Articles | Volume 15, issue 19
https://doi.org/10.5194/amt-15-5821-2022
https://doi.org/10.5194/amt-15-5821-2022
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

Viewed

Total article views: 2,097 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,522 509 66 2,097 63 55
  • HTML: 1,522
  • PDF: 509
  • XML: 66
  • Total: 2,097
  • BibTeX: 63
  • EndNote: 55
Views and downloads (calculated since 04 Feb 2022)
Cumulative views and downloads (calculated since 04 Feb 2022)

Viewed (geographical distribution)

Total article views: 2,097 (including HTML, PDF, and XML) Thereof 1,962 with geography defined and 135 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 20 Nov 2024
Download
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.