12 Apr 2023
 | 12 Apr 2023

A non-linear data driven approach to bias correction of XCO2 for OCO-2 NASA ACOS version 10

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

Abstract. Measurements of column averaged, dry air mole fraction of CO2 (termed XCO2) from the Orbiting Carbon Obersvatory-2 (OCO-2) contain systematic errors and regional scale biases; often induced by forward model error or nonlinearity in the retrieval. Operationally, these biases are corrected for by a multiple linear regression model fit to co-retrieved variables that are highly correlate with XCO2 error. The operational bias correction is fit in tandem with a hand-tuned quality filter which limits error variance and reduces the regime of interaction between state variables and error to one that is largely linear. While the operational correction and filter are successful in reducing biases in retrievals, they do not allow for throughput or correction of data in which biases become nonlinear in predictors or features. In this paper, we demonstrate a clear improvement in the reduction of error variance over the operational method using a robust data driven, non-linear method. We further illustrate how the operational quality filter can be relaxed when used in conjunction with a non-linear bias correction, which allows for an increase of sounding throughput by 16 % while maintaining the residual error of the operational correction. The method can readily be applied to future ACOS algorithm updates, OCO-2’s companion instrument OCO-3, and to other retrieved atmospheric state variables of interest.

William R. Keely et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-362', Anonymous Referee #2, 26 Apr 2023
  • RC2: 'Comment on egusphere-2023-362', Anonymous Referee #1, 04 May 2023

William R. Keely et al.

William R. Keely et al.


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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 non-linear method that improves correction against a set of ground truth proxies, and allows for high throughput of well corrected data.