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Improving Precipitation Nowcasting for High-Intensity Events Using Deep Generative Models with Balanced Loss and Temperature Data: A Case Study in the Netherlands

dc.contributor.authorCambier van Nooten, Charlotte
dc.contributor.authorSchreurs, Koert
dc.contributor.authorWijnands, Jasper S.
dc.contributor.authorLeijnse, Hidde
dc.contributor.authorSchmeits, Maurice
dc.contributor.authorWhan, Kirien
dc.contributor.authorShapovalova, Yuliya
dc.date.accessioned2024-12-24T15:08:18Z
dc.date.available2024-12-24T15:08:18Z
dc.date.copyright01 Oct. 2023
dc.date.issued2023
dc.identifier.otheraies-AIES-D-23-0017.1.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4301871
dc.languageEnglish
dc.publisherAmerican Meteorological Society
dc.titleImproving Precipitation Nowcasting for High-Intensity Events Using Deep Generative Models with Balanced Loss and Temperature Data: A Case Study in the Netherlands
dc.typeJournal Paper
dc.journal.volume2
dc.journal.issue4
dc.journal.titleArtificial Intelligence for the Earth Systems
dc.identifier.doi10.1175/AIES-D-23-0017.1
dc.journal.lastpagee230017
dc.treeArtificial Intelligence for the Earth Systems:;2023:;volume( 002 ):;issue: 004
dc.contenttypeFulltext


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