Spatially adaptive post-processing of ensemble forecasts for temperature

Michael Scheuerer and Luca Büermann

Journal of the Royal Statistical Society Series C, 2014, vol. 63, issue 3, 405-422

Abstract: type="main" xml:id="rssc12040-abs-0001">

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.

Date: 2014
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