{"id":"https://openalex.org/W4206228727","doi":"https://doi.org/10.1109/tgrs.2021.3138395","title":"A Remapping Technique of FY-3D MWRI Based on a Convolutional Neural Network for the Reduction of Representativeness Error","display_name":"A Remapping Technique of FY-3D MWRI Based on a Convolutional Neural Network for the Reduction of Representativeness Error","publication_year":2021,"publication_date":"2021-12-24","ids":{"openalex":"https://openalex.org/W4206228727","doi":"https://doi.org/10.1109/tgrs.2021.3138395"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2021.3138395","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2021.3138395","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/A5002239729","display_name":"Ke Chen","orcid":"https://orcid.org/0000-0002-3358-3885"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ke Chen","raw_affiliation_strings":["Science and Technology on Multi-Spectral Information Processing Laboratory, School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-3358-3885","affiliations":[{"raw_affiliation_string":"Science and Technology on Multi-Spectral Information Processing Laboratory, School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087955801","display_name":"Xu\u2010Lei Fan","orcid":"https://orcid.org/0000-0002-5080-8783"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xulei Fan","raw_affiliation_strings":["Science and Technology on Multi-Spectral Information Processing Laboratory, School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-5080-8783","affiliations":[{"raw_affiliation_string":"Science and Technology on Multi-Spectral Information Processing Laboratory, School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100452682","display_name":"Wei Han","orcid":"https://orcid.org/0000-0002-1966-446X"},"institutions":[{"id":"https://openalex.org/I141301092","display_name":"China Meteorological Administration","ror":"https://ror.org/00bx3rb98","country_code":"CN","type":"government","lineage":["https://openalex.org/I141301092"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Han","raw_affiliation_strings":["CMA Earth System Modeling and Prediction Centre (CEMC), and the State Key Laboratory of Severe Weather (LaSW), China Meteorological Administration, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-1966-446X","affiliations":[{"raw_affiliation_string":"CMA Earth System Modeling and Prediction Centre (CEMC), and the State Key Laboratory of Severe Weather (LaSW), China Meteorological Administration, Beijing, China","institution_ids":["https://openalex.org/I141301092"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059745746","display_name":"Hongyi Xiao","orcid":"https://orcid.org/0000-0001-5892-9988"},"institutions":[{"id":"https://openalex.org/I141301092","display_name":"China Meteorological Administration","ror":"https://ror.org/00bx3rb98","country_code":"CN","type":"government","lineage":["https://openalex.org/I141301092"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongyi Xiao","raw_affiliation_strings":["CMA Earth System Modeling and Prediction Centre (CEMC), and the State Key Laboratory of Severe Weather (LaSW), China Meteorological Administration, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CMA Earth System Modeling and Prediction Centre (CEMC), and the State Key Laboratory of Severe Weather (LaSW), China Meteorological Administration, Beijing, China","institution_ids":["https://openalex.org/I141301092"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.3265,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.79505062,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"60","issue":null,"first_page":"1","last_page":"11"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11234","display_name":"Precipitation Measurement and Analysis","score":0.9998999834060669,"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/T11234","display_name":"Precipitation Measurement and Analysis","score":0.9998999834060669,"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/T10466","display_name":"Meteorological Phenomena and Simulations","score":0.9998000264167786,"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/T11312","display_name":"Soil Moisture and Remote Sensing","score":0.9940000176429749,"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/representativeness-heuristic","display_name":"Representativeness heuristic","score":0.6014801859855652},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.545394241809845},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.49038100242614746},{"id":"https://openalex.org/keywords/notation","display_name":"Notation","score":0.4877358376979828},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47801315784454346},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4699319303035736},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3476318418979645},{"id":"https://openalex.org/keywords/arithmetic","display_name":"Arithmetic","score":0.12244582176208496},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11308833956718445}],"concepts":[{"id":"https://openalex.org/C37381756","wikidata":"https://www.wikidata.org/wiki/Q20203288","display_name":"Representativeness