## 5 Discussion
To help interpret similarities and differences, Figure 6 presents the averaged MODIS cloud top height for two separated conditions: (1) for those cases where both MODIS and AGRI identify cloud scenes; and (2) for those cases where the MODIS 1-km cloud mask identifies cloud scenes but AGRI  
Figure 5: Fractional agreement between AGRI 4-km and MODIS 1-km cloud mask separated (**a**) for clear-sky scenes and (**b**) for cloudy-sky scenes is presented. Spatial distribution of the collocated sample size for (**c**) clear-sky scenes and (**d**) cloudy-sky scenes is also presented. The fractional agreement was calculated at a 0.5\({}^{\circ}\) resolution based on three months (June, July, and August 2020) of collocated samples of the same dataset with Table 2. A grid cell with perfect agreement between AGRI and MODIS will have a fractional agreement of 1 (red), while boxes of poorer agreement are colored blue. Blank space means that there are no matched pixels under the corresponding conditions. misses (i.e., AGRI flags them as clear or probably clear or probably cloudy). It should be noted that the 1-km pixel-level MODIS cloud top heights were first weighted averaged through Equation (10) to derive the cloud top height of the collocated sample, and then the MODIS cloud top height of each grid cell was derived by averaging all matched sample MODIS CTH within the grid box. The weighted average cloud height approach is to better characterize the cloud top height of those clouds that AGRI missed. By comparing Figure 6a with Figure 6b, it can be found that clouds that can be simultaneously detected by both MODIS and AGRI were those with higher CTH. The higher CTH in Figure 6a was most pronounced for the southeastern part of the TP (25\({}^{\circ}\)N-32\({}^{\circ}\)N, 85\({}^{\circ}\)E-105\({}^{\circ}\)E). This is due to the unique large topography of the TP that causes significant thermal-dynamic forcing on the atmosphere in summer over the steep southern slope and makes this region associated with frequent convective activities in summer [66; 67]. For these convective clouds with high CTH (>12 km), AGRI and MODIS can obtain good agreement. AGRI tends to miss low CTH clouds (<5 km), which is more pronounced at lower altitudes (<4 km) in areas located away from the center part of the TP, particularly in the southwest TP. In addition, for the central region of the TP (29\({}^{\circ}\)N-36\({}^{\circ}\)N, 79\({}^{\circ}\)E-97\({}^{\circ}\)E) (i.e., the main part of the TP with an altitude of \(\geq\)4 km), the clouds missed by AGRI had a relatively lower CTH than those identified by both approaches over the same areas. This is likely due to the lack of thermal contrast between the clouds and the surface. The cloud top is usually colder than the surface, and will be colder with increasing cloud top height. A good contrast between clouds and surface will benefit cloud detection. The TP is associated with cold surfaces, so the higher the elevation, the colder. The cold background conditions of the TP make cloud and clear scene discrimination problematic, especially for the low CTH clouds that are relatively warmer than high CTH clouds over it. Figure 7 presents the mean absolute error and mean bias error of AGRI cloud top height compared with MODIS CTH separated into grid cells. Three months (June, July, and August 2020) of collocated samples of the same dataset with Table 3 were divided into 0.5\({}^{\circ}\) grid cells. As shown in Figure 7, each grid box presents the MAE (Figure 7a) and MBE (Figure 7b) for all the matched samples within the box. It can be found that the MAE presented a similar pattern of CTH disagreement distribution with MBE. In general, there were few CTH differences, especially over the eastern part of the TP. The largest differences were due to significant underestimation by AGRI occurring over the southern, southwestern, and northern region of the TP (Figure 7b).