Articles | Volume 15, issue 19
https://doi.org/10.5194/amt-15-5821-2022
© Author(s) 2022. 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-15-5821-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Oct 2022
Research article |
| 14 Oct 2022
| 14 Oct 2022
Machine learning-based prediction of Alpine foehn events using GNSS troposphere products: first results for Altdorf, Switzerland
Matthias Aichinger-Rosenberger
CORRESPONDING AUTHOR
Institute of Geodesy and Photogrammetry, ETH Zürich, 8093 Zurich, Switzerland
Elmar Brockmann
Swiss Federal Office of Topography (swisstopo), 3084 Wabern bei Bern, Switzerland
Laura Crocetti
Institute of Geodesy and Photogrammetry, ETH Zürich, 8093 Zurich, Switzerland
Benedikt Soja
Institute of Geodesy and Photogrammetry, ETH Zürich, 8093 Zurich, Switzerland
Gregor Moeller
Institute of Geodesy and Photogrammetry, ETH Zürich, 8093 Zurich, Switzerland
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Short summary
This study develops an innovative approach for the detection and prediction of foehn winds. The approach uses products generated from GNSS (Global Navigation Satellite Systems) in combination with machine learning-based classification algorithms to detect and predict foehn winds at Altdorf, Switzerland. Results are encouraging and comparable to similar studies using meteorological data, which might qualify the method as an additional tool for short-term foehn forecasting in the future.
This study develops an innovative approach for the detection and prediction of foehn winds. The...
