Data fusion of CO_2 retrieved from GOSAT and AIRS: using regression analysis and fixed rank kriging

摘要

This paper proposes an improved statistical method for fusing carbon dioxide (CO_2) data retrieved from two major instruments, the Greenhouse gases Observing SATellite (GOSAT) and the Atmospheric Infrared Sounder (AIRS). These two datasets were fused to obtain CO_2 concentrations near the surface, which is a region that is especially important for studies on carbon sources and sinks. Overall, the CO_2 monthly average values from GOSAT are all lower than those from AIRS from 2010 to 2012. The datasets show the similar seasonal cycles of carbon dioxide and show an increasing trend with a determination coefficient of 0.45. A strong correlation was determined by adding the climatic factors as independent variables for regression analysis. The correlation coefficients between the CO_2 values from AIRS and GOSAT significantly increased in response. The true CO_2 data processes were then predicted using the fixed rank kriging method. This showed that the data-fusion CO_2 product provides more reasonable information and that the corresponding mean squared prediction errors are smaller than those from the single GOSAT CO_2 dataset.