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.
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.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
18 Nov 2022
Model evaluation paper |
| 18 Nov 2022
| 18 Nov 2022
A comprehensive evaluation of the use of Lagrangian particle dispersion models for inverse modeling of greenhouse gas emissions
Department of Meteorology and Geophysics, University of Vienna, Vienna, Austria
Andreas Plach
Department of Meteorology and Geophysics, University of Vienna, Vienna, Austria
Physics Institute, Climate and Environmental Physics, University of Bern, Bern, Switzerland
Rona L. Thompson
Norwegian Institute for Air Research NILU, Kjeller, Norway
Andreas Stohl
Department of Meteorology and Geophysics, University of Vienna, Vienna, Austria
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Cited
18 citations as recorded by crossref.
- Monitoring the impact of EU F-gas regulation on HFC-134a emissions through a comparison of top-down and bottom-up estimates S. Annadate et al. https://doi.org/10.1186/s12302-025-01081-1
- Long-term analysis of atmospheric propane over Southern Europe based on observations conducted at the WMO-GAW station of Monte Cimone E. Mancinelli et al. https://doi.org/10.5194/acp-26-4105-2026
- FLEXPART version 11: improved accuracy, efficiency, and flexibility L. Bakels et al. https://doi.org/10.5194/gmd-17-7595-2024
- Review of spatial scale dispersion models (ATDMs) to simulate environmental dispersion and deposition of radionuclides and the overview of GIS coupling with dispersion models H. Muhammad et al. https://doi.org/10.1016/j.jandt.2025.03.004
- Enabling fast greenhouse gas emissions inference from satellites with GATES: a Graph-Neural-Network Atmospheric Transport Emulation System E. Fillola et al. https://doi.org/10.5194/gmd-19-1893-2026
- Sustained Increases in Hydrofluorocarbon Emissions from China and Implications for Global Emissions X. Hu et al. https://doi.org/10.1021/acs.est.4c08981
- Emission estimates of ozone-depleting hydrochlorofluorocarbons for China inferred from new observations and inverse modeling Y. Chen et al. https://doi.org/10.1016/j.atmosenv.2025.121578
- Impact of transport model resolution and a priori assumptions on inverse modeling of Swiss F-gas emissions I. Katharopoulos et al. https://doi.org/10.5194/acp-23-14159-2023
- Spatial-temporal source term estimation using deep neural network prior and its application to Chernobyl wildfires A. Brožová et al. https://doi.org/10.1016/j.jhazmat.2025.137510
- Emissions of Ozone-Layer-Depleting Methyl Chloroform (CH3CCl3) in China Inferred from High-Frequency In-Situ Observations S. Jin et al. https://doi.org/10.1021/acs.estlett.5c00776
- Bayesian reconstruction of atmospheric radionuclide releases from sparse observations using nonuniform continuous priors for both spatial location and temporal release Y. Xu & S. Fang https://doi.org/10.1016/j.jhazmat.2026.141898
- A Review of City-Scale Methane Flux Inversion Based on Top-Down Methods X. Li et al. https://doi.org/10.3390/rs17183152
- Quantifying European SF6 emissions from 2005 to 2021 using a large inversion ensemble M. Vojta et al. https://doi.org/10.5194/acp-25-15197-2025
- Sustained Decline in CFC-12 Emissions from China: Evidence for Successful Phase-out under the Montreal Protocol and Global Implications M. Ma et al. https://doi.org/10.1021/acs.est.5c15532
- A global re-analysis of regionally resolved emissions and atmospheric mole fractions of SF6 for the period 2005–2021 M. Vojta et al. https://doi.org/10.5194/acp-24-12465-2024
- Dispersion based recurrent neural network model for methane monitoring in Albertan tailings ponds E. Saha et al. https://doi.org/10.1016/j.jenvman.2025.127748
- A Sensitivity Study of a Bayesian Inversion Model Used to Estimate Emissions of Synthetic Greenhouse Gases at the European Scale S. Annadate et al. https://doi.org/10.3390/atmos15010051
- Analysis of Regional Surface CO2 Fluxes Using the MEGA Satellite Data Assimilation System L. Hu et al. https://doi.org/10.3390/rs17223720
18 citations as recorded by crossref.
