Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 15, issue 18
Articles | Volume 15, issue 18
https://doi.org/10.5194/amt-15-5219-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-5219-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 18
Research article
 | 
15 Sep 2022
Research article |  | 15 Sep 2022

On the potential of a neural-network-based approach for estimating XCO2 from OCO-2 measurements

François-Marie Bréon, Leslie David, Pierre Chatelanaz, and Frédéric Chevallier

Viewed

Total article views: 2,092 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,534 499 59 2,092 49 54
  • HTML: 1,534
  • PDF: 499
  • XML: 59
  • Total: 2,092
  • BibTeX: 49
  • EndNote: 54
Views and downloads (calculated since 27 Oct 2021)
Cumulative views and downloads (calculated since 27 Oct 2021)

Viewed (geographical distribution)

Total article views: 2,092 (including HTML, PDF, and XML) Thereof 2,085 with geography defined and 7 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 29 Jun 2024
Download
Short summary
The estimate of atmospheric CO2 from space measurement is difficult. Current methods are based on a detailed description of the atmospheric radiative transfer. These are affected by significant biases and errors and are very computer intensive. Instead we have proposed using a neural network approach. A first attempt led to confusing results. Here we provide an interpretation for these results and describe a new version that leads to high-quality estimates.
Read more