Weather and Climate Dynamics
Weather and Climate Dynamics
WCD
Articles | Volume 6, issue 2
Articles | Volume 6, issue 2
https://doi.org/10.5194/wcd-6-571-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wcd-6-571-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 6, issue 2
Research article
 | 
21 May 2025
Research article |  | 21 May 2025

Weather type reconstruction using machine learning approaches

Lucas Pfister, Lena Wilhelm, Yuri Brugnara, Noemi Imfeld, and Stefan Brönnimann

Viewed

Total article views: 5,296 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
3,712 1,187 397 5,296 317 149 281
  • HTML: 3,712
  • PDF: 1,187
  • XML: 397
  • Total: 5,296
  • Supplement: 317
  • BibTeX: 149
  • EndNote: 281
Views and downloads (calculated since 21 May 2024)
Cumulative views and downloads (calculated since 21 May 2024)

Viewed (geographical distribution)

Total article views: 5,296 (including HTML, PDF, and XML) Thereof 5,208 with geography defined and 88 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 06 May 2026
Download
Short summary
Our work compares different machine learning approaches for creating long-term classifications of daily atmospheric circulation patterns using input data from surface meteorological observations. Our comparison reveals that a feedforward neural network performs best at this task. Using this model, we present a daily reconstruction of a commonly used weather type classification for central Europe that dates back to 1728.
Read more
Share