Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 22
Articles | Volume 15, issue 22
https://doi.org/10.5194/gmd-15-8295-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-15-8295-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 22
Model evaluation paper
 | 
18 Nov 2022
Model evaluation paper |  | 18 Nov 2022

A comprehensive evaluation of the use of Lagrangian particle dispersion models for inverse modeling of greenhouse gas emissions

Martin Vojta, Andreas Plach, Rona L. Thompson, and Andreas Stohl

Viewed

Total article views: 2,969 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,240 660 69 2,969 126 79 76
  • HTML: 2,240
  • PDF: 660
  • XML: 69
  • Total: 2,969
  • Supplement: 126
  • BibTeX: 79
  • EndNote: 76
Views and downloads (calculated since 16 Jun 2022)
Cumulative views and downloads (calculated since 16 Jun 2022)

Viewed (geographical distribution)

Total article views: 2,969 (including HTML, PDF, and XML) Thereof 2,796 with geography defined and 173 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 30 Jun 2025
Download
Short summary
In light of recent global warming, we aim to improve methods for modeling greenhouse gas emissions in order to support the successful implementation of the Paris Agreement. In this study, we investigate certain aspects of a Bayesian inversion method that uses computer simulations and atmospheric observations to improve estimates of greenhouse gas emissions. We explore method limitations, discuss problems, and suggest improvements.
Read more
Share
Special issue
The Lagrangian particle dispersion model FLEXPART