Articles | Volume 15, issue 22
https://doi.org/10.5194/gmd-15-8295-2022
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, Andreas Plach, Rona L. Thompson, and Andreas Stohl

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Cited articles

Advanced Global Atmospheric Gases Experiment (AGAGE): Medusa GC-MS SF6 time series, AGAGE [data set], https://agage2.eas.gatech.edu/data_archive/agage/gc-ms-medusa/complete/, all stations, year 2011 and 2012, last access: 27 April 2022. a
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Brunner, D., Arnold, T., Henne, S., Manning, A., Thompson, R. L., Maione, M., O'Doherty, S., and Reimann, S.: Comparison of four inverse modelling systems applied to the estimation of HFC-125, HFC-134a, and SF6 emissions over Europe, Atmos. Chem. Phys., 17, 10651–10674, https://doi.org/10.5194/acp-17-10651-2017, 2017. a, b, c
CIESIN: Center for International Earth Science Information Network, Columbia University, Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11, NASA Socioeconomic Data and Applications Center (SEDAC), Palisades, NY, https://doi.org/10.7927/H49C6VHW (last access: 27 April 2022), 2018. a
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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.
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