Articles | Volume 25, issue 19
https://doi.org/10.5194/acp-25-12737-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.Efficient use of a Lagrangian particle dispersion model for atmospheric inversions using satellite observations of column mixing ratios
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- Final revised paper (published on 10 Oct 2025)
- Supplement to the final revised paper
- Preprint (discussion started on 25 Feb 2025)
- Supplement to the preprint
Interactive discussion
Status: closed
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
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RC1: 'Comment on egusphere-2025-147', Anonymous Referee #1, 20 Mar 2025
- AC3: 'Reply on RC1', Rona Thompson, 12 May 2025
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RC2: 'Comment on egusphere-2025-147', Anonymous Referee #2, 31 Mar 2025
- AC1: 'Reply on RC2', Rona Thompson, 12 May 2025
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RC3: 'Comment on egusphere-2025-147', Anonymous Referee #3, 08 Apr 2025
- AC2: 'Reply on RC3', Rona Thompson, 12 May 2025
Peer review completion
AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Rona Thompson on behalf of the Authors (12 May 2025)
Author's response
Author's tracked changes
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ED: Referee Nomination & Report Request started (12 May 2025) by Farahnaz Khosrawi
RR by Anonymous Referee #1 (04 Jun 2025)

RR by Anonymous Referee #3 (04 Jun 2025)

ED: Reconsider after major revisions (04 Jun 2025) by Farahnaz Khosrawi

AR by Rona Thompson on behalf of the Authors (09 Jul 2025)
Author's response
Author's tracked changes
Manuscript
ED: Publish as is (10 Jul 2025) by Farahnaz Khosrawi

AR by Rona Thompson on behalf of the Authors (18 Jul 2025)
Manuscript
General comments
This study present an innovative method for estimating total column methane (XCH4) using a Lagrangian Particle Dispersion Model (LPDM), FLEXPART, and way to assimilate the data in an atmospheric inverse model, FLEXINVERT. The case study is carried out for Siberia for 2022. The comparison against the “traditional” ground-based inversion showed broad agreement with the inversion using TROPOMI data, and consequently reliability and a good potential in the presented method for estimation of regional CH4 fluxes. The method sounds applicable for other LPDMs, and could contribute significantly to the atmospheric inverse modelling community with new ways to infer greenhouse gas flux information from satellite data. The fact that LPDMs can be run in much higher resolution than Eulerian transport models will be an advantage for incorporating information from future satellites with much higher spatial resolution. Therefore, this paper is worth of prompt publication after considering a few points below.
Specific comments
L160: Why do you use area-weighed averages? I understand it is somewhat reasonable for mixing ratios, but for averaging kernels and presser weighting, I do not fully understand how the area would be affected. Could you also explain how did you take into account differences in number of observations within the aggregated cells?
Section 3: Did you include temporal correlation of the state vectors? Please add information somewhere.
L195: Could you add a figure on spatial resolution? Where were lowest and highest resolutions? Were the resolutions same for the TROPOMI and ground-based inversions, despite the fact that they would have differences in “how strongly the fluxes influence the observations” due to differences in locations and quantity (surface vs total column) of the observations? Please clarify.
L209-213: In later sections, it is said that the background mixing ratios were also optimised. What were the uncertainties in the boundary conditions?
L218: Why did you chose the grid cell sizes of 0.25° and 0.5°? FLEXPART is run at 0.5° and smallest optimisation spatial resolution is also 0.5°, so why did you chose to have observations at higher spatial resolutions? Can FLEXPART resolve differences well (or what is done) if there are more than one observations within a 0.5° x 0.5° grid cell?
L223: I suppose number of observation vary a lot within the study period. Please add information about number of observations also perhaps in 14-days temporal resolution, which is your flux optimisation resolution. I would also like to see for both TROPOMI and ground-based observations.
L225-232:
Section 3.1.3:
Section 3.2: I understand that you only optimise total fluxes, but as you find some spatial differences in the flux increments between the TROPOMI and ground-based inversions, can you speculate whether emissions from oil and gas sources have different seasonal patters in the two inversions?
L286, L306: Could you add uncertainty estimates as well?
L295-299: Are the number of TROPOMI observations less than those from the ground-based stations? Do you argue that number of the observations was persistent for all months? I suppose flux uncertainties are larger in summer (as a whole domain) due to contribution of wetlands (although it is not so clear from Figure 6)? How would the retrieval biases possibly play a role that were discussed in e.g. Lindqvist et al. (2024)?
L318-321: Related to questions above, why do you think that the flux were not as well constrained in the TROPOMI inversions? Satellite data are suppose to have good spatial coverage compared to ground-based data, and with much large number of data, it should, in principal, constrain the fluxes better than the ground-based data. But it is not the case here. Is this a general feature or something specific to high northern latitudes?
Technical comments
Introduction: Please add information about focus/simulation years
L67: Please add references to FLEXPART.
Equations: Please use bold fonts for vectors and matrices.
L210: ...ERA5 at 0.5° x 0.5° and… ?
Table 1. What are the altitudes here? Elevation of the site or height from which FLEXPART trajectories were calculated?
Figure 2: Are these of super-observations? Please clarify.
Figure 4:
Please consider combining Figures 5 and 8, and Figures 7 and 9. It would be easy to compare between TROPOMI-based and ground-based inversions that way.
References
Lindqvist, H., et al.: Evaluation of Sentinel-5P TROPOMI Methane Observations at Northern High Latitudes, Remote Sensing, 16, 2979, https://doi.org/10.3390/rs16162979, 2024.