Cloud parameterizations in large-scale models often try to predict the amount of sub-grid scale variability in cloud properties to address the significant non-linear effects of radiation and precipitation. Statistical cloud schemes provide an attractive framework to self-consistently predict the variability in radiation and microphysics but require accurate predictions of the width and asymmetry of the distribution of cloud properties.

Data from the Atmospheric Radiation Measurement (ARM) program are used to assess the variability in boundary layer cloud properties for a well-mixed stratocumulus observed at the Oklahoma ARM site during the March 2000 Intensive Observing Period. Cloud boundaries, liquid water content, and liquid water path are retrieved from the millimeter wavelength cloud radar and the microwave radiometer. Satellite observations provide a complementary assessment of the horizontal variability in cloud optical depth. This data is used to test predictions of variability from a statistical cloud scheme with prognostic variance that is being tested in the GFDL global atmospheric model.