Articles | Volume 14, issue 4
https://doi.org/10.5194/amt-14-3169-2021
https://doi.org/10.5194/amt-14-3169-2021
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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Cited articles

Anagnostou, E. N. and Krajewski, W. F.: Real-Time Radar Rainfall Estimation. Part I: Algorithm Formulation, J. Atmos. Ocean. Tech., 16, 189–197, https://doi.org/10.1175/1520-0426(1999)016<0189:RTRREP>2.0.CO;2, 1999. a
Anagnostou, M. N., Kalogiros, J., Anagnostou, E. N., Tarolli, M., Papadopoulos, A., and Borga, M.: Performance evaluation of high-resolution rainfall estimation by X-band dual-polarization radar for flash flood applications in mountainous basins, J. Hydrol., 394, 4–16, https://doi.org/10.1016/j.jhydrol.2010.06.026, 2010. a
Baldauf, M., Seifert, A., Förstner, J., Majewski, D., Raschendorfer, M., and Reinhardt, T.: Operational convective-scale numerical weather prediction with the COSMO model: description and sensitivities, Mon. Weather Rev., 139, 3887–3905, https://doi.org/10.1175/MWR-D-10-05013.1, 2011. a
Barton, Y., Sideris, I. V., Germann, U., and Martius, O.: A method for real-time temporal disaggregation of blended radar–rain gauge precipitation fields, Meteorol. Appl., 27, e1843, https://doi.org/10.1002/met.1843, 2020. a, b
Besic, N., Figueras i Ventura, J., Grazioli, J., Gabella, M., Germann, U., and Berne, A.: Hydrometeor classification through statistical clustering of polarimetric radar measurements: a semi-supervised approach, Atmos. Meas. Tech., 9, 4425–4445, https://doi.org/10.5194/amt-9-4425-2016, 2016. a
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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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