Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 5
Articles | Volume 18, issue 5
https://doi.org/10.5194/gmd-18-1661-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-1661-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 5
Development and technical paper
 | 
12 Mar 2025
Development and technical paper |  | 12 Mar 2025

FootNet v1.0: development of a machine learning emulator of atmospheric transport

Tai-Long He, Nikhil Dadheech, Tammy M. Thompson, and Alexander J. Turner

Viewed

Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.

Total article views: 3,721 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,868 775 78 3,721 137 67 141
  • HTML: 2,868
  • PDF: 775
  • XML: 78
  • Total: 3,721
  • Supplement: 137
  • BibTeX: 67
  • EndNote: 141
Views and downloads (calculated since 01 Jul 2024)
Cumulative views and downloads (calculated since 01 Jul 2024)

Viewed (geographical distribution)

Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.

Total article views: 3,721 (including HTML, PDF, and XML) Thereof 3,715 with geography defined and 6 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 02 May 2026
Download
Short summary
It is computationally expensive to infer greenhouse gas (GHG) emissions using atmospheric observations. This is partly due to the detailed model used to represent atmospheric transport. We demonstrate how a machine learning (ML) model can be used to simulate high-resolution atmospheric transport. This type of ML model will help estimate GHG emissions using dense observations, which are becoming increasingly common with the proliferation of urban monitoring networks and geostationary satellites.
Read more
Share