{"id":"AIBD-1238","title":"The National Hurricane Center leans its Hurricane Isaias forecast on Google DeepMind's ensemble, which calls a Category 2 landfall on the Florida Panhandle from the first advisory while physics-based models point to Louisiana","summary":"The NHC leaned its Hurricane Isaias forecast on Google DeepMind's ensemble, which called a Category 2 Florida Panhandle landfall from the first advisory while physics-based models pointed west.","date_observed":"2026-10-09","date_basis":"event","date_precision":"day","benefit_status":"reported","account":"Hurricane Isaias formed in the southwestern Gulf of Mexico in October 2026 and made landfall near Destin, Florida, as a Category 2 storm on 9 October. The National Hurricane Center's forecast discussions record its reliance on the Google DeepMind ensemble, which it designates GDMI. On the morning of 7 October the forecasters nudged their track eastward, though not as far as the DeepMind ensemble mean, which stood with the AI models on the right side of the guidance. That evening they wrote that the forecast leaned more heavily on the corrected consensus and the DeepMind aids, which were performing better, on the faster and right side of the guidance. On 8 October they raised the forecast peak to major hurricane strength because a majority of DeepMind ensemble members showed it. CNN reported on 8 October that the NHC had shaped its predictions to match the DeepMind model rather than the European and GFS models and had forecast a Category 2 storm from the first advisory. NPR reported on 26 October that physics-based models had initially sent the storm toward southeastern Louisiana, that the DeepMind model had called the Florida Panhandle from the start, and that it beat the physics-based models on the path. The NHC's director told NPR that the model did less well than the NHC's blended forecast on intensity. Michael Lowry, a hurricane expert who has advised the NHC, called it the best performing model that forecasters have seen.","category":"benefit created","unexpected":false,"purpose":"Google DeepMind's cyclone ensemble forecasts the track and intensity of tropical cyclones for the NHC's use alongside physics-based models.","system":"Google DeepMind tropical cyclone model, ensemble mean (NHC designation GDMI)","model_version":null,"developer":"Google DeepMind","deployer":"NOAA National Hurricane Center","location":"Northern Gulf Coast, United States, with landfall near Destin, Florida","domain":"Weather forecasting / tropical cyclones","segment":null,"registry_ids":{},"ratings":[{"group":"organizational","depth":2,"reason":"The NHC's official forecast leaned on the ensemble, and the track it gave, a landfall on the Florida Panhandle, verified while physics-based models pointed to Louisiana."},{"group":"societal","depth":null,"reason":"No source documents that the forecast changed what people in the storm's path did."}],"ai_role":{"status":"assessed","statement":"The NHC's own discussions say that its forecast leaned on the DeepMind aids for track and raised its intensity forecast on the ensemble's members. Human forecasters blended the guidance and issued the forecast, and the NHC's director says that the model did less well than the blend on intensity."},"evidence":[{"kind":"report","citation":"National Hurricane Center, Hurricane Isaias Discussion Number 6, 10 PM CDT, 7 October 2026.","title":"Hurricane Isaias Discussion Number 6","url":"https://www.nhc.noaa.gov/archive/2026/al09/al092026.discus.006.shtml","date":"2026-10-07","written_by":"deployer","quote":"The NHC forecast continues to lean more heavily on the better-performing corrected consensus (HCCA) and Google DeepMind (GDMI) aids, remaining on the faster and right side of the guidance envelope."},{"kind":"report","citation":"National Hurricane Center, Tropical Storm Isaias Discussion Number 4, 10 AM CDT, 7 October 2026.","title":"Tropical Storm Isaias Discussion Number 4","url":"https://www.nhc.noaa.gov/archive/2026/al09/al092026.discus.004.shtml","date":"2026-10-07","written_by":"deployer","quote":"The new NHC forecast is nudged a bit eastward, but isn't as far east as the latest HFIP Corrected Consensus Approach (HCCA) or Google DeepMind ensemble mean (GDMI)."},{"kind":"report","citation":"National Hurricane Center, Hurricane Isaias Discussion Number 10, 10 PM CDT, 8 October 2026.","title":"Hurricane Isaias Discussion Number 10","url":"https://www.nhc.noaa.gov/archive/2026/al09/al092026.discus.010.shtml","date":"2026-10-08","written_by":"deployer","quote":"A majority of Google DeepMind ensemble members show Isaias briefly reaching major hurricane status early Friday (along with some of the hurricane regional models), and this is now reflected in the NHC forecast."},{"kind":"news report","citation":"Meszaros, J., How AI is transforming hurricane forecasts, WUSF and NPR, 26 October 2026.","title":"How AI is transforming hurricane forecasts","url":"https://www.wusf.org/2026-10-26/how-ai-is-transforming-hurricane-forecasts","date":"2026-10-26","written_by":"independent","quote":"In early October, Hurricane Isaias made landfall near Destin, Fla., as a Category 2 storm. Conventional forecasting models initially predicted it would head further west, toward southeastern Louisiana. But Google DeepMind's artificial intelligence model called it right out of the gate: the hurricane was going to hit the western Florida Panhandle."