{"id":"https://openalex.org/W1980439602","doi":"https://doi.org/10.1109/cidu.2012.6382199","title":"Machine learning enhancement of Storm Scale Ensemble precipitation forecasts","display_name":"Machine learning enhancement of Storm Scale Ensemble precipitation forecasts","publication_year":2012,"publication_date":"2012-10-01","ids":{"openalex":"https://openalex.org/W1980439602","doi":"https://doi.org/10.1109/cidu.2012.6382199","mag":"1980439602"},"language":"en","primary_location":{"id":"doi:10.1109/cidu.2012.6382199","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cidu.2012.6382199","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 Conference on Intelligent Data Understanding","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/A5013616568","display_name":"David John Gagne","orcid":"https://orcid.org/0000-0002-0469-2740"},"institutions":[{"id":"https://openalex.org/I8692664","display_name":"University of Oklahoma","ror":"https://ror.org/02aqsxs83","country_code":"US","type":"education","lineage":["https://openalex.org/I8692664"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"David John Gagne","raw_affiliation_strings":["School of Meteorology, University of Oklahoma, Norman, OK, USA","School of Meteorology, University of Oklahoma, Norman, USA"],"affiliations":[{"raw_affiliation_string":"School of Meteorology, University of Oklahoma, Norman, OK, USA","institution_ids":["https://openalex.org/I8692664"]},{"raw_affiliation_string":"School of Meteorology, University of Oklahoma, Norman, USA","institution_ids":["https://openalex.org/I8692664"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070181594","display_name":"Amy McGovern","orcid":"https://orcid.org/0000-0001-6675-7119"},"institutions":[{"id":"https://openalex.org/I8692664","display_name":"University of Oklahoma","ror":"https://ror.org/02aqsxs83","country_code":"US","type":"education","lineage":["https://openalex.org/I8692664"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Amy McGovern","raw_affiliation_strings":["School of Computer Science, University of Oklahoma, Norman, OK, USA","School of Computer Science, University of Oklahoma, Norman, USA;"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, University of Oklahoma, Norman, OK, USA","institution_ids":["https://openalex.org/I8692664"]},{"raw_affiliation_string":"School of Computer Science, University of Oklahoma, Norman, USA;","institution_ids":["https://openalex.org/I8692664"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001931607","display_name":"Ming Xue","orcid":"https://orcid.org/0000-0003-1976-3238"},"institutions":[{"id":"https://openalex.org/I8692664","display_name":"University of Oklahoma","ror":"https://ror.org/02aqsxs83","country_code":"US","type":"education","lineage":["https://openalex.org/I8692664"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ming Xue","raw_affiliation_strings":["Center for the Analysis and Prediction of Storms, School of Meteorology, University of Oklahoma, Norman, OK, USA","Center for the Analysis and Prediction of Storms, School of Meteorology, University of Oklahoma, Norman, USA"],"affiliations":[{"raw_affiliation_string":"Center for the Analysis and Prediction of Storms, School of Meteorology, University of Oklahoma, Norman, OK, USA","institution_ids":["https://openalex.org/I8692664"]},{"raw_affiliation_string":"Center for the Analysis and Prediction of Storms, School of Meteorology, University of Oklahoma, Norman, USA","institution_ids":["https://openalex.org/I8692664"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5013616568"],"corresponding_institution_ids":["https://openalex.org/I8692664"],"apc_list":null,"apc_paid":null,"fwci":0.6861,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.7309988,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"7","issue":null,"first_page":"39","last_page":"46"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10466","display_name":"Meteorological Phenomena and Simulations","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/T10466","display_name":"Meteorological Phenomena and Simulations","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/T11234","display_name":"Precipitation Measurement and