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- Michael Scheuerer
- Ruprecht-Karls-Universität Heidelberg Germany
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- Luca Büermann
- Ruprecht-Karls-Universität Heidelberg Germany
抄録
<jats:title>Summary</jats:title><jats:p>We propose a statistical post-processing method that yields locally calibrated probabilistic forecasts of temperature, based on the output of an ensemble prediction system. It represents the mean of the predictive distributions as a sum of short-term averages of local temperatures and ensemble prediction system driven terms. For the spatial interpolation of temperature averages and local forecast uncertainty parameters we use an intrinsic Gaussian random-field model with a location-dependent nugget effect that accounts for small-scale variability. Applied to the COSMO-DE ensemble, our method yields locally calibrated and sharp probabilistic forecasts and compares favourably with other approaches.</jats:p>
収録刊行物
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- Journal of the Royal Statistical Society Series C: Applied Statistics
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Journal of the Royal Statistical Society Series C: Applied Statistics 63 (3), 405-422, 2013-09-30
Oxford University Press (OUP)