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    Vertically Resolved Convective–Stratiform Echo-Type Identification and Convectivity Retrieval for Vertically Pointing Radars

    Source: Journal of Atmospheric and Oceanic Technology:;2022:;volume( 039 ):;issue: 011::page 1705
    Author:
    Ulrike Romatschke
    ,
    Michael J. Dixon
    DOI: 10.1175/JTECH-D-22-0019.1
    Publisher: American Meteorological Society
    Abstract: Using data from the airborne HIAPER Cloud Radar (HCR), a partitioning algorithm (ECCO-V) that provides vertically resolved convectivity and convective versus stratiform radar-echo classification is developed for vertically pointing radars. The algorithm is based on the calculation of reflectivity and radial velocity texture fields that measure the horizontal homogeneity of cloud and precipitation features. The texture fields are translated into convectivity, a numerical measure of the convective or stratiform nature of each data point. The convective–stratiform classification is obtained by thresholding the convectivity field. Subcategories of low, mid-, and high stratiform, shallow, mid-, deep, and elevated convective, and mixed echoes are introduced, which are based on the melting-layer and divergence-level altitudes. As the algorithm provides vertically resolved classifications, it is capable of identifying different types of vertically layered echoes, and convective features that are embedded in stratiform cloud layers. Its robustness was tested on data from four HCR field campaigns that took place in different meteorological and climatological regimes. The algorithm was adapted for use in spaceborne and ground-based radars, proving its versatility, as it is adaptable not only to different radar types and wavelengths, but also different research applications.
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      Vertically Resolved Convective–Stratiform Echo-Type Identification and Convectivity Retrieval for Vertically Pointing Radars

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    https://yetl.yabesh.ir/yetl1/handle/yetl/4289674
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    contributor authorUlrike Romatschke
    contributor authorMichael J. Dixon
    date accessioned2023-04-12T18:26:31Z
    date available2023-04-12T18:26:31Z
    date copyright2022/10/28
    date issued2022
    identifier otherJTECH-D-22-0019.1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4289674
    description abstractUsing data from the airborne HIAPER Cloud Radar (HCR), a partitioning algorithm (ECCO-V) that provides vertically resolved convectivity and convective versus stratiform radar-echo classification is developed for vertically pointing radars. The algorithm is based on the calculation of reflectivity and radial velocity texture fields that measure the horizontal homogeneity of cloud and precipitation features. The texture fields are translated into convectivity, a numerical measure of the convective or stratiform nature of each data point. The convective–stratiform classification is obtained by thresholding the convectivity field. Subcategories of low, mid-, and high stratiform, shallow, mid-, deep, and elevated convective, and mixed echoes are introduced, which are based on the melting-layer and divergence-level altitudes. As the algorithm provides vertically resolved classifications, it is capable of identifying different types of vertically layered echoes, and convective features that are embedded in stratiform cloud layers. Its robustness was tested on data from four HCR field campaigns that took place in different meteorological and climatological regimes. The algorithm was adapted for use in spaceborne and ground-based radars, proving its versatility, as it is adaptable not only to different radar types and wavelengths, but also different research applications.
    publisherAmerican Meteorological Society
    titleVertically Resolved Convective–Stratiform Echo-Type Identification and Convectivity Retrieval for Vertically Pointing Radars
    typeJournal Paper
    journal volume39
    journal issue11
    journal titleJournal of Atmospheric and Oceanic Technology
    identifier doi10.1175/JTECH-D-22-0019.1
    journal fristpage1705
    journal lastpage1716
    page1705–1716
    treeJournal of Atmospheric and Oceanic Technology:;2022:;volume( 039 ):;issue: 011
    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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