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

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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

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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