Articles | Volume 24, issue 4
https://doi.org/10.5194/acp-24-2759-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-24-2759-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Mar 2024
Research article |
| 01 Mar 2024
| 01 Mar 2024
European CH4 inversions with ICON-ART coupled to the CarbonTracker Data Assimilation Shell
Empa, Swiss Federal Laboratories for Materials Science and Technology, Dübendorf, Switzerland
Wouter Peters
Environmental Sciences Group, Department of Meteorology and Air Quality, Wageningen University & Research, Wageningen, the Netherlands
Centre for Isotope Research, University of Groningen, Groningen, the Netherlands
Ingrid Luijkx
Environmental Sciences Group, Department of Meteorology and Air Quality, Wageningen University & Research, Wageningen, the Netherlands
Stephan Henne
Empa, Swiss Federal Laboratories for Materials Science and Technology, Dübendorf, Switzerland
Huilin Chen
Centre for Isotope Research, University of Groningen, Groningen, the Netherlands
Samuel Hammer
Institut für Umweltphysik, Heidelberg University, Heidelberg, Germany
Empa, Swiss Federal Laboratories for Materials Science and Technology, Dübendorf, Switzerland
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Cited
14 citations as recorded by crossref.
- High-resolution inversion of methane emissions over Europe using the Community Inversion Framework and FLEXPART A. Mengistu et al. https://doi.org/10.5194/acp-26-12295-2026
- German methane fluxes estimated top-down using ICON–ART – Part 1: Ensemble-enhanced scaling inversion V. Bruch et al. https://doi.org/10.5194/acp-25-17159-2025
- German methane fluxes estimated top-down using ICON–ART – Part 2: Inversion results for 2021 V. Bruch et al. https://doi.org/10.5194/acp-25-17187-2025
- Improving the ensemble square root filter (EnSRF) in the Community Inversion Framework: a case study with ICON-ART 2024.01 J. Thanwerdas et al. https://doi.org/10.5194/gmd-18-1505-2025
- CH4 emissions from Northern Europe wetlands: compared data assimilation approaches G. Monteil et al. https://doi.org/10.5194/acp-25-14251-2025
- The ZiCOS-M CO2 sensor network: measurement performance and CO2 variability across Zurich S. Grange et al. https://doi.org/10.5194/acp-25-2781-2025
- High-resolution greenhouse gas flux inversions using a machine learning surrogate model for atmospheric transport N. Dadheech et al. https://doi.org/10.5194/acp-25-5159-2025
- Emiproc: A Python package for emission inventory processing C. Lionel et al. https://doi.org/10.21105/joss.07509
- Estimation of CO2 fluxes in the cities of Zurich and Paris using the ICON-ART CTDAS inverse modelling framework N. Ponomarev et al. https://doi.org/10.5194/acp-26-547-2026
- The atmospheric composition component of the ICON modeling framework: ICON-ART version 2025.10 G. Hoshyaripour et al. https://doi.org/10.5194/gmd-19-1645-2026
- Flow-dependent observation errors for greenhouse gas inversions in an ensemble Kalman smoother M. Steiner et al. https://doi.org/10.5194/acp-24-12447-2024
- An inter-comparison of inverse models for estimating European CH4 emissions E. Ioannidis et al. https://doi.org/10.5194/essd-18-167-2026
- Exploring atmospheric CH4 monitoring network expansion in Italy using inverse modelling J. Thanwerdas et al. https://doi.org/10.5194/acp-26-10477-2026
- High-resolution regional inversion reveals overestimation of anthropogenic methane emissions in China S. Feng et al. https://doi.org/10.5194/acp-25-15121-2025
14 citations as recorded by crossref.
- High-resolution inversion of methane emissions over Europe using the Community Inversion Framework and FLEXPART A. Mengistu et al. https://doi.org/10.5194/acp-26-12295-2026
- German methane fluxes estimated top-down using ICON–ART – Part 1: Ensemble-enhanced scaling inversion V. Bruch et al. https://doi.org/10.5194/acp-25-17159-2025
- German methane fluxes estimated top-down using ICON–ART – Part 2: Inversion results for 2021 V. Bruch et al. https://doi.org/10.5194/acp-25-17187-2025
- Improving the ensemble square root filter (EnSRF) in the Community Inversion Framework: a case study with ICON-ART 2024.01 J. Thanwerdas et al. https://doi.org/10.5194/gmd-18-1505-2025
- CH4 emissions from Northern Europe wetlands: compared data assimilation approaches G. Monteil et al. https://doi.org/10.5194/acp-25-14251-2025
- The ZiCOS-M CO2 sensor network: measurement performance and CO2 variability across Zurich S. Grange et al. https://doi.org/10.5194/acp-25-2781-2025
- High-resolution greenhouse gas flux inversions using a machine learning surrogate model for atmospheric transport N. Dadheech et al. https://doi.org/10.5194/acp-25-5159-2025
- Emiproc: A Python package for emission inventory processing C. Lionel et al. https://doi.org/10.21105/joss.07509
- Estimation of CO2 fluxes in the cities of Zurich and Paris using the ICON-ART CTDAS inverse modelling framework N. Ponomarev et al. https://doi.org/10.5194/acp-26-547-2026
- The atmospheric composition component of the ICON modeling framework: ICON-ART version 2025.10 G. Hoshyaripour et al. https://doi.org/10.5194/gmd-19-1645-2026
- Flow-dependent observation errors for greenhouse gas inversions in an ensemble Kalman smoother M. Steiner et al. https://doi.org/10.5194/acp-24-12447-2024
- An inter-comparison of inverse models for estimating European CH4 emissions E. Ioannidis et al. https://doi.org/10.5194/essd-18-167-2026
- Exploring atmospheric CH4 monitoring network expansion in Italy using inverse modelling J. Thanwerdas et al. https://doi.org/10.5194/acp-26-10477-2026
- High-resolution regional inversion reveals overestimation of anthropogenic methane emissions in China S. Feng et al. https://doi.org/10.5194/acp-25-15121-2025
Saved (final revised paper)
Latest update: 20 Sep 2026
Short summary
The Paris Agreement increased interest in estimating greenhouse gas (GHG) emissions of individual countries, but top-down emission estimation is not yet considered policy-relevant. It is therefore paramount to reduce large errors and to build systems that are based on the newest atmospheric transport models. In this study, we present the first application of ICON-ART in the inverse modeling of GHG fluxes with an ensemble Kalman filter and present our results for European CH4 emissions.
The Paris Agreement increased interest in estimating greenhouse gas (GHG) emissions of...
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