{"id":"https://openalex.org/W7160908008","doi":"https://doi.org/10.48550/arxiv.2605.10297","title":"QuantWeather: Quantile-Aware Probabilistic Forecasting for Subseasonal Precipitation","display_name":"QuantWeather: Quantile-Aware Probabilistic Forecasting for Subseasonal Precipitation","publication_year":2026,"publication_date":"2026-05-11","ids":{"openalex":"https://openalex.org/W7160908008","doi":"https://doi.org/10.48550/arxiv.2605.10297"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.10297","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.10297","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.2605.10297","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135918249","display_name":"Lei Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Lei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124392647","display_name":"Xinyu Su","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Su, Xinyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135938890","display_name":"Xiaohui Zhong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhong, Xiaohui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135983782","display_name":"Hao Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Hao","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":0.9135000109672546,"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":0.9135000109672546,"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/T11234","display_name":"Precipitation Measurement and Analysis","score":0.016499999910593033,"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/T11483","display_name":"Tropical and Extratropical Cyclones Research","score":0.01640000008046627,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.8859000205993652},{"id":"https://openalex.org/keywords/probabilistic-forecasting","display_name":"Probabilistic forecasting","score":0.7926999926567078},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.6367999911308289},{"id":"https://openalex.org/keywords/quantitative-precipitation-forecast","display_name":"Quantitative precipitation forecast","score":0.623199999332428},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"Ensemble forecasting","score":0.6043999791145325},{"id":"https://openalex.org/keywords/forecast-skill","display_name":"Forecast skill","score":0.4772000014781952},{"id":"https://openalex.org/keywords/uncertainty-quantification","display_name":"Uncertainty quantification","score":0.4277999997138977},{"id":"https://openalex.org/keywords/stochastic-modelling","display_name":"Stochastic modelling","score":0.41339999437332153},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.41119998693466187}],"concepts":[{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.8859000205993652},{"id":"https://openalex.org/C122282355","wikidata":"https://www.wikidata.org/wiki/Q7246855","display_name":"Probabilistic forecasting","level":3,"score":0.7926999926567078},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.6367999911308289},{"id":"https://openalex.org/C140178040","wikidata":"https://www.wikidata.org/wiki/Q18402512","display_name":"Quantitative precipitation forecast","level":3,"score":0.623199999332428},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.6043999791145325},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5845999717712402},{"id":"https://openalex.org/C170061395","wikidata":"https://www.wikidata.org/wiki/Q5468164","display_name":"Forecast skill","level":2,"score":0.4772000014781952},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.4277999997138977},{"id":"https://openalex.org/C127491075","wikidata":"https://www.wikidata.org/wiki/Q7617825","display_name":"Stochastic modelling","level":2,"score":0.41339999437332153},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.41119998693466187},{"id":"https://openalex.org/C107054158","wikidata":"https://www.wikidata.org/wiki/Q25257","display_name":"Precipitation","level":2,"score":0.36800000071525574},{"id":"https://openalex.org/C8272713","wikidata":"https://www.wikidata.org/wiki/Q176737","display_name":"Stochastic process","level":2,"score":0.365200012922287},{"id":"https://openalex.org/C2777052490","wikidata":"https://www.wikidata.org/wiki/Q5072826","display_name":"Chaotic","level":2,"score":0.3630000054836273},{"id":"https://openalex.org/C143017306","wikidata":"https://www.wikidata.org/wiki/Q3318133","display_name":"Probabilistic relevance model","level":4,"score":0.35910001397132874},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35659998655319214},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.3440999984741211},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34369999170303345},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.32499998807907104},{"id":"https://openalex.org/C21001229","wikidata":"https://www.wikidata.org/wiki/Q182868","display_name":"Weather forecasting","level":2,"score":0.32100000977516174},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.3147999942302704},{"id":"https://openalex.org/C177803969","wikidata":"https://www.wikidata.org/wiki/Q29205","display_name":"Uncertainty analysis","level":2,"score":0.29249998927116394},{"id":"https://openalex.org/C112353543","wikidata":"https://www.wikidata.org/wiki/Q5468162","display_name":"Forecast period","level":4,"score":0.2768999934196472},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.27250000834465027},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.26919999718666077},{"id":"https://openalex.org/C166851805","wikidata":"https://www.wikidata.org/wiki/Q5468165","display_name":"Forecast verification","level":3,"score":0.2655999958515167},{"id":"https://openalex.org/C120954023","wikidata":"https://www.wikidata.org/wiki/Q1127277","display_name":"Consensus forecast","level":2,"score":0.2648000121116638},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.26339998841285706},{"id":"https://openalex.org/C51865526","wikidata":"https://www.wikidata.org/wiki/Q5379962","display_name":"Ensemble average","level":2,"score":0.26190000772476196},{"id":"https://openalex.org/C60782215","wikidata":"https://www.wikidata.org/wiki/Q3333679","display_name":"Probabilistic method","level":3,"score":0.2502000033855438}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.10297","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.10297","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.2605.10297","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.10297","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":[{"id":"https://metadata.un.org/sdg/13","display_name":"Climate action","score":0.7637494206428528}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Subseasonal":[0],"precipitation":[1],"forecasting":[2,87,135],"is":[3],"inherently":[4],"uncertain":[5],"due":[6,34],"to":[7,35,73],"chaotic":[8],"atmospheric":[9],"dynamics,":[10],"making":[11],"reliable":[12,53],"uncertainty":[13,23,54],"estimation":[14],"essential":[15],"for":[16],"real-world":[17],"applications.":[18],"Existing":[19],"approaches":[20],"typically":[21],"represent":[22],"through":[24],"ensemble":[25,40],"forecasts":[26],"rather":[27],"than":[28],"directly":[29,50],"modeling":[30],"predictive":[31],"distributions.":[32],"However,":[33],"systematic":[36],"model":[37],"biases,":[38],"raw":[39],"outputs":[41,114],"are":[42,70,98],"often":[43],"not":[44],"well":[45],"calibrated":[46],"and":[47,75,95,103,122,142],"cannot":[48],"be":[49],"interpreted":[51],"as":[52],"estimates.":[55],"As":[56],"a":[57,90,117],"result,":[58],"operational":[59],"systems":[60],"rely":[61],"on":[62,66],"post-hoc":[63],"calibration":[64],"based":[65],"reforecast":[67],"datasets,":[68],"which":[69],"computationally":[71],"expensive":[72],"generate":[74],"maintain.":[76],"To":[77],"address":[78],"these":[79],"limitations,":[80],"we":[81],"propose":[82],"QuantWeather,":[83],"an":[84],"end-to-end":[85],"probabilistic":[86,94,113,134],"framework":[88,107],"with":[89,100,116],"dual-head":[91],"design.":[92],"The":[93,106],"deterministic":[96],"heads":[97],"supervised":[99],"separate":[101],"objectives":[102],"optimized":[104],"jointly.":[105],"further":[108],"supports":[109],"stochastic":[110,119],"sampling,":[111],"enabling":[112],"even":[115],"single":[118],"forward":[120],"pass":[121],"allowing":[123],"optional":[124],"multi-sample":[125],"aggregation.":[126],"Extensive":[127],"experiments":[128],"show":[129],"that":[130],"QuantWeather":[131],"demonstrates":[132],"superior":[133],"skill":[136],"while":[137],"substantially":[138],"reducing":[139],"inference-time":[140],"computational":[141],"storage":[143],"costs.":[144]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-13T00:00:00"}