Articles | Volume 15, issue 4
Geosci. Model Dev., 15, 1677–1687, 2022
https://doi.org/10.5194/gmd-15-1677-2022
Geosci. Model Dev., 15, 1677–1687, 2022
https://doi.org/10.5194/gmd-15-1677-2022
Development and technical paper
25 Feb 2022
Development and technical paper | 25 Feb 2022

A machine-learning-guided adaptive algorithm to reduce the computational cost of integrating kinetics in global atmospheric chemistry models: application to GEOS-Chem versions 12.0.0 and 12.9.1

Lu Shen et al.

Viewed

Total article views: 2,042 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,410 595 37 2,042 117 6 15
  • HTML: 1,410
  • PDF: 595
  • XML: 37
  • Total: 2,042
  • Supplement: 117
  • BibTeX: 6
  • EndNote: 15
Views and downloads (calculated since 16 Feb 2021)
Cumulative views and downloads (calculated since 16 Feb 2021)

Viewed (geographical distribution)

Total article views: 2,042 (including HTML, PDF, and XML) Thereof 1,856 with geography defined and 186 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 30 Nov 2022
Short summary
The high computational cost of chemical integration is a long-standing limitation in global atmospheric chemistry models. Here we present an adaptive and efficient algorithm that can reduce the computational time of atmospheric chemistry by 50 % and maintain the error below 2 % for important species, inspired by machine learning clustering techniques and traditional asymptotic analysis ideas.