Articles | Volume 16, issue 6
https://doi.org/10.5194/amt-16-1461-2023
https://doi.org/10.5194/amt-16-1461-2023
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

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Cited articles

Breiman, L.: Random forests, Mach. Learn., 45, 5–32, 2001. 
Chen, S., Natraj, V., Zeng, Z.-C., and Yung, Y. L.: Machine learning-based aerosol characterization using OCO-2 O2 A-band observations, J. Quant. Spectrosc. Ra., 279, 108049, https://doi.org/10.1016/j.jqsrt.2021.108049, 2022. 
Cronk, H.: OCO-2/MODIS Collocation Products User Guide, Version 3, ftp://ftp.cira.colostate.edu/ftp/TTaylor/publications/ (last access: 13 March 2023), 2018. 
Dubey, M., Henderson, B., Green, D., Butterfield, Z., Keppel-Aleks, G., Allen, N., Blavier, J. F., Roehl, C., Wunch, D., and Lindenmaier, R.: TCCON data from Manaus (BR), Release GGG2014R0, CaltechDATA [data set], https://doi.org/10.14291/tccon.ggg2014.manaus01.R0/1149274, 2014a. 
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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.