1. Understanding Cirrus Cloud Behavior using A-Train and Geostationary Satellite and NCEP/NCAR Reanalysis Data Betsy Dupont and Jay Mace, University of Utah 13 th AMS Conference on Cloud Physics, Portland, OR, 2010 Approach: Consider the A-Train constellation as a single synergistic observational suite. Augment the instantaneous view of A-Train with the temporal view from geostationary.

2. Clustering of Cirrus Events Cirrus defined using CloudSat/CALIPSO cloud mask and ECMWF temperature data 100km cirrus events from the Atlantic Basin in 2007 K-means cluster analysis based on P-dBZ patterns (Zhang et al., 2007)

3. Resulting Clusters 123 564

4. Vertical Distribution of IWC Water Content (IWC) in Cirrus Clusters

5. Given the cloud distributions, what are the large-scale dynamics? Deep CirrusCirrus with boundary layer cloud Associated with upper- level ridge Associated with surface warm front

6. Question: What are the relationships between cirrus properties (and their temporal change) and the large-scale dynamics? Next: Track Cirrus in time following Soden (1998) approach Case study of a dissipating cirrus event

7. Cluster 1 Cirrus Dissipating Event

8. Large-Scale Meteorology

9. Case study: a dissipating cirrus event

10. Cloud Level Dynamics NCEP/NCAR Reanalysis 18Z back in timeforward in time Evolution

11. Summary: Combining A-Train with Geostationary time series allows us to place events within a temporal context adding knowledge of changes in the cloud field. Compositing with large-scale reveals relationships between the cloud field and meteorology. Work in Progress: Do models simulate the relationship between large-scale dynamic and cirrus evolution? What are the microphysical processes that control the differences in growing and dissipating cases?