Combining XCO2 Measurements Derived from SCIAMACHY and GOSAT for Potentially Generating Global CO2 Maps with High Spatiotemporal Resolution

Abstract

Global warming induced by atmospheric CO2 has attracted increasing attention of researchers all over the world. Although space-based technology provides the ability to map atmospheric CO2 globally, the number of valid CO2 measurements is generally limited for certain instruments owing to the presence of clouds, which in turn constrain the studies of global CO2 sources and sinks. Thus, it is a potentially promising work to combine the currently available CO2 measurements. In this study, a strategy for fusing SCIAMACHY and GOSAT CO2 measurements is proposed by fully considering the CO2 global bias, averaging kernel, and spatiotemporal variations as well as the CO2 retrieval errors. Based on this method, a global CO2 map with certain UTC time can also be generated by employing the pattern of the CO2 daily cycle reflected by Carbon Tracker (CT) data. The results reveal that relative to GOSAT, the global spatial coverage of the combined CO2 map increased by 41.3% and 47.7% on a daily and monthly scale, respectively, and even higher when compared with that relative to SCIAMACHY. The findings in this paper prove the effectiveness of the combination method in supporting the generation of global full-coverage XCO2 maps with higher temporal and spatial sampling by jointly using these two space-based XCO2 datasets.

Mapping Global Atmospheric CO2 Concentration at High Spatiotemporal Resolution

“…Therefore, a necessary adjustment is performed to adjust the two observations for a common a priori profile (i.e., the newly generated fused profile). Wang et al [13] employed the CT CO 2 profiles (2° × 3°) to assist data fusion. They indicated that a priori CO 2 profile of both the ACOS and BESD are first interpolated or extrapolated to the level of the CT CO 2 profile according to their pressure layers.…”

“…In this study, a new CO 2 profile database with 1° × 1° resolution is used by integrating the CO 2 profiles of CT, ACOS, BESD, and NIES v02.xx products. Thus, the adjustment equation can be expressed as follows [13]:…”

“…The limited satellite observations restrict the generation of level 3 (L3) XCO 2 maps with high spatiotemporal resolution when only a single satellite-based XCO 2 dataset is considered. Considering the weakness of single satellites, Wang et al [13] proposed a physical fusing algorithm to generate a continuous spatiotemporal CO 2 dataset. However, in certain regions, even after combining GOSAT with SCIAMACHY retrievals at time scales of up to a few months, some gaps remain, thereby influencing the analysis of global and regional spatiotemporal characteristics of XCO 2 concentrations.…”

“…Nevertheless, the fact is that these single satellite-based strategies could not ensure sufficient data for stable semivariogram estimation during Kriging interpolation because the number of valid data points is limited for a single satellite [13,18], which may cause large uncertainties. Rather than using L2 XCO 2 from a single dataset (e.g., GOSAT), as performed in existing literature, fusing available CO 2 measurements derived from various space-based data would facilitate the generation of highly reliable full coverage (L3) maps with high spatiotemporal resolution.…”

“…Rather than using L2 XCO 2 from a single dataset (e.g., GOSAT), as performed in existing literature, fusing available CO 2 measurements derived from various space-based data would facilitate the generation of highly reliable full coverage (L3) maps with high spatiotemporal resolution. Wang et al [13] proved that the spatial coverage of the fused data is wider than that of a single satellite. Thus, using fused data to generate a global land full-coverage map of XCO 2 distribution based on the Kriging approach is a better choice.…”