},{"kind":"news report","citation":"Meszaros, J., How AI is transforming hurricane forecasts, WUSF and NPR, 26 October 2026. On the comparison.","title":"How AI is transforming hurricane forecasts","url":"https://www.wusf.org/2026-10-26/how-ai-is-transforming-hurricane-forecasts","date":"2026-10-26","written_by":"independent","quote":"An AI model from Google Deepmind beat out traditional physics-based models at predicting the path of the storm, though it didn't do as well in forecasting the storm's strength."},{"kind":"news report","citation":"Meszaros, J., How AI is transforming hurricane forecasts, WUSF and NPR, 26 October 2026. Quoting the NHC's director on intensity.","title":"How AI is transforming hurricane forecasts","url":"https://www.wusf.org/2026-10-26/how-ai-is-transforming-hurricane-forecasts","date":"2026-10-26","written_by":"deployer","quote":"While it topped other models in tracking the path of Isaias, it didn't do as well as the NHC's blended forecast in determining the intensity, or strength, of the storm.","supports_benefit":false},{"kind":"news report","citation":"Freedman, A., Why the National Hurricane Center bet big on AI to forecast Isaias, CNN, 8 October 2026 (as carried by KSL).","title":"Why the National Hurricane Center bet big on AI to forecast Isaias","url":"https://www.ksl.com/article/51634059/why-the-national-hurricane-center-bet-big-on-ai-to-forecast-isaias","date":"2026-10-08","written_by":"independent","quote":"They shaped their predictions to closely match an AI hurricane model developed by Google DeepMind, rather than leaning on the classic physics-based models many Americans are more familiar with, such as the European and GFS."},{"kind":"news report","citation":"Freedman, A., Why the National Hurricane Center bet big on AI to forecast Isaias, CNN, 8 October 2026 (as carried by KSL). On the first advisory.","title":"Why the National Hurricane Center bet big on AI to forecast Isaias","url":"https://www.ksl.com/article/51634059/why-the-national-hurricane-center-bet-big-on-ai-to-forecast-isaias","date":"2026-10-08","written_by":"independent","quote":"Relying heavily on DeepMind meant NHC forecast Isaias would become a Category 2 from the first advisory on Tuesday, when it didn't even have a name yet."}],"replication":{"produced_by":["A cyclone ensemble trained on decades of atmospheric analyses and historical tracks, run for each forecast cycle","Human forecasters who blend the AI guidance with physics-based models and corrected consensus aids"],"must_hold":["Forecasters must have the ensemble's output in time for each advisory","The ensemble's track skill must hold for the storm at hand, since its intensity guidance trailed the blend"],"echoes":[]},"paired_incident":null,"headline":"NHC leans on *Google DeepMind's ensemble* and calls Isaias's landfall","metric":"a Category 2 Florida Panhandle landfall from the first advisory","comparator":{"against":"the physics-based models, the European and GFS","before":"a weaker storm on a track toward southeastern Louisiana","after":"a Category 2 landfall on the western Florida Panhandle, which verified near Destin","basis":"measured","quote":"In early October, Hurricane Isaias made landfall near Destin, Fla., as a Category 2 storm. Conventional forecasting models initially predicted it would head further west, toward southeastern Louisiana. But Google DeepMind's artificial intelligence model called it right out of the gate: the hurricane was going to hit the western Florida Panhandle.","url":"https://www.wusf.org/2026-10-26/how-ai-is-transforming-hurricane-forecasts"},"later":{"status":"persisted","date":"2026-10-26","note":"NPR reported on 26 October 2026 that the model had beaten the physics-based models on the storm's path.","quote":"An AI model from Google Deepmind beat out traditional physics-based models at predicting the path of the storm, though it didn't do as well in forecasting the storm's strength.","url":"https://www.wusf.org/2026-10-26/how-ai-is-transforming-hurricane-forecasts"},"learns_from_data":{"value":"shown","note":"The developers' Nature paper says that the cyclone model is trained on ECMWF analyses and historical cyclone tracks.","quote":"It is jointly trained on a combination of two data sources (a process we refer to as co-training): decades of global atmospheric analysis data from the ECMWF","url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13581599/"},"benefit_categories":["environmental_impact"],"size":{"reach":{"band":1,"fact":"One agency, the National Hurricane Center, leaned its forecast on the ensemble.","quote":"The NHC forecast continues to lean more heavily on the better-performing corrected consensus (HCCA) and Google DeepMind (GDMI) aids, remaining on the faster and right side of the guidance envelope.","url":"https://www.nhc.noaa.gov/archive/2026/al09/al092026.discus.006.shtml","reason":null},"duration":{"band":2,"fact":"The forecasts covered the storm's days from formation to landfall.","quote":"In early October, Hurricane Isaias made landfall near Destin, Fla., as a Category 2 storm. Conventional forecasting models initially predicted it would head further west, toward southeastern Louisiana. But Google DeepMind's artificial intelligence model called it right out of the gate: the hurricane was going to hit the western Florida Panhandle.","url":"https://www.wusf.org/2026-10-26/how-ai-is-transforming-hurricane-forecasts","reason":null},"distribution":{"band":null,"fact":null,"quote":null,"url":null,"reason":"The sources compare no groups along the coast."