Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 17
Articles | Volume 15, issue 17
https://doi.org/10.5194/gmd-15-6677-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-6677-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 17
Development and technical paper
 | 
05 Sep 2022
Development and technical paper |  | 05 Sep 2022

Downscaling atmospheric chemistry simulations with physically consistent deep learning

Andrew Geiss, Sam J. Silva, and Joseph C. Hardin

Viewed

Total article views: 3,532 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,611 853 68 3,532 54 43
  • HTML: 2,611
  • PDF: 853
  • XML: 68
  • Total: 3,532
  • BibTeX: 54
  • EndNote: 43
Views and downloads (calculated since 23 Mar 2022)
Cumulative views and downloads (calculated since 23 Mar 2022)

Viewed (geographical distribution)

Total article views: 3,532 (including HTML, PDF, and XML) Thereof 3,257 with geography defined and 275 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 20 Nov 2024
Download
Short summary
This work demonstrates the use of modern machine learning techniques to enhance the resolution of atmospheric chemistry simulations. We evaluate the schemes for an 8 x 10 increase in resolution and find that they perform substantially better than conventional methods. Methods are introduced to target machine learning methods towards this type of problem, most notably by ensuring they do not break known physical constraints.
Read more