Articles | Volume 17, issue 5
https://doi.org/10.5194/gmd-17-1995-2024
https://doi.org/10.5194/gmd-17-1995-2024
Model description paper
 | 
05 Mar 2024
Model description paper |  | 05 Mar 2024

Deep learning applied to CO2 power plant emissions quantification using simulated satellite images

Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet

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Cited articles

Beirle, S., Borger, C., Dörner, S., Li, A., Hu, Z., Liu, F., Wang, Y., and Wagner, T.: Pinpointing nitrogen oxide emissions from space, Science Advances, 5, eaax9800, https://doi.org/10.1126/sciadv.aax9800 2019. a
Broquet, G., Bréon, F.-M., Renault, E., Buchwitz, M., Reuter, M., Bovensmann, H., Chevallier, F., Wu, L., and Ciais, P.: The potential of satellite spectro-imagery for monitoring CO2 emissions from large cities, Atmos. Meas. Tech., 11, 681–708, https://doi.org/10.5194/amt-11-681-2018, 2018. a
Brunner, D., Kuhlmann, G., Marshall, J., Clément, V., Fuhrer, O., Broquet, G., Löscher, A., and Meijer, Y.: Accounting for the vertical distribution of emissions in atmospheric CO2 simulations, Atmos. Chem. Phys., 19, 4541–4559, https://doi.org/10.5194/acp-19-4541-2019, 2019. a
Chevallier, F., Remaud, M., O'Dell, C. W., Baker, D., Peylin, P., and Cozic, A.: Objective evaluation of surface- and satellite-driven carbon dioxide atmospheric inversions, Atmos. Chem. Phys., 19, 14233–14251, https://doi.org/10.5194/acp-19-14233-2019, 2019. a
Chevallier, F., Broquet, G., Zheng, B., Ciais, P., and Eldering, A.: Large CO2 Emitters as Seen From Satellite: Comparison to a Gridded Global Emission Inventory, Geophys. Res. Lett., 49, e2021GL097540, https://doi.org/10.1029/2021GL097540, 2022. a
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Short summary
Our research presents an innovative approach to estimating power plant CO2 emissions from satellite images of the corresponding plumes such as those from the forthcoming CO2M satellite constellation. The exploitation of these images is challenging due to noise and meteorological uncertainties. To overcome these obstacles, we use a deep learning neural network trained on simulated CO2 images. Our method outperforms alternatives, providing a positive perspective for the analysis of CO2M images.