Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 2
Articles | Volume 15, issue 2
https://doi.org/10.5194/gmd-15-649-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-649-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 2
Development and technical paper
 | 
26 Jan 2022
Development and technical paper |  | 26 Jan 2022

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. Baker, Emily Bell, Kenneth J. Davis, Joel F. Campbell, Bing Lin, and Jeremy Dobler

Data sets

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

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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.
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