{"id":"https://openalex.org/W7119002085","doi":"https://doi.org/10.1175/jtech-d-24-0142.1","title":"Novel Multiview Machine Learning Classification of Snowflakes: Harnessing Convolutional Neural Networks and Multiangle Multicamera Instruments","display_name":"Novel Multiview Machine Learning Classification of Snowflakes: Harnessing Convolutional Neural Networks and Multiangle Multicamera Instruments","publication_year":2026,"publication_date":"2026-01-07","ids":{"openalex":"https://openalex.org/W7119002085","doi":"https://doi.org/10.1175/jtech-d-24-0142.1"},"language":null,"primary_location":{"id":"doi:10.1175/jtech-d-24-0142.1","doi":"https://doi.org/10.1175/jtech-d-24-0142.1","is_oa":false,"landing_page_url":"https://doi.org/10.1175/jtech-d-24-0142.1","pdf_url":null,"source":{"id":"https://openalex.org/S135937692","display_name":"Journal of Atmospheric and Oceanic Technology","issn_l":"0739-0572","issn":["0739-0572","1520-0426"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","dongbi-b","jufo-1","norway-1","poland-100","russia-white-list-1"],"host_organization":"https://openalex.org/P4310320260","host_organization_name":"American Meteorological Society","host_organization_lineage":["https://openalex.org/P4310320260"],"host_organization_lineage_names":["American Meteorological Society"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Atmospheric and Oceanic Technology","raw_type":"journal-article"},"volume":null,"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/A5048570026","display_name":"Hein Thant","orcid":"https://orcid.org/0009-0004-8541-0160","observed_orcids":["https://orcid.org/0009-0004-8541-0160"]},"institutions":[{"id":"https://openalex.org/I92446798","display_name":"Colorado State University","ror":"https://ror.org/03k1gpj17","country_code":"US","type":"education","lineage":["https://openalex.org/I92446798"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hein Thant","raw_affiliation_strings":["Electrical and Computer Engineering Department, Colorado State University, Fort Collins, Colorado"],"raw_orcid":"https://orcid.org/0009-0004-8541-0160","affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering Department, Colorado State University, Fort Collins, Colorado","institution_ids":["https://openalex.org/I92446798"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027524682","display_name":"Branislav M. Notaro\u0161","orcid":"https://orcid.org/0000-0002-5755-961X","observed_orcids":["https://orcid.org/0000-0002-5755-961X"]},"institutions":[{"id":"https://openalex.org/I92446798","display_name":"Colorado State University","ror":"https://ror.org/03k1gpj17","country_code":"US","type":"education","lineage":["https://openalex.org/I92446798"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Branislav M. Notaro\u0161","raw_affiliation_strings":["Electrical and Computer Engineering Department, Colorado State University, Fort Collins, Colorado"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering Department, Colorado State University, Fort Collins, Colorado","institution_ids":["https://openalex.org/I92446798"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I92446798"],"apc_list":null,"apc_paid":null,"fwci":3.2004,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.85832988,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":96},"biblio":{"volume":"43","issue":"2","first_page":"149","last_page":"168"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.44429999589920044,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.44429999589920044,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11234","display_name":"Precipitation Measurement and Analysis","score":0.42410001158714294,"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/T10644","display_name":"Cryospheric studies and observations","score":0.053199999034404755,"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/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.9359999895095825},{"id":"https://openalex.org/keywords/frozen-hydrometeors","display_name":"frozen hydrometeors","score":0.8830000162124634},{"id":"https://openalex.org/keywords/ice-particle-microphysics","display_name":"ice particle microphysics","score":0.7279999852180481},{"id":"https://openalex.org/keywords/quantitative-precipitation-estimation","display_name":"quantitative precipitation estimation","score":0.8960000276565552},{"id":"https://openalex.org/keywords/polarimetric-radar","display_name":"polarimetric radar","score":0.7620000243186951}],"concepts":[{"id":"https://openalex.org/C54059038","wikidata":"https://www.wikidata.org/wiki/Q550147","display_name":"Snowflake","level":3,"score":0.9779999852180481},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.770799994468689},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7024000287055969},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6890000104904175},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3982999920845032},{"id":"https://openalex.org/C197046000","wikidata":"https://www.wikidata.org/wiki/Q7561","display_name":"Snow","level":2,"score":0.3831000030040741},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.36890000104904175},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3652999997138977},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.3546999990940094},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.304500013589859},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.30300000309944153},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