Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 4
Articles | Volume 15, issue 4
https://doi.org/10.5194/gmd-15-1677-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-1677-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 4
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, Daniel J. Jacob, Mauricio Santillana, Kelvin Bates, Jiawei Zhuang, and Wei Chen

Viewed

Total article views: 2,879 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,966 852 61 2,879 215 36 46
  • HTML: 1,966
  • PDF: 852
  • XML: 61
  • Total: 2,879
  • Supplement: 215
  • BibTeX: 36
  • EndNote: 46
Views and downloads (calculated since 16 Feb 2021)
Cumulative views and downloads (calculated since 16 Feb 2021)

Viewed (geographical distribution)

Total article views: 2,879 (including HTML, PDF, and XML) Thereof 2,665 with geography defined and 214 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 29 Jun 2024
Download
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.
Read more