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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 Apr 2021
Research article |
| 29 Apr 2021
| 29 Apr 2021
RainForest: a random forest algorithm for quantitative precipitation estimation over Switzerland
Daniel Wolfensberger
LTE, Ecole polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland
MeteoSwiss, via ai Monti 146, Locarno, Switzerland
Marco Gabella
MeteoSwiss, via ai Monti 146, Locarno, Switzerland
Marco Boscacci
MeteoSwiss, via ai Monti 146, Locarno, Switzerland
Urs Germann
MeteoSwiss, via ai Monti 146, Locarno, Switzerland
LTE, Ecole polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland
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- Data fusion of satellite imagery and downscaling for generating highly fine-scale precipitation X. Zhang et al. 10.1016/j.jhydrol.2024.130665
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- Research on Water Resource Modeling Based on Machine Learning Technologies Z. Liu et al. 10.3390/w16030472
- Application of Machine Learning Techniques to Improve Multi-Radar Mosaic Precipitation Estimates in Shanghai R. Wang et al. 10.3390/atmos14091364
- Intercomparison of Deep Learning Architectures for the Prediction of Precipitation Fields With a Focus on Extremes N. Otero & P. Horton 10.1029/2023WR035088
- Near Real-Time Estimation of High Spatiotemporal Resolution Rainfall from Cloud Top Properties of the Msg Satellite and Commercial Microwave Link Rainfall Intensities K. Kumah et al. 10.2139/ssrn.4098667
- Exploiting radar polarimetry for nowcasting thunderstorm hazards using deep learning N. Rombeek et al. 10.5194/nhess-24-133-2024
- Assessing Machine Learning Models for Gap Filling Daily Rainfall Series in a Semiarid Region of Spain J. Bellido-Jiménez et al. 10.3390/atmos12091158
- Introduction of Materials Genome Technology and Its Applications in the Field of Biomedical Materials Y. Qiu et al. 10.3390/ma16051906
- The effect of altitude on the uncertainty of radar-based precipitation estimates over Switzerland E. Ghaemi et al. 10.1080/01431161.2023.2203339
- Estimation of Precipitation Area Using S-Band Dual-Polarization Radar Measurements J. Song et al. 10.3390/rs13112039
- SSAS: Spatiotemporal Scale Adaptive Selection for Improving Bias Correction on Precipitation Y. Liu et al. 10.1109/TCYB.2021.3072483
- Weather Radar in Complex Orography U. Germann et al. 10.3390/rs14030503
- A comparison of five models in predicting surface dead fine fuel moisture content of typical forests in Northeast China J. Fan et al. 10.3389/ffgc.2023.1122087
- Remote sensing and machine learning method to support sea surface pCO2 estimation in the Yellow Sea W. Li et al. 10.3389/fmars.2023.1181095
- Near real-time estimation of high spatiotemporal resolution rainfall from cloud top properties of the MSG satellite and commercial microwave link rainfall intensities K. Kumah et al. 10.1016/j.atmosres.2022.106357
- Projection of future water availability in the Amu Darya Basin O. Salehie et al. 10.1002/joc.8490
- A new approach for quantitative precipitation estimation from radar reflectivity using a gated recurrent unit network T. Dinh et al. 10.1016/j.jhydrol.2023.129887
- A novel approach to precipitation prediction using a coupled CEEMDAN-GRU-Transformer model with permutation entropy algorithm J. Zhao et al. 10.2166/wst.2023.257
- Parks Under Stress: Air Temperature Regulation of Urban Green Spaces Under Conditions of Drought and Summer Heat R. Kraemer & N. Kabisch 10.3389/fenvs.2022.849965
- Calibration of X-Band Radar for Extreme Events in a Spatially Complex Precipitation Region in North Peru: Machine Learning vs. Empirical Approach R. Rollenbeck et al. 10.3390/atmos12121561
- Hybrid EMD-RF Model for Predicting Annual Rainfall in Kerala, India A. Jayasree et al. 10.3390/app13074572
Latest update: 29 Jun 2024
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.
In this work, we present a novel quantitative precipitation estimation method for Switzerland...
