期刊
ATMOSPHERIC MEASUREMENT TECHNIQUES
卷 16, 期 6, 页码 1461-1476出版社
COPERNICUS GESELLSCHAFT MBH
DOI: 10.5194/amt-16-1461-2023
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The OCO-2 satellite measures radiance in different bands to estimate column-averaged atmospheric CO2 dry-air mole fractions (XCO2). However, calibration issues and mismatches between the retrieval algorithm and observed radiances cause biases in the retrieved XCO2 values. Multiple linear regression is used to mitigate these biases. A bias correction model based on interpretable non-linear machine learning is developed to address 3D cloud biases, reducing unphysical variability over land and sea. The approach is applicable to other greenhouse gas experiments.
The Orbiting Carbon Observatory-2 (OCO-2) makes space-based radiance measurements in the oxygen A band and the weak and strong carbon dioxide (CO2) bands. Using a physics-based retrieval algorithm these measurements are inverted to column-averaged atmospheric CO2 dry-air mole fractions (XCO2). However, the retrieved XCO2 values are biased due to calibration issues and mismatches between the physics-based retrieval radiances and observed radiances. Using multiple linear regression, the biases are empirically mitigated. However, a recent analysis revealed remaining biases in the proximity of clouds caused by 3D cloud radiative effects (Massie et al., 2021) in the processing version B10. Using an interpretable non-linear machine learning approach, we develop a bias correction model to address these 3D cloud biases. The model is able to reduce unphysical variability over land and sea by 20 % and 40 %, respectively. Additionally, the 3D cloud bias-corrected XCO2 values show agreement with independent ground-based observations from the Total Carbon Column Observation Network (TCCON). Overall, we find that the published OCO-2 data record underestimates XCO2 over land by -0.3 ppm in the tropics and northward of 45 degrees N. The approach can be expanded to a more general bias correction and is generalizable to other greenhouse gas experiments, such as GeoCarb, GOSAT-3, and CO2M.
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