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    Improvement of Liquid Water Content Retrieval Accuracy by Multilevel Detection in Cloud Tomography

    Source: Journal of Atmospheric and Oceanic Technology:;2012:;volume( 030 ):;issue: 002::page 301
    Author:
    Zhou, Jun
    ,
    Lei, Hengchi
    ,
    Ji, Lei
    DOI: 10.1175/JTECH-D-12-00054.1
    Publisher: American Meteorological Society
    Abstract: new multilevel detection scheme for cloud tomography is developed. This scheme solves problems intrinsic to conventional single-level detection, such as the lateral boundary problem and the low accuracy of liquid water content (LWC) retrieval for clouds without distinct liquid water cores. Sensitivity studies show that the new multilevel detection scheme can significantly enhance the well posedness of the inverse problem and increases the accuracy of the retrieval. These improvements are achieved not only for clouds with distinct liquid water cores but also for clouds with weak or no liquid water cores, which are difficult to accurately reconstruct using a single-level detection scheme. The settlement of the lateral boundary problem also leads to a natural and easy way of solving the detection time limit problem in cloud tomography. By using a multi-aircraft flight (MAF) scheme, segmental retrieval can be applied to make the applicable scope of cloud tomography much broader. Considering the detection time limit and the cost in practice, the feasible flight scheme at present is MAF with two detection levels. Although only one detection level is added to the conventional single-level scheme, the accuracy of LWC retrieval can be improved by 1.4%?13.1%.
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      Improvement of Liquid Water Content Retrieval Accuracy by Multilevel Detection in Cloud Tomography

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4228075
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    contributor authorZhou, Jun
    contributor authorLei, Hengchi
    contributor authorJi, Lei
    date accessioned2017-06-09T17:24:33Z
    date available2017-06-09T17:24:33Z
    date copyright2013/02/01
    date issued2012
    identifier issn0739-0572
    identifier otherams-84709.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4228075
    description abstractnew multilevel detection scheme for cloud tomography is developed. This scheme solves problems intrinsic to conventional single-level detection, such as the lateral boundary problem and the low accuracy of liquid water content (LWC) retrieval for clouds without distinct liquid water cores. Sensitivity studies show that the new multilevel detection scheme can significantly enhance the well posedness of the inverse problem and increases the accuracy of the retrieval. These improvements are achieved not only for clouds with distinct liquid water cores but also for clouds with weak or no liquid water cores, which are difficult to accurately reconstruct using a single-level detection scheme. The settlement of the lateral boundary problem also leads to a natural and easy way of solving the detection time limit problem in cloud tomography. By using a multi-aircraft flight (MAF) scheme, segmental retrieval can be applied to make the applicable scope of cloud tomography much broader. Considering the detection time limit and the cost in practice, the feasible flight scheme at present is MAF with two detection levels. Although only one detection level is added to the conventional single-level scheme, the accuracy of LWC retrieval can be improved by 1.4%?13.1%.
    publisherAmerican Meteorological Society
    titleImprovement of Liquid Water Content Retrieval Accuracy by Multilevel Detection in Cloud Tomography
    typeJournal Paper
    journal volume30
    journal issue2
    journal titleJournal of Atmospheric and Oceanic Technology
    identifier doi10.1175/JTECH-D-12-00054.1
    journal fristpage301
    journal lastpage312
    treeJournal of Atmospheric and Oceanic Technology:;2012:;volume( 030 ):;issue: 002
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian