YaBeSH Engineering and Technology Library

    • Journals
    • PaperQuest
    • YSE Standards
    • YaBeSH
    • Login
    View Item 
    •   YE&T Library
    • AMS
    • Journal of Applied Meteorology and Climatology
    • View Item
    •   YE&T Library
    • AMS
    • Journal of Applied Meteorology and Climatology
    • View Item
    • All Fields
    • Source Title
    • Year
    • Publisher
    • Title
    • Subject
    • Author
    • DOI
    • ISBN
    Advanced Search
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Archive

    Meteosat-Based Characterization of the Spatiotemporal Evolution of Warm Convective Cloud Fields over Central Europe

    Source: Journal of Applied Meteorology and Climatology:;2016:;volume( 055 ):;issue: 010::page 2181
    Author:
    Bley, Sebastian
    ,
    Deneke, Hartwig
    ,
    Senf, Fabian
    DOI: 10.1175/JAMC-D-15-0335.1
    Publisher: American Meteorological Society
    Abstract: he spatiotemporal evolution of warm convective cloud fields over central Europe is investigated on the basis of 30 cases using observations from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the geostationary Meteosat platforms. Cloud fields are tracked in successive satellite images using cloud motion vectors. The time-lagged autocorrelation is calculated for spectral reflectance and cloud property fields using boxes of 16 ? 16 pixels and adopting both Lagrangian and Eulerian perspectives. The 0.6-?m reflectance, cloud optical depth, and water path show a similar characteristic Lagrangian decorrelation time of about 30 min. In contrast, significantly lower decorrelation times are observed for the cloud effective radius and droplet density. It is shown that the Eulerian decorrelation time can be decomposed into an advective component and a convective component using the spatial autocorrelation function. In an Eulerian frame cloud fields generally decorrelate faster than in a Lagrangian one. The Eulerian decorrelation time contains contributions from the spatial decorrelation of the cloud field advected by the horizontal wind. A typical spatial decorrelation length of 7 km is observed, which suggests that sampling of SEVIRI observations is better in the temporal domain than in the spatial domain when investigating small-scale convective clouds. An along-track time series of box-averaged cloud liquid water path is derived and compared with the time series that would be measured at a fixed location. Supported by previous results, it is argued that this makes it possible to discriminate between local changes such as condensation and evaporation on the one hand and advective changes on the other hand.
    • Download: (2.588Mb)
    • Show Full MetaData Hide Full MetaData
    • Item Order
    • Go To Publisher
    • Price: 5000 Rial
    • Statistics

      Meteosat-Based Characterization of the Spatiotemporal Evolution of Warm Convective Cloud Fields over Central Europe

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4217638
    Collections
    • Journal of Applied Meteorology and Climatology

    Show full item record

    contributor authorBley, Sebastian
    contributor authorDeneke, Hartwig
    contributor authorSenf, Fabian
    date accessioned2017-06-09T16:51:13Z
    date available2017-06-09T16:51:13Z
    date copyright2016/10/01
    date issued2016
    identifier issn1558-8424
    identifier otherams-75315.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4217638
    description abstracthe spatiotemporal evolution of warm convective cloud fields over central Europe is investigated on the basis of 30 cases using observations from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the geostationary Meteosat platforms. Cloud fields are tracked in successive satellite images using cloud motion vectors. The time-lagged autocorrelation is calculated for spectral reflectance and cloud property fields using boxes of 16 ? 16 pixels and adopting both Lagrangian and Eulerian perspectives. The 0.6-?m reflectance, cloud optical depth, and water path show a similar characteristic Lagrangian decorrelation time of about 30 min. In contrast, significantly lower decorrelation times are observed for the cloud effective radius and droplet density. It is shown that the Eulerian decorrelation time can be decomposed into an advective component and a convective component using the spatial autocorrelation function. In an Eulerian frame cloud fields generally decorrelate faster than in a Lagrangian one. The Eulerian decorrelation time contains contributions from the spatial decorrelation of the cloud field advected by the horizontal wind. A typical spatial decorrelation length of 7 km is observed, which suggests that sampling of SEVIRI observations is better in the temporal domain than in the spatial domain when investigating small-scale convective clouds. An along-track time series of box-averaged cloud liquid water path is derived and compared with the time series that would be measured at a fixed location. Supported by previous results, it is argued that this makes it possible to discriminate between local changes such as condensation and evaporation on the one hand and advective changes on the other hand.
    publisherAmerican Meteorological Society
    titleMeteosat-Based Characterization of the Spatiotemporal Evolution of Warm Convective Cloud Fields over Central Europe
    typeJournal Paper
    journal volume55
    journal issue10
    journal titleJournal of Applied Meteorology and Climatology
    identifier doi10.1175/JAMC-D-15-0335.1
    journal fristpage2181
    journal lastpage2195
    treeJournal of Applied Meteorology and Climatology:;2016:;volume( 055 ):;issue: 010
    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