CLOUD DETECTION USING MEASURED AND MODELED STATE PARAMETERS

Improvements of cloud model parameterizations require basic knowledge of the relationships between measured and modeled meteorological state parameters and the cloud properties in a given volume of air. The uncertainty in the relationships, especially large for cirrus clouds, is exacerbated by the differences between actual soundings and those produced by analyses and by differences in the actual and retrieved cloud properties. With the availability of high temporal and spatial resolution analyses and satellite cloud retrievals, it is imperative to better understand those relationships. This paper examines the statistical dependencies of various cloud properties, such as cloud amount, on temperature, relative humidity, and horizontal and vertical wind velocities. Data from balloon soundings (BBS), surface-based retrievals (SBR), Rapid Update Cycle (RUC) atmospheric profiles, and Visible Infrared Solar-Infrared Split Window Technique (VISST) cloud retrievals derived from GOES multi-spectral satellite images over the ARM SGP central facility are used in this study. A reference set of statistics describing the dependence of cloud properties on state variables is generated from the BBS and SBR data since they provide the most detailed and accurate characterizations. Similar statistics are generated using the BBS and VISST data, the SBR and RUC data, and the VISST and RUC data. Differences in these various statistics provide a measure of the uncertainties in the relationships derived from similar datasets. Comparisons of the RUC-generated and VISST IWP/LWP will also be examined to determine the differences in the predicted and observed IWP/LWP. The statistical dependencies will then be used help interpret the differences. The results of this study should be valuable for improving cloud parameterizations and for the development of improved methods for validating model-generated IWP/LWP.