1. Analysis of microphysical data in an orographic environment to evaluate a polarization radar-based hydrometeor classification scheme Sabine Göke, David M. Plummer Department of Atmospheric Sciences, University of Illinois, Urbana- Champaign, IL Scott M. Ellis, and Jothiram Vivekanandan National Center for Atmospheric Research, Boulder, CO 4 th ERAD Conference, Barcelona, Spain, 18 - 22 Sept. 2006

2. Conceptual Model Medina and Houze, QJRMS 2003 Houze and Medina, JAS 2005 Orographic precipitation mechanisms (“wet” MAP and IMPROVE II)

3. Hydrometeor Identification Vivekanandan et al., BAMS 1999 Irreg. crystals Dry snow Wet snow Graupel Rain Ground clutter Alps Reflectivity

4. Riming versus Aggregation (Hobbs, 1974)1 mm (Straka et al., JAM 2000) Supercooled droplets

5. Matching Repeat within [T –  T, T +  T] Horizontal distance: 1 km Vertical Distance: 250 m  T = 90 sec, 150 sec Time T Position: lat, lon, altitude Wind: u, v, w Time T -  T Shortest distance

6. Comparison (Aggregates)

8. Comparison (Rimed particles)

10. Future Steps 1.Finishing cataloging all matched data. 2.Fine tuning the algorithm for orographic environment (in collaboration with Scott Ellis and Vivek). 3.Determining the uncertainty of the hydrometeor classification algorithm output. 4.Using more than one radar volume to classify hydrometeor types. 5.Testing the conceptual model as proposed by Houze (in collaboration with Bob Houze and his research group).