Articles | Volume 4, issue 4
https://doi.org/10.5194/wcd-4-981-2023
https://doi.org/10.5194/wcd-4-981-2023
Research article
 | 
20 Nov 2023
Research article |  | 20 Nov 2023

Identification of high-wind features within extratropical cyclones using a probabilistic random forest – Part 2: Climatology over Europe

Lea Eisenstein, Benedikt Schulz, Joaquim G. Pinto, and Peter Knippertz

Viewed

Total article views: 782 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
567 179 36 782 19 22
  • HTML: 567
  • PDF: 179
  • XML: 36
  • Total: 782
  • BibTeX: 19
  • EndNote: 22
Views and downloads (calculated since 24 Mar 2023)
Cumulative views and downloads (calculated since 24 Mar 2023)

Viewed (geographical distribution)

Total article views: 782 (including HTML, PDF, and XML) Thereof 770 with geography defined and 12 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 08 May 2024
Download
Short summary
Mesoscale high-wind features within extratropical cyclones can cause immense damage. In Part 1 of this work, we introduced RAMEFI (RAndom-forest-based MEsoscale wind Feature Identification), an objective, flexible identification tool for these wind features based on a probabilistic random forest. Here, we use RAMEFI to compile a climatology of the features over 19 extended winter seasons over western and central Europe, focusing on relative occurrence, affected areas and further characteristics.