Analysis","score":0.9994999766349792,"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/T10029","display_name":"Climate variability and models","score":0.9994000196456909,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/quantitative-precipitation-forecast","display_name":"Quantitative precipitation forecast","score":0.6717963218688965},{"id":"https://openalex.org/keywords/probabilistic-forecasting","display_name":"Probabilistic forecasting","score":0.5284854173660278},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5057034492492676},{"id":"https://openalex.org/keywords/storm","display_name":"Storm","score":0.49840331077575684},{"id":"https://openalex.org/keywords/precipitation","display_name":"Precipitation","score":0.49256235361099243},{"id":"https://openalex.org/keywords/consensus-forecast","display_name":"Consensus forecast","score":0.4752894639968872},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.4615175426006317},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.46059998869895935},{"id":"https://openalex.org/keywords/forecast-skill","display_name":"Forecast skill","score":0.4320940673351288},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.41595539450645447},{"id":"https://openalex.org/keywords/climatology","display_name":"Climatology","score":0.3480781614780426},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.335033118724823},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.329438716173172},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2591676115989685},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.224511057138443},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20779985189437866},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.10455784201622009}],"concepts":[{"id":"https://openalex.org/C140178040","wikidata":"https://www.wikidata.org/wiki/Q18402512","display_name":"Quantitative precipitation forecast","level":3,"score":0.6717963218688965},{"id":"https://openalex.org/C122282355","wikidata":"https://www.wikidata.org/wiki/Q7246855","display_name":"Probabilistic forecasting","level":3,"score":0.5284854173660278},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5057034492492676},{"id":"https://openalex.org/C105306849","wikidata":"https://www.wikidata.org/wiki/Q81054","display_name":"Storm","level":2,"score":0.49840331077575684},{"id":"https://openalex.org/C107054158","wikidata":"https://www.wikidata.org/wiki/Q25257","display_name":"Precipitation","level":2,"score":0.49256235361099243},{"id":"https://openalex.org/C120954023","wikidata":"https://www.wikidata.org/wiki/Q1127277","display_name":"Consensus forecast","level":2,"score":0.4752894639968872},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.4615175426006317},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.46059998869895935},{"id":"https://openalex.org/C170061395","wikidata":"https://www.wikidata.org/wiki/Q5468164","display_name":"Forecast skill","level":2,"score":0.4320940673351288},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.41595539450645447},{"id":"https://openalex.org/C49204034","wikidata":"https://www.wikidata.org/wiki/Q52139","display_name":"Climatology","level":1,"score":0.3480781614780426},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.335033118724823},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.329438716173172},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2591676115989685},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.224511057138443},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20779985189437866},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.10455784201622009},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cidu.2012.6382199","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cidu.2012.6382199","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 Conference on Intelligent Data Understanding","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4699999988079071,"display_name":"Climate