Spatially Adaptive Post-Processing of Ensemble Forecasts for Temperature. Issue 3 (30th September 2013)

• Record Type: Journal Article
• Authors: Scheuerer, Michael; Büermann, Luca
• Is Part Of: Journal of the Royal Statistical Society. Volume 63:Issue 3(2014:May)
• Page Start: 405
• Page End: 422
• Publication Date: 2013-09-30
• Subjects: Continuous ranked probability score -- Ensemble prediction system -- Gaussian process -- Non-homogeneous Gaussian regression -- Predictive distribution; Statistics -- Periodicals; 519.5
• DOI: 10.1111/rssc.12040
• Languages: English
• ISSNs: 0035-9254

Abstract

Summary: 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.