{"id":"https://openalex.org/W2594353256","doi":"https://doi.org/10.1109/bigdata47090.2019.9005568","title":"Application of Multi-channel 3D-cube Successive Convolution Network for Convective Storm Nowcasting","display_name":"Application of Multi-channel 3D-cube Successive Convolution Network for Convective Storm Nowcasting","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W2594353256","doi":"https://doi.org/10.1109/bigdata47090.2019.9005568","mag":"2594353256"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata47090.2019.9005568","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9005568","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1702.04517","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100441494","display_name":"Wei Zhang","orcid":"https://orcid.org/0000-0001-7390-7613"},"institutions":[{"id":"https://openalex.org/I59028903","display_name":"Ocean University of China","ror":"https://ror.org/04rdtx186","country_code":"CN","type":"education","lineage":["https://openalex.org/I59028903"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wei Zhang","raw_affiliation_strings":["Ocean University of China, Qingdao, China"],"affiliations":[{"raw_affiliation_string":"Ocean University of China, Qingdao, China","institution_ids":["https://openalex.org/I59028903"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100657627","display_name":"Lei Han","orcid":"https://orcid.org/0000-0002-6141-4595"},"institutions":[{"id":"https://openalex.org/I59028903","display_name":"Ocean University of China","ror":"https://ror.org/04rdtx186","country_code":"CN","type":"education","lineage":["https://openalex.org/I59028903"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Han","raw_affiliation_strings":["Ocean University of China, Qingdao, China"],"affiliations":[{"raw_affiliation_string":"Ocean University of China, Qingdao, China","institution_ids":["https://openalex.org/I59028903"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020045932","display_name":"Juanzhen Sun","orcid":"https://orcid.org/0000-0001-6048-6362"},"institutions":[{"id":"https://openalex.org/I107766831","display_name":"NSF National Center for Atmospheric Research","ror":"https://ror.org/05cvfcr44","country_code":"US","type":"facility","lineage":["https://openalex.org/I107766831","https://openalex.org/I1311060795","https://openalex.org/I2799356940","https://openalex.org/I4210141337","https://openalex.org/I4210150888"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Juanzhen Sun","raw_affiliation_strings":["National Center for Atmospheric Research, Boulder, CO, USA"],"affiliations":[{"raw_affiliation_string":"National Center for Atmospheric Research, Boulder, CO, USA","institution_ids":["https://openalex.org/I107766831"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050963307","display_name":"Hanyang Guo","orcid":"https://orcid.org/0000-0002-5687-2655"},"institutions":[{"id":"https://openalex.org/I59028903","display_name":"Ocean University of China","ror":"https://ror.org/04rdtx186","country_code":"CN","type":"education","lineage":["https://openalex.org/I59028903"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hanyang Guo","raw_affiliation_strings":["Ocean University of China, Qingdao, China"],"affiliations":[{"raw_affiliation_string":"Ocean University of China, Qingdao, China","institution_ids":["https://openalex.org/I59028903"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076673098","display_name":"Jie Dai","orcid":"https://orcid.org/0000-0001-9848-6894"},"institutions":[{"id":"https://openalex.org/I59028903","display_name":"Ocean University of China","ror":"https://ror.org/04rdtx186","country_code":"CN","type":"education","lineage":["https://openalex.org/I59028903"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Dai","raw_affiliation_strings":["Ocean University of China, Qingdao, China"],"affiliations":[{"raw_affiliation_string":"Ocean University of China, Qingdao, China","institution_ids":["https://openalex.org/I59028903"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100441494"],"corresponding_institution_ids":["https://openalex.org/I59028903"],"apc_list":null,"apc_paid":null,"fwci":2.3628,"has_fulltext":false,"cited_by_count":40,"citation_normalized_percentile":{"value":0.87681936,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1705","last_page":"1710"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10466","display_name":"Meteorological Phenomena and Simulations","score":0.9750000238418579,"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":0.9750000238418579,"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/T14042","display_name":"Technology and Security Systems","score":0.961899995803833,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T13650","display_name":"Computational Physics and Python Applications","score":0.9532999992370605,"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/nowcasting","display_name":"Nowcasting","score":0.9434477090835571},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.7119922637939453},{"id":"https://openalex.org/keywords/storm","display_name":"Storm","score":0.6670932173728943},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5981377363204956},{"id":"https://openalex.org/keywords/convective-storm-detection","display_name":"Convective storm detection","score":0.583120584487915},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.5665493011474609},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.46366646885871887},{"id":"https://openalex.org/keywords/convection","display_name":"Convection","score":0.4324280321598053},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.3792968988418579},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.28784406185150146},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.21673709154129028},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2071516215801239},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.1343730390071869},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.08349496126174927}],"concepts":[{"id":"https://openalex.org/C2781013037","wikidata":"https://www.wikidata.org/wiki/Q1433331","display_name":"Nowcasting","level":2,"score":0.9434477090835571},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.7119922637939453},{"id":"https://openalex.org/C105306849","wikidata":"https://www.wikidata.org/wiki/Q81054","display_name":"Storm","level":2,"score":0.6670932173728943},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5981377363204956},{"id":"https://openalex.org/C192932206","wikidata":"https://www.wikidata.org/wiki/Q16951299","display_name":"Convective