Articles | Volume 27, issue 1
https://doi.org/10.5194/npg-27-23-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/npg-27-23-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Feb 2020
Research article |
| 05 Feb 2020
| 05 Feb 2020
Remember the past: a comparison of time-adaptive training schemes for non-homogeneous regression
Department of Statistics, Universität Innsbruck, Innsbruck, Austria
Department of Atmospheric and Cryospheric Sciences, Universität Innsbruck, Innsbruck, Austria
Sebastian Lerch
Institute for Stochastics, Karlsruher Institut für Technologie, Karlsruhe, Germany
Georg J. Mayr
Department of Atmospheric and Cryospheric Sciences, Universität Innsbruck, Innsbruck, Austria
Thorsten Simon
Department of Statistics, Universität Innsbruck, Innsbruck, Austria
Department of Atmospheric and Cryospheric Sciences, Universität Innsbruck, Innsbruck, Austria
Reto Stauffer
Department of Statistics, Universität Innsbruck, Innsbruck, Austria
Digital Science Center, Universität Innsbruck, Innsbruck, Austria
Achim Zeileis
Department of Statistics, Universität Innsbruck, Innsbruck, Austria
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Cited
19 citations as recorded by crossref.
- Robust weather-adaptive post-processing using model output statistics random forests T. Muschinski et al. 10.5194/npg-30-503-2023
- Incorporating the North Atlantic Oscillation into the post‐processing of MOGREPS‐G wind speed forecasts S. Allen et al. 10.1002/qj.3983
- Simulation-based comparison of multivariate ensemble post-processing methods S. Lerch et al. 10.5194/npg-27-349-2020
- The EUPPBench postprocessing benchmark dataset v1.0 J. Demaeyer et al. 10.5194/essd-15-2635-2023
- Operational statistical postprocessing of temperature ensemble forecasts with station‐specific predictors K. Ylinen et al. 10.1002/met.1971
- Calibrated ensemble forecasts of the height of new snow using quantile regression forests and ensemble model output statistics G. Evin et al. 10.5194/npg-28-467-2021
- Skill of Global Raw and Postprocessed Ensemble Predictions of Rainfall in the Tropics P. Vogel et al. 10.1175/WAF-D-20-0082.1
- Skewed and Mixture of Gaussian Distributions for Ensemble Postprocessing M. Taillardat 10.3390/atmos12080966
- Comparison of multivariate post‐processing methods using global ECMWF ensemble forecasts M. Lakatos et al. 10.1002/qj.4436
- Post-processing numerical weather prediction ensembles for probabilistic solar irradiance forecasting B. Schulz et al. 10.1016/j.solener.2021.03.023
- Generative machine learning methods for multivariate ensemble postprocessing J. Chen et al. 10.1214/23-AOAS1784
- Recalibrating wind‐speed forecasts using regime‐dependent ensemble model output statistics S. Allen et al. 10.1002/qj.3806
- Lead‐time‐continuous statistical postprocessing of ensemble weather forecasts J. Wessel et al. 10.1002/qj.4701
- Correcting for model changes in statistical postprocessing – an approach based on response theory J. Demaeyer & S. Vannitsem 10.5194/npg-27-307-2020
- On the implementation of post-processing of runoff forecast ensembles J. Skøien et al. 10.1175/JHM-D-21-0008.1
- A comparison of statistical and dynamical downscaling methods for short‐term weather forecasts in the US Northeast M. Alessi & A. DeGaetano 10.1002/met.1976
- Preface: Advances in post-processing and blending of deterministic and ensemble forecasts S. Hemri et al. 10.5194/npg-27-519-2020
- Predicting power ramps from joint distributions of future wind speeds T. Muschinski et al. 10.5194/wes-7-2393-2022
- D‐vine‐copula‐based postprocessing of wind speed ensemble forecasts D. Jobst et al. 10.1002/qj.4521
19 citations as recorded by crossref.
- Robust weather-adaptive post-processing using model output statistics random forests T. Muschinski et al. 10.5194/npg-30-503-2023
- Incorporating the North Atlantic Oscillation into the post‐processing of MOGREPS‐G wind speed forecasts S. Allen et al. 10.1002/qj.3983
- Simulation-based comparison of multivariate ensemble post-processing methods S. Lerch et al. 10.5194/npg-27-349-2020
- The EUPPBench postprocessing benchmark dataset v1.0 J. Demaeyer et al. 10.5194/essd-15-2635-2023
- Operational statistical postprocessing of temperature ensemble forecasts with station‐specific predictors K. Ylinen et al. 10.1002/met.1971
- Calibrated ensemble forecasts of the height of new snow using quantile regression forests and ensemble model output statistics G. Evin et al. 10.5194/npg-28-467-2021
- Skill of Global Raw and Postprocessed Ensemble Predictions of Rainfall in the Tropics P. Vogel et al. 10.1175/WAF-D-20-0082.1
- Skewed and Mixture of Gaussian Distributions for Ensemble Postprocessing M. Taillardat 10.3390/atmos12080966
- Comparison of multivariate post‐processing methods using global ECMWF ensemble forecasts M. Lakatos et al. 10.1002/qj.4436
- Post-processing numerical weather prediction ensembles for probabilistic solar irradiance forecasting B. Schulz et al. 10.1016/j.solener.2021.03.023
- Generative machine learning methods for multivariate ensemble postprocessing J. Chen et al. 10.1214/23-AOAS1784
- Recalibrating wind‐speed forecasts using regime‐dependent ensemble model output statistics S. Allen et al. 10.1002/qj.3806
- Lead‐time‐continuous statistical postprocessing of ensemble weather forecasts J. Wessel et al. 10.1002/qj.4701
- Correcting for model changes in statistical postprocessing – an approach based on response theory J. Demaeyer & S. Vannitsem 10.5194/npg-27-307-2020
- On the implementation of post-processing of runoff forecast ensembles J. Skøien et al. 10.1175/JHM-D-21-0008.1
- A comparison of statistical and dynamical downscaling methods for short‐term weather forecasts in the US Northeast M. Alessi & A. DeGaetano 10.1002/met.1976
- Preface: Advances in post-processing and blending of deterministic and ensemble forecasts S. Hemri et al. 10.5194/npg-27-519-2020
- Predicting power ramps from joint distributions of future wind speeds T. Muschinski et al. 10.5194/wes-7-2393-2022
- D‐vine‐copula‐based postprocessing of wind speed ensemble forecasts D. Jobst et al. 10.1002/qj.4521
Latest update: 19 Apr 2024
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...
