{"id":"https://openalex.org/W4415883286","doi":"https://doi.org/10.1109/tgrs.2025.3628905","title":"Heterogeneous Spatiotemporal Graph Learning for Localized Sparse Meteorological Forecasting","display_name":"Heterogeneous Spatiotemporal Graph Learning for Localized Sparse Meteorological Forecasting","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4415883286","doi":"https://doi.org/10.1109/tgrs.2025.3628905"},"language":null,"primary_location":{"id":"doi:10.1109/tgrs.2025.3628905","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2025.3628905","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100359796","display_name":"Sheng Li","orcid":"https://orcid.org/0000-0001-8845-4362"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Sheng Li","raw_affiliation_strings":["College of Meteorology and Oceanography, National University of Defense Technology, Changsha, China"],"affiliations":[{"raw_affiliation_string":"College of Meteorology and Oceanography, National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100340635","display_name":"Qian Li","orcid":"https://orcid.org/0000-0002-9530-4925"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qian Li","raw_affiliation_strings":["College of Meteorology and Oceanography, National University of Defense Technology, Changsha, China"],"affiliations":[{"raw_affiliation_string":"College of Meteorology and Oceanography, National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100948903","display_name":"Zhencai Du","orcid":null},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210152758","display_name":"Institute of Atmospheric Physics","ror":"https://ror.org/0424h4e32","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210152758"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhencai Du","raw_affiliation_strings":["Institute of Atmospheric Physics Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute of Atmospheric Physics Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210152758","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064994218","display_name":"Zeming Zhou","orcid":"https://orcid.org/0000-0002-9264-0608"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zeming Zhou","raw_affiliation_strings":["College of Meteorology and Oceanography, National University of Defense Technology, Changsha, China"],"affiliations":[{"raw_affiliation_string":"College of Meteorology and Oceanography, National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100340858","display_name":"Min Wang","orcid":"https://orcid.org/0000-0001-6874-4282"},"institutions":[{"id":"https://openalex.org/I200845125","display_name":"Nanjing University of Information Science and Technology","ror":"https://ror.org/02y0rxk19","country_code":"CN","type":"education","lineage":["https://openalex.org/I200845125"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Min Wang","raw_affiliation_strings":["School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I200845125"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100425279","display_name":"Liang Zhang","orcid":"https://orcid.org/0000-0003-2812-5748"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Zhang","raw_affiliation_strings":["College of Meteorology and Oceanography, National University of Defense Technology, Changsha, China"],"affiliations":[{"raw_affiliation_string":"College of Meteorology and Oceanography, National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100689765","display_name":"Wei Li","orcid":"https://orcid.org/0000-0001-9786-585X"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Li","raw_affiliation_strings":["College of Meteorology and Oceanography, National University of Defense Technology, Changsha, China"],"affiliations":[{"raw_affiliation_string":"College of Meteorology and Oceanography, National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5100359796"],"corresponding_institution_ids":["https://openalex.org/I170215575"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.34339056,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"63","issue":null,"first_page":"1","last_page":"18"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.27709999680519104,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.27709999680519104,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.18770000338554382,"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"}},{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.10899999737739563,"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/graph","display_name":"Graph","score":0.5608999729156494},{"id":"https://openalex.org/keywords/data-assimilation","display_name":"Data assimilation","score":0.5371000170707703},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.46810001134872437},{"id":"https://openalex.org/keywords/weather-forecasting","display_name":"Weather forecasting","score":0.4187000095844269},{"id":"https://openalex.org/keywords/heterogeneous-network","display_name":"Heterogeneous network","score":0.4120999872684479},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.40709999203681946},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.4058000147342682},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.40450000762939453}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7343000173568726},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5608999729156494},{"id":"https://openalex.org/C24552861","wikidata":"https://www.wikidata.org/wiki/Q2670177","display_name":"Data assimilation","level":2,"score":0.5371000170707703},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4936000108718872},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46810001134872437},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43869999051094055},{"id":"https://openalex.org/C21001229","wikidata":"https://www.wikidata.org/wiki/Q182868","display_name":"Weather forecasting","level":2,"score":0.4187000095844269},{"id":"https://openalex.org/C158207573","wikidata":"https://www.wikidata.org/wiki/Q5747224","display_name":"Heterogeneous