## 3 Results
### Validation of CBH Using Spaceborne Cloud Radar and Lidar Measurements
The CloudSat-CALIPSO is able to penetrate the high cirrus and correctly detects the base of the low-level clouds. In contrast, the signal received by the AHI and MODIS is heavily dominated by the cold brightness temperature of the high-level cirrus, which leads to a very high CTH from CNN. This large CTH error propagates directly to the final CBH, leading to its severe overestimation. Due to a battery issue with CloudSat, there have been no available nighttime observations in the 2B-GEOPROF-LIDAR product since April 2011. To demonstrate the capability of the proposed algorithm to retrieve AHI CBH under both daytime and nighttime conditions, we conducted a comparison using the state-of-the-art EarthCARE-CPR 2A cloud product (i.e., CPR_TC_2A). Figure 6 compares the AHI nighttime retrieval products with EarthCARE-CPR measurements on 19 March 2025, at 13:14 UTC. Consistent with the previous comparisons against CloudSat-CALIPSO, the retrieved AHI CBH effectively mitigates the errors derived from AHI CTH retrievals for pixels between 187\({}^{\circ}\)E and 188\({}^{\circ}\)E. However, when the thick cloud is situated close to the ground (i.e., between 190\({}^{\circ}\)E and 191\({}^{\circ}\)E), the retrieved AHI CBH tends to overestimate the cloud base boundary, even when the corresponding AHI CTH retrievals are accurate. To reveal the performance of our algorithm in retrieving nighttime CBH, Figure 7 evaluates the AHI CBH retrievals using the EarthCARE-CPR measurements. The pixel-by-pixel  
Figure 6: A nighttime example of the (**a**) CTH, (**b**) CWP, and (**c**) CBH retrievals based on the AHI infrared observations at 13:14 UTC on 19 March 2025. The red line in (**a–c**) is the coincident ground track of EarthCARE. Panel (**d**) shows a cross-sectional comparison between the AHI-retrieved CTH (blue dots) and CBH (red dots) and the EarthCARE CPR-measured cloud vertical structures (gray area). The purple shading represents that the case occurred during nighttime. comparisons are conducted for clouds at daytime and nighttime, respectively. The results show that the accuracy of the AHI-derived nighttime CBH retrievals (\(-0.35\pm 1.84\) km) is slightly lower than that of the daytime CBH retrievals (\(-0.21\pm 1.66\) km). The difference between daytime and nighttime CBH retrieval accuracies is mainly attributed to the difference between daytime and nighttime cloud vertical characteristics (i.e., CTH, CBH, and CGT). As shown in Figure 8a,b, the EarthCARE measurements indicate that there is a higher proportion of high-level thin clouds and fewer thick clouds at nighttime than at daytime. Furthermore, Figure 8c,d presents a bias distribution heatmap of the retrieved AHI CBH, constrained by the EarthCARE CTH and CGT. The results show that the distribution of the biases of the day and night CBH retrievals is similar. However, the proposed algorithm tends to overestimate CBH for optically thick clouds while underestimating CBH for optically high-level thin clouds. The overestimation for optically thick clouds is mainly caused by the limitations of infrared radiance in penetrating thick clouds, which leads to underestimated CWP retrievals [10]. In addition, some previous studies have found that the MODIS cloud property products tend to underestimate the CTH of high-level thin clouds [32]. As one of the major input variables, the underestimation of the retrieved CTH would result in underestimated AHI CBH retrievals for high-level optically thin clouds. To conclude, the hybrid CBH retrieval algorithm can be effectively applied to nighttime AHI observations, although its performance may be affected by the diurnal variation of the cloud vertical structures. Figure 8: _Cont._  
Figure 7: Pixel-by-pixel comparison of AHI CBH retrievals and the EarthCARE CPR measurements for (**a**) daytime and (**b**) nighttime conditions. In all subplots, the black solid line is the 1:1 line, and the red solid line represent the linear regression.