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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Cited articles

Athey, S., Tibshirani, J., and Wager, S.: Generalized random forests, Ann. Stat., 47, 1148–1178, https://doi.org/10.1214/18-AOS1709, 2019. a
Baran, S. and Nemoda, D.: Censored and shifted gamma distribution based EMOS model for probabilistic quantitative precipitation forecasting, Environmetrics, 27, 280–292, https://doi.org/10.1002/env.2391, 2016. a
Bauer, P., Thorpe, A., and Brunet, G.: The Quiet Revolution of Numerical Weather Prediction, Nature, 525, 47–55, https://doi.org/10.1038/nature14956, 2015. a
Breiman, L.: Bagging Predictors, Mach. Learn., 24, 123–140, https://doi.org/10.1007/bf00058655, 1996. a
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/a:1010933404324, 2001. a, b
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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.