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Improving Subseasonal Soil Moisture and Evaporative Stress Index Forecasts through Machine Learning: The Role of Initial Land State versus Dynamical Model Output

dc.contributor.authorLorenz, David J.
dc.contributor.authorOtkin, Jason A.
dc.contributor.authorZaitchik, Benjamin F.
dc.contributor.authorHain, Christopher
dc.contributor.authorHolmes, Thomas R. H.
dc.contributor.authorAnderson, Martha C.
dc.date.accessioned2024-12-24T15:05:11Z
dc.date.available2024-12-24T15:05:11Z
dc.date.copyright01 Aug. 2024
dc.date.issued2024
dc.identifier.otherhydr-JHM-D-23-0074.1.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4301798
dc.languageEnglish
dc.publisherAmerican Meteorological Society
dc.titleImproving Subseasonal Soil Moisture and Evaporative Stress Index Forecasts through Machine Learning: The Role of Initial Land State versus Dynamical Model Output
dc.typeJournal Paper
dc.journal.volume25
dc.journal.issue8
dc.journal.titleJournal of Hydrometeorology
dc.identifier.doi10.1175/JHM-D-23-0074.1
dc.journal.fristpage1147
dc.journal.lastpage1163
dc.treeJournal of Hydrometeorology:;2024:;volume( 025 ):;issue: 008
dc.contenttypeFulltext


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