},"frequency":{"band":1,"fact":"One storm, Isaias, was forecast with the ensemble.","quote":"They shaped their predictions to closely match an AI hurricane model developed by Google DeepMind, rather than leaning on the classic physics-based models many Americans are more familiar with, such as the European and GFS.","url":"https://www.ksl.com/article/51634059/why-the-national-hurricane-center-bet-big-on-ai-to-forecast-isaias","reason":null}},"value":null,"developer_country":"United Kingdom","deployer_country":"United States","unrated_because":null,"brief_notes":null,"brief":null,"confidence":"reported","shade":"medium","claim_status":"Independently reported","claim_basis":"Sources: two independent news reports and three reports from the deployer. For the benefit itself, the cited sources include no independent study or research report and no finding by an external body.","case_jurisdictions":["United States"],"design":null,"certainty":null,"certainty_reasons":[],"profile":{"case_id":"AIBD-1238","source_ids":["AIBD-1238-source-0001","AIBD-1238-source-0002","AIBD-1238-source-0003","AIBD-1238-source-0004","AIBD-1238-source-0005","AIBD-1238-source-0006","AIBD-1238-source-0007","AIBD-1238-source-0008"],"families":{"strategic assessments":{"state":"unresolved","record_ids":[]},"evaluations":{"state":"unresolved","record_ids":[]},"monetary observations":{"state":"unresolved","record_ids":[]},"institutional decisions":{"state":"unresolved","record_ids":[]}},"fields":{"actors[]":{"state":"value_present","value":{"kind":"record_fields","fields":{"developer":"Google DeepMind","deployer":"NOAA National Hurricane Center"}}},"actors[].ownership_control":{"state":"unresolved","reason":"This is an explicit unresolved assignment: the sources on record do not settle this question."},"ai_contribution":{"state":"value_present","value":{"kind":"record_fields","fields":{"ai_role":{"status":"assessed","statement":"The NHC's own discussions say that its forecast leaned on the DeepMind aids for track and raised its intensity forecast on the ensemble's members. Human forecasters blended the guidance and issued the forecast, and the NHC's director says that the model did less well than the blend on intensity."}}}},"ai_relationship":{"state":"value_present","value":{"kind":"record_fields","fields":{"category":"benefit created","ai_role":{"status":"assessed","statement":"The NHC's own discussions say that its forecast leaned on the DeepMind aids for track and raised its intensity forecast on the ensemble's members. Human forecasters blended the guidance and issued the forecast, and the NHC's director says that the model did less well than the blend on intensity."}}}},"assessments[].alternatives":{"state":"unresolved","reason":"This is an explicit unresolved assignment: the sources on record do not settle this question."},"assessments[].assessor_date":{"state":"unresolved","reason":"This is an explicit unresolved assignment: the sources on record do not settle this question."},"assessments[].depth":{"state":"value_present","value":{"kind":"record_fields","fields":{"ratings":[{"group":"organizational","depth":2,"reason":"The NHC's official forecast leaned on the ensemble, and the track it gave, a landfall on the Florida Panhandle, verified while physics-based models pointed to Louisiana."},{"group":"societal","reason":"No source documents that the forecast changed what people in the storm's path did."}]}}},"assessments[].display_confidence":{"state":"value_present","value":{"kind":"structured","data":{"confidence":"medium","evidence_word":"reported","claim_status":"Independently reported","basis":"Sources: two independent news reports and three reports from the deployer. For the benefit itself, the cited sources include no independent study or research report and no finding by an external body.","interpretation":"Existing display rule preserved; the richer outcome-specific confidence question still needs review."}}},"assessments[].rationale":{"state":"value_present","value":{"kind":"record_fields","fields":{"ratings":[{"group":"organizational","depth":2,"reason":"The NHC's official forecast leaned on the ensemble, and the track it gave, a landfall on the Florida Panhandle, verified while physics-based models pointed to Louisiana."},{"group":"societal","reason":"No source documents that the forecast changed what people in the storm's path did."}]}}},"assessments[].rubric_version":{"state":"unresolved","reason":"This is an explicit unresolved assignment: the sources on record do not settle this question."},"assessments[].status_basis":{"state":"unresolved","reason":"This is an explicit unresolved assignment: the sources on record do not settle this question."},"assessments[].target":{"state":"value_present","value":{"kind":"record_fields","fields":{"ratings":[{"group":"organizational","depth":2,"reason":"The NHC's official forecast leaned on the ensemble, and the track it gave, a landfall on the Florida Panhandle, verified while physics-based models pointed to Louisiana."},{"group":"societal","reason":"No source documents that the forecast changed what people in the storm's path did."}]}}},"availability":{"state":"unresolved","reason":"This is an explicit unresolved assignment: the sources on record do not settle this question."