New publication
"Machine learning-based prediction of Alpine foehn events using GNSS troposphere products: first results for Altdorf, Switzerland" by Aichinger-Rosenberger et al. (2022)
Foehn winds are a meteorological phenomenon rarely studied using Global Navigation Satellite Systems (GNSS). However, since they are associated with significant humidity gradients between two sides of a mountain range, tropospheric estimates from GNSS are also affected by their occurrence. In this initial study we develop a new, machine learning-based method for detection and prediction of foehn events at the Swiss station Altdorf by utilising long-term time series of high-quality GNSS troposphere products. We used data from the Automated GNSS Network Switzerland (AGNES) and various GNSS sites from neighbouring countries, as well as operational foehn index reference data, in both a gradient-boosting and support vector classifier model.
Results are promising, especially when reprocessed GNSS products are utilised and the most dense station setup is used. Detection- and alarm-based measures reach levels between 66 % - 80 % for both tested algorithms and thus are comparable to those from studies using data from meteorological stations and numerical weather prediction. The availability and quality of GNSS products in near-real time (NRT) currently limit the usability and performance of the approach in operational prediction. Specific additional investigations also outline benefits for the results when including geographically relevant stations (e.g. high-altitude stations) in the utilised datasets.
Much more detailed information and results can now be found in the corresponding AMT paper, which can be found here: external page https://amt.copernicus.org/articles/15/5821/2022/.
