Articles | Volume 3, issue 4
Weather Clim. Dynam., 3, 1157–1182, 2022
https://doi.org/10.5194/wcd-3-1157-2022
Weather Clim. Dynam., 3, 1157–1182, 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 et al.

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wcd-2022-29', Anonymous Referee #1, 01 Jul 2022
    • AC1: 'Reply on RC1', Lea Eisenstein, 16 Aug 2022
  • RC2: 'Comment on wcd-2022-29', Ambrogio Volonté, 25 Jul 2022
    • AC2: 'Reply on RC2', Lea Eisenstein, 16 Aug 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Lea Eisenstein on behalf of the Authors (16 Aug 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (22 Aug 2022) by Silvio Davolio
RR by Anonymous Referee #1 (31 Aug 2022)
RR by Ambrogio Volonté (05 Sep 2022)
ED: Publish subject to minor revisions (review by editor) (06 Sep 2022) by Silvio Davolio
AR by Lea Eisenstein on behalf of the Authors (16 Sep 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (19 Sep 2022) by Silvio Davolio
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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.