Journal cover Journal topic
Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 1.699 IF 1.699
  • IF 5-year value: 1.559 IF 5-year
    1.559
  • CiteScore value: 1.61 CiteScore
    1.61
  • SNIP value: 0.884 SNIP 0.884
  • IPP value: 1.49 IPP 1.49
  • SJR value: 0.648 SJR 0.648
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 52 Scimago H
    index 52
  • h5-index value: 21 h5-index 21
Discussion papers
https://doi.org/10.5194/npg-2019-49
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-2019-49
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 02 Oct 2019

Submitted as: research article | 02 Oct 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Nonlinear Processes in Geophysics (NPG).

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

Moritz N. Lang1,2, Sebastian Lerch3, Georg J. Mayr2, Thorsten Simon1,2, Reto Stauffer1,4, and Achim Zeileis1 Moritz N. Lang et al.
  • 1Department of Statistics, Universität Innsbruck, Innsbruck, Austria
  • 2Department of Atmospheric and Cryospheric Sciences, Universität Innsbruck, Innsbruck, Austria
  • 3Institute for Stochastics, Karlsruher Institut für Technologie, Karlsruhe, Germany
  • 4Digital Science Center, Universität Innsbruck, Innsbruck, Austria

Abstract. Non-homogeneous regression is a frequently-used post-processing method for increasing the predictive skill of probabilistic ensemble weather forecasts. To adjust for seasonally varying error characteristics between ensemble forecasts and corresponding observations, different time-adaptive training schemes, including the classical sliding training window, have been developed for non-homogeneous regression. This study compares three such training approaches with the sliding-window approach for the application of post-processing near-surface air temperature forecasts across Central Europe. The predictive performance is evaluated conditional on three different groups of stations located in plains, in mountain foreland, and within mountainous terrain, as well as on changes in the ensemble forecast system of the European Centre for Medium-Range Weather Forecasts (ECMWF) used as input for the post-processing.

The results show that time-adaptive training schemes using data over multiple years stabilize the temporal evolution of the coefficient estimates, yielding an increased predictive performance for all station types tested compared to the classical sliding-window approach based on the most recent days only. While this may not be surprising under fully stable model conditions, it is shown that remembering the past from multiple years of training data is typically also superior to the classical sliding-window when the ensemble prediction system is affected by certain model changes. Thus, reducing the variance of the non-homogeneous regression estimates due to increased training data appears to be more important than reducing its bias by adapting rapidly to the most current training data only.

Moritz N. Lang et al.
Interactive discussion
Status: open (until 27 Nov 2019)
Status: open (until 27 Nov 2019)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement
Moritz N. Lang et al.
Moritz N. Lang et al.
Viewed  
Total article views: 274 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
219 49 6 274 5 3
  • HTML: 219
  • PDF: 49
  • XML: 6
  • Total: 274
  • BibTeX: 5
  • EndNote: 3
Views and downloads (calculated since 02 Oct 2019)
Cumulative views and downloads (calculated since 02 Oct 2019)
Viewed (geographical distribution)  
Total article views: 196 (including HTML, PDF, and XML) Thereof 196 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Cited  
Saved  
No saved metrics found.
Discussed  
No discussed metrics found.
Latest update: 12 Nov 2019
Publications Copernicus
Download
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.
Statistical post-processing aims to increase the predictive skill of probabilistic ensemble...
Citation