{ "id": "AIBD-0089", "title": "NHC's 2025 season verification: Google DeepMind ensemble mean outperformed official short-range Atlantic track forecasts in NHC's first operational use of AI models", "summary": "NHC's 2025 verification: Google DeepMind ensemble mean beat official Atlantic track forecasts at 12–72 h; first AI models in NHC operations.", "date_observed": "2026-03-30", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The National Hurricane Center's Forecast Verification Report for the 2025 season is dated 30 March 2026. It states that 2025 marked the first time NHC incorporated AI-based models into real-time operations. In the Atlantic track comparison, the Google DeepMind ensemble mean (GDMI) slightly outperformed the official forecast at lead times from 12 to 72 h. Official forecasts outperformed the consensus aids TVCA, HCCA and FSSE. In the eastern North Pacific, GDMI performed considerably better than every other individual model. It beat the official forecast and all consensus aids at 48–120 h. For intensity it was the top individual model from 12 to 48 h. GDMI was not available early in the season, so the first storms are excluded from these comparisons.", "category": "general benefit", "unexpected": false, "purpose": "Experimental AI tropical cyclone forecast model providing ensemble track and intensity guidance to NHC forecasters.", "system": "Google DeepMind AI hurricane forecast model, ensemble mean designated GDMI in NHC guidance", "model_version": null, "developer": "DeepMind", "deployer": "NOAA National Hurricane Center", "location": "Miami, United States", "domain": "Weather forecasting / tropical cyclones", "segment": "Public-sector meteorological service", "benefit_type": "Improved Task Accuracy", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "Aggregate verification statistics, no discrete event." }, { "group": "organizational", "depth": null, "reason": "Aggregate verification statistics, no discrete event." }, { "group": "societal", "depth": null, "reason": "Aggregate verification statistics, no discrete event." } ], "ai_role": { "status": "assessed", "statement": "NHC's own homogeneous verification isolates GDMI's track and intensity performance against official forecasts, consensus aids and other models. The sample excludes early-season storms, when GDMI was unavailable." }, "evidence": [ { "kind": "report", "citation": "Cangialosi, J. P., Martinez, J., Reinhart, B., Papin, P. (2026). National Hurricane Center Forecast Verification Report: 2025 Hurricane Season, Section 1 (Introduction), p. 7. NOAA/NHC, 30 March 2026.", "title": "NHC Forecast Verification Report, 2025 Hurricane Season (AI models statement)", "url": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf", "date": "2026-03-30", "written_by": "deployer", "quote": "The 2025 season marked the first time that NHC incorporated AI-based models into real-time operations." }, { "kind": "report", "citation": "Cangialosi, J. P., Martinez, J., Reinhart, B., Papin, P. (2026). National Hurricane Center Forecast Verification Report: 2025 Hurricane Season, Atlantic track verification. NOAA/NHC, 30 March 2026.", "title": "NHC Forecast Verification Report, 2025 Hurricane Season (Atlantic track)", "url": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf", "date": "2026-03-30", "written_by": "deployer", "quote": "there was one model, the Google DeepMind ensemble mean (GDMI), that slightly outperformed NHC at the shorter lead times from 12 to 72 h" }, { "kind": "report", "citation": "Cangialosi, J. P., Martinez, J., Reinhart, B., Papin, P. (2026). National Hurricane Center Forecast Verification Report: 2025 Hurricane Season, eastern North Pacific track verification. NOAA/NHC, 30 March 2026.", "title": "NHC Forecast Verification Report, 2025 Hurricane Season (eastern North Pacific track)", "url": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf", "date": "2026-03-30", "written_by": "deployer", "quote": "GDMI performed considerably better than all of the other individual models and beat the official forecast and all of the consensus aids at 48-120 h." }, { "kind": "press release", "citation": "National Weather Service (n.d.). AI in Hurricane Forecasting at the National Hurricane Center: A Q&A with NHC Science Operation Officer Wallace Hogsett. NOAA/NWS News Around NOAA.", "title": "AI in Hurricane Forecasting at the National Hurricane Center", "url": "https://www.weather.gov/news/261102-AI-Hurricane-Forecasting", "date": null, "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." } ], "replication": { "produced_by": [ "Real-time availability of the Google DeepMind ensemble to NHC for most of the 2025 season", "NHC's standard homogeneous verification procedure applied to early-model versions of the guidance" ], "must_hold": [ "Consistent timely delivery of the AI guidance (NHC notes some AI systems were not consistently available)", "Results from one season. NHC's Science Operations Officer says it takes a long time to fully evaluate new models" ], "echoes": [ { "date": "2024-09-08", "setting": "Independent CIRA evaluation of earlier open-source AI models (GraphCast-operational, Pangu-Weather, FourCastNet) on May-November 2023 cyclones: track errors comparable to best operational models", "model_version": "GraphCast-operational, Pangu-Weather, FourCastNet v1/v2-small", "outcome": "recurred", "source": "https://arxiv.org/abs/2409.06735" }, { "date": "2025-10-23", "setting": "Hurricane Melissa: NHC blended GDMI into official track and intensity forecasts", "model_version": null, "outcome": "recurred", "source": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.010.shtml" } ] }, "paired_incident": { "database": "AI Incident Database", "identifier": "536", "title": "NJ Transit's Use of Modeling Software Miscalculated Storm Surge Threat Level", "url": "https://incidentdatabase.ai/cite/536", "severity": null, "group": "organizational", "basis": "National Weather Service storm-surge model underestimated Hurricane Sandy's threat and NJ Transit lost rail equipment; NHC's hurricane forecast guidance serves the same operational decisions." }, "headline": "DeepMind's GDMI beats NHC Atlantic track forecasts at 12–72 h", "metric": "beat official Atlantic track forecasts at 12–72 h", "comparator": { "against": "the NHC official track forecast (OFCL) in the 2025 Atlantic homogeneous model comparison", "before": "OFCL errors of 20.4, 31.1, 41.9, 49.2, 62.2 and 81.4 n mi at 12, 24, 36, 48, 60 and 72 h", "after": "GDMI errors of 19.6, 26.4, 34.8, 41.4, 50.8 and 66.5 n mi at the same lead times", "basis": "measured", "quote": "The official forecasts were highly skillful, even outperforming the typically reliable consensus aids TVCA, HCCA, and FSSE. However, there was one model, the Google DeepMind ensemble mean (GDMI), that slightly outperformed NHC at the shorter lead times from 12 to 72 h.", "url": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf" }, "later": { "status": "unknown", "date": null, "note": "Nothing after the 30 March 2026 report was found in the cited sources.", "quote": null, "url": null }, "value": null, "developer_country": "United Kingdom", "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-20", "change": "Paired with AI Incident Database incident 536 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/536", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Relocated the “first time” sentence to Section 1 (Introduction), p. 7 of the verification report, replaced the unsupported “multiple seasons” caution with the Science Operations Officer's statement that it takes a long time to fully evaluate new models, and noted in the taxonomy note that the date observed is the report's date while the verified forecasts were issued during the 2025 season.", "source": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf; https://www.weather.gov/news/261102-AI-Hurricane-Forecasting", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against from the cited sources.", "source": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Task Accuracy under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Google_DeepMind; https://www.nhc.noaa.gov/aboutintro.shtml", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to DeepMind.", "source": null, "editor": "AIBD" } ], "confidence": "claimed", "shade": "low", "claim_status": "The deployer's account", "claim_basis": "Sources: the deployer's report and the deployer's press release. 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