The response of low-level cloud properties to aerosol loading remains ambiguous, particularly due to the confounding influence of meteorological factors and water vapor availability. We utilize long-term data from Ka-band Zenith Radar, Clouds and the Earth’s Radiant Energy System, Modern-Era Retrospective analysis for Research and Applications Version 2, and European Centre for Medium-Range Weather Forecasts Reanalysis v5 to evaluate aerosol’s effects on low-level clouds under the constrains of meteorological conditions and liquid water path (LWP) over the Semi-Arid Climate and Environment Observatory of Lanzhou University during 2014–2019. To better constrain meteorological variability, we apply Principal Component Analysis to derive the first principal component (PC1), which strongly correlates with cloud properties, thereby enabling more accurate assessment of aerosol–cloud interaction (ACI) under constrained meteorological conditions delineated by PC1. Analysis suggests that under favorable meteorological conditions for low-level cloud formation (low PC1) and moderate LWP levels (25–150 g/m²), ACI is characterized by a significantly negative ACI index, with the cloud effective radius (CER) increasing in response to rising aerosol concentrations. When constrained by both PC1 and LWP, the relationship between CER and the aerosol optical depth shows a distinct bifurcation into positive and negative correlations. Different aerosol types show contrasting effects: dust aerosols increase CER under favorable meteorological conditions, whereas sulfate, organic carbon, and black carbon aerosols consistently decrease it, even under high-LWP conditions.

Cloud overlap, referring to distinct cloud layers occurring over the same location, is essential for accurately calculating the atmospheric radiation transfer in numerical models, which, in turn, enhances our ability to predict future climate change. In this study, we analyze multi-year cloud overlap properties observed from the Ka-band Zenith Radar (KAZR) at the Semi-Arid Climate and Environment Observatory of Lanzhou University’s (SACOL) site. We conduct a series of statistical analyses and determine the suitable temporal-spatial resolution of 1 h with a 360 m scale for data analysis. Our findings show that the cloud overlap parameter and total cloud fraction are maximized during winter-spring and minimized in summer-autumn, and the extreme value of decorrelation length usually lags one or two seasons. Additionally, we find the cloud overlap assumption has distinct effects on the cloud fraction bias for different cloud types. The random overlap leads to the minimum bias of the cloud fraction for Low-Middle-High (LMH), Low-Middle (LM), and Middle-High (MH) clouds, while the maximum overlap is for Low (L), Middle (M), and High (H) clouds. We also incorporate observations from satellite-based active sensors, including CloudSat, Cloud-Aerosol Lidar, and Infrared Pathfinder Satellite Observations (CALIPSO), to refine our study area and specific cases by considering the total cloud fraction and sample size from different datasets. Our analysis reveals that the representativeness of random overlap strengthens and then weakens with increasing layer separations. The decorrelation length varies with the KAZR, CloudSat-CALIPSO, CloudSat, and CALIPSO datasets, measuring 1.43 km, 2.18 km, 2.58 km, and 1.11 km, respectively. For H, MH, and LMH clouds, the average cloud overlap parameter from CloudSat-CALIPSO aligns closely with KAZR. For L, M, and LM clouds, when the level separation of cloud layer pairs are less than 1 km, the representative assumption obtained from different datasets are maximum overlap.