{"id":"https://openalex.org/W7126047858","doi":"https://doi.org/10.1109/wpmc67460.2025.11351229","title":"Enhancing Liquid Water Content Estimation Accuracy through Large-Scale Radar Data and Advanced Processing","display_name":"Enhancing Liquid Water Content Estimation Accuracy through Large-Scale Radar Data and Advanced Processing","publication_year":2025,"publication_date":"2025-11-09","ids":{"openalex":"https://openalex.org/W7126047858","doi":"https://doi.org/10.1109/wpmc67460.2025.11351229"},"language":null,"primary_location":{"id":"doi:10.1109/wpmc67460.2025.11351229","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wpmc67460.2025.11351229","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 28th International Symposium on Wireless Personal Multimedia Communications (WPMC)","raw_type":"proceedings-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/A5012439153","display_name":"Yosuke Oya","orcid":null},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Yosuke Oya","raw_affiliation_strings":["Waseda University,School of Fundamental Science and Engineering,Tokyo,Japan"],"affiliations":[{"raw_affiliation_string":"Waseda University,School of Fundamental Science and Engineering,Tokyo,Japan","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5105140479","display_name":"Zheng Wen","orcid":"https://orcid.org/0009-0002-0909-3576"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Zheng Wen","raw_affiliation_strings":["Waseda University,Faculty of Science and Engineering,Tokyo,Japan"],"affiliations":[{"raw_affiliation_string":"Waseda University,Faculty of Science and Engineering,Tokyo,Japan","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082145620","display_name":"Da Peng","orcid":"https://orcid.org/0009-0008-9478-637X"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Dingjie Peng","raw_affiliation_strings":["Waseda University,Graduate School of Fundamental Science and Engineering,Tokyo,Japan"],"affiliations":[{"raw_affiliation_string":"Waseda University,Graduate School of Fundamental Science and Engineering,Tokyo,Japan","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124244080","display_name":"Xun Su","orcid":null},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Xun Su","raw_affiliation_strings":["Waseda University,Graduate School of Fundamental Science and Engineering,Tokyo,Japan"],"affiliations":[{"raw_affiliation_string":"Waseda University,Graduate School of Fundamental Science and Engineering,Tokyo,Japan","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007479406","display_name":"Kazuhiko Tamesue","orcid":"https://orcid.org/0000-0002-9842-5490"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kazuhiko Tamesue","raw_affiliation_strings":["Waseda University,Faculty of Science and Engineering,Tokyo,Japan"],"affiliations":[{"raw_affiliation_string":"Waseda University,Faculty of Science and Engineering,Tokyo,Japan","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079080816","display_name":"Hiroyuki Kasai","orcid":"https://orcid.org/0000-0003-1161-6823"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hiroyuki Kasai","raw_affiliation_strings":["Waseda University,Faculty of Science and Engineering,Tokyo,Japan"],"affiliations":[{"raw_affiliation_string":"Waseda University,Faculty of Science and Engineering,Tokyo,Japan","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124203932","display_name":"Wataru Kameyama","orcid":null},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Wataru Kameyama","raw_affiliation_strings":["Waseda University,Faculty of Science and Engineering,Tokyo,Japan"],"affiliations":[{"raw_affiliation_string":"Waseda University,Faculty of Science and Engineering,Tokyo,Japan","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052661964","display_name":"Takuro Sato","orcid":"https://orcid.org/0000-0001-5973-164X"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takuro Sato","raw_affiliation_strings":["Waseda University,Faculty of Science and Engineering,Tokyo,Japan"],"affiliations":[{"raw_affiliation_string":"Waseda University,Faculty of Science and Engineering,Tokyo,Japan","institution_ids":["https://openalex.org/I150744194"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5012439153"],"corresponding_institution_ids":["https://openalex.org/I150744194"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.7226246,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11234","display_name":"Precipitation Measurement and Analysis","score":0.8406999707221985,"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":0.8406999707221985,"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/T10347","display_name":"Atmospheric aerosols and clouds","score":0.05900000035762787,"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/T10466","display_name":"Meteorological Phenomena and Simulations","score":0.03290000185370445,"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/radar","display_name":"Radar","score":0.5666000247001648},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5234000086784363},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.5063999891281128},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.49230000376701355},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.4584999978542328},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.45559999346733093},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.44519999623298645},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.40290001034736633},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.3644999861717224}],"concepts":[{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.6126000285148621},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.5666000247001648},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.5509999990463257},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5234000086784363},