Articles | Volume 17, issue 8
https://doi.org/10.5194/amt-17-2317-2024
© 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/amt-17-2317-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Apr 2024
Research article |
| 18 Apr 2024
| 18 Apr 2024
Greenhouse gas retrievals for the CO2M mission using the FOCAL method: first performance estimates
Stefan Noël
CORRESPONDING AUTHOR
Institute of Environmental Physics, University of Bremen, FB 1, P.O. Box 330440, 28334 Bremen, Germany
Michael Buchwitz
Institute of Environmental Physics, University of Bremen, FB 1, P.O. Box 330440, 28334 Bremen, Germany
Michael Hilker
Institute of Environmental Physics, University of Bremen, FB 1, P.O. Box 330440, 28334 Bremen, Germany
Maximilian Reuter
Institute of Environmental Physics, University of Bremen, FB 1, P.O. Box 330440, 28334 Bremen, Germany
Michael Weimer
Institute of Environmental Physics, University of Bremen, FB 1, P.O. Box 330440, 28334 Bremen, Germany
Heinrich Bovensmann
Institute of Environmental Physics, University of Bremen, FB 1, P.O. Box 330440, 28334 Bremen, Germany
John P. Burrows
Institute of Environmental Physics, University of Bremen, FB 1, P.O. Box 330440, 28334 Bremen, Germany
Hartmut Bösch
Institute of Environmental Physics, University of Bremen, FB 1, P.O. Box 330440, 28334 Bremen, Germany
Ruediger Lang
EUMETSAT, Eumetsat Allee 1, 64295 Darmstadt, Germany
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Cited
12 citations as recorded by crossref.
- China’s net-zero budget Z. Liu et al. https://doi.org/10.1038/s43017-026-00791-1
- Retrieving the atmospheric concentrations of carbon dioxide and methane from the European Copernicus CO2M satellite mission using artificial neural networks M. Reuter et al. https://doi.org/10.5194/amt-18-241-2025
- Rovibrational Energies of Carbon Dioxide with Kilohertz Accuracy F. Cao et al. https://doi.org/10.1063/5.0322874
- MARVEL analysis of high-resolution rovibrational spectra of 17O13C18O and 17O13C17O A. Azzam et al. https://doi.org/10.1016/j.jqsrt.2025.109485
- End-to-end performance modeling for the TanSat-2 NO2 instrument: an evaluation of key design parameters for an elliptical orbit mission Y. Li et al. https://doi.org/10.1364/AO.582658
- 基于多源数据的城市CO2源汇高空间分辨网格的构建方法研究 孙. Sun Erchang et al. https://doi.org/10.3788/AOS241741
- Advancing the Arctic Methane Permafrost Challenge (AMPAC) With Future Satellite Missions A. Bartsch et al. https://doi.org/10.1109/JSTARS.2025.3538897
- A study of measurement scenarios for the future CO2M mission: avoidance of detector saturation and the impact on XCO2 retrievals M. Weimer et al. https://doi.org/10.5194/amt-18-3321-2025
- State-wide California 2020 carbon dioxide budget estimated with OCO-2 and OCO-3 satellite data M. Johnson et al. https://doi.org/10.5194/acp-25-8475-2025
- Long-term solar-induced fluorescence data record from GOME-2A and GOME-2B (2007–2023) using the SIFTER v3 algorithm J. Anema et al. https://doi.org/10.5194/essd-18-5643-2026
- Importance of subpixel Earth surface reflectance and altitude for atmospheric trace gas retrievals from passive satellite instruments M. Weimer et al. https://doi.org/10.5194/amt-19-5425-2026
- The 626M24 dataset of validated transitions and empirical rovibrational energy levels of 16O12C16O A. Azzam et al. https://doi.org/10.1038/s41597-025-04755-w
12 citations as recorded by crossref.
- China’s net-zero budget Z. Liu et al. https://doi.org/10.1038/s43017-026-00791-1
- Retrieving the atmospheric concentrations of carbon dioxide and methane from the European Copernicus CO2M satellite mission using artificial neural networks M. Reuter et al. https://doi.org/10.5194/amt-18-241-2025
- Rovibrational Energies of Carbon Dioxide with Kilohertz Accuracy F. Cao et al. https://doi.org/10.1063/5.0322874
- MARVEL analysis of high-resolution rovibrational spectra of 17O13C18O and 17O13C17O A. Azzam et al. https://doi.org/10.1016/j.jqsrt.2025.109485
- End-to-end performance modeling for the TanSat-2 NO2 instrument: an evaluation of key design parameters for an elliptical orbit mission Y. Li et al. https://doi.org/10.1364/AO.582658
- 基于多源数据的城市CO2源汇高空间分辨网格的构建方法研究 孙. Sun Erchang et al. https://doi.org/10.3788/AOS241741
- Advancing the Arctic Methane Permafrost Challenge (AMPAC) With Future Satellite Missions A. Bartsch et al. https://doi.org/10.1109/JSTARS.2025.3538897
- A study of measurement scenarios for the future CO2M mission: avoidance of detector saturation and the impact on XCO2 retrievals M. Weimer et al. https://doi.org/10.5194/amt-18-3321-2025
- State-wide California 2020 carbon dioxide budget estimated with OCO-2 and OCO-3 satellite data M. Johnson et al. https://doi.org/10.5194/acp-25-8475-2025
- Long-term solar-induced fluorescence data record from GOME-2A and GOME-2B (2007–2023) using the SIFTER v3 algorithm J. Anema et al. https://doi.org/10.5194/essd-18-5643-2026
- Importance of subpixel Earth surface reflectance and altitude for atmospheric trace gas retrievals from passive satellite instruments M. Weimer et al. https://doi.org/10.5194/amt-19-5425-2026
- The 626M24 dataset of validated transitions and empirical rovibrational energy levels of 16O12C16O A. Azzam et al. https://doi.org/10.1038/s41597-025-04755-w
Saved (final revised paper)
Latest update: 04 Sep 2026
Short summary
FOCAL-CO2M is one of the three operational retrieval algorithms which will be used to derive XCO2 and XCH4 from measurements of the forthcoming European CO2M mission. We present results of applications of FOCAL-CO2M to simulated spectra, from which confidence is gained that the algorithm is able to fulfil the challenging requirements on systematic errors for the CO2M mission (spatio-temporal bias ≤ 0.5 ppm for XCO2 and ≤ 5 ppb for XCH4).
FOCAL-CO2M is one of the three operational retrieval algorithms which will be used to derive...