- Monitoring the impact of EU F-gas regulation on HFC-134a emissions through a comparison of top-down and bottom-up estimates S. Annadate et al. https://doi.org/10.1186/s12302-025-01081-1
- Long-term analysis of atmospheric propane over Southern Europe based on observations conducted at the WMO-GAW station of Monte Cimone E. Mancinelli et al. https://doi.org/10.5194/acp-26-4105-2026
- FLEXPART version 11: improved accuracy, efficiency, and flexibility L. Bakels et al. https://doi.org/10.5194/gmd-17-7595-2024
- Review of spatial scale dispersion models (ATDMs) to simulate environmental dispersion and deposition of radionuclides and the overview of GIS coupling with dispersion models H. Muhammad et al. https://doi.org/10.1016/j.jandt.2025.03.004
- Enabling fast greenhouse gas emissions inference from satellites with GATES: a Graph-Neural-Network Atmospheric Transport Emulation System E. Fillola et al. https://doi.org/10.5194/gmd-19-1893-2026
- Sustained Increases in Hydrofluorocarbon Emissions from China and Implications for Global Emissions X. Hu et al. https://doi.org/10.1021/acs.est.4c08981
- Emission estimates of ozone-depleting hydrochlorofluorocarbons for China inferred from new observations and inverse modeling Y. Chen et al. https://doi.org/10.1016/j.atmosenv.2025.121578
- Impact of transport model resolution and a priori assumptions on inverse modeling of Swiss F-gas emissions I. Katharopoulos et al. https://doi.org/10.5194/acp-23-14159-2023
- Spatial-temporal source term estimation using deep neural network prior and its application to Chernobyl wildfires A. Brožová et al. https://doi.org/10.1016/j.jhazmat.2025.137510
- Emissions of Ozone-Layer-Depleting Methyl Chloroform (CH3CCl3) in China Inferred from High-Frequency In-Situ Observations S. Jin et al. https://doi.org/10.1021/acs.estlett.5c00776
- Bayesian reconstruction of atmospheric radionuclide releases from sparse observations using nonuniform continuous priors for both spatial location and temporal release Y. Xu & S. Fang https://doi.org/10.1016/j.jhazmat.2026.141898
- A Review of City-Scale Methane Flux Inversion Based on Top-Down Methods X. Li et al. https://doi.org/10.3390/rs17183152
- Quantifying European SF6 emissions from 2005 to 2021 using a large inversion ensemble M. Vojta et al. https://doi.org/10.5194/acp-25-15197-2025
- Sustained Decline in CFC-12 Emissions from China: Evidence for Successful Phase-out under the Montreal Protocol and Global Implications M. Ma et al. https://doi.org/10.1021/acs.est.5c15532
- A global re-analysis of regionally resolved emissions and atmospheric mole fractions of SF6 for the period 2005–2021 M. Vojta et al. https://doi.org/10.5194/acp-24-12465-2024
- Dispersion based recurrent neural network model for methane monitoring in Albertan tailings ponds E. Saha et al. https://doi.org/10.1016/j.jenvman.2025.127748
- A Sensitivity Study of a Bayesian Inversion Model Used to Estimate Emissions of Synthetic Greenhouse Gases at the European Scale S. Annadate et al. https://doi.org/10.3390/atmos15010051
- Analysis of Regional Surface CO2 Fluxes Using the MEGA Satellite Data Assimilation System L. Hu et al. https://doi.org/10.3390/rs17223720
Saved (final revised paper)
Latest update: 03 Jun 2026
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.
In light of recent global warming, we aim to improve methods for modeling greenhouse gas...
Special issue