87th AMS Annual Meeting

Saturday, 13 January 2007
Validation of a Polarimetric Radar Hail Detection Algorithm Using a Fuzzy Logic Approach
Alicia M. Dale, Texas A&M University, College Station, TX; and L. D. Carey and V. Chandrasekar

Identification and nowcasting of severe (³ 1.9 cm) hail in convection is a high priority for the National Weather Service (NWS) because of the associated property damage.  Traditional radar reflectivity-based techniques are often insufficient to detect hail signatures and hence forecast potentially dangerous storms.  Because of their added remote sensing capabilities, polarimetric radars are useful in differentiating between hydrometeors types (e.g., rain vs. hail).  The NWS Weather Surveillance Radar 1988 Doppler (WSR-88D) network is currently planned for an upgrade to polarimetric capability by about 2010.  As a result, ongoing research is aimed at further improving interpretation of polarimetric radar information from an operational perspective and validating techniques.  For this study, polarimetric data were obtained from the Colorado State University CSU-CHILL radar, which is an NSF sponsored research facility located on the Front Range of Colorado.  Three case studies from April 19th, April 20th, and May 10th of 2005 were chosen and compared to hail reports from the Community Collaborative Rain, Hail & Snow Network (CoCoRaHS) project.  CSU-CHILL data [horizontal reflectivity (ZH), differential reflectivity (ZDR), linear depolarization ratio (LDR), specific differential phase (KDP), and the co-polar correlation coefficient (ρhv)] were gridded and then a hydrometeor identification program was applied to the gridded data.  A fuzzy logic technique was used to differentiate between hydrometeors (e.g., rain, small hail, large hail) at low levels.  Results from the fuzzy logic algorithm were then compared to ground reports from CoCoRaHS volunteers.  Findings from the study will be presented at the conference.

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