Monday, 3 June 2002: 10:45 AM
Parameterizing the moments of cirrus cloud property distributions in terms of cloud layer thickness and layer mean temperature using cloud microphysical properties derived from ARM data
Representing cirrus clouds correctly in large scale atmospheric models requires an understanding of how predicted water contents are distributed in terms of cloud fraction and particle size within a grid box. Parameterizations require that these characterizations be made in terms of the variables predicted by the model. These resolved scale variables typically represent space and time scales that are large with respect to those scales that normally govern the evolution of cirrus cloud elements. Typically, for a given large scale situation a broad continuum of cirrus cloud properties are observed. However, most parameterizations derived from data predict only a single moment of this continuum, namely the mean values. Using 4 continuous years of cirrus cloud property retrievals derived from ARM millimeter cloud radar data, a parameterization of the moments of cirrus cloud bulk microphysical properties is derived. Building on the results of earlier work, this parameterization is couched in terms of the layer mean temperature and layer thickness and returns the statistical moments of a probability distribution so that the characteristics of the variability can be understood from the parameterization. While this parameterization can be used as a predictive tool, our primary purpose is to present a characterization of the relationships found between the derived cloud properties and the large scale meteorology for model validation purposes.