ADVANCES IN ATMOSPHERIC SCIENCES, VOL. 34, MARCH 2017, 347–359 • Original Paper • A Comparison of Cloud Layers from Ground and Satellite Active Remote Sensing at the Southern Great Plains ARM Site Jinqiang ZHANG 1,2,3 , Xiang’ao XIA ∗1,3 , and Hongbin CHEN 1,3 1 Key Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China 2 Center of Technical Support and Service, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China 3 Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, China (Received 29 January 2016; revised 18 August 2016; accepted 13 September 2016) ABSTRACT Using the data collected over the Southern Great Plains ARM site from 2006 to 2010, the surface Active Remote Sensing of Cloud (ARSCL) and CloudSat-CALIPSO satellite (CC) retrievals of total cloud and six specified cloud types [low, mid– low (ML), high–mid–low (HML), mid, high–mid (HM) and high] were compared in terms of cloud fraction (CF), cloud-base height (CBH), cloud-top height (CTH) and cloud thickness (CT), on different temporal scales, to identify their respective advantages and limitations. Good agreement between the two methods was exhibited in the total CF. However, large discrep- ancies were found between the cloud distributions of the two methods at a high (240-m) vertical grid spacing. Compared to the satellites, ARSCL retrievals detected more boundary layer clouds, while they underestimated high clouds. In terms of the six specific cloud types, more low- and mid-level clouds but less HML- and high-level clouds were detected by ARSCL than by CC. In contrast, the ARSCL retrievals of ML- and HM-level clouds agreed more closely with the estimations from the CC product. Lower CBHs tended to be reported by the surface data for low-, ML- and HML-level clouds; however, higher CTHs were often recorded by the satellite product for HML-, HM- and high-level clouds. The mean CTs for low- and ML-level cloud were similar between the two products; however, the mean CTs for HML-, mid-, HM- and high-level clouds from ARSCL were smaller than those from CC. Key words: surface, satellite, active remote sensing, cloud Citation: Zhang, J. Q., X. A. Xia, and H. B. Chen, 2017: A comparison of cloud layers from ground and satellite active remote sensing at the Southern Great Plains ARM site. Adv. Atmos. Sci., 34(3), 347–359, doi: 10.1007/s00376-016-6030-1. 1. Introduction Clouds are crucial components of Earth’s climate system due to their profound influences on the hydrological cycle and planetary radiation budget by reflecting the incoming so- lar radiation and absorbing the upwelling infrared radiation (Wang et al., 2014). In addition, the vertical structure and distribution of clouds within the atmosphere interact with at- mospheric dynamics (Kalesse and Kollias, 2013). Sherwood et al. (2014) highlighted the importance of low clouds and their feedbacks in the analysis of climate sensitivity. Sassen and Wang (2012) found that mid-level clouds cover about a quarter of Earth’s surface and represent a significant contri- bution to the planet’s energy budget. High cirrus clouds often produce a warming effect on the climate system (Huo and Lu, 2014). ∗ Corresponding author: Xiang’ao XIA Email: xxa@mail.iap.ac.cn A profound knowledge of cloud structure is undoubtedly required for furthering our understanding of cloud climate ef- fects, since these effects are highly dependent on the cloud structure. Unfortunately, cloud profiles are poorly under- stood at present and remain a primary source of uncertainty in global weather and climate studies (Stephens, 2005). This is mainly because the observation and modeling of cloud pro- files is a challenging task, due to the diversity and complexity of cloud distributions, and it is therefore unsurprising that in- consistency exists among different observational and model datasets. To facilitate the use of satellite data to evaluate mod- els in a consistent way, the CFMIP community developed an integrated satellite simulator, COSP (Bodas-Salcedo et al., 2011). By simulating the observations of several satellite- borne active and passive sensors, COSP enables quantitative evaluation of clouds, humidity and precipitation processes in many types of numerical models, from high-resolution mod- els (∼1 km resolution) to coarse-resolution models. Another advantage of COSP is that it facilitates model intercompar- © Institute of Atmospheric Physics/Chinese Academy of Sciences, and Science Press and Springer-Verlag Berlin Heidelberg 2017