storm detection","level":3,"score":0.583120584487915},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.5665493011474609},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.46366646885871887},{"id":"https://openalex.org/C10899652","wikidata":"https://www.wikidata.org/wiki/Q160329","display_name":"Convection","level":2,"score":0.4324280321598053},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3792968988418579},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.28784406185150146},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.21673709154129028},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2071516215801239},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.1343730390071869},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.08349496126174927}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/bigdata47090.2019.9005568","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9005568","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1702.04517","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1702.04517","pdf_url":"https://arxiv.org/pdf/1702.04517","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1702.04517","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1702.04517","pdf_url":"https://arxiv.org/pdf/1702.04517","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"score":0.7200000286102295,"display_name":"Climate action","id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":54,"referenced_works":["https://openalex.org/W1139309024","https://openalex.org/W1485009520","https://openalex.org/W1568255176","https://openalex.org/W1641696774","https://openalex.org/W1686810756","https://openalex.org/W1799366690","https://openalex.org/W1920022804","https://openalex.org/W1982479097","https://openalex.org/W1983364832","https://openalex.org/W1990748933","https://openalex.org/W1999511976","https://openalex.org/W2002521620","https://openalex.org/W2009276230","https://openalex.org/W2012130302","https://openalex.org/W2016053056","https://openalex.org/W2016184960","https://openalex.org/W2024414272","https://openalex.org/W2032701621","https://openalex.org/W2042172159","https://openalex.org/W2058913535","https://openalex.org/W2070158856","https://openalex.org/W2078308830","https://openalex.org/W2090659718","https://openalex.org/W2097117768","https://openalex.org/W2102605133","https://openalex.org/W2112796928","https://openalex.org/W2123045220","https://openalex.org/W2131704473","https://openalex.org/W2133693888","https://openalex.org/W2153529786","https://openalex.org/W2154579312","https://openalex.org/W2158698691","https://openalex.org/W2163605009","https://openalex.org/W2174781512","https://openalex.org/W2176523672","https://openalex.org/W2211722331","https://openalex.org/W2229637417","https://openalex.org/W2232647241","https://openalex.org/W2345707736","https://openalex.org/W2919115771","https://openalex.org/W2963382180","https://openalex.org/W2963542991","https://openalex.org/W4252780833","https://openalex.org/W4293282014","https://openalex.org/W6628877408","https://openalex.org/W6629368666","https://openalex.org/W6637373629","https://openalex.org/W6638444622","https://openalex.org/W6640300118","https://openalex.org/W6653251060","https://openalex.org/W6677995690","https://openalex.org/W6679901780","https://openalex.org/W6684191040","https://openalex.org/W6689579922"],"related_works":["https://openalex.org/W2802190565","https://openalex.org/W2797123029","https://openalex.org/W3207011416","https://openalex.org/W3104851051","https://openalex.org/W2160434530","https://openalex.org/W1971891908","https://openalex.org/W4322001589","https://openalex.org/W2068212092","https://openalex.org/W1024226195","https://openalex.org/W4323043499"],"abstract_inverted_index":{"very":[0,99],"short-term":[1],"weather":[2],"forecasting":[3],"or":[4],"nowcasting":[5,190],"has":[6],"attracted":[7],"substantial":[8],"attention":[9],"in":[10],"various":[11],"fields.":[12],"Existing":[13],"methods":[14,172],"can":[15,48],"nowcast":[16,36,83],"storm":[17,37,63,84,192],"advection":[18,88],"based":[19],"on":[20],"radar":[21,29,120],"data.":[22,103],"Due":[23],"to":[24,35,61,72,82,138],"the":[25,28,43,91,98],"limitations":[26],"of":[27,69,93,183,189,191],"observations,":[30],"it":[31],"is":[32,59,68],"still":[33],"challenging":[34],"initiation":[38,64],"and":[39,65,87,121,195],"growth.":[40,66],"However,":[41],"as":[42,134],"real-time":[44],"re-analysis":[45,75,122],"meteorological":[46,102],"data":[47,123,131],"now":[49],"provide":[50],"valuable":[51],"atmospheric":[52],"boundary":[53],"layer":[54],"thermal":[55],"dynamic":[56],"information,":[57],"which":[58,115,144],"essential":[60],"predict":[62],"It":[67],"great":[70],"importance":[71],"leverage":[73],"these":[74],"data.This":[76],"paper":[77],"describes":[78],"our":[79,142,184],"first":[80],"attempt":[81],"initiation,":[85,193],"growth,":[86,194],"simultaneously":[89],"under":[90],"framework":[92],"convolutional":[94,154],"neural":[95],"network":[96,114],"using":[97],"large":[100],"multi-source":[101],"To":[104],"this":[105],"end,":[106],"we":[107,158],"construct":[108],"a":[109],"multi-channel":[110,135],"3D-cube":[111],"successive":[112,153],"convolution":[113],"leveraging":[116],"both":[117],"raw":[118],"3D":[119,136,149],"directly":[124],"without":[125,156],"any":[126],"handcraft":[127],"feature":[128],"engineering.":[129],"These":[130],"are":[132,145],"formulated":[133],"cubes,":[137],"be":[139],"fed":[140],"into":[141],"network,":[143],"convolved":[146],"by":[147],"cross-channel":[148],"convolutions.":[150],"By":[151],"stacking":[152],"layers":[155],"pooling,":[157],"build":[159],"an":[160],"end-to-end":[161],"trainable":[162],"model":[163],"for":[164],"nowcasting.":[165],"Experimental":[166],"results":[167,188],"show":[168,186],"that":[169],"deep":[170],"learning":[171],"achieve":[173],"better":[174],"performance":[175],"than":[176],"traditional":[177],"extrapolation":[178],"methods.":[179],"The":[180],"qualitative":[181],"analyses":[182],"approach":[185],"encouraging":[187],"advection.":[196]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":3}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2017-03-16T00:00:00"}
