{"id":"https://openalex.org/W7162782502","doi":"https://doi.org/10.48550/arxiv.2605.30184","title":"Can AI Weather Models Predict Beyond Two Weeks? A Quantitative Benchmark and Analysis of Long Rollouts","display_name":"Can AI Weather Models Predict Beyond Two Weeks? A Quantitative Benchmark and Analysis of Long Rollouts","publication_year":2026,"publication_date":"2026-05-28","ids":{"openalex":"https://openalex.org/W7162782502","doi":"https://doi.org/10.48550/arxiv.2605.30184"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.30184","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30184","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.30184","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5024278031","display_name":"Fanny Lehmann","orcid":"https://orcid.org/0000-0002-8296-6110"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lehmann, Fanny","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041585740","display_name":"F\u0131rat \u00d6zdemir","orcid":"https://orcid.org/0000-0001-6643-7318"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ozdemir, Firat","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137355734","display_name":"Yun Cheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheng, Yun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137390967","display_name":"Torsten Hoefler","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hoefler, Torsten","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137336703","display_name":"Sebastian Schemm","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Schemm, Sebastian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135248872","display_name":"Benedikt Soja","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Soja, Benedikt","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137366582","display_name":"Siddhartha Mishra","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mishra, Siddhartha","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10799","display_name":"Data Visualization and Analytics","score":0.18880000710487366,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10799","display_name":"Data Visualization and Analytics","score":0.18880000710487366,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.07970000058412552,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.04910000041127205,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5250999927520752},{"id":"https://openalex.org/keywords/weather-prediction","display_name":"Weather prediction","score":0.5054000020027161},{"id":"https://openalex.org/keywords/numerical-weather-prediction","display_name":"Numerical weather prediction","score":0.42980000376701355},{"id":"https://openalex.org/keywords/weather-forecasting","display_name":"Weather forecasting","score":0.4138000011444092},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.40630000829696655},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.37139999866485596},{"id":"https://openalex.org/keywords/unified-model","display_name":"Unified Model","score":0.3449000120162964}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6525999903678894},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5250999927520752},{"id":"https://openalex.org/C2987469573","wikidata":"https://www.wikidata.org/wiki/Q182868","display_name":"Weather prediction","level":2,"score":0.5054000020027161},{"id":"https://openalex.org/C147947694","wikidata":"https://www.wikidata.org/wiki/Q837552","display_name":"Numerical weather prediction","level":2,"score":0.42980000376701355},{"id":"https://openalex.org/C21001229","wikidata":"https://www.wikidata.org/wiki/Q182868","display_name":"Weather forecasting","level":2,"score":0.4138000011444092},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.40630000829696655},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3824999928474426},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.374099999666214},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.37139999866485596},{"id":"https://openalex.org/C45493050","wikidata":"https://www.wikidata.org/wiki/Q7884934","display_name":"Unified Model","level":2,"score":0.3449000120162964},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3409000039100647},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.33730000257492065},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.31769999861717224},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.29319998621940613},{"id":"https://openalex.org/C127491075","wikidata":"https://www.wikidata.org/wiki/Q7617825","display_name":"Stochastic modelling","level":2,"score":0.2870999872684479},{"id":"https://openalex.org/C550656013","wikidata":"https://www.wikidata.org/wiki/Q1513578","display_name":"Weather modification","level":2,"score":0.25949999690055847},{"id":"https://openalex.org/C2993336609","wikidata":"https://www.wikidata.org/wiki/Q11663","display_name":"Weather patterns","level":3,"score":0.2565999925136566},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.2533000111579895},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.25119999051094055}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.30184","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30184","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.30184","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30184","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"While":[0],"AI":[1,54,129],"weather":[2,55,105,130],"models":[3,71,77,92,102],"excel":[4],"at":[5],"short-to-medium":[6],"range":[7],"forecasts":[8],"(up":[9],"to":[10,85,93],"15":[11],"days),":[12],"they":[13],"frequently":[14],"suffer":[15],"from":[16,89],"ill-defined":[17],"\"instabilities\"":[18],"when":[19,81],"rolled":[20],"out":[21],"over":[22],"longer":[23],"horizons.":[24],"This":[25],"work":[26],"addresses":[27],"the":[28,64,109],"lack":[29],"of":[30,46,51,66],"a":[31],"formal":[32],"taxonomy":[33],"by":[34],"categorizing":[35],"these":[36,91],"failures":[37],"into":[38],"three":[39],"distinct":[40],"regimes:":[41],"blow-up,":[42],"drift,":[43],"and":[44],"loss":[45],"seasonality,":[47],"through":[48,116],"year-long":[49],"rollouts":[50],"nine":[52],"state-of-the-art":[53,125],"models.":[56],"Our":[57],"analysis":[58],"reveals":[59],"that":[60,100],"stability":[61],"hinges":[62],"on":[63,108,119],"treatment":[65],"small":[67],"spatio-temporal":[68],"scales:":[69],"unstable":[70],"amplify":[72],"high-frequency":[73],"energy,":[74],"while":[75],"stable":[76,101],"act":[78],"as":[79],"denoisers":[80],"noise":[82],"is":[83],"added":[84],"their":[86],"inputs.":[87],"Far":[88],"reducing":[90],"mere":[94],"stochastic":[95],"parrots,":[96],"our":[97,114],"findings":[98,115],"highlight":[99],"generate":[103],"unique":[104],"trajectories,":[106],"conditioned":[107],"initial":[110],"state.":[111],"We":[112],"verify":[113],"ablation":[117],"studies":[118],"architectural":[120],"design":[121],"choices,":[122],"conducted":[123],"using":[124],"Vision":[126],"Transformer":[127],"(ViT)":[128],"model":[131],"architectures.":[132]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-30T00:00:00"}
