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    Prediction of Consensus Tropical Cyclone Intensity Forecast Error

    Source: Weather and Forecasting:;2014:;volume( 029 ):;issue: 003::page 750
    Author:
    Goerss, James S.
    ,
    Sampson, Charles R.
    DOI: 10.1175/WAF-D-13-00058.1
    Publisher: American Meteorological Society
    Abstract: he extent to which the tropical cyclone (TC) intensity forecast error of IVCN and S5YY, consensus models routinely used by forecasters at the National Hurricane Center and the Joint Typhoon Warning Center, respectively, can be predicted is determined. A number of predictors of consensus intensity forecast error, which must be quantities that are available prior to the official forecast deadline, were examined for the Atlantic and eastern North Pacific basins for 2008?11 and the western North Pacific basin for 2012. Leading predictors were found to be forecast TC intensity and intensity change, initial intensity and latitude of the TC, and consensus model spread, defined to be the average of the absolute intensity differences between the member forecasts and the consensus forecast. Using stepwise linear regression and the full pool of predictors, regression models were found for each forecast length to predict the IVCN and S5YY TC intensity forecast errors. Using the regression models, intervals were determined centered on the IVCN and S5YY forecasts that contained the verifying TC intensity about 67% of the time. Based on the size of these intervals, a forecaster can determine the confidence that can be placed upon the IVCN or S5YY forecasts. Independent data testing yielded results only slightly degraded from those of dependent data testing, highlighting the capability of these methods in practical forecasting applications.
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      Prediction of Consensus Tropical Cyclone Intensity Forecast Error

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4231681
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    contributor authorGoerss, James S.
    contributor authorSampson, Charles R.
    date accessioned2017-06-09T17:36:23Z
    date available2017-06-09T17:36:23Z
    date copyright2014/06/01
    date issued2014
    identifier issn0882-8156
    identifier otherams-87955.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4231681
    description abstracthe extent to which the tropical cyclone (TC) intensity forecast error of IVCN and S5YY, consensus models routinely used by forecasters at the National Hurricane Center and the Joint Typhoon Warning Center, respectively, can be predicted is determined. A number of predictors of consensus intensity forecast error, which must be quantities that are available prior to the official forecast deadline, were examined for the Atlantic and eastern North Pacific basins for 2008?11 and the western North Pacific basin for 2012. Leading predictors were found to be forecast TC intensity and intensity change, initial intensity and latitude of the TC, and consensus model spread, defined to be the average of the absolute intensity differences between the member forecasts and the consensus forecast. Using stepwise linear regression and the full pool of predictors, regression models were found for each forecast length to predict the IVCN and S5YY TC intensity forecast errors. Using the regression models, intervals were determined centered on the IVCN and S5YY forecasts that contained the verifying TC intensity about 67% of the time. Based on the size of these intervals, a forecaster can determine the confidence that can be placed upon the IVCN or S5YY forecasts. Independent data testing yielded results only slightly degraded from those of dependent data testing, highlighting the capability of these methods in practical forecasting applications.
    publisherAmerican Meteorological Society
    titlePrediction of Consensus Tropical Cyclone Intensity Forecast Error
    typeJournal Paper
    journal volume29
    journal issue3
    journal titleWeather and Forecasting
    identifier doi10.1175/WAF-D-13-00058.1
    journal fristpage750
    journal lastpage762
    treeWeather and Forecasting:;2014:;volume( 029 ):;issue: 003
    contenttypeFulltext
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