A Regional Gap-Filling Method Based on Spatiotemporal Variogram Model of $\hbox{CO}_{2}$ Columns

A precise and high-resolution spatiotemporal distribution of atmospheric carbon dioxide (CO
₂
) is important in identifying and quantifying the CO
₂
source and sinks on regional scales and emissions from discrete point sources. We propose the use of a regional gap-filling method by modeling the spatiotemporal correlation structures of column-averaged CO
₂
dry air mole fractions (Xco
₂
) on a regional scale, using data from the Atmospheric CO
₂
Observations from Space retrievals of the Greenhouse Gases Observing Satellite (ACOS-GOSAT) measurements over mainland China. The accuracy of the gap-filling results is verified by cross-validation and comparison with ground-based measurements. As the results of the spatiotemporal gap-filling method are applied to mainland China, the correlation coefficient (r
²
) between the predicted values and true ones is greater than 0.85, the mean absolute prediction error is less than 1.5 ppm in cross-validation, and the seasonal cycle of the gap-filled data is generally in agreement with ground-based measurements. Finally, we compare the prediction accuracy based on our method with that based on the commonly used spatial-only kriging to further demonstrate the improved prediction accuracy. The applied regional gap-filling method, which makes full use of the multitemporal ACOS-GOSAT data, can generate a regional regular spatial distribution map of (Xco
₂
) at high spatial and temporal resolutions.