},"brief":{"state":"unresolved","reason":"This is an explicit unresolved assignment: the sources on record do not settle this question."},"case_kind":{"state":"value_present","value":{"kind":"structured","data":{"classification":"deployment","answer":"A deployment. The NHC used the ensemble in its official forecasts of a real storm.","scope":"The period and activity described in the case."}},"display":{"answer":"A deployment. The NHC used the ensemble in its official forecasts of a real storm.","inferred":false},"source_refs":["AIBD-1238-source-0001"]},"case_links[]":{"state":"unresolved","reason":"This is an explicit unresolved assignment: the sources on record do not settle this question."},"claims[].evidence_links":{"state":"unresolved","reason":"This is an explicit unresolved assignment: the sources on record do not settle this question."},"claims[].evidence_strength":{"state":"value_present","value":{"kind":"structured","data":{"answer":"Independently reported. Sources: two independent news reports and three reports from the deployer. For the benefit itself, the cited sources include no independent study or research report and no finding by an external body.","evidence_word":"reported","claim_status":"Independently reported"}},"display":{"answer":"Independently reported. Sources: two independent news reports and three reports from the deployer. For the benefit itself, the cited sources include no independent study or research report and no finding by an external body.","inferred":false},"source_refs":["AIBD-1238-source-0001","AIBD-1238-source-0002","AIBD-1238-source-0003","AIBD-1238-source-0004","AIBD-1238-source-0005","AIBD-1238-source-0006","AIBD-1238-source-0007","AIBD-1238-source-0008"]},"claims[].missingness":{"state":"value_present","value":{"kind":"text","text":"The model led on the storm's track but trailed the NHC's blended forecast on intensity, and the comparison rests on one storm. No source documents what people in the storm's path did with the forecast."},"display":{"answer":"The model led on the storm's track but trailed the NHC's blended forecast on intensity, and the comparison rests on one storm. No source documents what people in the storm's path did with the forecast.","inferred":false},"source_refs":["AIBD-1238-source-0004"]},"claims[].proposition":{"state":"unresolved","reason":"This is an explicit unresolved assignment: the sources on record do not settle this question."},"cross_border_flows[]":{"state":"unresolved","reason":"This is an explicit unresolved assignment: the sources on record do not settle this question."},"decisions[].action_outcome":{"state":"unresolved","family":"institutional decisions"},"decisions[].grounds":{"state":"unresolved","family":"institutional decisions"},"decisions[].institution_task":{"state":"unresolved","family":"institutional decisions"},"decisions[].success_criterion":{"state":"unresolved","family":"institutional decisions"},"deployment_stage":{"state":"value_present","value":{"kind":"structured","data":{"classification":"routine use","answer":"The system was in routine use. NPR reports that the NHC has incorporated the model into its official forecasts since 2025.","scope":"The period and activity described in the case."}},"display":{"answer":"The system was in routine use. NPR reports that the NHC has incorporated the model into its official forecasts since 2025.","inferred":false},"source_refs":["AIBD-1238-source-0001"]},"economics[].amount":{"state":"unresolved","family":"monetary observations"},"economics[].costs[]":{"state":"unresolved","family":"monetary observations"},"economics[].counterfactual":{"state":"unresolved","family":"monetary observations"},"economics[].currency_price_basis":{"state":"unresolved","family":"monetary observations"},"economics[].gross_net":{"state":"unresolved","family":"monetary observations"},"economics[].measure_type":{"state":"unresolved","family":"monetary observations"},"economics[].period_unit":{"state":"unresolved","family":"monetary observations"},"economics[].realization":{"state":"unresolved","family":"monetary observations"},"economics[].recipient_payer":{"state":"unresolved","family":"monetary observations"},"economics[].return_payback":{"state":"unresolved","family":"monetary observations"},"economics[].valuation_method":{"state":"unresolved","family":"monetary observations"},"evaluations[].ai_uplift":{"state":"unresolved","family":"evaluations"},"evaluations[].comparator":{"state":"unresolved","family":"evaluations"},"evaluations[].design":{"state":"unresolved","family":"evaluations"},"evaluations[].effect":{"state":"unresolved","family":"evaluations"},"evaluations[].exposure":{"state":"unresolved","family":"evaluations"},"evaluations[].id":{"state":"unresolved","family":"evaluations"},"evaluations[].measurement_method":{"state":"unresolved","family":"evaluations"},"evaluations[].metric":{"state":"unresolved","family":"evaluations"},"evaluations[].reproduction_materials":{"state":"unresolved","family":"evaluations"},"evaluations[].setting_sample":{"state":"unresolved","family":"evaluations"},"evaluations[].uncertainty_limitations":{"state":"unresolved","family":"evaluations"},"external_references[]":{"state":"unresolved","reason":"This is an explicit unresolved assignment: the sources on record do not settle this question."