Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 25, issue 10
Articles | Volume 25, issue 10
https://doi.org/10.5194/acp-25-5159-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-25-5159-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 25, issue 10
Research article
 | 
21 May 2025
Research article |  | 21 May 2025

High-resolution greenhouse gas flux inversions using a machine learning surrogate model for atmospheric transport

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

Viewed

Total article views: 4,279 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,864 586 829 4,279 59 83
  • HTML: 2,864
  • PDF: 586
  • XML: 829
  • Total: 4,279
  • BibTeX: 59
  • EndNote: 83
Views and downloads (calculated since 26 Sep 2024)
Cumulative views and downloads (calculated since 26 Sep 2024)

Viewed (geographical distribution)

Total article views: 4,279 (including HTML, PDF, and XML) Thereof 4,126 with geography defined and 153 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 12 Mar 2026
Download
Short summary
We developed an efficient GHG (greenhouse gas) flux inversion framework using a machine-learning emulator (FootNet) as a surrogate for an atmospheric transport model, resulting in a 650 × speedup. Paradoxically, the flux inversion using the ML (machine-learning) model outperforms the full-physics model in our case study. We attribute this to the ML model mitigating transport errors in the GHG flux inversion.
Read more
Share
Altmetrics
Final-revised paper
Preprint