{"id":"https://openalex.org/W7162646835","doi":"https://doi.org/10.48550/arxiv.2605.28153","title":"Skillful high-resolution weather forecasting independent of physical models","display_name":"Skillful high-resolution weather forecasting independent of physical models","publication_year":2026,"publication_date":"2026-05-27","ids":{"openalex":"https://openalex.org/W7162646835","doi":"https://doi.org/10.48550/arxiv.2605.28153"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.28153","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.28153","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":null,"license_id":null,"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.2605.28153","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137204141","display_name":"Pengcheng Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Pengcheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013266721","display_name":"Siqi Xiang","orcid":"https://orcid.org/0000-0003-4704-6412"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiang, Siqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089309862","display_name":"Weixin Jin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jin, Weixin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104214254","display_name":"Zekun Ni","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ni, Zekun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137307928","display_name":"Jiang Bian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bian, Jiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137198546","display_name":"Zuliang Fang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fang, Zuliang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137226868","display_name":"Hongyu Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Hongyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137206959","display_name":"Bin Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Bin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137262803","display_name":"Richard E. Turner","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Turner, Richard E.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137236393","display_name":"Jonathan Weyn","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Weyn, Jonathan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137192482","display_name":"Haiyu Dong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dong, Haiyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109226002","display_name":"Kit Thambiratnam","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Thambiratnam, Kit","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137224449","display_name":"Qi Zhang","orcid":"https://orcid.org/0000-0002-8513-1573"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Qi","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/T10466","display_name":"Meteorological Phenomena and Simulations","score":0.7804999947547913,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10466","display_name":"Meteorological Phenomena and Simulations","score":0.7804999947547913,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10029","display_name":"Climate variability and models","score":0.08020000159740448,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.03359999880194664,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/numerical-weather-prediction","display_name":"Numerical weather prediction","score":0.7024000287055969},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.679099977016449},{"id":"https://openalex.org/keywords/weather-forecasting","display_name":"Weather forecasting","score":0.5659999847412109},{"id":"https://openalex.org/keywords/global-forecast-system","display_name":"Global Forecast System","score":0.4909999966621399},{"id":"https://openalex.org/keywords/north-american-mesoscale-model","display_name":"North American Mesoscale Model","score":0.4754999876022339},{"id":"https://openalex.org/keywords/weather-prediction","display_name":"Weather prediction","score":0.4717000126838684},{"id":"https://openalex.org/keywords/probabilistic-forecasting","display_name":"Probabilistic forecasting","score":0.40959998965263367}],"concepts":[{"id":"https://openalex.org/C147947694","wikidata":"https://www.wikidata.org/wiki/Q837552","display_name":"Numerical weather prediction","level":2,"score":0.7024000287055969},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.679099977016449},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5741999745368958},{"id":"https://openalex.org/C21001229","wikidata":"https://www.wikidata.org/wiki/Q182868","display_name":"Weather forecasting","level":2,"score":0.5659999847412109},{"id":"https://openalex.org/C163109420","wikidata":"https://www.wikidata.org/wiki/Q287011","display_name":"Global Forecast System","level":3,"score":0.4909999966621399},{"id":"https://openalex.org/C197661373","wikidata":"https://www.wikidata.org/wiki/Q7053811","display_name":"North American Mesoscale Model","level":4,"score":0.4754999876022339},{"id":"https://openalex.org/C2987469573","wikidata":"https://www.wikidata.org/wiki/Q182868","display_name":"Weather prediction","level":2,"score":0.4717000126838684},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.4668000042438507},{"id":"https://openalex.org/C122282355","wikidata":"https://www.wikidata.org/wiki/Q7246855","display_name":"Probabilistic forecasting","level":3,"score":0.40959998965263367},{"id":"https://openalex.org/C102561126","wikidata":"https://www.wikidata.org/wiki/Q17164865","display_name":"Tropical cyclone forecast model","level":3,"score":0.3806000053882599},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.3723999857902527},{"id":"https://openalex.org/C107054158","wikidata":"https://www.wikidata.org/wiki/Q25257","display_name":"Precipitation","level":2,"score":0.36809998750686646},{"id":"https://openalex.org/C170061395","wikidata":"https://www.wikidata.org/wiki/Q5468164","display_name":"Forecast skill","level":2,"score":0.35120001435279846},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.34610000252723694},{"id":"https://openalex.org/C37505551","wikidata":"https://www.wikidata.org/wiki/Q1453537","display_name":"Model output statistics","level":3,"score":0.3359000086784363},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.28189998865127563},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.27959999442100525},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.27900001406669617},{"id":"https://openalex.org/C140178040","wikidata":"https://www.wikidata.org/wiki/Q18402512","display_name":"Quantitative precipitation forecast","level":3,"score":0.2628999948501587}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.28153","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.28153","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.28153","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.28153","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","display_name":"Climate action","score":0.7416879534721375}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"and":[1,26,37,63,69,97,124,136,145,151,166],"timely":[2],"weather":[3,14,45],"forecasts":[4],"are":[5,78],"critical":[6],"for":[7,21,57,130],"high-impact":[8],"decisions":[9],"in":[10,29,104,114],"modern":[11],"society.":[12],"Machine-learning-based":[13],"prediction":[15,46],"is":[16],"emerging":[17],"as":[18],"an":[19],"alternative":[20],"producing":[22],"initial":[23],"conditions,":[24],"forecasts,":[25],"even":[27,49],"both":[28,95],"end-to-end":[30,50],"systems.":[31],"These":[32],"methods":[33],"deliver":[34],"predictions":[35],"faster":[36],"often":[38],"with":[39],"higher":[40],"skill":[41],"than":[42],"traditional":[43,168],"numerical":[44],"(NWP).":[47],"However,":[48],"models":[51],"typically":[52],"rely":[53],"on":[54],"NWP-generated":[55],"reanalyses":[56],"supervision,":[58],"thereby":[59],"inheriting":[60],"the":[61,120,161],"biases":[62],"resolution":[64],"limitations":[65],"of":[66],"those":[67],"NWPs,":[68],"limiting":[70],"adaptation":[71],"to":[72,84,149,163],"settings":[73],"where":[74],"suitable":[75],"reanalysis":[76],"products":[77],"unavailable,":[79],"infrequently":[80],"updated,":[81],"or":[82,107],"expensive":[83],"produce.":[85],"Here":[86],"we":[87],"introduce":[88],"ObsCast,":[89],"a":[90,143],"regional":[91,117,153],"system":[92],"that":[93],"generates":[94],"analysis":[96],"predictions,":[98],"without":[99,160],"using":[100],"any":[101],"NWP-derived":[102],"data":[103],"either":[105],"training":[106],"inference,":[108],"while":[109],"still":[110],"achieving":[111],"state-of-the-art":[112],"performance":[113],"short-term":[115],"high-resolution":[116],"modeling.":[118],"Over":[119],"contiguous":[121],"United":[122],"States":[123],"Europe,":[125],"ObsCast":[126],"outperforms":[127],"operational":[128],"NWP":[129],"near-surface":[131],"variables":[132],"through":[133],"18":[134],"h":[135],"produces":[137],"skillful":[138],"precipitation":[139],"forecasts.":[140],"It":[141],"provides":[142],"simpler":[144],"more":[146],"adaptable":[147],"route":[148],"build":[150],"refine":[152],"forecasting":[154,169],"services":[155],"directly":[156],"from":[157],"local":[158],"observations,":[159],"need":[162],"develop":[164],"complex":[165],"costly":[167],"pipelines.":[170]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-29T00:00:00"}