Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 2
Articles | Volume 15, issue 2
https://doi.org/10.5194/gmd-15-509-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-509-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 2
Methods for assessment of models
 | 
25 Jan 2022
Methods for assessment of models |  | 25 Jan 2022

An aerosol classification scheme for global simulations using the K-means machine learning method

Jingmin Li, Johannes Hendricks, Mattia Righi, and Christof G. Beer

Viewed

Total article views: 2,700 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,073 553 74 2,700 76 36 43
  • HTML: 2,073
  • PDF: 553
  • XML: 74
  • Total: 2,700
  • Supplement: 76
  • BibTeX: 36
  • EndNote: 43
Views and downloads (calculated since 23 Jul 2021)
Cumulative views and downloads (calculated since 23 Jul 2021)

Viewed (geographical distribution)

Total article views: 2,700 (including HTML, PDF, and XML) Thereof 2,533 with geography defined and 167 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 29 Jun 2024
Download
Short summary
The growing complexity of global aerosol models results in a large number of parameters that describe the aerosol number, size, and composition. This makes the analysis, evaluation, and interpretation of the model results a challenge. To overcome this difficulty, we apply a machine learning classification method to identify clusters of specific aerosol types in global aerosol simulations. Our results demonstrate the spatial distributions and characteristics of these identified aerosol clusters.
Read more