Abstract
When statistically postprocessing temperature forecasts, it is almost always assumed that the future temperature follows a Gaussian distribution conditional on the output of an ensemble prediction system. Recent studies, however, have demonstrated that it can at times be beneficial to employ alternative parametric families when postprocessing temperature forecasts that are either asymmetric or heavier-tailed than the normal distribution. In this article, we compare choices of the parametric distribution used within the ensemble model output statistics (EMOS) framework to statistically postprocess 2-m temperature forecast fields generated by the Met Office’s regional, convection-permitting ensemble prediction system, MOGREPS-UK Specifically, we study the normal, logistic, and skew-logistic distributions. A flexible alternative is also introduced that first applies a Yeo–Johnson transformation to the temperature forecasts prior to postprocessing, so that they more readily conform to the assumptions made by established postprocessing methods. It is found that accounting for the skewness of temperature when postprocessing can enhance the performance of the resulting forecast field, particularly during summer and winter and in mountainous regions.
Allen’s current affiliation: Institute of Mathematical Statistics and Actuarial Science, University of Bern, Bern, Switzerland.
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