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Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation (2005)

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by Tilmann Gneiting , Anton H. Westveld III , Adrian E. Raftery , Tom Goldman
Venue:MONTHLY WEATHER REVIEW
Citations:80 - 14 self
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BibTeX

@TECHREPORT{Gneiting05calibratedprobabilistic,
    author = {Tilmann Gneiting and Anton H. Westveld III and Adrian E. Raftery and Tom Goldman},
    title = {Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation},
    institution = {MONTHLY WEATHER REVIEW},
    year = {2005}
}

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Abstract

Ensemble prediction systems typically show positive spread-error correlation, but they are subject to forecast bias and underdispersion, and therefore uncalibrated. This work proposes the use of ensemble model output statistics (EMOS), an easy to imple-ment post-processing technique that addresses both forecast bias and underdispersion and takes account of the spread-skill relationship. The technique is based on multiple lin-ear regression and akin to the superensemble approach that has traditionally been used for deterministic-style forecasts. The EMOS technique yields probabilistic forecasts that take the form of Gaussian predictive probability density functions (PDFs) for continuous weather variables, and can be applied to gridded model output. The EMOS predictive mean is an optimal, bias-corrected weighted average of the ensemble member forecasts, with coefficients that are constrained to be nonnegative and associated with the member model skill. The EMOS predictive mean provides a highly accurate deterministic-style forecast. The EMOS predictive variance is a linear function of the ensemble spread. For fitting the EMOS coefficients, the method of minimum CRPS estimation is introduced.

Keyphrases

minimum crp estimation    ensemble model output statistic    probabilistic forecasting    emos predictive mean    emos coefficient    imple-ment post-processing technique    positive spread-error correlation    ensemble member forecast    bias-corrected weighted average    emos technique yield probabilistic forecast    forecast bias    ensemble spread    continuous weather variable    spread-skill relationship    accurate deterministic-style forecast    emos predictive variance    linear function    deterministic-style forecast    gaussian predictive probability density function    superensemble approach    member model skill    gridded model output    ensemble prediction system    multiple lin-ear regression   

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