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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

    Source: Journal of Hydrometeorology:;2024:;volume( 025 ):;issue: 008::page 1147
    Author:
    Lorenz, David J.
    ,
    Otkin, Jason A.
    ,
    Zaitchik, Benjamin F.
    ,
    Hain, Christopher
    ,
    Holmes, Thomas R. H.
    ,
    Anderson, Martha C.
    DOI: 10.1175/JHM-D-23-0074.1
    Publisher: American Meteorological Society
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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

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4301798
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    contributor authorLorenz, David J.
    contributor authorOtkin, Jason A.
    contributor authorZaitchik, Benjamin F.
    contributor authorHain, Christopher
    contributor authorHolmes, Thomas R. H.
    contributor authorAnderson, Martha C.
    date accessioned2024-12-24T15:05:11Z
    date available2024-12-24T15:05:11Z
    date copyright01 Aug. 2024
    date issued2024
    identifier otherhydr-JHM-D-23-0074.1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4301798
    languageEnglish
    publisherAmerican Meteorological Society
    titleImproving Subseasonal Soil Moisture and Evaporative Stress Index Forecasts through Machine Learning: The Role of Initial Land State versus Dynamical Model Output
    typeJournal Paper
    journal volume25
    journal issue8
    journal titleJournal of Hydrometeorology
    identifier doi10.1175/JHM-D-23-0074.1
    journal fristpage1147
    journal lastpage1163
    treeJournal of Hydrometeorology:;2024:;volume( 025 ):;issue: 008
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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