Investigating Cirrus cloud behavior using A-train and geostationary satellite data

Knowledge of how the large-scale dynamics are coupled with microphysical properties is necessary for parameterizing cirrus in climate models. In this study, the synergy of the CloudSat and CALIPSO instruments is exploited for identifying cirrus. Mesoscale-size cirrus events are defined using a combined CloudSat-CALIPSO cloud mask and temperature data for one year in the Atlantic basin. I!Cirrus events are tracked using an algorithm, which follows patterns of 6.2?m brightness temperature in consecutive water vapor images. NCEP/NCAR reanalysis data is used to determine the environments in which the cirrus events exist. The cirrus events are sorted based on pressure- radar reflectivity patterns using a k-means cluster algorithm. The six clusters that are identified include Single-Layer Cirrus, Thick Cirrus and Low Cloud, High Cirrus, Deep Cirrus, Mixed Cloud and Thin Cirrus, and Low Cloud. A cluster algorithm is also applied to the large-scale dynamics to determine the basic synoptic states for cirrus. This analysis results in six dynamic clusters including Deep Wave Cirrus, Developing Tropical Cirrus, Subtropical Jet Cirrus, Zonal Jet/Stationary Front Cirrus, Dissipating Tropical Cirrus, and Ridge Crest Cirrus. We find that large-scale dynamic types do not necessarily predetermine the cirrus cloud properties.