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    Comparison of Methodologies for Probabilistic Quantitative Precipitation Forecasting

    Source: Weather and Forecasting:;2002:;volume( 017 ):;issue: 004::page 783
    Author:
    Applequist, Scott
    ,
    Gahrs, Gregory E.
    ,
    Pfeffer, Richard L.
    ,
    Niu, Xu-Feng
    DOI: 10.1175/1520-0434(2002)017<0783:COMFPQ>2.0.CO;2
    Publisher: American Meteorological Society
    Abstract: Twenty-four-hour probabilistic quantitative precipitation forecasts (PQPFs) for accumulations exceeding thresholds of 0.01, 0.05, and 0.10 in. are produced for 154 meteorological stations over the eastern and central regions of the United States. Comparisons of skill are made among forecasts generated using five different linear and nonlinear statistical methodologies, namely, linear regression, discriminant analysis, logistic regression, neural networks, and a classifier system. The predictors for the different statistical models were selected from a large pool of analyzed and predicted variables generated by the Nested Grid Model (NGM) during the four cool seasons (December?March) from 1992/93 to 1995/96. Because linear regression is the current method used by the National Weather Service, it is chosen as the benchmark by which the other methodologies are compared. The results indicate that logistic regression performs best among all methodologies. Most notable is that it performs significantly better at the 99% confidence limits than linear regression, attaining Brier skill scores of 0.413, 0.480, and 0.478 versus 0.378, 0.440, and 0.457 for linear regression, at thresholds of 0.01, 0.05, and 0.10 in., respectively. Attributes diagrams reveal that linear regression gives a greater number of forecast probabilities closer to climatology than does logistic regression at all three thresholds. Moreover, these forecasts are more biased toward lower-than-observed probabilities and are further from the ?perfect reliability? line in almost all probability categories than are the forecasts made by logistic regression. For the other methodologies, the classifier system also showed significantly greater skill than did linear regression, and discriminant analysis and neural networks gave mixed results.
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      Comparison of Methodologies for Probabilistic Quantitative Precipitation Forecasting

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4170234
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    contributor authorApplequist, Scott
    contributor authorGahrs, Gregory E.
    contributor authorPfeffer, Richard L.
    contributor authorNiu, Xu-Feng
    date accessioned2017-06-09T15:02:05Z
    date available2017-06-09T15:02:05Z
    date copyright2002/08/01
    date issued2002
    identifier issn0882-8156
    identifier otherams-3265.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4170234
    description abstractTwenty-four-hour probabilistic quantitative precipitation forecasts (PQPFs) for accumulations exceeding thresholds of 0.01, 0.05, and 0.10 in. are produced for 154 meteorological stations over the eastern and central regions of the United States. Comparisons of skill are made among forecasts generated using five different linear and nonlinear statistical methodologies, namely, linear regression, discriminant analysis, logistic regression, neural networks, and a classifier system. The predictors for the different statistical models were selected from a large pool of analyzed and predicted variables generated by the Nested Grid Model (NGM) during the four cool seasons (December?March) from 1992/93 to 1995/96. Because linear regression is the current method used by the National Weather Service, it is chosen as the benchmark by which the other methodologies are compared. The results indicate that logistic regression performs best among all methodologies. Most notable is that it performs significantly better at the 99% confidence limits than linear regression, attaining Brier skill scores of 0.413, 0.480, and 0.478 versus 0.378, 0.440, and 0.457 for linear regression, at thresholds of 0.01, 0.05, and 0.10 in., respectively. Attributes diagrams reveal that linear regression gives a greater number of forecast probabilities closer to climatology than does logistic regression at all three thresholds. Moreover, these forecasts are more biased toward lower-than-observed probabilities and are further from the ?perfect reliability? line in almost all probability categories than are the forecasts made by logistic regression. For the other methodologies, the classifier system also showed significantly greater skill than did linear regression, and discriminant analysis and neural networks gave mixed results.
    publisherAmerican Meteorological Society
    titleComparison of Methodologies for Probabilistic Quantitative Precipitation Forecasting
    typeJournal Paper
    journal volume17
    journal issue4
    journal titleWeather and Forecasting
    identifier doi10.1175/1520-0434(2002)017<0783:COMFPQ>2.0.CO;2
    journal fristpage783
    journal lastpage799
    treeWeather and Forecasting:;2002:;volume( 017 ):;issue: 004
    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