A Regional Gap-Filling Method Based on Spatiotemporal Variogram Model of <inline-formula> <tex-math notation="TeX">$\hbox{CO}_{2}$</tex-math></inline-formula> Columns

Abstract

A precise and high-resolution spatiotemporal distribution of atmospheric carbon dioxide (CO2) is important in identifying and quantifying the CO2 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 CO2 dry air mole fractions (Xco2) on a regional scale, using data from the Atmospheric CO2 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 (r2) 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 (Xco2) at high spatial and temporal resolutions.