},"follow_up[]":{"state":"value_present","value":{"kind":"record_fields","fields":{"later":{"status":"persisted","date":"2026-10-26","note":"NPR reported on 26 October 2026 that the model had beaten the physics-based models on the storm's path.","quote":"An AI model from Google Deepmind beat out traditional physics-based models at predicting the path of the storm, though it didn't do as well in forecasting the storm's strength.","url":"https://www.wusf.org/2026-10-26/how-ai-is-transforming-hurricane-forecasts"}}}},"human_oversight":{"state":"value_present","value":{"kind":"text","text":"NHC forecasters blended the DeepMind aids with other guidance and issued the forecast themselves, leaning on the ensemble for track and raising the peak intensity when a majority of its members showed it."},"display":{"answer":"NHC forecasters blended the DeepMind aids with other guidance and issued the forecast themselves, leaning on the ensemble for track and raising the peak intensity when a majority of its members showed it.","inferred":false},"evidence_note":"Read from the benefit's account, the editor's statement of the system's part and the quoted sources on 7 November 2026."},"id":{"state":"value_present","value":{"kind":"record_fields","fields":{"id":"AIBD-1238"}}},"infrastructure_dependencies[]":{"state":"unresolved","reason":"This is an explicit unresolved assignment: the sources on record do not settle this question."},"outcomes[].beneficiaries":{"state":"unresolved","reason":"This is an explicit unresolved assignment: the sources on record do not settle this question."},"outcomes[].beneficiary_count":{"state":"unresolved","reason":"This is an explicit unresolved assignment: the sources on record do not settle this question."},"outcomes[].claim":{"state":"value_present","value":{"kind":"record_fields","fields":{"account":"Hurricane Isaias formed in the southwestern Gulf of Mexico in October 2026 and made landfall near Destin, Florida, as a Category 2 storm on 9 October. The National Hurricane Center's forecast discussions record its reliance on the Google DeepMind ensemble, which it designates GDMI. On the morning of 7 October the forecasters nudged their track eastward, though not as far as the DeepMind ensemble mean, which stood with the AI models on the right side of the guidance. That evening they wrote that the forecast leaned more heavily on the corrected consensus and the DeepMind aids, which were performing better, on the faster and right side of the guidance. On 8 October they raised the forecast peak to major hurricane strength because a majority of DeepMind ensemble members showed it. CNN reported on 8 October that the NHC had shaped its predictions to match the DeepMind model rather than the European and GFS models and had forecast a Category 2 storm from the first advisory. NPR reported on 26 October that physics-based models had initially sent the storm toward southeastern Louisiana, that the DeepMind model had called the Florida Panhandle from the start, and that it beat the physics-based models on the path. The NHC's director told NPR that the model did less well than the NHC's blended forecast on intensity. Michael Lowry, a hurricane expert who has advised the NHC, called it the best performing model that forecasters have seen."}}},"outcomes[].distribution":{"state":"unresolved","reason":"This is an explicit unresolved assignment: the sources on record do not settle this question."},"outcomes[].duration":{"state":"unresolved","reason":"This is an explicit unresolved assignment: the sources on record do not settle this question."},"outcomes[].id":{"state":"unresolved","reason":"This is an explicit unresolved assignment: the sources on record do not settle this question."},"outcomes[].stakeholder_group":{"state":"value_present","value":{"kind":"record_fields","fields":{"ratings":[{"group":"organizational","depth":2,"reason":"The NHC's official forecast leaned on the ensemble, and the track it gave, a landfall on the Florida Panhandle, verified while physics-based models pointed to Louisiana."},{"group":"societal","reason":"No source documents that the forecast changed what people in the storm's path did."}]}}},"outcomes[].status":{"state":"value_present","value":{"kind":"record_fields","fields":{"benefit_status":"reported"}}},"outcomes[].tradeoffs":{"state":"value_present","value":{"kind":"text","text":"No costs or harms are reported in the sources."},"display":{"answer":"No costs or harms are reported in the sources.","inferred":false},"source_refs":["AIBD-1238-source-0001","AIBD-1238-source-0002","AIBD-1238-source-0003","AIBD-1238-source-0004","AIBD-1238-source-0005","AIBD-1238-source-0006","AIBD-1238-source-0007","AIBD-1238-source-0008"]},"outcomes[].unexpected":{"state":"value_present","value":{"kind":"record_fields","fields":{"unexpected":false,"purpose":"Google DeepMind's cyclone ensemble forecasts the track and intensity of tropical cyclones for the NHC's use alongside physics-based models."}}},"places[]":{"state":"value_present","value":{"kind":"record_fields","fields":{"location":"Northern Gulf Coast, United States, with landfall near Destin, Florida","developer_country":"United Kingdom","deployer_country":"United States"}}},"purpose":{"state":"value_present","value":{"kind":"record_fields","fields":{"purpose":"Google DeepMind's cyclone ensemble forecasts the track and intensity of tropical cyclones for the NHC's use alongside physics-based models."