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    Atmospheric Liquid Water Retrieval Using a Gated Experts Neural Network

    Source: Journal of Atmospheric and Oceanic Technology:;2002:;volume( 019 ):;issue: 004::page 457
    Author:
    Moreau, E.
    ,
    Mallet, C.
    ,
    Thiria, S.
    ,
    Mabboux, B.
    ,
    Badran, F.
    ,
    Klapisz, C.
    DOI: 10.1175/1520-0426(2002)019<0457:ALWRUA>2.0.CO;2
    Publisher: American Meteorological Society
    Abstract: Gated experts (GE) neural networks have been developed in order to retrieve atmospheric liquid water content over ocean from radiometer data. Gated experts neural networks are statistical models, which can model any general class of function. This paper focuses on the case where the complex transfer functions can be split on different simpler functions in order to improve the accuracy. Two atmospheric quantities are considered: the integrated cloud liquid water (iclw) and the surface rain rate (RR). In the case of iclw, the GE neural network finds two modes, splitting the problem into low and high iclw values. The physical meaning of those modes is discussed. A comparison with a standard regression algorithm and a multilayer perceptron neural network is done on simulated data and an ?indirect comparison? is done using Special Sensor Microwave Imager (SSM/I) data. In the case of RR, the focus is on the ability of GE neural networks to perform a classification between rainy and nonrainy situations. Tropical Rainfall Measuring Mission (TRMM) data are used for rain-rate validation: rain-rate retrieval from the GE algorithm applied to actual TRMM Microwave Imager (TMI) measurements are compared with collocated precipitation radar (PR) rain rate.
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      Atmospheric Liquid Water Retrieval Using a Gated Experts Neural Network

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4155845
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    • Journal of Atmospheric and Oceanic Technology

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    contributor authorMoreau, E.
    contributor authorMallet, C.
    contributor authorThiria, S.
    contributor authorMabboux, B.
    contributor authorBadran, F.
    contributor authorKlapisz, C.
    date accessioned2017-06-09T14:27:50Z
    date available2017-06-09T14:27:50Z
    date copyright2002/04/01
    date issued2002
    identifier issn0739-0572
    identifier otherams-1970.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4155845
    description abstractGated experts (GE) neural networks have been developed in order to retrieve atmospheric liquid water content over ocean from radiometer data. Gated experts neural networks are statistical models, which can model any general class of function. This paper focuses on the case where the complex transfer functions can be split on different simpler functions in order to improve the accuracy. Two atmospheric quantities are considered: the integrated cloud liquid water (iclw) and the surface rain rate (RR). In the case of iclw, the GE neural network finds two modes, splitting the problem into low and high iclw values. The physical meaning of those modes is discussed. A comparison with a standard regression algorithm and a multilayer perceptron neural network is done on simulated data and an ?indirect comparison? is done using Special Sensor Microwave Imager (SSM/I) data. In the case of RR, the focus is on the ability of GE neural networks to perform a classification between rainy and nonrainy situations. Tropical Rainfall Measuring Mission (TRMM) data are used for rain-rate validation: rain-rate retrieval from the GE algorithm applied to actual TRMM Microwave Imager (TMI) measurements are compared with collocated precipitation radar (PR) rain rate.
    publisherAmerican Meteorological Society
    titleAtmospheric Liquid Water Retrieval Using a Gated Experts Neural Network
    typeJournal Paper
    journal volume19
    journal issue4
    journal titleJournal of Atmospheric and Oceanic Technology
    identifier doi10.1175/1520-0426(2002)019<0457:ALWRUA>2.0.CO;2
    journal fristpage457
    journal lastpage467
    treeJournal of Atmospheric and Oceanic Technology:;2002:;volume( 019 ):;issue: 004
    contenttypeFulltext
    DSpace software copyright © 2002-2015  DuraSpace
    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