Weather and Climate Dynamics
Weather and Climate Dynamics
WCD
Articles | Volume 3, issue 4
Articles | Volume 3, issue 4
https://doi.org/10.5194/wcd-3-1157-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wcd-3-1157-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 3, issue 4
Research article
 | 
19 Oct 2022
Research article |  | 19 Oct 2022

Identification of high-wind features within extratropical cyclones using a probabilistic random forest – Part 1: Method and case studies

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

Viewed

Total article views: 6,697 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
4,870 1,740 87 6,697 123 157
  • HTML: 4,870
  • PDF: 1,740
  • XML: 87
  • Total: 6,697
  • BibTeX: 123
  • EndNote: 157
Views and downloads (calculated since 17 May 2022)
Cumulative views and downloads (calculated since 17 May 2022)

Viewed (geographical distribution)

Total article views: 6,697 (including HTML, PDF, and XML) Thereof 6,477 with geography defined and 220 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 02 May 2026
Download
Short summary
Mesoscale high-wind features within extratropical cyclones can cause immense damage. Here, we present RAMEFI, a novel approach to objectively identify the wind features based on a probabilistic random forest. RAMEFI enables a wide range of applications such as probabilistic predictions for the occurrence or a multi-decadal climatology of these features, which will be the focus of Part 2 of the study, with the goal of improving wind and, specifically, wind gust forecasts in the long run.
Read more
Share