Cloud base height (CBH) is crucial for parameterizing the cloud vertical structure (CVS), but knowledge concerning the temporal and spatial distribution of CBH is still poor owing to the lack of large-scale and continuous CBH observations. Taking advantage of high temporal and spatial resolution observations from the Advanced Himawari Imager (AHI) on board the geostationary Himawari-8 satellite, this study investigated the climatology of CBH by applying a novel CBH retrieval algorithm to AHI observations. We first evaluated the accuracy of the AHI-derived CBH retrievals using the active measurements of CVS from the CloudSat and CALIPSO satellites, and the results indicated that our CBH retrievals for single-layer clouds perform well, with a mean bias of 0.3 ± 1.9 km. Therefore, the CBH climatology was compiled based on AHI-derived CBH retrievals for single-layer clouds for the time period between September 2015 and August 2018. Overall, the distribution of CBH is tightly associated with cloud phase, cloud type, and cloud top height and also exhibits significant geographical distribution and temporal variation. Clouds at low latitudes are generally higher than those at middle and high latitudes, with CBHs peaking in summer and lowest in winter. In addition, the surface type affects the distribution of CBH. The proportion of low clouds over the ocean is larger than that over the land, while high cloud occurs most frequently over the coastal area. Due to periodic changes in environmental conditions, cloud types also undergo significant diurnal changes, resulting in periodic changes in the vertical structure of clouds.

Most existing cloud base height (CBH) retrieval algorithms are only applicable for daytime satellite observations due to their dependence on visible observations. This study presents a novel algorithm to retrieve day and night CBH using infrared observations of the geostationary Advanced Himawari Imager (AHI). The algorithm is featured by integrating deep learning techniques with a physical model. The algorithm first utilizes a convolutional neural network-based model to extract cloud top height (CTH) and cloud water path (CWP) from the AHI infrared observations. Then, a physical model is introduced to relate cloud geometric thickness (CGT) to CWP by constructing a look-up table of effective cloud water content (ECWC). Thus, the CBH can be obtained by subtracting CGT from CTH. The results demonstrate good agreement between our AHI CBH retrievals and the spaceborne active remote sensing measurements, with a mean bias of −0.14 ± 1.26 km for CloudSat-CALIPSO observations at daytime and −0.35 ± 1.84 km for EarthCARE measurements at nighttime. Additional validation against ground-based millimeter wave cloud radar (MMCR) measurements further confirms the effectiveness and reliability of the proposed algorithm across varying atmospheric conditions and temporal scales.