## 4 Experimental Results and Analysis
### Accuracy Evaluation for Merged AOD  
Another primary objective of the GL-BME fusion algorithm is to achieve high-quality merged AOD products. AERONET and MAN AOD observations are adopted to validate the merged AOD products and compare the accuracy between the merged AODs and original satellite-based AODs. The accuracy is quantitatively assessed by utilizing a set of evaluation metrics for statistical analysis, including the Pearson correlation coefficient (R), root-mean-square error (RMSE), mean absolute error (MAE), and expected error (EE) envelope combined with underestimation and overestimation ratios. The spatiotemporal matching strategy between all the satellite-based AODs and ground-based AODs is the same as the strategy mentioned in Section 3.2.2.  
Figure 5: Daily averaged AOD spatial completeness ratio in 2017 for the original Himawari-8 AHI AOD (green), merged AOD blending AHI, MODIS, and CALIOP AOD based on GL-BME (red) and merged AOD via only AHI AOD based on BME (blue).  
Figure 6: Spatial distribution of the annually averaged coverage ratio in 2017 for the (**a**) original Himawari-8 L2 AHI AOD datasets and (**b**) merged AOD datasets after GL-BME fusion, combined with (**c**) coverage improvement from original AHI AOD to merged AOD.  
Figure 7 shows the validation results of the original satellite-based and merged AOD datasets against AERONET and MAN AOD observations over the whole experiment region during 2017. As shown in Figure 7a-c, the R, MAE, RMSE, above EE, within EE, and below EE based on 40,839 matchups from the original AHI AODs are 0.81, 0.13, 0.20, 22%, 56%, and 22%, respectively. For the 1078 matchups from the MODIS AODs, the R, MAE, RMSE, above EE, within EE, and below EE are 0.92, 0.09, 0.13, 9%, 65%, and 27%, respectively. For the 34 matchups from the CALIOP AOD datasets, the R, MAE, RMSE, above EE, within EE, and below EE are 0.79, 0.12, 0.19, 26%, 62%, and 22%, respectively. Figure 7d shows the overall accuracy of the merged AOD datasets over the whole experimental period and domain. The R, MAE, RMSE, above EE, within EE, and below EE based on 40,839 matchups are 0.80, 0.12, 0.20, 20%, 58%, and 22%, respectively. Additionally, Figure 7e demonstrates the accuracy of the merged AODs over areas where the initial AHI AODs are missing, with an R of 0.77 and approximately 56% of 34,018 matchups falling within the EE envelope. Moreover, the number of matchups below the EE envelope is higher than those above the EE envelope, with the underestimation mostly occurring at high aerosol loadings of >0.8. This is possibly due to the strict quality control process removing the cloud-influenced anomalies accompanied by the screening of some of the truly high AOD values. Admittedly, the comparison among Figure 7a,d,e demonstrates there exists a slight decrease in the accuracy of the merged AOD compared to the accuracy of the original AHI AOD, which is probably caused by AOD expectation estimation tending to be affected by spatiotemporally adjacent potential low AOD values and strict cloud screening preprocess to exclude some high AOD values to a certain extent. Overall, the merged AODs possess a relatively higher ratio within the EE envelop than the original AHI AODs, which indicates that the GL-BME fusion process could ensure the accuracy of the merged AOD dataset on the basis of dramatically enhancing the spatial coverage.  
Figure 7: Validation results of the (**a**) original Himawari-8 L2 AHI, (**b**) MODIS, (**c**) CALIOP, (**d**) merged AODs, and (**e**) merged AODs s for areas where the initial Himawari-8 AHI, MODIS, and CALIOP AODs are all missing with AERONET and MAN AODs as benchmarks. The black, blue, and red lines represent the 1:1 reference, EE envelop, and regression lines of the AOD matchups, respectively.