Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 18, issue 1
Articles | Volume 18, issue 1
https://doi.org/10.5194/amt-18-241-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-18-241-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 1
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

Viewed

Total article views: 645 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
433 139 73 645 6 6
  • HTML: 433
  • PDF: 139
  • XML: 73
  • Total: 645
  • BibTeX: 6
  • EndNote: 6
Views and downloads (calculated since 02 Aug 2024)
Cumulative views and downloads (calculated since 02 Aug 2024)

Viewed (geographical distribution)

Total article views: 645 (including HTML, PDF, and XML) Thereof 673 with geography defined and -28 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 15 Jan 2025
Download
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.
Read more