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    Finite Samples and Uncertainty Estimates for Skill Measures for Seasonal Prediction

    Source: Monthly Weather Review:;2009:;volume( 137 ):;issue: 008::page 2622
    Author:
    Kumar, Arun
    DOI: 10.1175/2009MWR2814.1
    Publisher: American Meteorological Society
    Abstract: The expected value for various measures of skill for seasonal climate predictions is determined by the signal-to-noise ratio. The expected value, however, is only realized for long verification time series. In practice, the verifications for specific seasons?for example, forecasts for the December?February seasonal mean?seldom exceed a sample size of 30. The estimates of skill measure based on small verification time series, because of sampling errors, can have large departures from their expected value. An analysis of spread in the estimates of skill measures with the length of verification time series and for different signal-to-noise ratios is made. The analysis is based on the Monte Carlo approach and skill measures for deterministic, categorical, and probabilistic forecasts are considered. It is shown that the behavior of spread for various skill measures can be very different and it is not always the largest for the small values of signal-to-noise ratios.
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      Finite Samples and Uncertainty Estimates for Skill Measures for Seasonal Prediction

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4211168
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    contributor authorKumar, Arun
    date accessioned2017-06-09T16:31:51Z
    date available2017-06-09T16:31:51Z
    date copyright2009/08/01
    date issued2009
    identifier issn0027-0644
    identifier otherams-69493.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4211168
    description abstractThe expected value for various measures of skill for seasonal climate predictions is determined by the signal-to-noise ratio. The expected value, however, is only realized for long verification time series. In practice, the verifications for specific seasons?for example, forecasts for the December?February seasonal mean?seldom exceed a sample size of 30. The estimates of skill measure based on small verification time series, because of sampling errors, can have large departures from their expected value. An analysis of spread in the estimates of skill measures with the length of verification time series and for different signal-to-noise ratios is made. The analysis is based on the Monte Carlo approach and skill measures for deterministic, categorical, and probabilistic forecasts are considered. It is shown that the behavior of spread for various skill measures can be very different and it is not always the largest for the small values of signal-to-noise ratios.
    publisherAmerican Meteorological Society
    titleFinite Samples and Uncertainty Estimates for Skill Measures for Seasonal Prediction
    typeJournal Paper
    journal volume137
    journal issue8
    journal titleMonthly Weather Review
    identifier doi10.1175/2009MWR2814.1
    journal fristpage2622
    journal lastpage2631
    treeMonthly Weather Review:;2009:;volume( 137 ):;issue: 008
    contenttypeFulltext
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