Articles | Volume 30, issue 4
https://doi.org/10.5194/npg-30-503-2023
https://doi.org/10.5194/npg-30-503-2023
Research article
 | 
20 Nov 2023
Research article |  | 20 Nov 2023

Robust weather-adaptive post-processing using model output statistics random forests

Thomas Muschinski, Georg J. Mayr, Achim Zeileis, and Thorsten Simon

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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.