Global climate models typically do not correctly simulate cloudiness associated with midlatitude synoptic systems because coarse grid spacing prevents them from resolving mesoscale dynamics and current parameterizations of subgrid mesoscale processes are inadequate. Comparison of modeled and observed cloud properties averaged over similar regimes (e.g., compositing) aids the diagnosis of simulation errors and identification of meteorological forcing responsible for producing particular cloud conditions. This study uses a k-means clustering algorithm to objectively classify satellite cloud scenes into distinct regimes based on gridbox mean cloud fraction, cloud reflectivity, and cloud top pressure. The spatial domain is the densely instrumented Southern Great Plains site of the Atmospheric Radiation Measurement Program, and the time period is the cool season months (November-March) of 1999-2001. As a complement to the satellite retrievals of cloud properties, lidar and cloud radar data are analyzed to examine the vertical structure of the cloud layers. Meteorological data from the Constraint Variational Analysis is averaged for each cluster to provide insight on the large-scale dynamics and advective tendencies coincident with specific cloud types. Meteorological conditions associated with high and low subgrid spatial variability are also investigated for each cluster. Cloud outputs from a single column model version of the GFDL AM2 atmospheric model forced with observed meteorological boundary conditions were compared to observations for each cluster in order to determine the accuracy with which the model reproduces attributes of specific cloud regimes.