Articles | Volume 30, issue 4
https://doi.org/10.5194/npg-30-503-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-30-503-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Nov 2023
Research article |
| 20 Nov 2023
| 20 Nov 2023
Robust weather-adaptive post-processing using model output statistics random forests
Thomas Muschinski
CORRESPONDING AUTHOR
Department of Atmospheric and Cryospheric Sciences, Universität Innsbruck, Innsbruck, Austria
Department of Economics, Statistical Methods and Econometrics, Karlsruhe Institute of Technology, Karlsruhe, Germany
Georg J. Mayr
Department of Atmospheric and Cryospheric Sciences, Universität Innsbruck, Innsbruck, Austria
Achim Zeileis
Department of Statistics, Universität Innsbruck, Innsbruck, Austria
Thorsten Simon
Department of Statistics, Universität Innsbruck, Innsbruck, Austria
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Related subject area
Subject: Predictability, probabilistic forecasts, data assimilation, inverse problems | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Big data and artificial intelligence
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Short summary
Statistical post-processing is necessary to generate probabilistic forecasts from physical numerical weather prediction models. To allow for more flexibility, there has been a shift in post-processing away from traditional parametric regression models towards modern machine learning methods. By fusing these two approaches, we developed model output statistics random forests, a new post-processing method that is highly flexible but at the same time also very robust and easy to interpret.
Statistical post-processing is necessary to generate probabilistic forecasts from physical...
