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    Extracting Microphysical Impacts in Large-Eddy Simulations of Shallow Convection

    Source: Journal of the Atmospheric Sciences:;2014:;Volume( 071 ):;issue: 012::page 4493
    Author:
    Grabowski, Wojciech W.
    DOI: 10.1175/JAS-D-14-0231.1
    Publisher: American Meteorological Society
    Abstract: simple methodology is proposed to extract impacts of cloud microphysics on macrophysical cloud-field properties in large-eddy simulations of shallow convection. These impacts are typically difficult to assess because of natural variability of the simulated cloud field. The idea is to use two sets of thermodynamic variables driven by different microphysical schemes or by a single scheme with different parameters as applied here. The first set is coupled to the dynamics as in the standard model, and the second set is applied diagnostically?that is, driven by the flow but without the feedback on the flow dynamics. Having the two schemes operating in the same flow pattern allows for extracting the impact with high confidence. For illustration, the method is applied to simulations of precipitating shallow convection applying a simple bulk representation of warm-rain processes. Because of natural variability, the traditional approach provides an uncertain estimate of the impact of cloud droplet concentration on the mean cloud-field rainfall even with an ensemble of simulations. In contrast, the impact is well constrained while applying the new methodology. The method can even detect minuscule changes of the mean cloud cover and liquid water path despite their large temporal fluctuations and different evolutions within the ensemble.
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      Extracting Microphysical Impacts in Large-Eddy Simulations of Shallow Convection

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4219685
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    contributor authorGrabowski, Wojciech W.
    date accessioned2017-06-09T16:57:56Z
    date available2017-06-09T16:57:56Z
    date copyright2014/12/01
    date issued2014
    identifier issn0022-4928
    identifier otherams-77158.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4219685
    description abstractsimple methodology is proposed to extract impacts of cloud microphysics on macrophysical cloud-field properties in large-eddy simulations of shallow convection. These impacts are typically difficult to assess because of natural variability of the simulated cloud field. The idea is to use two sets of thermodynamic variables driven by different microphysical schemes or by a single scheme with different parameters as applied here. The first set is coupled to the dynamics as in the standard model, and the second set is applied diagnostically?that is, driven by the flow but without the feedback on the flow dynamics. Having the two schemes operating in the same flow pattern allows for extracting the impact with high confidence. For illustration, the method is applied to simulations of precipitating shallow convection applying a simple bulk representation of warm-rain processes. Because of natural variability, the traditional approach provides an uncertain estimate of the impact of cloud droplet concentration on the mean cloud-field rainfall even with an ensemble of simulations. In contrast, the impact is well constrained while applying the new methodology. The method can even detect minuscule changes of the mean cloud cover and liquid water path despite their large temporal fluctuations and different evolutions within the ensemble.
    publisherAmerican Meteorological Society
    titleExtracting Microphysical Impacts in Large-Eddy Simulations of Shallow Convection
    typeJournal Paper
    journal volume71
    journal issue12
    journal titleJournal of the Atmospheric Sciences
    identifier doi10.1175/JAS-D-14-0231.1
    journal fristpage4493
    journal lastpage4499
    treeJournal of the Atmospheric Sciences:;2014:;Volume( 071 ):;issue: 012
    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