Articles | Volume 27, issue 1
https://doi.org/10.5194/npg-27-23-2020
https://doi.org/10.5194/npg-27-23-2020
Research article
 | 
05 Feb 2020
Research article |  | 05 Feb 2020

Remember the past: a comparison of time-adaptive training schemes for non-homogeneous regression

Moritz N. Lang, Sebastian Lerch, Georg J. Mayr, Thorsten Simon, Reto Stauffer, and Achim Zeileis

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Latest update: 28 Mar 2024
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Short summary
Statistical post-processing aims to increase the predictive skill of probabilistic ensemble weather forecasts by learning the statistical relation between historical pairs of observations and ensemble forecasts within a given training data set. This study compares four different training schemes and shows that including multiple years of data in the training set typically yields a more stable post-processing while it loses the ability to quickly adjust to temporal changes in the underlying data.