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

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Latest update: 04 Nov 2024
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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.
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