Preprints
https://doi.org/10.5194/amt-2022-33
https://doi.org/10.5194/amt-2022-33
 
04 Feb 2022
04 Feb 2022
Status: this preprint is currently under review for the journal AMT.

Prediction of Alpine Foehn from time series of GNSS troposphere products using machine learning

Matthias Aichinger-Rosenberger1, Elmar Brockmann2, Laura Crocetti1, Benedikt Soja1, and Gregor Moeller1 Matthias Aichinger-Rosenberger et al.
  • 1Institute of Geodesy and Photogrammetry, ETH Zürich, 8093 Zurich, Switzerland
  • 2Swiss Federal Office of Topography (swisstopo), 3084 Wabern bei Bern, Switzerland

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.

Matthias Aichinger-Rosenberger et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2022-33', Anonymous Referee #1, 17 Feb 2022
  • RC2: 'Comment on amt-2022-33', Anonymous Referee #2, 27 Apr 2022

Matthias Aichinger-Rosenberger et al.

Matthias Aichinger-Rosenberger et al.

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Short summary
This study delevops 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 at Altdorf, Switzerland. Results are very encouringing and comparable to similar approaches using meteorological data, which qualifies the method as an additional independent tool for short-term foehn forecasting.