This study compares CL51 ceilometer observations made at Scott Base, Antarctica, with statistics from the ERA5, JRA55, and MERRA2 reanalyses. To enhance the comparison we use a lidar instrument simulator to derive cloud statistics from the reanalyses which account for instrumental factors. The cloud occurrence in the three reanalyses is slightly overestimated above 3km, but displays a larger underestimation below 3 km relative to observations. Unlike previous studies, we see no relationship between relative humidity and cloud occurrence biases, suggesting that the cloud biases do not result from the representation of moisture. We also show that the seasonal variation of cloud occurrence and cloud fraction, defined as the vertically integrated cloud occurrence, are small in both the observations and the reanalyses. We also examine the quality of the cloud representation for a set of synoptic states derived from ERA5 surface winds. The variability associated with grouping cloud occurrence based on synoptic state is much larger than the seasonal variation, highlighting synoptic state is a strong control of cloud occurrence. All the reanalyses continue to display underestimates below 3km and overestimates above 3km for each synoptic state. But, the variability in ERA5 statistics matches the changes in the observations better than the other reanalyses. We also use a machine learning scheme to estimate the quantity of super-cooled liquid water cloud from the ceilometer observations. Ceilometer low-level super-cooled liquid water cloud occurrences are considerably larger than values derived from the reanalyses, further highlighting the poor representation of low-level clouds in the reanalyses.