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NPG | Articles | Volume 27, issue 1
Nonlin. Processes Geophys., 27, 23–34, 2020
https://doi.org/10.5194/npg-27-23-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue: Advances in post-processing and blending of deterministic...

Nonlin. Processes Geophys., 27, 23–34, 2020
https://doi.org/10.5194/npg-27-23-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

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 et al.

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Cited articles

Baran, S. and Möller, A.: Bivariate Ensemble Model Output Statistics Approach for Joint Forecasting of Wind Speed and Temperature, Meteorol. Atmos. Phys., 129, 99–112, https://doi.org/10.1007/s00703-016-0467-8, 2017. a
Barnes, C., Brierley, C. M., and Chandler, R. E.: New approaches to postprocessing of multi-model ensemble forecasts, Q. J. Roy. Meteor. Soc., 145, 3479–3498, https://doi.org/10.1002/qj.3632, 2019. a
Demaeyer, J. and Vannitsem, S.: Correcting for Model Changes in Statistical Post-Processing – An approach based on Response Theory, Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npg-2019-57, in review, 2019. a
Gebetsberger, M., Messner, J. W., Mayr, G. J., and Zeileis, A.: Estimation Methods for Nonhomogeneous Regression Models: Minimum Continuous Ranked Probability Score versus Maximum Likelihood, Mon. Weather Rev., 146, 4323–4338, https://doi.org/10.1175/MWR-D-17-0364.1, 2018. a
Gneiting, T. and Katzfuss, M.: Probabilistic Forecasting, Annu. Rev. Stat. Appl., 1, 125–151, https://doi.org/10.1146/annurev-statistics-062713-085831, 2014. a, b
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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.
Statistical post-processing aims to increase the predictive skill of probabilistic ensemble...
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