## 4 Experimental Results and Analysis
### Error Analysis and Performance of AOD Fusion  
Four-moment cases from different seasons are selected, in which the valid AOD pixels from the original Himawari-8 AHI possessed diverse spatial distributions to explore the AOD fusion details of the GL-BME. Figure 8a,b illustrates that there are data blanks in the original Himawari-8 AHI AOD, with the spatial coverage ranging from 27.3% to 34.2%. In contrast, the spatial coverage of the merged AOD datasets shows significant improvements, even up to 90% or more, which provide the possibility for spatiotemporally continuous analysis of aerosols. As shown in the third line of Figure 8, the estimation error variance (EEV) is adopted to evaluate the reliability of AOD expectation in the BME estimation. The EEV characterizes the estimation variance of the GL-BME fusion framework, which is calculated based on the number of available AODs within a certain spatiotemporal range centered at an estimated AOD pixel in the original satellite data as well as their spatiotemporal correlations. To be specific, when the original AOD is available, no fusion process is performed, and the EEV is 0 by default in this situation, and the EEV is updated along with the BME fusion process only when the original AOD is not available. Accordingly, the unreliable AOD estimations recovered by the GL-BME fusion algorithm are further excluded when the corresponding EEV is over 0.03 in this study, which are illustrated as blank areas in the second line of Figure 8. From the four selected moments throughout the experiment region, the highest EEV occurs over areas where there are not sufficient spatiotemporally adjacent AOD pixels, while the lowest EEV appears over areas where there exist enough valid adjacent AOD pixels in the original Himawari-8 AHI AOD with uniform spatiotemporal correlations. Additionally, a comparison between the spatial distribution of the monthly merged AOD and monthly original L2 AHI AOD is conducted to further demonstrate the performance of the proposed GL-BME fusion process in restoring the real aerosol loading information. As illustrated in Figure 9, there remains a number of data blanks in the monthly averaged original AHI AOD maps, whereas the monthly averaged merged AOD maps after GL-BME fusion achieve complete coverage in each month. Furthermore, there are some specific improvements. First, sporadic noises with extremely high values caused by thin clouds or cloud edges are mixed with normal pixels in the original datasets, inevitably leading to an overestimation of the local pollution levels. The quality control strategy in this study screens these abnormal points during the fusion process, thus en  
Figure 8: Maps of the original Himawari-8 AHI AOD (first line), merged AOD (second line), and estimation error variance (EEV) of the merged AOD data (third line) in the experimental region on 15 February, 25 May, 20 October, and 3 December of 2017.  
abling the merged AOD to characterize the regional features of aerosols more accurately and stably. Second, the original AOD values are abnormally high in the immediate offshore area, wherein sediment is prone to accumulate, such as the sea areas near the Yangtze River Delta. In contrast, the merged AOD mitigates the excessive overestimation to a certain extent in individual months, particularly in August, September, and December. Third, the GL-BME fusion framework recovers the AOD values of the areas with relatively low aerosol loadings, such as the ocean and rural areas. Furthermore, there exist significant differences in some regions between the merged AOD and the original AHI AOD. For instance, it recovers relatively high values in heavily polluted areas (e.g., Beijing-Tianjin-Hebei region), obviously in January, February, March, and July.