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    Horizontal and Vertical Scaling of Cloud Geometry Inferred from CloudSat Data

    Source: Journal of the Atmospheric Sciences:;2018:;volume 075:;issue 007::page 2187
    Author:
    Guillaume, A.
    ,
    Kahn, B. H.
    ,
    Yue, Q.
    ,
    Fetzer, E. J.
    ,
    Wong, S.
    ,
    Manipon, G. J.
    ,
    Hua, H.
    ,
    Wilson, B. D.
    DOI: 10.1175/JAS-D-17-0111.1
    Publisher: American Meteorological Society
    Abstract: AbstractA method is described to characterize the scale dependence of cloud chord length using cloud-type classification reported with the 94-GHz CloudSat radar. The cloud length along the CloudSat track is quantified using horizontal and vertical structures of cloud classification separately for each cloud type and for all clouds independent of cloud type. While the individual cloud types do not follow a clear power-law behavior as a function of horizontal or vertical scale, a robust power-law scaling of cloud chord length is observed when cloud type is not considered. The exponent of horizontal length is approximated by ? ≈ 1.66 ± 0.00 across two orders of magnitude (~10?1000 km). The exponent of vertical thickness is approximated by ? ≈ 2.23 ± 0.03 in excess of one order of magnitude (~1?14 km). These exponents are in agreement with previous studies using numerical models, satellites, dropsondes, and in situ aircraft observations. These differences in horizontal and vertical cloud scaling are consistent with scaling of temperature and horizontal wind in the horizontal dimension and with scaling of buoyancy flux in the vertical dimension. The observed scale dependence should serve as a guide to test and evaluate scale-cognizant climate and weather numerical prediction models.
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      Horizontal and Vertical Scaling of Cloud Geometry Inferred from CloudSat Data

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4261731
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    contributor authorGuillaume, A.
    contributor authorKahn, B. H.
    contributor authorYue, Q.
    contributor authorFetzer, E. J.
    contributor authorWong, S.
    contributor authorManipon, G. J.
    contributor authorHua, H.
    contributor authorWilson, B. D.
    date accessioned2019-09-19T10:07:08Z
    date available2019-09-19T10:07:08Z
    date copyright4/17/2018 12:00:00 AM
    date issued2018
    identifier otherjas-d-17-0111.1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4261731
    description abstractAbstractA method is described to characterize the scale dependence of cloud chord length using cloud-type classification reported with the 94-GHz CloudSat radar. The cloud length along the CloudSat track is quantified using horizontal and vertical structures of cloud classification separately for each cloud type and for all clouds independent of cloud type. While the individual cloud types do not follow a clear power-law behavior as a function of horizontal or vertical scale, a robust power-law scaling of cloud chord length is observed when cloud type is not considered. The exponent of horizontal length is approximated by ? ≈ 1.66 ± 0.00 across two orders of magnitude (~10?1000 km). The exponent of vertical thickness is approximated by ? ≈ 2.23 ± 0.03 in excess of one order of magnitude (~1?14 km). These exponents are in agreement with previous studies using numerical models, satellites, dropsondes, and in situ aircraft observations. These differences in horizontal and vertical cloud scaling are consistent with scaling of temperature and horizontal wind in the horizontal dimension and with scaling of buoyancy flux in the vertical dimension. The observed scale dependence should serve as a guide to test and evaluate scale-cognizant climate and weather numerical prediction models.
    publisherAmerican Meteorological Society
    titleHorizontal and Vertical Scaling of Cloud Geometry Inferred from CloudSat Data
    typeJournal Paper
    journal volume75
    journal issue7
    journal titleJournal of the Atmospheric Sciences
    identifier doi10.1175/JAS-D-17-0111.1
    journal fristpage2187
    journal lastpage2197
    treeJournal of the Atmospheric Sciences:;2018:;volume 075:;issue 007
    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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