Spatially adaptive post-processing of ensemble forecasts for temperature

• Verfasst von: Scheuerer, Michael [VerfasserIn]
• Verfasst von: Büermann, Luca [VerfasserIn]
• Titel: Spatially adaptive post-processing of ensemble forecasts for temperature
• Verf.angabe: Michael Scheuerer and Luca Büermann
• Jahr: 2014
• Jahr des Originals: 2013
• Umfang: 18 S.
• Fussnoten: First published: 30 September 2013 ; Gesehen am 18.09.2020
• Titel Quelle: Enthalten in: Royal Statistical SocietyJournal of the Royal Statistical Society / C
• Ort Quelle: Oxford : Wiley-Blackwell, 1952
• Jahr Quelle: 2014
• Band/Heft Quelle: 63(2014), 3, Seite 405-422
• ISSN Quelle: 1467-9876
• DOI: doi:10.1111/rssc.12040
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Continuous ranked probability score
• Sach-SW: Ensemble prediction system
• Sach-SW: Gaussian process
• Sach-SW: Non-homogeneous Gaussian regression
• Sach-SW: Predictive distribution
• K10plus-PPN: 1733293973

Abstract

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.