}}},"record_dates":{"state":"value_present","value":{"kind":"record_fields","fields":{"date_observed":"2026-10-09","date_basis":"event","date_precision":"day"}}},"replication.attempts[]":{"state":"unresolved","reason":"This is an explicit unresolved assignment: the sources on record do not settle this question."},"replication.conditions":{"state":"value_present","value":{"kind":"record_fields","fields":{"replication":{"produced_by":["A cyclone ensemble trained on decades of atmospheric analyses and historical tracks, run for each forecast cycle","Human forecasters who blend the AI guidance with physics-based models and corrected consensus aids"],"must_hold":["Forecasters must have the ensemble's output in time for each advisory","The ensemble's track skill must hold for the storm at hand, since its intensity guidance trailed the blend"],"echoes":[]}}}},"sector":{"state":"value_present","value":{"kind":"record_fields","fields":{"domain":"Weather forecasting / tropical cyclones"}}},"sources[].access_version":{"state":"unresolved","reason":"This is an explicit unresolved assignment: the sources on record do not settle this question."},"sources[].citation_locator":{"state":"value_present","value":{"kind":"record_fields","fields":{"evidence":[{"kind":"report","citation":"National Hurricane Center, Hurricane Isaias Discussion Number 6, 10 PM CDT, 7 October 2026.","title":"Hurricane Isaias Discussion Number 6","url":"https://www.nhc.noaa.gov/archive/2026/al09/al092026.discus.006.shtml","date":"2026-10-07","written_by":"deployer","quote":"The NHC forecast continues to lean more heavily on the better-performing corrected consensus (HCCA) and Google DeepMind (GDMI) aids, remaining on the faster and right side of the guidance envelope."},{"kind":"report","citation":"National Hurricane Center, Tropical Storm Isaias Discussion Number 4, 10 AM CDT, 7 October 2026.","title":"Tropical Storm Isaias Discussion Number 4","url":"https://www.nhc.noaa.gov/archive/2026/al09/al092026.discus.004.shtml","date":"2026-10-07","written_by":"deployer","quote":"The new NHC forecast is nudged a bit eastward, but isn't as far east as the latest HFIP Corrected Consensus Approach (HCCA) or Google DeepMind ensemble mean (GDMI)."},{"kind":"report","citation":"National Hurricane Center, Hurricane Isaias Discussion Number 10, 10 PM CDT, 8 October 2026.","title":"Hurricane Isaias Discussion Number 10","url":"https://www.nhc.noaa.gov/archive/2026/al09/al092026.discus.010.shtml","date":"2026-10-08","written_by":"deployer","quote":"A majority of Google DeepMind ensemble members show Isaias briefly reaching major hurricane status early Friday (along with some of the hurricane regional models), and this is now reflected in the NHC forecast."},{"kind":"news report","citation":"Meszaros, J., How AI is transforming hurricane forecasts, WUSF and NPR, 26 October 2026.","title":"How AI is transforming hurricane forecasts","url":"https://www.wusf.org/2026-10-26/how-ai-is-transforming-hurricane-forecasts","date":"2026-10-26","written_by":"independent","quote":"In early October, Hurricane Isaias made landfall near Destin, Fla., as a Category 2 storm. Conventional forecasting models initially predicted it would head further west, toward southeastern Louisiana. But Google DeepMind's artificial intelligence model called it right out of the gate: the hurricane was going to hit the western Florida Panhandle."},{"kind":"news report","citation":"Meszaros, J., How AI is transforming hurricane forecasts, WUSF and NPR, 26 October 2026. On the comparison.","title":"How AI is transforming hurricane forecasts","url":"https://www.wusf.org/2026-10-26/how-ai-is-transforming-hurricane-forecasts","date":"2026-10-26","written_by":"independent","quote":"An AI model from Google Deepmind beat out traditional physics-based models at predicting the path of the storm, though it didn't do as well in forecasting the storm's strength."},{"kind":"news report","citation":"Meszaros, J., How AI is transforming hurricane forecasts, WUSF and NPR, 26 October 2026. Quoting the NHC's director on intensity.","title":"How AI is transforming hurricane forecasts","url":"https://www.wusf.org/2026-10-26/how-ai-is-transforming-hurricane-forecasts","date":"2026-10-26","written_by":"deployer","quote":"While it topped other models in tracking the path of Isaias, it didn't do as well as the NHC's blended forecast in determining the intensity, or strength, of the storm.","supports_benefit":false},{"kind":"news report","citation":"Freedman, A., Why the National Hurricane Center bet big on AI to forecast Isaias, CNN, 8 October 2026 (as carried by KSL).","title":"Why the National Hurricane Center bet big on AI to forecast Isaias","url":"https://www.ksl.com/article/51634059/why-the-national-hurricane-center-bet-big-on-ai-to-forecast-isaias","date":"2026-10-08","written_by":"independent","quote":"They shaped their predictions to closely match an AI hurricane model developed by Google DeepMind, rather than leaning on the classic physics-based models many Americans are more familiar with, such as the European and GFS."},{"kind":"news report","citation":"Freedman, A., Why the National Hurricane Center bet big on AI to forecast Isaias, CNN, 8 October 2026 (as carried by KSL). On the first advisory.","title":"Why the National Hurricane Center bet big on AI to forecast Isaias","url":"https://www.ksl.com/article/51634059/why-the-national-hurricane-center-bet-big-on-ai-to-forecast-isaias","date":"2026-10-08","written_by":"independent","quote":"Relying heavily on DeepMind meant NHC forecast Isaias would become a Category 2 from the first advisory on Tuesday, when it didn't even have a name yet."