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    Fitting and Analyzing LES Using Multiple Trivariate Gaussians

    Source: Journal of the Atmospheric Sciences:;2014:;Volume( 072 ):;issue: 003::page 1094
    Author:
    Firl, Grant J.
    ,
    Randall, David A.
    DOI: 10.1175/JAS-D-14-0192.1
    Publisher: American Meteorological Society
    Abstract: ssumed-PDF methods for the parameterization of subgrid-scale processes in atmospheric models provide many benefits. Many currently used assumed-PDF schemes reconcile the high number of required PDF parameters with the relative paucity of input moments by employing simplifying assumptions that are difficult to test. This paper explores the possibility of constructing a trivariate double-Gaussian PDF from the first three orders of moments without simplifying assumptions and proves that no unique solution exists. In an effort to provide a path for future improvement of current assumed-PDF schemes, the expectation maximization (EM) algorithm for Gaussian mixture models is used with LES output of shallow cumulus, stratocumulus, and deep convection cases to determine ?best fit? PDFs using from one through four Gaussian clusters. The EM PDFs are evaluated using PDF-diagnosed higher-order moments, PDF-diagnosed cloud statistics, and the Akaike information criterion. It was found that two Gaussian clusters were almost always adequate to represent both higher-order moments and cloud statistics like cloud fraction, water content, and vertical fluxes of cloud water and buoyancy in layered clouds such as stratocumulus and deep convective anvils. However, higher-order moments and higher-order cloud statistics were only properly represented when three or four Gaussians were used in the upper regions of shallow cumulus layers and throughout the active portion of deep convection. Evidence is also provided that several common assumptions employed to diagnose trivariate double-Gaussian PDFs from a minimum number of input moments are weak.
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      Fitting and Analyzing LES Using Multiple Trivariate Gaussians

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    contributor authorFirl, Grant J.
    contributor authorRandall, David A.
    date accessioned2017-06-09T16:57:48Z
    date available2017-06-09T16:57:48Z
    date copyright2015/03/01
    date issued2014
    identifier issn0022-4928
    identifier otherams-77128.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4219652
    description abstractssumed-PDF methods for the parameterization of subgrid-scale processes in atmospheric models provide many benefits. Many currently used assumed-PDF schemes reconcile the high number of required PDF parameters with the relative paucity of input moments by employing simplifying assumptions that are difficult to test. This paper explores the possibility of constructing a trivariate double-Gaussian PDF from the first three orders of moments without simplifying assumptions and proves that no unique solution exists. In an effort to provide a path for future improvement of current assumed-PDF schemes, the expectation maximization (EM) algorithm for Gaussian mixture models is used with LES output of shallow cumulus, stratocumulus, and deep convection cases to determine ?best fit? PDFs using from one through four Gaussian clusters. The EM PDFs are evaluated using PDF-diagnosed higher-order moments, PDF-diagnosed cloud statistics, and the Akaike information criterion. It was found that two Gaussian clusters were almost always adequate to represent both higher-order moments and cloud statistics like cloud fraction, water content, and vertical fluxes of cloud water and buoyancy in layered clouds such as stratocumulus and deep convective anvils. However, higher-order moments and higher-order cloud statistics were only properly represented when three or four Gaussians were used in the upper regions of shallow cumulus layers and throughout the active portion of deep convection. Evidence is also provided that several common assumptions employed to diagnose trivariate double-Gaussian PDFs from a minimum number of input moments are weak.
    publisherAmerican Meteorological Society
    titleFitting and Analyzing LES Using Multiple Trivariate Gaussians
    typeJournal Paper
    journal volume72
    journal issue3
    journal titleJournal of the Atmospheric Sciences
    identifier doi10.1175/JAS-D-14-0192.1
    journal fristpage1094
    journal lastpage1116
    treeJournal of the Atmospheric Sciences:;2014:;Volume( 072 ):;issue: 003
    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
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