Articles | Volume 13, issue 11
https://doi.org/10.5194/gmd-13-5813-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-5813-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
26 Nov 2020
Model description paper |
| 26 Nov 2020
| 26 Nov 2020
PMIF v1.0: assessing the potential of satellite observations to constrain CO2 emissions from large cities and point sources over the globe using synthetic data
Yilong Wang
CORRESPONDING AUTHOR
Laboratoire des Sciences du Climat et de l'Environnement,
CEA-CNRS-UVSQ- Université Paris Saclay, 91191, Gif-sur-Yvette CEDEX,
France
Key Laboratory of Land Surface Pattern and Simulation, Institute of
Geographic Sciences and Natural Resources Research, Chinese Academy of
Sciences, Beijing, China
Grégoire Broquet
Laboratoire des Sciences du Climat et de l'Environnement,
CEA-CNRS-UVSQ- Université Paris Saclay, 91191, Gif-sur-Yvette CEDEX,
France
François-Marie Bréon
Laboratoire des Sciences du Climat et de l'Environnement,
CEA-CNRS-UVSQ- Université Paris Saclay, 91191, Gif-sur-Yvette CEDEX,
France
Franck Lespinas
Laboratoire des Sciences du Climat et de l'Environnement,
CEA-CNRS-UVSQ- Université Paris Saclay, 91191, Gif-sur-Yvette CEDEX,
France
Canadian Centre for Meteorological and Environmental Prediction, 2121 Transcanada Highway, Dorval, QC, H9P 1J3, Canada
Michael Buchwitz
Institute of Environmental Physics (IUP), University of Bremen FB1,
Otto Hahn Allee 1, 28334 Bremen, Germany
Maximilian Reuter
Institute of Environmental Physics (IUP), University of Bremen FB1,
Otto Hahn Allee 1, 28334 Bremen, Germany
Yasjka Meijer
European Space Agency (ESA), Noordwijk, the Netherlands
Armin Loescher
European Space Agency (ESA), Noordwijk, the Netherlands
Greet Janssens-Maenhout
European Commission, Joint Research Centre, Directorate Sustainable
Resources, via E. Fermi 2749 (T.P. 123), 21027 Ispra, Italy
Laboratoire des Sciences du Climat et de l'Environnement,
CEA-CNRS-UVSQ- Université Paris Saclay, 91191, Gif-sur-Yvette CEDEX,
France
Philippe Ciais
Laboratoire des Sciences du Climat et de l'Environnement,
CEA-CNRS-UVSQ- Université Paris Saclay, 91191, Gif-sur-Yvette CEDEX,
France
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- Observing carbon dioxide emissions over China's cities and industrial areas with the Orbiting Carbon Observatory-2 B. Zheng et al. 10.5194/acp-20-8501-2020
20 citations as recorded by crossref.
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- Fossil fuel CO2 emissions over metropolitan areas from space: A multi-model analysis of OCO-2 data over Lahore, Pakistan R. Lei et al. 10.1016/j.rse.2021.112625
- CO2 and CO temporal variability over Mexico City from ground-based total column and surface measurements N. Taquet et al. 10.5194/acp-24-11823-2024
- Assimilation of atmospheric CO2 observations from space can support national CO2 emission inventories T. Kaminski et al. 10.1088/1748-9326/ac3cea
- High-resolution greenhouse gas flux inversions using a machine learning surrogate model for atmospheric transport N. Dadheech et al. 10.5194/acp-25-5159-2025
- Monitoring and quantifying CO2emissions of isolated power plants from space X. Lin et al. 10.5194/acp-23-6599-2023
- Benchmarking data-driven inversion methods for the estimation of local CO2 emissions from synthetic satellite images of XCO2 and NO2 D. Santaren et al. 10.5194/amt-18-211-2025
- Estimating CO2 Emissions from Large Scale Coal-Fired Power Plants Using OCO-2 Observations and Emission Inventories Y. Hu & Y. Shi 10.3390/atmos12070811
- Optimal design of surface CO2 observation network to constrain China’s land carbon sink Y. Wang et al. 10.1016/j.scib.2023.07.010
- A local- to national-scale inverse modeling system to assess the potential of spaceborne CO2 measurements for the monitoring of anthropogenic emissions D. Santaren et al. 10.5194/amt-14-403-2021
- Quantifying CO2 Emissions of Power Plants With CO2 and NO2 Imaging Satellites G. Kuhlmann et al. 10.3389/frsen.2021.689838
- Deep learning applied to CO2 power plant emissions quantification using simulated satellite images J. Dumont Le Brazidec et al. 10.5194/gmd-17-1995-2024
- Complementing XCO2 imagery with ground-based CO2 and 14CO2 measurements to monitor CO2 emissions from fossil fuels on a regional to local scale E. Potier et al. 10.5194/amt-15-5261-2022
- Automated detection of atmospheric NO2 plumes from satellite data: a tool to help infer anthropogenic combustion emissions D. Finch et al. 10.5194/amt-15-721-2022
- Assessing the Impact of Atmospheric CO2 and NO2 Measurements From Space on Estimating City-Scale Fossil Fuel CO2 Emissions in a Data Assimilation System T. Kaminski et al. 10.3389/frsen.2022.887456
- A CO2–Δ14CO2 inversion setup for estimating European fossil CO2 emissions C. Gómez-Ortiz et al. 10.5194/acp-25-397-2025
- Space-based Earth observation in support of the UNFCCC Paris Agreement M. Hegglin et al. 10.3389/fenvs.2022.941490
- Evaluation of simulated CO2 power plant plumes from six high-resolution atmospheric transport models D. Brunner et al. 10.5194/acp-23-2699-2023
- The Community Inversion Framework v1.0: a unified system for atmospheric inversion studies A. Berchet et al. 10.5194/gmd-14-5331-2021
- The ddeq Python library for point source quantification from remote sensing images (version 1.0) G. Kuhlmann et al. 10.5194/gmd-17-4773-2024
2 citations as recorded by crossref.
- The potential of a constellation of low earth orbit satellite imagers to monitor worldwide fossil fuel CO2 emissions from large cities and point sources F. Lespinas et al. 10.1186/s13021-020-00153-4
- Observing carbon dioxide emissions over China's cities and industrial areas with the Orbiting Carbon Observatory-2 B. Zheng et al. 10.5194/acp-20-8501-2020
Latest update: 29 May 2025