}]}}},"sources[].id":{"state":"value_present","value":{"kind":"identifiers","identifiers":["AIBD-1238-source-0001","AIBD-1238-source-0002","AIBD-1238-source-0003","AIBD-1238-source-0004","AIBD-1238-source-0005","AIBD-1238-source-0006","AIBD-1238-source-0007","AIBD-1238-source-0008"],"role":"Existing evidence items, in their original order"}},"sources[].language":{"state":"unresolved","reason":"This is an explicit unresolved assignment: the sources on record do not settle this question."},"sources[].relationship":{"state":"value_present","value":{"kind":"record_fields","fields":{"evidence":[{"kind":"report","citation":"National Hurricane Center, Hurricane Isaias Discussion Number 6, 10 PM CDT, 7 October 2026.","title":"Hurricane Isaias Discussion Number 6","url":"https://www.nhc.noaa.gov/archive/2026/al09/al092026.discus.006.shtml","date":"2026-10-07","written_by":"deployer","quote":"The NHC forecast continues to lean more heavily on the better-performing corrected consensus (HCCA) and Google DeepMind (GDMI) aids, remaining on the faster and right side of the guidance envelope."},{"kind":"report","citation":"National Hurricane Center, Tropical Storm Isaias Discussion Number 4, 10 AM CDT, 7 October 2026.","title":"Tropical Storm Isaias Discussion Number 4","url":"https://www.nhc.noaa.gov/archive/2026/al09/al092026.discus.004.shtml","date":"2026-10-07","written_by":"deployer","quote":"The new NHC forecast is nudged a bit eastward, but isn't as far east as the latest HFIP Corrected Consensus Approach (HCCA) or Google DeepMind ensemble mean (GDMI)."},{"kind":"report","citation":"National Hurricane Center, Hurricane Isaias Discussion Number 10, 10 PM CDT, 8 October 2026.","title":"Hurricane Isaias Discussion Number 10","url":"https://www.nhc.noaa.gov/archive/2026/al09/al092026.discus.010.shtml","date":"2026-10-08","written_by":"deployer","quote":"A majority of Google DeepMind ensemble members show Isaias briefly reaching major hurricane status early Friday (along with some of the hurricane regional models), and this is now reflected in the NHC forecast."},{"kind":"news report","citation":"Meszaros, J., How AI is transforming hurricane forecasts, WUSF and NPR, 26 October 2026.","title":"How AI is transforming hurricane forecasts","url":"https://www.wusf.org/2026-10-26/how-ai-is-transforming-hurricane-forecasts","date":"2026-10-26","written_by":"independent","quote":"In early October, Hurricane Isaias made landfall near Destin, Fla., as a Category 2 storm. Conventional forecasting models initially predicted it would head further west, toward southeastern Louisiana. But Google DeepMind's artificial intelligence model called it right out of the gate: the hurricane was going to hit the western Florida Panhandle."},{"kind":"news report","citation":"Meszaros, J., How AI is transforming hurricane forecasts, WUSF and NPR, 26 October 2026. On the comparison.","title":"How AI is transforming hurricane forecasts","url":"https://www.wusf.org/2026-10-26/how-ai-is-transforming-hurricane-forecasts","date":"2026-10-26","written_by":"independent","quote":"An AI model from Google Deepmind beat out traditional physics-based models at predicting the path of the storm, though it didn't do as well in forecasting the storm's strength."},{"kind":"news report","citation":"Meszaros, J., How AI is transforming hurricane forecasts, WUSF and NPR, 26 October 2026. Quoting the NHC's director on intensity.","title":"How AI is transforming hurricane forecasts","url":"https://www.wusf.org/2026-10-26/how-ai-is-transforming-hurricane-forecasts","date":"2026-10-26","written_by":"deployer","quote":"While it topped other models in tracking the path of Isaias, it didn't do as well as the NHC's blended forecast in determining the intensity, or strength, of the storm.","supports_benefit":false},{"kind":"news report","citation":"Freedman, A., Why the National Hurricane Center bet big on AI to forecast Isaias, CNN, 8 October 2026 (as carried by KSL).","title":"Why the National Hurricane Center bet big on AI to forecast Isaias","url":"https://www.ksl.com/article/51634059/why-the-national-hurricane-center-bet-big-on-ai-to-forecast-isaias","date":"2026-10-08","written_by":"independent","quote":"They shaped their predictions to closely match an AI hurricane model developed by Google DeepMind, rather than leaning on the classic physics-based models many Americans are more familiar with, such as the European and GFS."},{"kind":"news report","citation":"Freedman, A., Why the National Hurricane Center bet big on AI to forecast Isaias, CNN, 8 October 2026 (as carried by KSL). On the first advisory.","title":"Why the National Hurricane Center bet big on AI to forecast Isaias","url":"https://www.ksl.com/article/51634059/why-the-national-hurricane-center-bet-big-on-ai-to-forecast-isaias","date":"2026-10-08","written_by":"independent","quote":"Relying heavily on DeepMind meant NHC forecast Isaias would become a Category 2 from the first advisory on Tuesday, when it didn't even have a name yet."}]}}},"sources[].upstream_sources":{"state":"unresolved","reason":"This is an explicit unresolved assignment: the sources on record do not settle this question."