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    A Comparison of Two Techniques for Generating Nowcasting Ensembles. Part II: Analogs Selection and Comparison of Techniques

    Source: Monthly Weather Review:;2015:;volume( 143 ):;issue: 007::page 2890
    Author:
    Atencia, Aitor
    ,
    Zawadzki, Isztar
    DOI: 10.1175/MWR-D-14-00342.1
    Publisher: American Meteorological Society
    Abstract: owcasting is the short-range forecast obtained from the latest observed state. Currently, heuristic techniques, such as Lagrangian extrapolation, are the most commonly used for rainfall forecasting. However, the Lagrangian extrapolation technique does not account for changes in the motion field or growth and decay of precipitation. These errors are difficult to analytically model and are normally introduced by stochastic processes. According to the chaos theory, similar states, also called analogs, evolve in a similar way plus an error related with the predictability of the situation. Consequently, finding these states in a historical dataset provides a way of forecasting that includes all the physical processes such as growth and decay, among others.The difficulty of this approach lies in finding these analogs. In this study, recent radar observations are compared with a 15-yr radar dataset. Similar states within the dataset are selected according to their spatial rainfall patterns, temporal storm evolution, and synoptic patterns to generate ensembles. This ensemble of analog states is verified against observations for four different events. In addition, it is compared with the previously mentioned Lagrangian stochastic ensemble by means of different scores. This comparison shows the weaknesses and strengths of each technique. This could provide critical information for a future hybrid analog?stochastic nowcasting technique.
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      A Comparison of Two Techniques for Generating Nowcasting Ensembles. Part II: Analogs Selection and Comparison of Techniques

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4230642
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    contributor authorAtencia, Aitor
    contributor authorZawadzki, Isztar
    date accessioned2017-06-09T17:32:42Z
    date available2017-06-09T17:32:42Z
    date copyright2015/07/01
    date issued2015
    identifier issn0027-0644
    identifier otherams-87019.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4230642
    description abstractowcasting is the short-range forecast obtained from the latest observed state. Currently, heuristic techniques, such as Lagrangian extrapolation, are the most commonly used for rainfall forecasting. However, the Lagrangian extrapolation technique does not account for changes in the motion field or growth and decay of precipitation. These errors are difficult to analytically model and are normally introduced by stochastic processes. According to the chaos theory, similar states, also called analogs, evolve in a similar way plus an error related with the predictability of the situation. Consequently, finding these states in a historical dataset provides a way of forecasting that includes all the physical processes such as growth and decay, among others.The difficulty of this approach lies in finding these analogs. In this study, recent radar observations are compared with a 15-yr radar dataset. Similar states within the dataset are selected according to their spatial rainfall patterns, temporal storm evolution, and synoptic patterns to generate ensembles. This ensemble of analog states is verified against observations for four different events. In addition, it is compared with the previously mentioned Lagrangian stochastic ensemble by means of different scores. This comparison shows the weaknesses and strengths of each technique. This could provide critical information for a future hybrid analog?stochastic nowcasting technique.
    publisherAmerican Meteorological Society
    titleA Comparison of Two Techniques for Generating Nowcasting Ensembles. Part II: Analogs Selection and Comparison of Techniques
    typeJournal Paper
    journal volume143
    journal issue7
    journal titleMonthly Weather Review
    identifier doi10.1175/MWR-D-14-00342.1
    journal fristpage2890
    journal lastpage2908
    treeMonthly Weather Review:;2015:;volume( 143 ):;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
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