This study mainly focuses on the diurnal and long-term variation characteristics of the calibration performance of the geostationary (GEO) meteorological satellite Fengyun-4A/ Advanced Geostationary Radiation Imager (FY-4A/AGRI) infrared (IR) channels from June 1, 2017 to December 31, 2020. An improved algorithm is developed in this research based on the in situ observations from ocean drifters, which can capture the diurnal variations of the calibration performance of FY-4A/AGRI IR channels. The results suggest that there are significant and nonnegligible diurnal variations of the uncertainties and biases for the brightness temperature (TB) observed by the FY-4A/AGRI, especially the IR channels at 3.71, 8.61, 10.83, and $12.07 \mu \text{m}$ . Among them, the calibration performance from 16:00 to 18:00 UTC (around the time of local midnight at the subsatellite point of FY-4A) is worse at $3.71 \mu \text{m}$ , while TB biases from 04:00 to 16:00 UTC are relatively large at $12.07 \mu \text{m}$ . Moreover, after the operational calibration and update in June 2020, the long-term TB biases at channel 08 ( $3.71 \mu \text{m}$ ) obviously decrease from about 5–2.5 K, and the TB biases at channel 14 ( $13.54 \mu \text{m}$ ) increase from about 0.5–3 K, implying a possible positive or negative impact of the calibration update on the calibration performance of IR channels. Overall, this method based on the in situ drifter data can well monitor the on-orbit calibration performance of GEO satellite imaging sensor’s IR channels such as the FY-4A/AGRI, allowing an objective assessment on the effect of calibration update events.

Two groups of retrieval algorithms, physics based and machine learning (ML) based, each consisting of two independent approaches, have been developed to retrieve cloud base height (CBH) and its diurnal cycle from Himawari-8 geostationary satellite observations. Validations have been conducted using the joint CloudSat/Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) CBH products in 2017, ensuring independent assessments. Results show that the two ML-based algorithms exhibit markedly superior performance (the optimal method is with a correlation coefficient of R > 0.91 and an absolute bias of approximately 0.8 km) compared to the two physics-based algorithms. However, validations based on CBH data from the ground-based lidar at the Lijiang station in Yunnan Province and the cloud radar at the Nanjiao station in Beijing, China, explicitly present contradictory outcomes (R < 0.60). An identifiable issue arises with significant underestimations in the retrieved CBH by both ML-based algorithms, leading to an inability to capture the diurnal cycle characteristics of CBH. The strong consistence observed between CBH derived from ML-based algorithms and the spaceborne active sensors of CloudSat/CALIOP may be attributed to utilizing the same dataset for training and validation, sourced from the CloudSat/CALIOP products. In contrast, the CBH derived from the optimal physics-based algorithm demonstrates good agreement in diurnal variations in CBH with ground-based lidar/cloud radar observations during the daytime (with an R value of approximately 0.7). Therefore, the findings in this investigation from ground-based observations advocate for the more reliable and adaptable nature of physics-based algorithms in retrieving CBH from geostationary satellite measurements. Nevertheless, under ideal conditions, with an ample dataset of spaceborne cloud profiling radar observations encompassing the entire day for training purposes, the ML-based algorithms may hold promise for still delivering accurate CBH outputs.

A cloud structure construction algorithm adapted for the nighttime condition is proposed and evaluated. The algorithm expands the vertical information inferred from spaceborne radar and lidar via matching of infrared (IR) radiances and other properties at off-nadir locations with their counterparts that are collocated with active footprints. This nighttime spectral radiance matching (NSRM) method is tested using measurements from CloudSat/Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and Moderate Resolution Imaging Spectroradiometer (MODIS). Cloud layer heights are estimated up to 400 km on both sides of the ground track and reconstructed with the dead zone setting for an approximate evaluation of the reliability. By mimicking off-nadir pixels with a dead zone around pixels along the ground track, reconstruction of nadir profiles shows that, at 200 km from the ground track, the cloud top height (CTH) and the cloud base height (CBH) reconstructed by the NSRM method are within 1.49 km and 1.81 km of the original measurements, respectively. The constructed cloud structure is utilized for cloud classification in the nighttime. The same method is applied to the daytime measurements for comparison with collocated MODIS classification based on the International Satellite Cloud Climatology Project (ISCCP) standard. The comparison of eight cloud types over the expanded distance shows good agreement in general.