Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 14, issue 4
Articles | Volume 14, issue 4
https://doi.org/10.5194/amt-14-3169-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-3169-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 4
Research article
 | 
29 Apr 2021
Research article |  | 29 Apr 2021

RainForest: a random forest algorithm for quantitative precipitation estimation over Switzerland

Daniel Wolfensberger, Marco Gabella, Marco Boscacci, Urs Germann, and Alexis Berne

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Short summary
In this work, we present a novel quantitative precipitation estimation method for Switzerland that uses random forests, an ensemble-based machine learning technique. The estimator has been trained with a database of 4 years of ground and radar observations. The results of an in-depth evaluation indicate that, compared with the more classical method in use at MeteoSwiss, this novel estimator is able to reduce both the average error and bias of the predictions.
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