Two cases of boundary layer cloud from the ARM 2000 Cloud IOP are simulated using the CIMMS large eddy simulation (LES) model. Both occur after the passage of cold fronts and contain cloud regions colder than 0 degree Celsius. The University of North Dakota Citation aircraft flew on both days and provides microphysical data to verify the LES results. The LES is initialized with thermodynamic data from aircraft and aerosol spectra inferred from aircraft microphysical measurements. Large scale vertical motion appropriate for each case is imposed.

The first case (3 March) is a shallow layer of stratocumulus topping a well-mixed boundary layer, with much of the cloud above the 0 degree Celsius level. The LES produces a dynamic response typical of such cases and a vertical distribution of liquid water which correlates well with observations.

The second case (18 March) is somewhat atypical for LES study: a deep cloudy region occurring in a boundary layer that is stably stratified. A thin, shear-driven well-mixed layer is produced near the surface, and the stratification is less stable near cloud top, where radiative cooling acts to drive thin eddies. These dynamics are reflected in the vertical velocity variance and TKE fields, with much of the kinetic energy is concentrated just above the surface and near cloud top. The model produces significant surface drizzle and a double maxima in liquid water content (LWC), with peaks near 700 m and just below cloud top. The LWC profiles and droplet spectra appear to agree reasonably well with the aircraft data.

Large domain 2D runs will provide a valuable dataset to study the spatial variability of cloud structures. These data will be applied in concert with that from the ARM Millimeter-wave Cloud Radar (MMCR) to evaluate the amount of the radiatively important cloud variability that is captured by the the MMCR. Also, the validity of the frozen turbulence assumption in transforming temporal MMCR profiles into spatial cross sections will be evaluated for these cloud types. Ultimately, this information could be used to address issues of sub-grid cloud variability in mesoscale, NWP, and global climate models.