Published May 13, 2022
| Version v1
Software
Open
RAMEFI (RAndom-forest based MEsoscale wind Feature Identification)
- 1. Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Karlsruhe, Germany
- 2. Institute for Stochastics, Karlsruhe Institute of Technology, Karlsruhe, Germany
- 3. Heidelberg Institute of Theoretical Studies, Heidelberg, Germany
Description
This repository provides code and data at the time of submission accompanying the paper
Eisenstein, L., Schulz, B., Qadir, G. A., Pinto, J. G. and Knippertz, P. (2022). Objective identification of high-wind features within extratropical cyclones using a probabilistic random forest (RAMEFI). Part I: Method and illustrative case studies. Weather Clim. Dynam. Discuss. [preprint], https://doi.org/10.5194/wcd-2022-29, in review, 2022.
In particular, code for the implementation of the RAMEFI method and the data that was used in the study are available.
For further information, see README.md.
Notes
The research leading to these results has been accomplished within the project C5 "Dynamical feature-based ensemble postprocessing of wind gusts within European winter storms" of the Transregional Collaborative Research Center SFB/TRR 165 "Waves to Weather" funded by the German Science Foundation (DFG).
Files
ramefi.zip
Files
(2.2 GB)
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md5:67553b52545903da7edd63ad35173d66
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Additional details
Related works
- Is supplemented by
- Preprint: 10.5194/wcd-2022-29 (DOI)
- Requires
- Video/Audio: 10.5281/zenodo.6541277 (DOI)