network","level":4,"score":0.4120999872684479},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.40709999203681946},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.4058000147342682},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.40450000762939453},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36970001459121704},{"id":"https://openalex.org/C199845137","wikidata":"https://www.wikidata.org/wiki/Q145490","display_name":"Network topology","level":2,"score":0.36640000343322754},{"id":"https://openalex.org/C23131810","wikidata":"https://www.wikidata.org/wiki/Q818574","display_name":"Observational study","level":2,"score":0.3544999957084656},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.34200000762939453},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.32670000195503235},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.304500013589859},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.29600000381469727},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.28459998965263367},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.2809999883174896},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.26499998569488525},{"id":"https://openalex.org/C2780440489","wikidata":"https://www.wikidata.org/wiki/Q5227278","display_name":"Data-driven","level":2,"score":0.2590000033378601},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2581999897956848}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2025.3628905","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2025.3628905","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G134806700","display_name":null,"funder_award_id":"41305138","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2882385516","display_name":null,"funder_award_id":"2021JC0009","funder_id":"https://openalex.org/F4320322843","funder_display_name":"Natural Science Foundation of\u00a0Hunan Province"},{"id":"https://openalex.org/G4133884039","display_name":null,"funder_award_id":"U2242201","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6373430735","display_name":null,"funder_award_id":"2017M621700","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"},{"id":"https://openalex.org/G6410801369","display_name":null,"funder_award_id":"42075139","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8648714761","display_name":null,"funder_award_id":"42105146","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"},{"id":"https://openalex.org/F4320322843","display_name":"Natural Science Foundation of\u00a0Hunan Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W1545465591","https://openalex.org/W2058336014","https://openalex.org/W2747599906","https://openalex.org/W2901504064","https://openalex.org/W2907492528","https://openalex.org/W2912647535","https://openalex.org/W2913323966","https://openalex.org/W2963008249","https://openalex.org/W2965341826","https://openalex.org/W3003709258","https://openalex.org/W3025949386","https://openalex.org/W3080253043","https://openalex.org/W3124663847","https://openalex.org/W3155849328","https://openalex.org/W3217152393","https://openalex.org/W4206735705","https://openalex.org/W4313443611","https://openalex.org/W4382203079","https://openalex.org/W4383218913","https://openalex.org/W4385245566","https://openalex.org/W4386034829","https://openalex.org/W4387846860","https://openalex.org/W4388430377","https://openalex.org/W4388654737","https://openalex.org/W4388728292","https://openalex.org/W4388859807","https://openalex.org/W4389069156","https://openalex.org/W4389624392","https://openalex.org/W4401357932","https://openalex.org/W4401718375","https://openalex.org/W4403600951","https://openalex.org/W4403826918","https://openalex.org/W4407451392","https://openalex.org/W4410153250","https://openalex.org/W4411203469"],"related_works":[],"abstract_inverted_index":{"Localized":[0],"meteorological":[1,15,56,67,92,179,195,203],"forecasting":[2],"holds":[3],"significant":[4],"practical":[5],"value":[6],"for":[7,88],"human":[8],"socio-economic":[9],"activities.":[10],"Current":[11],"deep":[12],"learning-based":[13],"localized":[14,54,66,95,194],"prediction":[16,90,177,205],"methods":[17,61],"trained":[18],"with":[19],"structured":[20],"reanalysis":[21,35,86,113],"data":[22,33,36,50,87,111,114,156],"have":[23],"made":[24],"great":[25],"progress.":[26],"However,":[27],"the":[28,42],"assimilation":[29],"process":[30],"from":[31],"observational":[32,49,84,110],"to":[34,52,63,107,143,170],"may":[37],"introduce":[38,160],"uncertainty":[39],"errors.":[40],"Meanwhile,":[41],"inherent":[43],"sparsity":[44],"and":[45,85,112,129,174,197],"unstructured":[46],"nature":[47],"of":[48,91,178],"fail":[51],"reflect":[53],"sparse":[55,96,109],"conditions":[57],"accurately.":[58],"Therefore,":[59],"these":[60,71],"struggle":[62],"deliver":[64],"precise":[65],"forecasts.":[68],"To":[69],"address":[70],"issues,":[72],"we":[73,99,159],"propose":[74],"a":[75,102,123,136,161],"Heterogeneous":[76,138],"Spatio-Temporal":[77],"Graph":[78,104],"Neural":[79],"Network":[80],"(HSTGNN)":[81],"that":[82,190],"integrates":[83],"in-situ":[89,202],"variables":[93,180,204],"at":[94,181],"stations.":[97],"Specifically,":[98],"first":[100],"design":[101],"Hybrid":[103],"Constructor":[105],"(HGC)":[106],"map":[108],"into":[115],"heterogeneous":[116,149],"nodes,":[117],"establishing":[118],"node":[119],"association":[120],"topologies":[121],"through":[122],"dual-channel":[124],"mechanism":[125],"combining":[126],"geographical":[127],"graphs":[128],"adaptive":[130],"learning":[131],"graphs.":[132],"We":[133],"then":[134],"develop":[135],"Hierarchical":[137],"Message":[139],"Passing":[140],"Module":[141],"(H2MPM)":[142],"facilitate":[144],"differentiated":[145],"information":[146],"interaction":[147],"between":[148],"data,":[150],"effectively":[151],"capturing":[152],"spatial":[153],"dependencies":[154,173],"across":[155],"sources.":[157],"Finally,":[158],"temporal":[162,172],"linear":[163,168],"unit":[164],"based":[165],"on":[166,186],"single-layer":[167],"regression":[169],"extract":[171],"perform":[175],"sequence":[176],"each":[182],"station.":[183],"Experimental":[184],"results":[185],"two":[187],"datasets":[188],"demonstrate":[189],"HSTGNN":[191],"comprehensively":[192],"models":[193],"pattern":[196],"achieves":[198],"outstanding":[199],"performance":[200],"in":[201],"tasks.":[206]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-11-04T00:00:00"}