## 5 Discussion  
Interpretation of the cloud mask and cloud top height retrieval comparison results is complex. Biases and uncertainties result from a combination of spectral sensitivity differences and systematic algorithm biases from AGRI and MODIS as well as uncertainties from the spatial sampling differences. In addition, AGRI and MODIS cloud mask and cloud top height retrieval performance are dependent on both surface, cloud, and atmospheric properties. The complicated cloud regimes over the TP with complex terrain can increase the complexity of the interpretation of the comparison results. To further investigate the performance of the AGRI cloud mask over the TP, the AGRI cloud mask agreement distribution separated for cloudy-sky and clear-sky scenes is presented in Figure 5. Three months of collocated samples of the same dataset in Table 2 were divided into 0.5\({}^{\circ}\) grid boxes. As shown in Figure 5, each grid box presents the fractional agreement in cloudy-sky (Figure 5a)/clear-sky (Figure 5b) conditions for all the matched samples within the box. The spatial distribution of the matching sample size was also presented for clear-sky (Figure 5c) and cloudy-sky (Figure 5d) conditions. Figure 4: MAE (blue line), MBE (green line), and STD (red line) of AGRI CTH in different layers at an interval of 1 km for the same dataset in Figure 3.  
Figure 3: Validation of cloud top height (CTH) products of AGRI over the TP based MODIS CTH retrievals during the periods June, July, and August 2020. The color bar represents the number of collocated samples in every bin at an interval of 0.25 km, and the dashed line is the one-to-one ratio line. The total number of collocated samples (N), mean absolute error (MAE), mean bias error (MBE), standard deviation (STD), and correlation coefficient (CC) are marked in the upper left corner. The largest differences over the southern part of the TP (25\({}^{\circ}\)N-29\({}^{\circ}\)N, 79\({}^{\circ}\)E-102\({}^{\circ}\)E) for the clear-sky condition were partially due to sampling with only dozens or even fewer samples within a grid box instead of several hundred or even more than a thousand, as in other grid boxes. In summer, the warm and humid monsoon frequently reached the steep southern slope of the TP, making convective cloud system dominate there [22; 65], therefore, there were few MODIS clear pixels during the summer monsoon season over those areas (Figure 5c). Surprisingly, there was poor agreement for clear sky conditions over the western part of the TP (36\({}^{\circ}\)N-40\({}^{\circ}\)N, 75\({}^{\circ}\)E-85\({}^{\circ}\)E, the area where China's largest desert, the Taklimakan Desert, and largest basin, Tarim Basin, are located). Although it is expected that the desert and basin area should be associated with warm surfaces, causing high thermal contrast between clouds and the surface, leading to better clear-sky detection; in fact, the differences over the under clear-sky condition were obvious. This is likely due to the fact that AGRI thresholds of the infrared tests are not applicable for the desert area, leading to poor agreement. It was found that cloud occurrence frequencies obtained from AGRI reached the peak at night and were the least during the day over that area. Radiation cooling at night will cause a significant temperature drop in desert areas. Additionally, the low temperature of the desert at night results in poor thermal contrast between the clouds and surface, which will increase the difficulty for cloudy-sky/clear-sky detection. It is necessary to further study what caused these differences in future work. It is obvious that AGRI and MODIS were in better agreement for cloudy scenes than for clear-sky scenes. The fractional agreement was better than 0.9 over much of the TP for cloudy-sky condition. While AGRI and MODIS were generally in good agreement under this circumstance, there were some small regions located in the southern, southwestern, and northeastern part of the TP with poor agreement.