Sabine Göke, David M. Plummer
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Transcript of Sabine Göke, David M. Plummer
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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
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Conceptual Model
Medina and Houze, QJRMS 2003Houze and Medina, JAS 2005
Orographic precipitation mechanisms
(“wet” MAP and IMPROVE II)
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Hydrometeor Identification
Vivekanandan et al., BAMS 1999
Irreg. crystals
Dry snow
Wet snow
Graupel
Rain
Ground clutter
AlpsReflectivity
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Riming versus Aggregation
(Hobbs, 1974) 1 mm
(Straka et al., JAM 2000)
Supercooled droplets
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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
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Comparison (Aggregates)
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Comparison (Rimed particles)
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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).