Single column models are the "models of choice" for examining cloud affects on climate. They can be used as a stand-alone single column model or as a component of full general circulation climate and forecast models. In recent years several groups have incorporated complex bin-resolving microphysics in single-column models. This approach, while be computationally attractive, is limited by the fact that single-column models do not provide an explicit representation of cloud-scale vertical velocities which are needed to determine cloud supersaturations correctly, and droplet residence times which is important to hydrometeor growth. However, if cloud-scale vertical velocity spectra can be parameterized, a modified microphysics/cloud dynamics scheme for use in single-column models can be created.
An assumption that the spectrum of vertical velocity is Gaussian has been made in the past. Using the RAMS model developed at Colorado State University configured as a LES/CRM, we investigate the validity of this Gaussian assumption. We derive the probability density function (PDF) of resolved vertical velocity for two cloud systems: Fog and Cirrus. The impact of turbulent intensity, cloud stability, wind shear, and microphysical processes are considered.