Articles | Volume 16, issue 13
https://doi.org/10.5194/gmd-16-3997-2023
https://doi.org/10.5194/gmd-16-3997-2023
Model description paper
 | 
14 Jul 2023
Model description paper |  | 14 Jul 2023

Segmentation of XCO2 images with deep learning: application to synthetic plumes from cities and power plants

Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Marc Bocquet, Jinghui Lian, Grégoire Broquet, Gerrit Kuhlmann, Alexandre Danjou, and Thomas Lauvaux

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Cited articles

Agustí-Panareda, A., Massart, S., Chevallier, F., Boussetta, S., Balsamo, G., Beljaars, A., Ciais, P., Deutscher, N. M., Engelen, R., Jones, L., Kivi, R., Paris, J.-D., Peuch, V.-H., Sherlock, V., Vermeulen, A. T., Wennberg, P. O., and Wunch, D.: Forecasting global atmospheric CO2, Atmos. Chem. Phys., 14, 11959–11983, https://doi.org/10.5194/acp-14-11959-2014, 2014. 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
Butz, A., Scheidweiler, L., Baumgartner, A., Feist, D. G., Gottschaldt, K.-D., Jöckel, P., Kern, B., Köhler, C., Krutz, D., Lichtenberg, G., Marshall, J., Paproth, C., Slijkhuis, S., Sebastian, I., Strandgren, J., Wilzewski, J. S., and Roiger, A.: CO2Image: a next generation imaging spectrometer for CO2 point source quantification, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6324, https://doi.org/10.5194/egusphere-egu22-6324, 2022. a
Chevallier, F.: Validation report for the inverted CO2 fluxes, v18r1 – version 1.0, Copernicus Atmosphere Monitoring Service, p. 20, https://atmosphere.copernicus.eu/sites/default/files/2019-01/CAMS73_2018SC1_D73.1.4.1-2017-v0_201812_v1_final.pdf (last access: 10 July 2023), 2018. a
Chollet, F.: Deep Learning with Python, 1st edn., Manning Publications, 384 pp., ISBN 978-1617294433, 2017. a, b, c
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Short summary
Monitoring of CO2 emissions is key to the development of reduction policies. Local emissions, from cities or power plants, may be estimated from CO2 plumes detected in satellite images. CO2 plumes generally have a weak signal and are partially concealed by highly variable background concentrations and instrument errors, which hampers their detection. To address this problem, we propose and apply deep learning methods to detect the contour of a plume in simulated CO2 satellite images.