Accounting for skew when post-processing MOGREPS-UK temperature forecast fields
Allen, S; Evans, GR; Buchanan, P; et al.Kwasniok, F
Date: 1 August 2021
Article
Journal
Monthly Weather Review
Publisher
American Meteorological Society
Publisher DOI
Abstract
When statistically post-processing 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 ...
When statistically post-processing 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 post-processing 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 post-process 2m 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 post-processing, so that they more readily conform to the assumptions made by established post-processing methods. It is found that accounting for the skewness of temperature
when post-processing can enhance the performance of the resulting forecast field, particularly
during summer and winter and in mountainous regions.
Mathematics and Statistics
Faculty of Environment, Science and Economy
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