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.5063999891281128},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.49230000376701355},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.46140000224113464},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.4584999978542328},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.45559999346733093},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.44519999623298645},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.42910000681877136},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.40290001034736633},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.3644999861717224},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.35280001163482666},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.3427000045776367},{"id":"https://openalex.org/C21001229","wikidata":"https://www.wikidata.org/wiki/Q182868","display_name":"Weather forecasting","level":2,"score":0.33399999141693115},{"id":"https://openalex.org/C85502700","wikidata":"https://www.wikidata.org/wiki/Q2995540","display_name":"Liquid water content","level":3,"score":0.3310000002384186},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.32679998874664307},{"id":"https://openalex.org/C24939127","wikidata":"https://www.wikidata.org/wiki/Q373499","display_name":"Water content","level":2,"score":0.31139999628067017},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.3073999881744385},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.2985999882221222},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.28839999437332153},{"id":"https://openalex.org/C138827492","wikidata":"https://www.wikidata.org/wiki/Q6661985","display_name":"Data processing","level":2,"score":0.2840000092983246},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.2791999876499176},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.27059999108314514},{"id":"https://openalex.org/C2778152352","wikidata":"https://www.wikidata.org/wiki/Q5165061","display_name":"Content (measure theory)","level":2,"score":0.26969999074935913},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26570001244544983},{"id":"https://openalex.org/C92237259","wikidata":"https://www.wikidata.org/wiki/Q863343","display_name":"Weather radar","level":3,"score":0.2646999955177307},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2623000144958496},{"id":"https://openalex.org/C118365302","wikidata":"https://www.wikidata.org/wiki/Q4817115","display_name":"Atmospheric model","level":2,"score":0.25699999928474426},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2565000057220459},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.2540000081062317},{"id":"https://openalex.org/C6350597","wikidata":"https://www.wikidata.org/wiki/Q339495","display_name":"Altitude (triangle)","level":2,"score":0.25279998779296875}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wpmc67460.2025.11351229","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wpmc67460.2025.11351229","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 28th International Symposium on Wireless Personal Multimedia Communications (WPMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.42944347858428955,"display_name":"Clean water and sanitation","id":"https://metadata.un.org/sdg/6"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W1995960218","https://openalex.org/W1999440036","https://openalex.org/W2039845302","https://openalex.org/W2102295079","https://openalex.org/W2112020841","https://openalex.org/W2138644576","https://openalex.org/W2289243322","https://openalex.org/W4399602313"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"estimation":[1,146],"of":[2,55,64,151],"Liquid":[3],"Water":[4],"Content":[5],"(LWC)":[6],"is":[7],"vital":[8],"for":[9],"understanding":[10],"cloud":[11,86],"microphysics":[12],"and":[13,85,100,105,113,138,143],"improving":[14],"weather":[15],"prediction.":[16],"Our":[17],"previous":[18,118],"study":[19],"[1]":[20],"used":[21],"a":[22,44,52,131],"Bayesian":[23,152],"Neural":[24],"Network":[25],"(BNN)":[26],"with":[27],"dual-frequency":[28],"radar":[29],"(35":[30],"GHz,":[31],"95":[32],"GHz)":[33],"to":[34,116],"estimate":[35],"LWC,":[36],"achieving":[37],"high":[38],"accuracy":[39],"but":[40],"being":[41],"limited":[42],"by":[43],"small":[45],"dataset.":[46],"In":[47],"this":[48],"study,":[49],"we":[50],"constructed":[51],"large-scale":[53,135],"dataset":[54],"129,357":[56],"samples":[57],"using":[58],"observations":[59,137],"from":[60],"the":[61,72,117,148],"U.S.":[62],"Department":[63],"Energy\u2019s":[65],"Atmospheric":[66],"Radiation":[67],"Measurement":[68],"(ARM)":[69],"program":[70],"at":[71],"Cape":[73],"Cod":[74],"site":[75],"(2016\u20132017).":[76],"After":[77],"preprocessing\u2014including":[78],"temporal":[79],"synchronization,":[80],"altitude":[81],"interpolation,":[82],"noise":[83],"reduction,":[84],"mask":[87],"extraction\u2014four":[88],"models":[89],"were":[90],"evaluated:":[91],"Random":[92],"Forest":[93],"(RF),":[94],"XGBoost":[95],"(XGB),":[96],"Decision":[97],"Tree":[98],"(DT),":[99],"Linear":[101],"Regression":[102],"(LR).":[103],"RF":[104],"XGB":[106],"achieved":[107],"R<sup":[108],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[109],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sup>":[110],"=":[111],"0.78":[112],"0.76,":[114],"comparable":[115],"BNN":[119],"results,":[120],"despite":[121],"not":[122],"relying":[123],"on":[124],"probabilistic":[125],"inference.":[126],"These":[127],"findings":[128],"demonstrate":[129],"that":[130],"data-centric":[132],"approach,":[133],"leveraging":[134],"ARM":[136],"ensemble":[139],"learning,":[140],"enables":[141],"robust":[142],"practical":[144],"LWC":[145],"without":[147],"computational":[149],"cost":[150],"models.":[153]},"counts_by_year":[],"updated_date":"2026-02-01T03:34:12.195049","created_date":"2026-01-30T00:00:00"}
