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    Potential Predictability of Seasonal Extreme Precipitation Accumulation in China

    Source: Journal of Hydrometeorology:;2017:;Volume( 018 ):;issue: 004::page 1071
    Author:
    Wei, Wenguang
    ,
    Yan, Zhongwei
    ,
    Jones, P. D.
    DOI: 10.1175/JHM-D-16-0141.1
    Publisher: American Meteorological Society
    Abstract: he potential predictability of seasonal extreme precipitation accumulation (SEPA) across mainland China is evaluated, based on daily precipitation observations during 1960?2013 at 675 stations. The potential predictability value (PPV) of SEPA is calculated for each station by decomposing the observed SEPA variance into a part associated with stochastic daily rainfall variability and another part associated with longer-time-scale climate processes. A Markov chain model is constructed for each station and a Monte Carlo simulation is applied to estimate the stochastic part of the variance. The results suggest that there are more potentially predictable regions for summer than for the other seasons, especially over southern China, the Yangtze River valley, the north China plain, and northwestern China. There are also regions of large PPVs in southern China for autumn and winter and in northwestern China for spring. The SEPA series for the regions of large PPVs are deemed not entirely stochastic, either with long-term trends (e.g., increasing trends in inland northwestern China) or significant correlation with well-known large-scale climate processes (e.g., East Asian winter monsoon for southern China in winter and El Niño for the Yangtze River valley in summer). This fact not only verifies the claim that the regions have potential predictability but also facilitates predictive studies of the regional extreme precipitation associated with large-scale climate processes.
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      Potential Predictability of Seasonal Extreme Precipitation Accumulation in China

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4225557
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    contributor authorWei, Wenguang
    contributor authorYan, Zhongwei
    contributor authorJones, P. D.
    date accessioned2017-06-09T17:17:16Z
    date available2017-06-09T17:17:16Z
    date copyright2017/04/01
    date issued2017
    identifier issn1525-755X
    identifier otherams-82442.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4225557
    description abstracthe potential predictability of seasonal extreme precipitation accumulation (SEPA) across mainland China is evaluated, based on daily precipitation observations during 1960?2013 at 675 stations. The potential predictability value (PPV) of SEPA is calculated for each station by decomposing the observed SEPA variance into a part associated with stochastic daily rainfall variability and another part associated with longer-time-scale climate processes. A Markov chain model is constructed for each station and a Monte Carlo simulation is applied to estimate the stochastic part of the variance. The results suggest that there are more potentially predictable regions for summer than for the other seasons, especially over southern China, the Yangtze River valley, the north China plain, and northwestern China. There are also regions of large PPVs in southern China for autumn and winter and in northwestern China for spring. The SEPA series for the regions of large PPVs are deemed not entirely stochastic, either with long-term trends (e.g., increasing trends in inland northwestern China) or significant correlation with well-known large-scale climate processes (e.g., East Asian winter monsoon for southern China in winter and El Niño for the Yangtze River valley in summer). This fact not only verifies the claim that the regions have potential predictability but also facilitates predictive studies of the regional extreme precipitation associated with large-scale climate processes.
    publisherAmerican Meteorological Society
    titlePotential Predictability of Seasonal Extreme Precipitation Accumulation in China
    typeJournal Paper
    journal volume18
    journal issue4
    journal titleJournal of Hydrometeorology
    identifier doi10.1175/JHM-D-16-0141.1
    journal fristpage1071
    journal lastpage1080
    treeJournal of Hydrometeorology:;2017:;Volume( 018 ):;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
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