## 3 Results
### Validation of CBH Using Spaceborne Cloud Radar and Lidar Measurements
This indicates that  
Figure 4: A daytime example of the **(a)** CTH, **(b)** CWP, and **(c)** CBH retrievals based on the AHI infrared observations at 03:00 UTC on 1 March 2017. The red line in **(a–c)** is the coincident ground track of CloudSat-CALIPSO. Panel **(d)** shows a cross-sectional comparison between the AHI-retrieved CTH (blue dots) and CBH (red dots) and the CloudSat-CALIPSO-measured cloud vertical profile (gray shading) from 145\({}^{\circ}\)E–175\({}^{\circ}\)E. The black crosses represent the collocated MYD06 CTH for additional comparison. our algorithm mitigates the potential errors associated with CTH retrievals and effectively characterizes the vertical structures of clouds. A statistical evaluation of our CBH retrievals is conducted using the collocated AHI-CPR-CALIOP observations in 2017, and the results are presented in Figure 5 and Table 3. Figure 5a shows a pixel-by-pixel comparison between the AHI CBH retrievals and those obtained from CloudSat-CALIPSO, and the results are compiled from all single-layer cloud pixels with CTH biases smaller than 2 km. The bias between the AHI CBH retrievals and the active measurements of CloudSat-CALIPSO is \(-0.14\), and the RMSE is 1.26 km, indicating good consistency between the two results. To further evaluate the performance of the proposed algorithm, clouds over land or ocean are considered separately in Figure 5b,c. The results indicate that the accuracy of the CBH retrievals is higher for clouds over the ocean compared to those over land. This discrepancy may be attributed to the higher proportion of low clouds with CBH values smaller than 1.5 km over ocean than over land. Therefore, we further conduct a  
Figure 5: Pixel-by-pixel comparison between the CBH retrievals derived from the AHI observations and the active measurements derived from the CloudSat-CALIPSO 2B-GEOPROF-LIDAR products for samples from **(a)** all clouds, **(b)** clouds over land, **(c)** clouds over ocean, **(d)** high clouds (cirrus), **(e)** middle clouds (altostratus and altocumulus) and **(f)** low clouds (cumulus, stratocumulus, stratus). In all subplots, the black solid line is the 1:1 line, and the red solid line represent the linear regression. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Number** & **R** & **BIAS (km)** & **RMSE (km)** \\ \hline All clouds & 73,255 & 0.94 & \(-0.14\) & \(1.26\) \\ Clouds over land & 10,089 & 0.86 & \(-0.47\) & \(1.83\) \\ Clouds over the ocean & 63,166 & 0.95 & \(-0.08\) & \(1.14\) \\ High clouds & 11,388 & 0.65 & \(-0.95\) & \(1.85\) \\ Middle clouds & 14,368 & 0.51 & 0.28 & \(2.19\) \\ Low clouds & 47,499 & 0.73 & \(-0.07\) & \(0.49\) \\ \hline \hline \end{tabular} Note. High clouds refer to cirrus, middle clouds refer to alostratus and altocumulus, and low clouds are stratus, stratocumulus, and cumulus. Number represents the total matchups for various cloud types.  
\end{table} 
Table 3: Statistical comparison of the AHI CBH retrievals and the CloudSat-CALIPSO CBH measurements. comparison for clouds at different altitudes. According to the cloud types determined by the 2B-GEOPROF-LIDAR product based on the combined observations of CloudSat-CPR and CALIOP, the samples are divided into low clouds (cumulus, stratocumulus, stratus), middle clouds (altostratus and altocumulus), and high clouds (cirrus). As shown in Figure 5d-f, the algorithm is especially effective for low clouds, with an R as high as 0.73 and a BIAS as small as \(-\)0.07 km. The BIAS of \(-\)0.95 km for high clouds indicates that the AHI CBH retrievals tend to be underestimated, mainly due to the underestimation of CTH of high and thin clouds. The performance of the CBH retrievals for middle clouds is slightly worse than that of high clouds due to the more complex vertical distribution of altostratus and altocumulus clouds. In addition, there is an overestimation phenomenon for the CBH lower than 2 km, and this mainly stems from the middle clouds over the ocean, which are primarily caused by undetected multilayer clouds.