.30059999227523804},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.2962000072002411},{"id":"https://openalex.org/C28493345","wikidata":"https://www.wikidata.org/wiki/Q899381","display_name":"Polarimetry","level":3,"score":0.29440000653266907},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.28349998593330383},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2687999904155731},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.263700008392334},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.25859999656677246},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2567000091075897},{"id":"https://openalex.org/C63085389","wikidata":"https://www.wikidata.org/wiki/Q4287912","display_name":"Medoid","level":3,"score":0.2563000023365021},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.2531000077724457}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1175/jtech-d-24-0142.1","doi":"https://doi.org/10.1175/jtech-d-24-0142.1","is_oa":false,"landing_page_url":"https://doi.org/10.1175/jtech-d-24-0142.1","pdf_url":null,"source":{"id":"https://openalex.org/S135937692","display_name":"Journal of Atmospheric and Oceanic Technology","issn_l":"0739-0572","issn":["0739-0572","1520-0426"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","dongbi-b","jufo-1","norway-1","poland-100","russia-white-list-1"],"host_organization":"https://openalex.org/P4310320260","host_organization_name":"American Meteorological Society","host_organization_lineage":["https://openalex.org/P4310320260"],"host_organization_lineage_names":["American Meteorological Society"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Atmospheric and Oceanic Technology","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Abstract":[0],"Classification":[1],"of":[2,10,21,28,43,55,89,97,122,154,166,189,200,243,278,294,300],"snowflakes":[3,56,90,155],"based":[4,75,117,156],"on":[5,76,118,157,330],"their":[6,158],"geometric":[7,206,261],"shape,":[8],"degree":[9,173,264],"riming,":[11],"and":[12,19,25,47,85,109,145,161,163,174,203,210,219,235,266,292,297,302,314,324,333],"melt/dry":[13,175,211,267],"state":[14,268],"can":[15],"improve":[16],"understanding,":[17,290],"characterization,":[18,291],"quantification":[20,293],"other":[22,273],"geometrical,":[23,295],"microphysical,":[24,296],"scattering":[26,298],"properties":[27,299],"ice":[29,301],"particles.":[30],"For":[31],"example,":[32],"classification":[33,88,115,153,183,195,225,255,274,288],"provides":[34],"essential":[35,308],"ground-truth":[36],"data":[37],"for":[38,65,83,205,239,253,259,309,315],"interpreting":[39],"polarimetric":[40],"radar":[41],"signatures":[42],"snow":[44,303],"while":[45],"validating":[46],"advancing":[48],"radar-based":[49],"quantitative":[50,310],"precipitation":[51,311],"estimation.":[52],"High-resolution":[53],"photographs":[54,121,242],"obtained":[57],"by":[58,131],"emerging":[59],"multicamera":[60,133,237],"instruments":[61,238],"are":[62,307],"well":[63],"suited":[64],"snowflake":[66,99,114,168,182,224,260],"classification,":[67,234,262],"which,":[68],"coupled":[69],"with":[70,103,328],"recent":[71,228],"machine":[72,231,284],"learning":[73,139],"techniques":[74,149],"convolutional":[77],"neural":[78],"networks":[79],"(CNNs),":[80],"enable":[81],"methods":[82],"accurate":[84,162],"fast":[86],"automatic":[87],"using":[91],"images.":[92],"Given":[93],"that":[94,185],"the":[95,119,132,179,279],"appearance":[96],"a":[98,111,221],"generally":[100],"changes":[101],"significantly":[102,271],"viewing":[104],"angle,":[105],"this":[106],"work":[107,217],"proposes":[108,218],"presents":[110,220],"novel":[112,222],"multiview":[113,181,190,194,223,233,254,283],"methodology":[116,226],"high-resolution":[120,241],"frozen":[123,244],"hydrometeors":[124,245],"in":[125,230,246,276,319],"free-fall":[126,247],"from":[127,248],"multiple":[128,249],"views":[129],"collected":[130],"instruments.":[134],"The":[135,251],"approach":[136],"employs":[137],"machine/deep":[138],"algorithms":[140,313],"leveraging":[141,227],"multiangle":[142,236],"camera":[143,191],"systems":[144],"enhanced":[146],"supervised":[147],"CNN-based":[148],"to":[150],"achieve":[151],"precise":[152],"geometrical":[159],"categories":[160],"reliable":[164],"estimates":[165],"specific":[167],"properties,":[169],"such":[170],"as":[171],"riming":[172,208,263],"state.":[176],"This":[177,216],"represents":[178],"first":[180],"framework":[184],"takes":[186],"full":[187],"advantage":[188],"systems.":[192],"Presented":[193],"results":[196,252],"show":[197,256],"record":[198,257],"accuracies":[199,258],"98.57%,":[201],"98.22%,":[202],"95.83%":[204],"classes,":[207],"degree,":[209],"state,":[212],"respectively.":[213],"Significance":[214],"Statement":[215],"developments":[229],"learning,":[232],"acquiring":[240],"views.":[250],"estimation,":[265,269],"respectively,":[270],"outperforming":[272],"models":[275,323],"each":[277],"same":[280],"categories.":[281],"Automatic":[282],"learning\u2013based":[285],"winter":[286],"hydrometeor":[287],"enhances":[289],"hydrometeors.":[304],"These":[305],"improvements":[306],"estimation":[312],"microphysical":[316],"parameterizations":[317],"employed":[318],"numerical":[320],"winter-weather":[321],"forecast":[322],"regional":[325],"climate":[326],"projections,":[327],"impacts":[329],"economy,":[331],"safety,":[332],"everyday":[334],"life.":[335]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-10-08T09:06:37.294493","created_date":"2026-01-08T00:00:00"}
