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    A Bayesian Framework for Verification and Recalibration of Ensemble Forecasts: How Uncertain is NAO Predictability?

    Source: Journal of Climate:;2015:;volume( 029 ):;issue: 003::page 995
    Author:
    Siegert, Stefan
    ,
    Stephenson, David B.
    ,
    Sansom, Philip G.
    ,
    Scaife, Adam A.
    ,
    Eade, Rosie
    ,
    Arribas, Alberto
    DOI: 10.1175/JCLI-D-15-0196.1
    Publisher: American Meteorological Society
    Abstract: redictability estimates of ensemble prediction systems are uncertain because of limited numbers of past forecasts and observations. To account for such uncertainty, this paper proposes a Bayesian inferential framework that provides a simple 6-parameter representation of ensemble forecasting systems and the corresponding observations. The framework is probabilistic and thus allows for quantifying uncertainty in predictability measures, such as correlation skill and signal-to-noise ratios. It also provides a natural way to produce recalibrated probabilistic predictions from uncalibrated ensembles forecasts.The framework is used to address important questions concerning the skill of winter hindcasts of the North Atlantic Oscillation for 1992?2011 issued by the Met Office Global Seasonal Forecast System, version 5 (GloSea5), climate prediction system. Although there is much uncertainty in the correlation between ensemble mean and observations, there is strong evidence of skill: the 95% credible interval of the correlation coefficient of [0.19, 0.68] does not overlap zero. There is also strong evidence that the forecasts are not exchangeable with the observations: with over 99% certainty, the signal-to-noise ratio of the forecasts is smaller than the signal-to-noise ratio of the observations, which suggests that raw forecasts should not be taken as representative scenarios of the observations. Forecast recalibration is thus required, which can be coherently addressed within the proposed framework.
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      A Bayesian Framework for Verification and Recalibration of Ensemble Forecasts: How Uncertain is NAO Predictability?

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4224019
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    contributor authorSiegert, Stefan
    contributor authorStephenson, David B.
    contributor authorSansom, Philip G.
    contributor authorScaife, Adam A.
    contributor authorEade, Rosie
    contributor authorArribas, Alberto
    date accessioned2017-06-09T17:12:20Z
    date available2017-06-09T17:12:20Z
    date copyright2016/02/01
    date issued2015
    identifier issn0894-8755
    identifier otherams-81058.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4224019
    description abstractredictability estimates of ensemble prediction systems are uncertain because of limited numbers of past forecasts and observations. To account for such uncertainty, this paper proposes a Bayesian inferential framework that provides a simple 6-parameter representation of ensemble forecasting systems and the corresponding observations. The framework is probabilistic and thus allows for quantifying uncertainty in predictability measures, such as correlation skill and signal-to-noise ratios. It also provides a natural way to produce recalibrated probabilistic predictions from uncalibrated ensembles forecasts.The framework is used to address important questions concerning the skill of winter hindcasts of the North Atlantic Oscillation for 1992?2011 issued by the Met Office Global Seasonal Forecast System, version 5 (GloSea5), climate prediction system. Although there is much uncertainty in the correlation between ensemble mean and observations, there is strong evidence of skill: the 95% credible interval of the correlation coefficient of [0.19, 0.68] does not overlap zero. There is also strong evidence that the forecasts are not exchangeable with the observations: with over 99% certainty, the signal-to-noise ratio of the forecasts is smaller than the signal-to-noise ratio of the observations, which suggests that raw forecasts should not be taken as representative scenarios of the observations. Forecast recalibration is thus required, which can be coherently addressed within the proposed framework.
    publisherAmerican Meteorological Society
    titleA Bayesian Framework for Verification and Recalibration of Ensemble Forecasts: How Uncertain is NAO Predictability?
    typeJournal Paper
    journal volume29
    journal issue3
    journal titleJournal of Climate
    identifier doi10.1175/JCLI-D-15-0196.1
    journal fristpage995
    journal lastpage1012
    treeJournal of Climate:;2015:;volume( 029 ):;issue: 003
    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