},"strategic_assessments[]":{"state":"unresolved","family":"strategic assessments"},"summary":{"state":"value_present","value":{"kind":"record_fields","fields":{"summary":"The NHC leaned its Hurricane Isaias forecast on Google DeepMind's ensemble, which called a Category 2 Florida Panhandle landfall from the first advisory while physics-based models pointed west.","account":"Hurricane Isaias formed in the southwestern Gulf of Mexico in October 2026 and made landfall near Destin, Florida, as a Category 2 storm on 9 October. The National Hurricane Center's forecast discussions record its reliance on the Google DeepMind ensemble, which it designates GDMI. On the morning of 7 October the forecasters nudged their track eastward, though not as far as the DeepMind ensemble mean, which stood with the AI models on the right side of the guidance. That evening they wrote that the forecast leaned more heavily on the corrected consensus and the DeepMind aids, which were performing better, on the faster and right side of the guidance. On 8 October they raised the forecast peak to major hurricane strength because a majority of DeepMind ensemble members showed it. CNN reported on 8 October that the NHC had shaped its predictions to match the DeepMind model rather than the European and GFS models and had forecast a Category 2 storm from the first advisory. NPR reported on 26 October that physics-based models had initially sent the storm toward southeastern Louisiana, that the DeepMind model had called the Florida Panhandle from the start, and that it beat the physics-based models on the path. The NHC's director told NPR that the model did less well than the NHC's blended forecast on intensity. Michael Lowry, a hurricane expert who has advised the NHC, called it the best performing model that forecasters have seen."}}},"systems[].autonomy_permissions":{"state":"unresolved","reason":"This is an explicit unresolved assignment: the sources on record do not settle this question."},"systems[].component_role":{"state":"unresolved","reason":"This is an explicit unresolved assignment: the sources on record do not settle this question."},"systems[].deployment_configuration":{"state":"unresolved","reason":"This is an explicit unresolved assignment: the sources on record do not settle this question."},"systems[].inputs_outputs":{"state":"unresolved","reason":"This is an explicit unresolved assignment: the sources on record do not settle this question."},"systems[].model_name":{"state":"unresolved","reason":"This is an explicit unresolved assignment: the sources on record do not settle this question."},"systems[].model_version":{"state":"unresolved","reason":"This is an explicit unresolved assignment: the sources on record do not settle this question."},"systems[].product_name":{"state":"value_present","value":{"kind":"record_fields","fields":{"system":"Google DeepMind tropical cyclone model, ensemble mean (NHC designation GDMI)"}}},"systems[].provider":{"state":"unresolved","reason":"This is an explicit unresolved assignment: the sources on record do not settle this question."},"title":{"state":"value_present","value":{"kind":"record_fields","fields":{"title":"The National Hurricane Center leans its Hurricane Isaias forecast on Google DeepMind's ensemble, which calls a Category 2 landfall on the Florida Panhandle from the first advisory while physics-based models point to Louisiana","headline":"NHC leans on *Google DeepMind's ensemble* and calls Isaias's landfall"}}},"workflow":{"state":"value_present","value":{"kind":"record_fields","fields":{"account":"Hurricane Isaias formed in the southwestern Gulf of Mexico in October 2026 and made landfall near Destin, Florida, as a Category 2 storm on 9 October. The National Hurricane Center's forecast discussions record its reliance on the Google DeepMind ensemble, which it designates GDMI. On the morning of 7 October the forecasters nudged their track eastward, though not as far as the DeepMind ensemble mean, which stood with the AI models on the right side of the guidance. That evening they wrote that the forecast leaned more heavily on the corrected consensus and the DeepMind aids, which were performing better, on the faster and right side of the guidance. On 8 October they raised the forecast peak to major hurricane strength because a majority of DeepMind ensemble members showed it. CNN reported on 8 October that the NHC had shaped its predictions to match the DeepMind model rather than the European and GFS models and had forecast a Category 2 storm from the first advisory. NPR reported on 26 October that physics-based models had initially sent the storm toward southeastern Louisiana, that the DeepMind model had called the Florida Panhandle from the start, and that it beat the physics-based models on the path. The NHC's director told NPR that the model did less well than the NHC's blended forecast on intensity. Michael Lowry, a hurricane expert who has advised the NHC, called it the best performing model that forecasters have seen.","replication":{"produced_by":["A cyclone ensemble trained on decades of atmospheric analyses and historical tracks, run for each forecast cycle","Human forecasters who blend the AI guidance with physics-based models and corrected consensus aids"],"must_hold":["Forecasters must have the ensemble's output in time for each advisory","The ensemble's track skill must hold for the storm at hand, since its intensity guidance trailed the blend"],"echoes":[]}}}}}}}