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Li, Jingmin (2021) Cluster analysis data and code for global aerosol simulations using the K-means machine learning method. [Other]
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Abstract
This contains data and script for global aerosol clustering from the publication "An aerosol classification scheme for global simulations using the K-means machine learning method" (https://doi.org/10.5194/gmd-15-509-2022). The script applys a K-means clustering algorithm to data extracted from a global simulation with the EMAC-MADE3 global aerosol model (Beet al., 2020). For a range of k clusters two classification metrics are calculated: the sum of squared errors (SSE) and silhouette coefficient (SC).
| Item URL in elib: | https://elib.dlr.de/209996/ | ||||||||
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| Document Type: | Other | ||||||||
| Title: | Cluster analysis data and code for global aerosol simulations using the K-means machine learning method | ||||||||
| Authors: |
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| Date: | 2021 | ||||||||
| Refereed publication: | No | ||||||||
| Open Access: | No | ||||||||
| DOI: | 10.5281/zenodo.5582338 | ||||||||
| Status: | Published | ||||||||
| Keywords: | global aerosol classification procedure, aerosol regime, K-means, machine-learning | ||||||||
| HGF - Research field: | Aeronautics, Space and Transport | ||||||||
| HGF - Program: | Transport | ||||||||
| HGF - Program Themes: | Transport System | ||||||||
| DLR - Research area: | Transport | ||||||||
| DLR - Program: | V VS - Verkehrssystem | ||||||||
| DLR - Research theme (Project): | V - MoDa - Models and Data for Future Mobility_Supporting Services, R - Project MABAK | ||||||||
| Location: | Oberpfaffenhofen | ||||||||
| Institutes and Institutions: | Institute of Atmospheric Physics > Earth System Modelling | ||||||||
| Deposited By: | Li, Jingmin | ||||||||
| Deposited On: | 03 Dec 2024 13:38 | ||||||||
| Last Modified: | 03 Dec 2024 13:38 |
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