Identification of high-wind features within extratropical cyclones using a probabilistic random forest – Part 1: Method and case studies

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SEEK ID: https://publications.h-its.org/publications/1539

Filename: 2022WCD.pdf 

Format: PDF document

Size: 13.5 MB

SEEK ID: https://publications.h-its.org/publications/1539

DOI: 10.5194/wcd-3-1157-2022

Research Groups: Computational Statistics

Publication type: Journal

Journal: Weather and Climate Dynamics

Publisher: Copernicus GmbH

Citation: Weather and Climate Dynamics, 3(4):1157–1182

Date Published: 19th Oct 2022

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Registered Mode: manually

Authors: Lea Eisenstein, Benedikt Schulz, Ghulam A. Qadir, Joaquim G. Pinto, Peter Knippertz

Citation
Eisenstein, L., Schulz, B., Qadir, G. A., Pinto, J. G., & Knippertz, P. (2022). Identification of high-wind features within extratropical cyclones using a probabilistic random forest – Part 1: Method and case studies. In Weather and Climate Dynamics (Vol. 3, Issue 4, pp. 1157–1182). Copernicus GmbH. https://doi.org/10.5194/wcd-3-1157-2022
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Created: 20th Oct 2022 at 14:14

Last updated: 8th Mar 2024 at 15:41

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