Analysis of microphysical data in an Analysis of microphysical data in an orographic environment to evaluate a orographic environment to evaluate a polarization radar-based hydrometeor polarization radar-based hydrometeor

classification schemeclassification scheme

Sabine Göke, David M. PlummerSabine Göke, David M. PlummerDepartment of Atmospheric Sciences, University of Illinois, Urbana-Department of Atmospheric Sciences, University of Illinois, Urbana-

Champaign, ILChampaign, IL

Scott M. Ellis, and Jothiram VivekanandanScott M. Ellis, and Jothiram VivekanandanNational Center for Atmospheric Research, Boulder, CONational Center for Atmospheric Research, Boulder, CO

44thth ERAD Conference, Barcelona, Spain, 18 - 22 Sept. 2006 ERAD Conference, Barcelona, Spain, 18 - 22 Sept. 2006

Conceptual Model

Medina and Houze, QJRMS 2003Houze and Medina, JAS 2005

Orographic precipitation mechanisms

(“wet” MAP and IMPROVE II)

Hydrometeor Identification

Vivekanandan et al., BAMS 1999

Irreg. crystals

Dry snow

Wet snow

Graupel

Rain

Ground clutter

AlpsReflectivity

Riming versus Aggregation

(Hobbs, 1974) 1 mm

(Straka et al., JAM 2000)

Supercooled droplets

Dry low density graupel - small hail 20 - 35 _-0.5 - 1 > 0.95 0 - 0.5 < -25Dry snow (aggregates) < 35 0 - 1 > 0.95 0 - 0.2 < -25

)( 1 kmK DP)(dBZZ )(dBZ DR HV )(dBLDR

Matching

Repeat within [T – T, T + T]Horizontal distance: 1 kmVertical Distance: 250 mT = 90 sec, 150 sec

Time TPosition: lat, lon, altitudeWind: u, v, w

Time T - T

Shortest distance

Comparison (Aggregates)

Comparison (Rimed particles)

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).