Articles | Volume 15, issue 19
Research article
14 Oct 2022
Research article |  | 14 Oct 2022

Machine learning-based prediction of Alpine foehn events using GNSS troposphere products: first results for Altdorf, Switzerland

Matthias Aichinger-Rosenberger, Elmar Brockmann, Laura Crocetti, Benedikt Soja, and Gregor Moeller

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Cited articles

Aichinger-Rosenberger, M.: Usability of high-resolution GNSS-ZTD data in the AROME model, Master's thesis, University of Innsbruck, (last access: 2 September 2022), 2018. a
Baeza-Yates, R. A. and Ribeiro-Neto, B.: Modern Information Retrieval, Addison-Wesley Longman Publishing Co., Inc., USA,, 327–328, 1999. a
Barnes, L. R., Gruntfest, E. C., Hayden, M. H., Schultz, D. M., and Benight, C.: False Alarms and Close Calls: A Conceptual Model of Warning Accuracy, Weather Forecast., 22, 1140–1147,, 2007. a
Bennitt, G. V. and Jupp, A.: Operational Assimilation of GPS Zenith Total Delay Observations into the Met Office Numerical Weather Prediction Models, Mon. Weather Rev., 140, 2706–2719,, 2012. a
Bevis, M., Businger, S., Herring, T. A., Anthes, R. A., and Ware, R. H.: GPS Meteorology: Remote Sensing of Atmospheric Water Vapor Using the Global Positioning System, Geophys. Mag., 34, 359–425, 1992. a
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.