Articles | Volume 16, issue 23
https://doi.org/10.5194/amt-16-5725-2023
https://doi.org/10.5194/amt-16-5725-2023
Research article
 | 
29 Nov 2023
Research article |  | 29 Nov 2023

A nonlinear data-driven approach to bias correction of XCO2 for NASA's OCO-2 ACOS version 10

William R. Keely, Steffen Mauceri, Sean Crowell, and Christopher W. O'Dell

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Latest update: 04 Oct 2024
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Short summary
Measurement errors in satellite observations of CO2 attributed to co-estimated atmospheric variables are corrected using a linear regression on quality-filtered data. We propose a nonlinear method that improves correction against a set of ground truth proxies and allows for high throughput of well-corrected data.