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    Storm Labeling in Three Dimensions (SL3D): A Volumetric Radar Echo and Dual-Polarization Updraft Classification Algorithm

    Source: Monthly Weather Review:;2017:;volume( 145 ):;issue: 003::page 1127
    Author:
    Starzec, Mariusz
    ,
    Homeyer, Cameron R.
    ,
    Mullendore, Gretchen L.
    DOI: 10.1175/MWR-D-16-0089.1
    Publisher: American Meteorological Society
    Abstract: his study presents a new storm classification method for objectively stratifying three-dimensional radar echo into five categories: convection, convective updraft, precipitating stratiform, nonprecipitating stratiform, and ice-only anvil. The Storm Labeling in Three Dimensions (SL3D) algorithm utilizes volumetric radar data to classify radar echo based on storm height, depth, and intensity in order to provide a new method for updraft classification and improve upon the limitations of traditional storm classification algorithms. Convective updrafts are identified by searching for three known polarimetric radar signatures: weak-echo regions (bounded and unbounded) in the radar reflectivity factor at horizontal polarization , differential radar reflectivity columns, and specific differential phase columns. Additionally, leveraging the three-dimensional information allows SL3D to improve upon missed identifications of weak convection and intense stratiform rain in traditional two-dimensional classification schemes. This study presents the results of applying the SL3D algorithm to several cases of high-resolution three-dimensional composites of NEXRAD WSR-88D data in the contiguous United States. Comparisons with a traditional algorithm that uses two-dimensional maps of are also shown to illustrate the differences of the SL3D algorithm.
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      Storm Labeling in Three Dimensions (SL3D): A Volumetric Radar Echo and Dual-Polarization Updraft Classification Algorithm

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4230952
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    • Monthly Weather Review

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    contributor authorStarzec, Mariusz
    contributor authorHomeyer, Cameron R.
    contributor authorMullendore, Gretchen L.
    date accessioned2017-06-09T17:34:01Z
    date available2017-06-09T17:34:01Z
    date copyright2017/03/01
    date issued2017
    identifier issn0027-0644
    identifier otherams-87299.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4230952
    description abstracthis study presents a new storm classification method for objectively stratifying three-dimensional radar echo into five categories: convection, convective updraft, precipitating stratiform, nonprecipitating stratiform, and ice-only anvil. The Storm Labeling in Three Dimensions (SL3D) algorithm utilizes volumetric radar data to classify radar echo based on storm height, depth, and intensity in order to provide a new method for updraft classification and improve upon the limitations of traditional storm classification algorithms. Convective updrafts are identified by searching for three known polarimetric radar signatures: weak-echo regions (bounded and unbounded) in the radar reflectivity factor at horizontal polarization , differential radar reflectivity columns, and specific differential phase columns. Additionally, leveraging the three-dimensional information allows SL3D to improve upon missed identifications of weak convection and intense stratiform rain in traditional two-dimensional classification schemes. This study presents the results of applying the SL3D algorithm to several cases of high-resolution three-dimensional composites of NEXRAD WSR-88D data in the contiguous United States. Comparisons with a traditional algorithm that uses two-dimensional maps of are also shown to illustrate the differences of the SL3D algorithm.
    publisherAmerican Meteorological Society
    titleStorm Labeling in Three Dimensions (SL3D): A Volumetric Radar Echo and Dual-Polarization Updraft Classification Algorithm
    typeJournal Paper
    journal volume145
    journal issue3
    journal titleMonthly Weather Review
    identifier doi10.1175/MWR-D-16-0089.1
    journal fristpage1127
    journal lastpage1145
    treeMonthly Weather Review:;2017:;volume( 145 ):;issue: 003
    contenttypeFulltext
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