Articles | Volume 3, issue 4
https://doi.org/10.5194/wcd-3-1157-2022
https://doi.org/10.5194/wcd-3-1157-2022
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: 2,995 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,335 610 50 2,995 44 35
  • HTML: 2,335
  • PDF: 610
  • XML: 50
  • Total: 2,995
  • BibTeX: 44
  • EndNote: 35
Views and downloads (calculated since 17 May 2022)
Cumulative views and downloads (calculated since 17 May 2022)

Viewed (geographical distribution)

Total article views: 2,995 (including HTML, PDF, and XML) Thereof 2,901 with geography defined and 94 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 20 Nov 2024
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.