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Deep Learning of a 200-Member Ensemble with a Limited Historical Training to Improve the Prediction of Extreme Precipitation Events

dc.contributor.authorGhazvinian, Mohammadvaghef
dc.contributor.authorDelle Monache, Luca
dc.contributor.authorAfzali Gorooh, Vesta
dc.contributor.authorSteinhoff, Daniel
dc.contributor.authorSengupta, Agniv
dc.contributor.authorHu, Weiming
dc.contributor.authorSimpson, Matthew
dc.contributor.authorWeihs, Rachel
dc.contributor.authorPapadopoulos, Caroline
dc.contributor.authorMulrooney, Patrick
dc.contributor.authorKawzenuk, Brian
dc.contributor.authorMascioli, Nora
dc.contributor.authorRalph, Fred Martin
dc.date.accessioned2024-12-24T14:58:21Z
dc.date.available2024-12-24T14:58:21Z
dc.date.copyright01 Jul. 2024
dc.date.issued2024
dc.identifier.othermwre-MWR-D-23-0277.1.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4301618
dc.languageEnglish
dc.publisherAmerican Meteorological Society
dc.titleDeep Learning of a 200-Member Ensemble with a Limited Historical Training to Improve the Prediction of Extreme Precipitation Events
dc.typeJournal Paper
dc.journal.volume152
dc.journal.issue7
dc.journal.titleMonthly Weather Review
dc.identifier.doi10.1175/MWR-D-23-0277.1
dc.journal.fristpage1587
dc.journal.lastpage1605
dc.treeMonthly Weather Review:;2024:;volume( 152 ):;issue: 007
dc.contenttypeFulltext


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