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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Cited articles

Bach, L., Schraff, C., Keller, J. D., and Hense, A.: Towards a probabilistic regional reanalysis system for Europe: evaluation of precipitation from experiments, Tellus A, 68, 32209, https://doi.org/10.3402/tellusa.v68.32209, 2016. a
Beckert, A. A., Eisenstein, L., Oertel, A., Hewson, T., Craig, G. C., and Rautenhaus, M.: The three-dimensional structure of fronts in mid-latitude weather systems as represented by numerical weather prediction models, Weather Clim. Dynam. Discuss. [preprint], https://doi.org/10.5194/wcd-2022-36, in review, 2022. a, b
Bivand, R. and Lewin-Koh, N.: maptools: Tools for Handling Spatial Objects, r package version 1.1-2, https://CRAN.R-project.org/package=maptools (last access: 13 May 2022), 2021. a
Bjerknes, J.: On the Structure of Moving Cyclones, Mon. Weather Rev., 47, 95–99, https://doi.org/10.1175/1520-0493(1919)47<95:OTSOMC>2.0.CO;2, 1919. a
Bokeh Development Team: Bokeh: Python library for interactive visualization, python package version 2.3.2, https://bokeh.pydata.org/en/latest/ (last access: 13 May 2022), 2021. a, b
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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.