Articles | Volume 15, issue 22
Geosci. Model Dev., 15, 8295–8323, 2022
https://doi.org/10.5194/gmd-15-8295-2022

Special issue: The Lagrangian particle dispersion model FLEXPART

Geosci. Model Dev., 15, 8295–8323, 2022
https://doi.org/10.5194/gmd-15-8295-2022
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 et al.

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-275', Anonymous Referee #1, 18 Jul 2022
    • AC1: 'Reply on RC1', Martin Vojta, 28 Sep 2022
  • RC2: 'Comment on egusphere-2022-275', Anonymous Referee #2, 11 Sep 2022
    • AC2: 'Reply on RC2', Martin Vojta, 28 Sep 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Martin Vojta on behalf of the Authors (28 Sep 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (30 Sep 2022) by Andrea Stenke
RR by Anonymous Referee #1 (05 Oct 2022)
ED: Publish subject to technical corrections (21 Oct 2022) by Andrea Stenke
AR by Martin Vojta on behalf of the Authors (24 Oct 2022)  Author's response    Manuscript
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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.