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    Hail-Detection Algorithm for the GPM Core Observatory Satellite Sensors

    Source: Journal of Applied Meteorology and Climatology:;2017:;volume( 056 ):;issue: 007::page 1939
    Author:
    Mroz, Kamil;Battaglia, Alessandro;Lang, Timothy J.;Cecil, Daniel J.;Tanelli, Simone;Tridon, Frederic
    DOI: 10.1175/JAMC-D-16-0368.1
    Publisher: American Meteorological Society
    Abstract: AbstractBy exploiting an abundant number of extreme storms observed simultaneously by the Global Precipitation Measurement (GPM) mission Core Observatory satellite?s suite of sensors and by the ground-based S-band Next Generation Weather Radar (NEXRAD) network over the continental United States, proxies for the identification of hail are developed from the GPM Core Observatory satellite observables. The full capabilities of the GPM Core Observatory are tested by analyzing more than 20 observables and adopting the hydrometeor classification on the basis of ground-based polarimetric measurements being truth. The proxies have been tested using the critical success index (CSI) as a verification measure. The hail-detection algorithm that is based on the mean Ku-band reflectivity in the mixed-phase layer performs the best of all considered proxies (CSI of 45%). Outside the dual-frequency precipitation radar swath, the polarization-corrected temperature at 18.7 GHz shows the greatest potential for hail detection among all GPM Microwave Imager channels (CSI of 26% at a threshold value of 261 K). When dual-variable proxies are considered, the combination involving the mixed-phase reflectivity values at both Ku and Ka bands outperforms all of the other proxies, with a CSI of 49%. The best-performing radar?radiometer algorithm is based on the mixed-phase reflectivity at Ku band and on the brightness temperature (TB) at 10.7 GHz (CSI of 46%). When only radiometric data are available, the algorithm that is based on the TBs at 36.6 and 166 GHz is the most efficient, with a CSI of 27.5%.
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      Hail-Detection Algorithm for the GPM Core Observatory Satellite Sensors

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4246113
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    contributor authorMroz, Kamil;Battaglia, Alessandro;Lang, Timothy J.;Cecil, Daniel J.;Tanelli, Simone;Tridon, Frederic
    date accessioned2018-01-03T11:01:10Z
    date available2018-01-03T11:01:10Z
    date copyright5/22/2017 12:00:00 AM
    date issued2017
    identifier otherjamc-d-16-0368.1.pdf
    identifier urihttp://138.201.223.254:8080/yetl1/handle/yetl/4246113
    description abstractAbstractBy exploiting an abundant number of extreme storms observed simultaneously by the Global Precipitation Measurement (GPM) mission Core Observatory satellite?s suite of sensors and by the ground-based S-band Next Generation Weather Radar (NEXRAD) network over the continental United States, proxies for the identification of hail are developed from the GPM Core Observatory satellite observables. The full capabilities of the GPM Core Observatory are tested by analyzing more than 20 observables and adopting the hydrometeor classification on the basis of ground-based polarimetric measurements being truth. The proxies have been tested using the critical success index (CSI) as a verification measure. The hail-detection algorithm that is based on the mean Ku-band reflectivity in the mixed-phase layer performs the best of all considered proxies (CSI of 45%). Outside the dual-frequency precipitation radar swath, the polarization-corrected temperature at 18.7 GHz shows the greatest potential for hail detection among all GPM Microwave Imager channels (CSI of 26% at a threshold value of 261 K). When dual-variable proxies are considered, the combination involving the mixed-phase reflectivity values at both Ku and Ka bands outperforms all of the other proxies, with a CSI of 49%. The best-performing radar?radiometer algorithm is based on the mixed-phase reflectivity at Ku band and on the brightness temperature (TB) at 10.7 GHz (CSI of 46%). When only radiometric data are available, the algorithm that is based on the TBs at 36.6 and 166 GHz is the most efficient, with a CSI of 27.5%.
    publisherAmerican Meteorological Society
    titleHail-Detection Algorithm for the GPM Core Observatory Satellite Sensors
    typeJournal Paper
    journal volume56
    journal issue7
    journal titleJournal of Applied Meteorology and Climatology
    identifier doi10.1175/JAMC-D-16-0368.1
    journal fristpage1939
    journal lastpage1957
    treeJournal of Applied Meteorology and Climatology:;2017:;volume( 056 ):;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
    yabeshDSpacePersian