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    Estimating the Probability of Rain in an SSM/I FOV Using Logistic Regression

    Source: Journal of Applied Meteorology:;1995:;volume( 034 ):;issue: 011::page 2476
    Author:
    Crosby, David S.
    ,
    Ferraro, Ralph R.
    ,
    Wu, Helen
    DOI: 10.1175/1520-0450(1995)034<2476:ETPORI>2.0.CO;2
    Publisher: American Meteorological Society
    Abstract: The SSM/I has been used successfully to estimate precipitation and to determine the fields of view (FOV) that contain precipitating clouds. The use of multivariate logistic regression with the SSM/I brightness temperatures to estimate the probability that it is raining in an FOV is examined. The predictors used in this study are those that have been evaluated by other investigators to estimate rain events using other procedures. The logistic regression technique is applied to a matched set of SSM/I and radar data for a limited area from June to August 1989. For this limited dataset the results are quite good. In one example, if the predicted probability is less than 0.1, the radar data shows only 2 of 340 FOVs have precipitation. If the predicted probability is greater than 0.9, the radar data shows precipitation in 748 of 774 FOVS. These probabilities can be used for both instantaneous and climate timescale retrievals.
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      Estimating the Probability of Rain in an SSM/I FOV Using Logistic Regression

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4147545
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    contributor authorCrosby, David S.
    contributor authorFerraro, Ralph R.
    contributor authorWu, Helen
    date accessioned2017-06-09T14:05:28Z
    date available2017-06-09T14:05:28Z
    date copyright1995/11/01
    date issued1995
    identifier issn0894-8763
    identifier otherams-12229.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4147545
    description abstractThe SSM/I has been used successfully to estimate precipitation and to determine the fields of view (FOV) that contain precipitating clouds. The use of multivariate logistic regression with the SSM/I brightness temperatures to estimate the probability that it is raining in an FOV is examined. The predictors used in this study are those that have been evaluated by other investigators to estimate rain events using other procedures. The logistic regression technique is applied to a matched set of SSM/I and radar data for a limited area from June to August 1989. For this limited dataset the results are quite good. In one example, if the predicted probability is less than 0.1, the radar data shows only 2 of 340 FOVs have precipitation. If the predicted probability is greater than 0.9, the radar data shows precipitation in 748 of 774 FOVS. These probabilities can be used for both instantaneous and climate timescale retrievals.
    publisherAmerican Meteorological Society
    titleEstimating the Probability of Rain in an SSM/I FOV Using Logistic Regression
    typeJournal Paper
    journal volume34
    journal issue11
    journal titleJournal of Applied Meteorology
    identifier doi10.1175/1520-0450(1995)034<2476:ETPORI>2.0.CO;2
    journal fristpage2476
    journal lastpage2480
    treeJournal of Applied Meteorology:;1995:;volume( 034 ):;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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