heuristic","level":2,"score":0.6014801859855652},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.545394241809845},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.49038100242614746},{"id":"https://openalex.org/C45357846","wikidata":"https://www.wikidata.org/wiki/Q2001982","display_name":"Notation","level":2,"score":0.4877358376979828},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47801315784454346},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4699319303035736},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3476318418979645},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.12244582176208496},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11308833956718445}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2021.3138395","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2021.3138395","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":[{"id":"https://metadata.un.org/sdg/13","score":0.5199999809265137,"display_name":"Climate action"}],"awards":[{"id":"https://openalex.org/G5101139844","display_name":null,"funder_award_id":"2019YFC1510400","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G6053165725","display_name":null,"funder_award_id":"42075155","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/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1495761489","https://openalex.org/W1677182931","https://openalex.org/W1969123802","https://openalex.org/W2013201089","https://openalex.org/W2048942379","https://openalex.org/W2090988712","https://openalex.org/W2124512154","https://openalex.org/W2133665775","https://openalex.org/W2142226356","https://openalex.org/W2465998120","https://openalex.org/W2740301761","https://openalex.org/W2747994107","https://openalex.org/W2793433153","https://openalex.org/W2805713129","https://openalex.org/W2926933144","https://openalex.org/W2963814095","https://openalex.org/W2972752918","https://openalex.org/W2981657880","https://openalex.org/W2998570871","https://openalex.org/W3007895453","https://openalex.org/W3082229909","https://openalex.org/W3121331123","https://openalex.org/W6719983638"],"related_works":["https://openalex.org/W3159631231","https://openalex.org/W4306248409","https://openalex.org/W4211213551","https://openalex.org/W2062728131","https://openalex.org/W1824075546","https://openalex.org/W2103926897","https://openalex.org/W2101250918","https://openalex.org/W4376143407","https://openalex.org/W2894406327","https://openalex.org/W2796561009"],"abstract_inverted_index":{"The":[0,61,105,176,202],"assimilation":[1],"of":[2,76,212],"spaceborne":[3],"passive":[4],"microwave":[5,54],"measurements":[6,227],"often":[7],"suffers":[8],"from":[9],"representativeness":[10,49],"errors":[11],"due":[12],"to":[13,46,166],"the":[14,17,48,72,136,139,164,168,172,191,207,215,225],"mismatch":[15],"between":[16,143],"observation":[18,58,144,180,185,219],"footprints":[19],"and":[20,91,113,131,145,194,209],"numerical":[21],"weather":[22],"prediction":[23],"(NWP)":[24],"model":[25,130],"grids.":[26],"In":[27,156],"this":[28],"article,":[29],"a":[30,38],"new":[31],"brightness":[32,80],"temperature":[33,81,95],"remapping":[34,62,141],"technique":[35,63],"based":[36],"on":[37],"deep":[39],"convolutional":[40],"neural":[41],"network":[42,137,169],"(CNN)":[43],"is":[44,161],"proposed":[45],"reduce":[47],"error":[50],"in":[51,70,153,228],"FengYun-3D":[52],"(FY-3D)":[53],"radiation":[55],"imager":[56],"(MWRI)":[57],"data":[59,74,154,181],"assimilation.":[60,155],"uses":[64],"an":[65],"adapted":[66],"dataset":[67],"construction":[68],"method":[69],"which":[71,121],"training":[73],"consist":[75],"synthetic":[77,92,106],"NWP-model-grid-based":[78],"MWRI":[79,132,179,218],"(":[82,96],"<inline-formula":[83,97,107,114,147],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[84,98,108,115,148],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">":[85,99,109,116,149],"<tex-math":[86,100,110,117,150],"notation=\"LaTeX\">$T_{B}$":[87,118,151],"</tex-math></inline-formula>":[88,102,112,119,152],")":[89,103],"images":[90],"MWRI-observed":[93],"antenna":[94],"notation=\"LaTeX\">$T_{A}$":[101,111],"images.":[104],",":[120],"are":[122,182,220],"generated":[123],"through":[124,184],"radiative":[125],"transfer":[126],"for":[127],"TOVS":[128],"(RTTOV)":[129],"degradation":[133],"model,":[134],"make":[135],"learn":[138],"spatial":[140],"relationship":[142],"background":[146,187],"addition,":[157],"land\u2013sea":[158],"mask":[159],"information":[160],"input":[162],"into":[163],"CNN":[165],"help":[167],"better":[170],"analyze":[171],"coastline":[173],"area":[174],"data.":[175],"CNN-based":[177,216],"remapped":[178,217],"evaluated":[183],"minus":[186],"(OMB)":[188],"diagnosis":[189],"with":[190,214,224],"Global/Regional":[192],"Assimilation":[193],"PrEdiction":[195],"System":[196],"(GRAPES)":[197],"four-dimensional":[198],"variational":[199],"(4D-Var)":[200],"system.":[201],"experimental":[203],"results":[204],"illustrate":[205],"that":[206],"bias":[208],"standard":[210],"deviation":[211],"OMB":[213],"quantitatively":[221],"reduced":[222],"compared":[223],"raw":[226],"GRAPES":[229],"4D-Var.":[230]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
