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    Deep Learning–Based Summertime Turbulence Intensity Estimation Using Satellite Observations

    Source: Journal of Atmospheric and Oceanic Technology:;2023:;volume( 040 ):;issue: 011::page 1433
    Author:
    Lee, Yoonjin
    ,
    Kim, Soo-Hyun
    ,
    Noh, Yoo-Jeong
    ,
    Kim, Jung-Hoon
    DOI: 10.1175/JTECH-D-22-0137.1
    Publisher: American Meteorological Society
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      Deep Learning–Based Summertime Turbulence Intensity Estimation Using Satellite Observations

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4299983
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    • Journal of Atmospheric and Oceanic Technology

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    contributor authorLee, Yoonjin
    contributor authorKim, Soo-Hyun
    contributor authorNoh, Yoo-Jeong
    contributor authorKim, Jung-Hoon
    date accessioned2024-12-24T14:00:03Z
    date available2024-12-24T14:00:03Z
    date copyright01 Nov. 2023
    date issued2023
    identifier otheratot-JTECH-D-22-0137.1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4299983
    languageEnglish
    publisherAmerican Meteorological Society
    titleDeep Learning–Based Summertime Turbulence Intensity Estimation Using Satellite Observations
    typeJournal Paper
    journal volume40
    journal issue11
    journal titleJournal of Atmospheric and Oceanic Technology
    identifier doi10.1175/JTECH-D-22-0137.1
    journal fristpage1433
    journal lastpage1448
    treeJournal of Atmospheric and Oceanic Technology:;2023:;volume( 040 ):;issue: 011
    contenttypeFulltext
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    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian
     
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian