Preprints
https://doi.org/10.5194/gmd-2020-444
https://doi.org/10.5194/gmd-2020-444

Submitted as: development and technical paper 18 Feb 2021

Submitted as: development and technical paper | 18 Feb 2021

Review status: this preprint is currently under review for the journal GMD.

A new exponentially-decaying error correlation model for assimilating OCO-2 column-average CO2 data, using a length scale computed from airborne lidar measurements

David F. Baker1, Emily Bell1, Kenneth J. Davis2,3, Joel F. Campbell4, Bing Lin4, and Jeremy Dobler5 David F. Baker et al.
  • 1Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, USA
  • 2Department of Meteorology and Atmospheric Science, Pennsylvania State University, University Park, PA, USA
  • 3Earth and Environmental Systems Institute, Pennsylvania State University, University Park, PA, USA
  • 4NASA Langley Research Center, Hampton, VA, USA
  • 5Spectral Sensor Solutions LLC, Fort Wayne, IN, USA

Abstract. To check the accuracy of column-average dry air CO2 mole fractions (XCO2) retrieved from Orbiting Carbon Overvatory (OCO-2) data, a similar quantity has been measured from the Multi-functional Fiber Laser Lidar (MFLL) aboard aircraft flying underneath OCO-2 as part of the Atmospheric Carbon and Transport (ACT)-America flight campaigns. Here we do a lagged correlation analysis of these MFLL-OCO-2 column CO2 differences and find that their correlation spectrum falls off rapidly at along-track separation distances of under 10 km, with a correlation length scale of about 10 km, and less rapidly at longer separation distances, with a correlation length scale of about 20 km.

The OCO-2 satellite takes many CO2 measurements with small (~3 km2) fields of view (FOVs) in a thin (<10 km wide) swath running parallel to its orbit: up to 24 separate FOVs may be obtained per second (across a ~6.75 km distance on the ground), though clouds, aerosols, and other factors cause considerable data dropout. Errors in the CO2 retrieval method have long been thought to be correlated at these fine scales, and methods to account for these when assimilating these data into top-down atmospheric CO2 flux inversions have been developed. A common approach has been to average the data at coarser scales (e.g., in 10-second-long bins) along-track, then assign an uncertainty to the averaged value that accounts for the error correlations. Here we outline the methods used up to now for computing these 10-second averages and their uncertainties, including the constant-correlation-with-distance error model currently being used to summarize the OCO-2 version 9 XCO2 retrievals as part of the OCO-2 flux inversion model intercomparison project. We then derive a new one-dimensional error model using correlations that decay exponentially with separation distance, apply this model to the OCO-2 data using the correlation length scales derived from the MFLL-OCO-2 differences, and compare the results (for both the average and its uncertainty) to those given by the current constant-correlation error model. To implement this new model, the data are averaged first across 2-second spans, to collapse the cross-track distribution of the real data onto the 1-D path assumed by the new model. A small percentage of the data that cause nonphysical negative averaging weights in the model are thrown out. The correlation lengths over the ocean, which the land-based MFLL data do not clarify, are assumed to be twice those over the land.

The new correlation model gives 10-second XCO2 averages that are only a few tenths of a ppm different from the constant-correlation model. Over land, the uncertainties in the mean are also similar, suggesting that the +0.3 constant correlation coefficient currently used in the model there is accurate. Over the oceans, the twice-the-land correlation lengths that we assume here result in a significantly lower uncertainty on the mean than the +0.6 constant correlation currently gives – measurements similar to the MFLL ones are needed over the oceans to do better. Finally, we show how our 1-D exponential error correlation model may be used to account for correlations in those inversion methods that choose to assimilate each XCO2 retrieval individually, and to account for correlations between separate 10-second averages when these are assimilated instead.

David F. Baker 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 gmd-2020-444', Anonymous Referee #1, 24 Mar 2021
  • RC2: 'Comment on gmd-2020-444', Anonymous Referee #2, 03 May 2021
  • RC3: 'Correction to RC2 comment on gmd-2020-444', Anonymous Referee #2, 11 May 2021
  • AC1: 'Comment on gmd-2020-444', David Baker, 09 Jun 2021

David F. Baker et al.

Data sets

Computing a correlation length scale from MFLL-OCO2 CO2 differences, and accounting for correlated errors when assimilating OCO-2 data Baker, David F; Bell, Emily; Davis, Kenneth J.; Campbell, Joel F.; Lin, Bing; and Dobler, Jeremy https://doi.org/10.5281/zenodo.4399884

Model code and software

Computing a correlation length scale from MFLL-OCO2 CO2 differences, and accounting for correlated errors when assimilating OCO-2 data Baker, David F; Bell, Emily; Davis, Kenneth J.; Campbell, Joel F.; Lin, Bing; and Dobler, Jeremy https://doi.org/10.5281/zenodo.4399884

David F. Baker et al.

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Short summary
The OCO-2 satellite measures many closely-spaced column-averaged CO2 values around its orbit. To give these data proper weight in flux inversions, their error correlations must be accounted for. Here we lay out a 1-D error model with correlations that die out exponentially along-track to do so. A correlation length scale of ~20 km is derived from column CO2 measurements from an airborne lidar flown underneath OCO-2 for use in this model. The model's performance is compared to previous ones'.