":null,"developer_country":"United Kingdom","deployer_country":"United States","unrated_because":null,"brief_notes":null,"brief":null,"revisions":[{"date":"2026-10-31","change":"Added the record from the report of the National Hurricane Center.","source":"https://www.nhc.noaa.gov/data/tcr/AL052025_Erin.pdf","editor":"AIBD"},{"date":"2026-10-31","change":"Added an independent news report that supports the benefit.","source":null,"editor":"AIBD"}],"confidence":"reported","shade":"medium","claim_status":"Independently reported","claim_basis":"Sources: one independent news report, the deployer's report and the deployer's press release. 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NOAA/NWS, undated.","title":"AI in Hurricane Forecasting at the National Hurricane Center","url":"https://www.weather.gov/news/261102-AI-Hurricane-Forecasting","written_by":"deployer","quote":"NHC has partnered directly with Google DeepMind to develop a new AI hurricane forecast model that was used experimentally during the 2025 season.","supports_benefit":false},{"kind":"news report","citation":"Michael Lowry, 'New AI Model Shines During Hurricane Erin', Eye on the Tropics (newsletter), 26 August 2025. 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NHC worked with Google DeepMind to develop the model, which it uses as experimental guidance.","replication":{"produced_by":["NHC included GDMI among the guidance models in its homogeneous comparisons for Erin.","NHC measured each model's errors against Erin's best track and intensity."],"must_hold":["The comparison covers the forecasts for a single long-lived storm.","The official forecast remains NHC's own judgment drawn from many models."],"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":29,"reviewed":2,"reviewed_complete":2,"reviewed_partial":0,"record_mapped":27,"review_not_assessed":62,"not_assessed":0,"complete":false,"by_state":{"value_present":29,"not_reported":0,"unresolved":62,"not_assessed":0,"not_applicable":0,"withheld":0,"source_unavailable":0}}},"presentation_version":"c23a06424e0e839b9d8939b546c1f1550bd20921e586774e62a9d026bf246c54"}