Abstract. Cloud cover estimates of single-layer shallow cumuli obtained from narrow field-of-view (FOV) lidar-radar and wide-FOV Total Sky Imager (TSI) data are compared over an extended period (2000–2017 summers) at the established United States Atmospheric Radiation Measurement mid-continental Southern Great Plains site. We quantify the impacts of two factors on hourly and sub-hourly cloud cover estimates: 1) instrument-dependent cloud detection and data merging criteria, and 2) FOV configuration. Popular enhanced observations at this site combine the advantages of the ceilometer, micropulse lidar (MPL) and cloud radar in merged data products, and are used to calculate temporal cloud fractions (CF). Sky images provide the spatial fractional sky cover (FSC) within the visible sky dome. To assess the impact of the first factor on CF obtained from the merged data products, we consider two additional sub-periods (2000–2010 and 2011–2017 summers) that mark significant instrumentation and algorithmic advances in the cloud detection and data merging. We demonstrate that CF obtained from ceilometer data alone and FSC obtained from sky images provide the most similar and consistent cloud cover estimates: bias and root-mean-square difference (RMSD) are within 0.04 and 0.12, respectively. Whereas CF from merged MPL-ceilometer data provides the largest estimates of the mean cloud cover: about 0.12 (35 %) and 0.08 (24 %) greater than FSC for the first and second sub-periods, respectively. CF from merged ceilometer-MPL-radar data has the strongest sub-period dependence with a bias of 0.08 (24 %) compared to FSC for the first sub-period and shows no bias for the second sub-period. To quantify the FOV impact, a narrow-FOV FSC is derived from the TSI images. We demonstrate that FOV configuration does not modify the bias, but impacts the RMSD (0.1 hourly, 0.15 sub-hourly). In particular, the FOV impact is significant for sub-hourly observations, where 41 % of narrow- and wide-FOV FSC differ by more than 0.1. A new "quick-look" tool is introduced to visualize impacts of these two factors through integration of CF and FSC data with novel TSI-based images of the spatial variability in cloud cover.;