action","id":"https://metadata.un.org/sdg/13"}],"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":23,"referenced_works":["https://openalex.org/W1573647811","https://openalex.org/W1964028606","https://openalex.org/W1979562053","https://openalex.org/W2013765062","https://openalex.org/W2026058014","https://openalex.org/W2047634553","https://openalex.org/W2060430093","https://openalex.org/W2073241381","https://openalex.org/W2078451609","https://openalex.org/W2102201073","https://openalex.org/W2110668259","https://openalex.org/W2151695040","https://openalex.org/W2158840489","https://openalex.org/W2172654556","https://openalex.org/W2174913527","https://openalex.org/W2181151222","https://openalex.org/W2548328628","https://openalex.org/W2785613746","https://openalex.org/W2911964244","https://openalex.org/W4241996101","https://openalex.org/W4293282014","https://openalex.org/W6634147026","https://openalex.org/W6748567196"],"related_works":["https://openalex.org/W2157312879","https://openalex.org/W2069225437","https://openalex.org/W3091981235","https://openalex.org/W4308071692","https://openalex.org/W2197498248","https://openalex.org/W2132065513","https://openalex.org/W1980430798","https://openalex.org/W154791109","https://openalex.org/W3140246869","https://openalex.org/W2180087856"],"abstract_inverted_index":{"Precipitation":[0],"forecasts":[1,49,70,81,106,110,159,191,213,254],"provide":[2],"both":[3],"a":[4,12,43,174,194],"crucial":[5],"service":[6],"for":[7,23,28,128,151,262],"the":[8,18,29,51,75,103,109,112,137,152,166,169,197,230,237,248,253,257,263,266,276],"general":[9],"populace":[10],"and":[11,31,53,68,86,116,145,183,207,219,233,241,250,269],"challenging":[13],"forecasting":[14],"problem":[15],"due":[16],"to":[17,91,102,107,147,256],"complex,":[19],"multi-scale":[20],"interactions":[21],"required":[22],"precipitation":[24,56,80,105,142,153,172,267],"formation.":[25],"The":[26,58,78,123],"Center":[27],"Analysis":[30],"Prediction":[32],"of":[33,46,50,132,140,158,168,236,252,265],"Storms":[34],"(CAPS)":[35],"Storm":[36],"Scale":[37],"Ensemble":[38],"Forecast":[39],"(SSEF)":[40],"system":[41],"is":[42,176],"promising":[44],"method":[45],"providing":[47],"high-resolution":[48],"intensity":[52],"uncertainty":[54],"in":[55,162,247],"forecasts.":[57],"SSEF":[59,79,104,125],"incorporates":[60],"multiple":[61,95],"models":[62],"with":[63,178,202,216],"varied":[64],"parameterization":[65],"scheme":[66],"combinations":[67],"produces":[69],"every":[71],"4":[72],"km":[73],"over":[74],"continental":[76],"US.":[77],"exhibit":[82],"significant":[83],"negative":[84],"biases":[85],"placement":[87],"errors.":[88],"In":[89,136],"order":[90],"correct":[92,108],"these":[93],"issues,":[94],"machine":[96],"learning":[97],"algorithms":[98],"have":[99],"been":[100],"applied":[101],"using":[111],"NSSL":[113],"National":[114],"Mosaic":[115],"Multisensor":[117],"QPE":[118],"(NMQ)":[119],"grid":[120],"as":[121],"verification.":[122],"2010":[124],"was":[126],"used":[127,146],"training.":[129],"Two":[130],"levels":[131],"post-processing":[133],"are":[134,143,160,200,214,224],"performed.":[135],"first,":[138],"probabilities":[139],"any":[141],"determined":[144],"find":[148],"optimal":[149],"thresholds":[150],"areas.":[154,164],"Then,":[155],"three":[156],"types":[157],"produced":[161],"those":[163],"First,":[165],"probability":[167,211],"1-hour":[170],"accumulated":[171],"exceeding":[173],"threshold":[175],"predicted":[177],"random":[179,205],"forests,":[180,206],"logistic":[181],"regression,":[182,204],"multivariate":[184],"adaptive":[185],"regression":[186,221],"splines":[187],"(MARS).":[188],"Second,":[189],"deterministic":[190],"based":[192],"on":[193],"correction":[195],"from":[196,226,229,275],"ensemble":[198,259],"mean":[199],"made":[201,215],"linear":[203],"MARS.":[208],"Third,":[209],"fixed":[210],"interval":[212],"quantile":[217,220],"regressions":[218],"forests.":[222],"Models":[223],"generated":[225],"points":[227],"sampled":[228],"western,":[231],"central,":[232],"eastern":[234],"sections":[235],"domain.":[238],"Verification":[239],"statistics":[240],"case":[242],"study":[243],"results":[244],"show":[245],"improvements":[246],"reliability":[249],"skill":[251],"compared":[255],"original":[258],"while":[260],"controlling":[261],"over-prediction":[264],"areas":[268],"without":[270],"sacrificing":[271],"smaller":[272],"scale":[273],"details":[274],"model":[277],"runs.":[278]},"counts_by_year":[{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
