Articles | Volume 25, issue 19
https://doi.org/10.5194/acp-25-12737-2025
https://doi.org/10.5194/acp-25-12737-2025
Research article
 | 
10 Oct 2025
Research article |  | 10 Oct 2025

Efficient use of a Lagrangian particle dispersion model for atmospheric inversions using satellite observations of column mixing ratios

Rona L. Thompson, Nalini Krishnankutty, Ignacio Pisso, Philipp Schneider, Kerstin Stebel, Motoki Sasakawa, Andreas Stohl, and Stephen M. Platt

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Cited articles

Alexe, M., Bergamaschi, P., Segers, A., Detmers, R., Butz, A., Hasekamp, O., Guerlet, S., Parker, R., Boesch, H., Frankenberg, C., Scheepmaker, R. A., Dlugokencky, E., Sweeney, C., Wofsy, S. C., and Kort, E. A.: Inverse modelling of CH4 emissions for 2010–2011 using different satellite retrieval products from GOSAT and SCIAMACHY, Atmos. Chem. Phys., 15, 113–133, https://doi.org/10.5194/acp-15-113-2015, 2015. 
Apituley, A., Pedergnana, M., Sneep, M., Veefkind, J., Loyola, D., Hasekamp, O., Delgado, A. L., and Borsdorff, T.: Sentinel-5 precursor TROPOMI: Level 2 Product User Manual Methane, https://sentinels.copernicus.eu/documents/247904/2474726/Sentinel-5P-Level-2-Product-User-Manual-Methane.pdf (last access: 19 September 2025), 2021. 
Bergamaschi, P., Frankenberg, C., Meirink, J. F., Krol, M., Villani, M. G., Houweling, S., Dentener, F., Dlugokencky, E. J., Miller, J. B., Gatti, L. V., Engel, A., and Levin, I.: Inverse modeling of global and regional CH4 emissions using SCIAMACHY satellite retrievals, J. Geophys. Res.-Atmos., 114, D22301, https://doi.org/10.1029/2009jd012287, 2009. 
Brioude, J., Petron, G., Frost, G. J., Ahmadov, R., Angevine, W. M., Hsie, E. Y., Kim, S. W., Lee, S. H., McKeen, S. A., Trainer, M., Fehsenfeld, F. C., Holloway, J. S., Peischl, J., Ryerson, T. B., and Gurney, K. R.: A new inversion method to calculate emission inventories without a prior at mesoscale: Application to the anthropogenic CO2 emission from Houston, Texas, J. Geophys. Res.-Atmos., 117, D05312, https://doi.org/10.1029/2011jd016918, 2012. 
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Short summary
Satellite remote sensing of atmospheric mixing ratios of greenhouse gases (GHGs) can provide information on their emissions. This study presents a novel method to use atmospheric mixing ratios observed by satellites with a Lagrangian model of atmospheric transport to estimate GHG emissions. This method can reduce model errors resulting from how an observation is represented by an atmospheric model, thereby helping to reduce the errors in the GHG emissions derived.
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