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

Matthias Aichinger‐Rosenberger, E. Bröckmann, Laura Crocetti, Benedikt S. Soja, Gregor Möller

Atmospheric measurement techniques · 14 October 2022 · ETH Zurich

In one sentence. Classification algorithms detect and predict Alpine foehn events from GNSS troposphere products at Altdorf, reaching 70-85% detection and alarm measures.

Abstract

Abstract. Remote sensing of water vapor using the Global Navigation Satellite System (GNSS) is a well-established technique and reliable data source for Numerical Weather Prediction (NWP). One of the phenomena rarely studied using GNSS are foehn winds. Since foehn winds are associated with significant humidity gradients between lee/luv side of a mountain range, tropospheric estimates from GNSS are also affected by their occurrence. Time series reveal characteristic features like distinctive minima/maxima and significant decrease in correlation between the stations. However, detecting such signals becomes increasingly difficult for large data sets. Therefore, we suggest the application of machine learning algorithms for detection and prediction of foehn events from GNSS troposphere products. The present study uses long-term time series of high-quality GNSS troposphere products from the Automated GNSS Network Switzerland (AGNES) as well as records of operational foehn index to investigate the performance of several different classification algorithms based on appropriate statistical metrics. The two best-performing algorithms are fine-tuned and employed on two years of test data. The results show very promising results, especially when reprocessed GNSS products are utilized. Detection- and alarm-based measures reach levels of 70–85 % for both tested algorithms and thus are comparable to those from studies using data from meteorological stations and NWP. For operational prediction, some limitations due to the availability and quality of GNSS products in near-real time (NRT) exist. However, they might be mitigated to a significant extend by provision of additional NRT products and improved data processing in the future.

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