Articles | Volume 15, issue 2
Geosci. Model Dev., 15, 509–533, 2022
https://doi.org/10.5194/gmd-15-509-2022
Geosci. Model Dev., 15, 509–533, 2022
https://doi.org/10.5194/gmd-15-509-2022
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 et al.

Viewed

Total article views: 1,714 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,340 325 49 1,714 36 11 13
  • HTML: 1,340
  • PDF: 325
  • XML: 49
  • Total: 1,714
  • Supplement: 36
  • BibTeX: 11
  • EndNote: 13
Views and downloads (calculated since 23 Jul 2021)
Cumulative views and downloads (calculated since 23 Jul 2021)

Viewed (geographical distribution)

Total article views: 1,714 (including HTML, PDF, and XML) Thereof 1,565 with geography defined and 149 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 04 Dec 2022
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.