Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 16, issue 6
Articles | Volume 16, issue 6
https://doi.org/10.5194/amt-16-1461-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-16-1461-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 6
Research article
 | 
21 Mar 2023
Research article |  | 21 Mar 2023

Correcting 3D cloud effects in XCO2 retrievals from the Orbiting Carbon Observatory-2 (OCO-2)

Steffen Mauceri, Steven Massie, and Sebastian Schmidt

Data sets

3D cloud metrics for OCO-2 observations S. Massie, H. Cronk, A. Merrelli, K. S. Schmidt, H. Chen, and D. Baker https://doi.org/10.5281/zenodo.4008764

OCO-2 Level 2 bias-corrected XCO2 and other select fields from the fullphysics retrieval aggregated as daily files, Retrospective processing V10r OCO-2 Science Team, M. Gunson, and A. Eldering https://doi.org/10.5067/E4E140XDMPO2

2014 TCCON Data Release Total Carbon Column Observing Network (TCCON) Team https://doi.org/10.14291/TCCON.GGG2014

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Short summary
The Orbiting Carbon Observatory-2 makes space-based measurements of reflected sunlight. Using a retrieval algorithm these measurements are converted to CO2 concentrations in the atmosphere. However, the converted CO2 concentrations contain errors for observations close to clouds. Using a simple machine learning approach, we developed a model to correct these remaining errors. The model is able to reduce errors over land and ocean by 20 % and 40 %, respectively.
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