Spatially Adaptive Post-Processing of Ensemble Forecasts for Temperature
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- Michael Scheuerer
- Ruprecht-Karls-Universität Heidelberg Germany
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- Luca Büermann
- Ruprecht-Karls-Universität Heidelberg Germany
Abstract
<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>
Journal
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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)
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Details 詳細情報について
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- CRID
- 1360292621561099648
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- ISSN
- 14679876
- 00359254
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- Data Source
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- Crossref