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    Nonparametric Estimation of Raindrop Size Distributions from Dual-Polarization Radar Spectral Observations

    Source: Journal of Atmospheric and Oceanic Technology:;2007:;volume( 024 ):;issue: 006::page 1008
    Author:
    Moisseev, Dmitri N.
    ,
    Chandrasekar, V.
    DOI: 10.1175/JTECH2024.1
    Publisher: American Meteorological Society
    Abstract: This paper presents a method to retrieve raindrop size distributions (DSD) from slant profile dual-polarization Doppler spectra observations. It is shown that using radar measurements taken at a high elevation angle raindrop size distributions can be retrieved without making an assumption on the form of a DSD. In this paper it is shown that drop size distributions can be retrieved from Doppler power spectra by compensating for the effect of spectrum broadening and mean velocity shift. To accomplish that, spectrum deconvolution is used where the spectral broadening kernel width and wind velocity are estimated from spectral differential reflectivity measurements. Since convolution kernel is estimated from dual-polarization Doppler spectra observations and does not require observation of a clear-air signal, this method can be used by most radars capable of dual-polarization spectra measurements. To validate the technique, sensitivity of this method to the underlying assumptions and calibration errors is evaluated on realistic simulations of radar observations. Furthermore, performance of the method is illustrated on Colorado State University?University of Chicago?Illinois State Water Survey radar (CSU?CHILL) measurements of stratiform precipitation.
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      Nonparametric Estimation of Raindrop Size Distributions from Dual-Polarization Radar Spectral Observations

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4227740
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    contributor authorMoisseev, Dmitri N.
    contributor authorChandrasekar, V.
    date accessioned2017-06-09T17:23:35Z
    date available2017-06-09T17:23:35Z
    date copyright2007/06/01
    date issued2007
    identifier issn0739-0572
    identifier otherams-84407.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4227740
    description abstractThis paper presents a method to retrieve raindrop size distributions (DSD) from slant profile dual-polarization Doppler spectra observations. It is shown that using radar measurements taken at a high elevation angle raindrop size distributions can be retrieved without making an assumption on the form of a DSD. In this paper it is shown that drop size distributions can be retrieved from Doppler power spectra by compensating for the effect of spectrum broadening and mean velocity shift. To accomplish that, spectrum deconvolution is used where the spectral broadening kernel width and wind velocity are estimated from spectral differential reflectivity measurements. Since convolution kernel is estimated from dual-polarization Doppler spectra observations and does not require observation of a clear-air signal, this method can be used by most radars capable of dual-polarization spectra measurements. To validate the technique, sensitivity of this method to the underlying assumptions and calibration errors is evaluated on realistic simulations of radar observations. Furthermore, performance of the method is illustrated on Colorado State University?University of Chicago?Illinois State Water Survey radar (CSU?CHILL) measurements of stratiform precipitation.
    publisherAmerican Meteorological Society
    titleNonparametric Estimation of Raindrop Size Distributions from Dual-Polarization Radar Spectral Observations
    typeJournal Paper
    journal volume24
    journal issue6
    journal titleJournal of Atmospheric and Oceanic Technology
    identifier doi10.1175/JTECH2024.1
    journal fristpage1008
    journal lastpage1018
    treeJournal of Atmospheric and Oceanic Technology:;2007:;volume( 024 ):;issue: 006
    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