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Can a Machine Learning–Enabled Numerical Model Help Extend Effective Forecast Range through Consistently Trained Subgrid-Scale Models?

dc.contributor.authorQu, Yongquan
dc.contributor.authorShi, Xiaoming
dc.date.accessioned2024-12-24T15:13:47Z
dc.date.available2024-12-24T15:13:47Z
dc.date.copyright01 Jan. 2023
dc.date.issued2023
dc.identifier.otheraies-AIES-D-22-0050.1.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4302015
dc.languageEnglish
dc.publisherAmerican Meteorological Society
dc.titleCan a Machine Learning–Enabled Numerical Model Help Extend Effective Forecast Range through Consistently Trained Subgrid-Scale Models?
dc.typeJournal Paper
dc.journal.volume2
dc.journal.issue1
dc.journal.titleArtificial Intelligence for the Earth Systems
dc.identifier.doi10.1175/AIES-D-22-0050.1
dc.journal.lastpagee220050
dc.treeArtificial Intelligence for the Earth Systems:;2023:;volume( 002 ):;issue: 001
dc.contenttypeFulltext


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