RAMEFI (RAndom-forest based MEsoscale wind Feature Identification)
Eisenstein, Lea
1; Schulz, Benedikt
; Qadir, Ghulam A.; Pinto, Joaquim G. 1; Knippertz, Peter
1
1 Institut für Meteorologie und Klimaforschung (IMK), Karlsruher Institut für Technologie (KIT)
1; Schulz, Benedikt
; Qadir, Ghulam A.; Pinto, Joaquim G. 1; Knippertz, Peter
11 Institut für Meteorologie und Klimaforschung (IMK), Karlsruher Institut für Technologie (KIT)
Abstract:
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.
| Zugehörige Institution(en) am KIT | Institut für Meteorologie und Klimaforschung Troposphärenforschung (IMKTRO) |
| Publikationstyp | Forschungsdaten |
| Publikationsjahr | 2022 |
| Identifikator | KITopen-ID: 1000178170 |
| HGF-Programm | 12.11.34 (POF IV, LK 01) Improved predictions from weather to climate scales |
| Lizenz | Creative Commons Namensnennung 4.0 International |
| Schlagwörter | probabilistic random forest, identification, wind, Kriging, bokeh |
| Art der Forschungsdaten | Software |