Articles | Volume 18, issue 1
https://doi.org/10.5194/amt-18-241-2025
https://doi.org/10.5194/amt-18-241-2025
Research article
 | 
15 Jan 2025
Research article |  | 15 Jan 2025

Retrieving the atmospheric concentrations of carbon dioxide and methane from the European Copernicus CO2M satellite mission using artificial neural networks

Maximilian Reuter, Michael Hilker, Stefan Noël, Antonio Di Noia, Michael Weimer, Oliver Schneising, Michael Buchwitz, Heinrich Bovensmann, John P. Burrows, Hartmut Bösch, and Ruediger Lang

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Cited articles

Agustí-Panareda, A., McNorton, J., Balsamo, G., Baier, B. C., Bousserez, N., Boussetta, S., Brunner, D., Chevallier, F., Choulga, M., Diamantakis, M., Engelen, R., Flemming, J., Granier, C., Guevara, M., Denier van der Gon, H., Elguindi, N., Haussaire, J.-M., Jung, M., Janssens-Maenhout, G., Kivi, R., Massart, S., Papale, D., Parrington, M., Razinger, M., Sweeney, C., Vermeulen, A., and Walther, S.: Global nature run data with realistic high-resolution carbon weather for the year of the Paris Agreement, Scientific Data, 9, 160, https://doi.org/10.1038/s41597-022-01228-2, 2022.​​​​​​​ a
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Short summary
Carbon dioxide (CO2) and methane (CH4) are the main anthropogenic greenhouse gases. The European Copernicus CO2 monitoring satellite mission CO2M will provide measurements of their atmospheric concentrations, but the accuracy requirements are demanding and conventional retrieval methods computationally expensive. We present a new retrieval algorithm based on artificial neural networks that has the potential to meet the stringent requirements of the CO2M mission with minimal computational effort.