1. National Weather Association 31 st Annual Meeting 17 October 2006 Cleveland, Ohio Kevin Scharfenberg University of Oklahoma Cooperative Institute for Mesoscale Meteorological Studies and NOAA/National Severe Storms Laboratory, Norman, OK Kevin Scharfenberg University of Oklahoma Cooperative Institute for Mesoscale Meteorological Studies and NOAA/National Severe Storms Laboratory, Norman, OK Dual-pol WSR-88D Radar Algorithms

2. National Weather Association 31 st Annual Meeting 17 October 2006 Cleveland, Ohio The WSR-88D at the National Severe Storms Laboratory, KOUN, was upgraded to add polarization diversity in 2002. KOUN transmits each EM pulse with an orientation “slanted” at a 45 o angle to the horizontal surface. The backscatter is received on TWO receive channels: 1) Receives backscatter with electric field oriented on horizontal plane. 2) Receives backscatter with electric field oriented on vertical plane. / + | _ | _ KOUN The national WSR-88D network will be upgraded to include polarimetric capability beginning in approximately 2009. Background National Weather Association 31 st Annual Meeting 17 October 2006 Cleveland, Ohio

3. National Weather Association 31 st Annual Meeting 17 October 2006 Cleveland, Ohio Joint Polarization Experiment Operational test of KOUN Dual-pol WSR-88D Fall 2002 – Summer 2003 NWS WFO Norman, OK Operational test of KOUN Dual-pol WSR-88D Fall 2002 – Summer 2003 NWS WFO Norman, OK

4. National Weather Association 31 st Annual Meeting 17 October 2006 Cleveland, Ohio Data Preprocessing Smoothing of Base Data – For algorithm performance Noise and Bias Corrections “Texture” Products – For hydrometeor classification Attenuation Corrections Smoothing of Base Data – For algorithm performance Noise and Bias Corrections “Texture” Products – For hydrometeor classification Attenuation Corrections

5. National Weather Association 31 st Annual Meeting 17 October 2006 Cleveland, Ohio Also Produced First… Signal-to-noise ratio (SNR) product – For filtering noise out of other products Specific differential phase shift (K DP ) product – For rainfall accumulation estimation – Producing a good K DP product is difficult!! Signal-to-noise ratio (SNR) product – For filtering noise out of other products Specific differential phase shift (K DP ) product – For rainfall accumulation estimation – Producing a good K DP product is difficult!!

6. National Weather Association 31 st Annual Meeting 17 October 2006 Cleveland, Ohio Hydrometeor Classification Horizontal Reflectivity (Z h ) Correlation Coefficient (  hv ) 6.5 o

7. National Weather Association 31 st Annual Meeting 17 October 2006 Cleveland, Ohio Hydrometeor Classification

8. National Weather Association 31 st Annual Meeting 17 October 2006 Cleveland, Ohio Hydrometeor Classification No echo Light-mod rain Heavy rain “Big drops” Rain/hail Graupel Wet snow Dry snow Crystals Biologicals AP/Clutter Unknown No echo Light-mod rain Heavy rain “Big drops” Rain/hail Graupel Wet snow Dry snow Crystals Biologicals AP/Clutter Unknown

9. National Weather Association 31 st Annual Meeting 17 October 2006 Cleveland, Ohio Precip. Estimation Just as there are empirical relationships between rainfall rate & (horizontal) reflectivity… Just as there are empirical relationships between rainfall rate & (horizontal) reflectivity… R(Z h ) R = (0.171 Z h ) 0.714

10. National Weather Association 31 st Annual Meeting 17 October 2006 Cleveland, Ohio Precip. Estimation Just as there are empirical relationships between rainfall rate and reflectivity… …there are also empirical relationships between rainfall rate and dual-pol variables R(K DP ) R(Z h ) R = (0.171 Z h ) 0.714 R = 44 |K DP | 0.822 sign(K DP )

11. National Weather Association 31 st Annual Meeting 17 October 2006 Cleveland, Ohio Rainfall Estimation Dual-pol rainfall estimators provide the most improvement in heavy rain (hail?) –Use R(K DP ) in heavy rain –Use R(K DP, Z DR ) in moderate rain –Use R(Z h, Z DR ) in light rain We call it the “synthetic” dual-pol QPE algorithm Dual-pol rainfall estimators provide the most improvement in heavy rain (hail?) –Use R(K DP ) in heavy rain –Use R(K DP, Z DR ) in moderate rain –Use R(Z h, Z DR ) in light rain We call it the “synthetic” dual-pol QPE algorithm

12. National Weather Association 31 st Annual Meeting 17 October 2006 Cleveland, Ohio Rainfall Estimation Dual-pol rainfall estimators provide the most improvement near the radar –Use R(synthetic) within 120 km –Use R(K DP ) from 120-200 km range –Use R(Z h ) beyond 200 km We call it the “combined” dual-pol QPE algorithm Dual-pol rainfall estimators provide the most improvement near the radar –Use R(synthetic) within 120 km –Use R(K DP ) from 120-200 km range –Use R(Z h ) beyond 200 km We call it the “combined” dual-pol QPE algorithm

13. National Weather Association 31 st Annual Meeting 17 October 2006 Cleveland, Ohio Conclusions Data pre-processing important Very good estimation of hydrometeor type Improved rainfall accumulation estimation – Especially near the radar, and in heavy rain Algorithms still in their infancy Data pre-processing important Very good estimation of hydrometeor type Improved rainfall accumulation estimation – Especially near the radar, and in heavy rain Algorithms still in their infancy

14. National Weather Association 31 st Annual Meeting 17 October 2006 Cleveland, Ohio Conclusions Implications: – Fewer flash flood warning false alarms – Reliable hail detection – High resolution precip typing in winter storms – Much cleaner data displays More on the rainfall accumulation aspect: – Tomorrow at 9:15 am Implications: – Fewer flash flood warning false alarms – Reliable hail detection – High resolution precip typing in winter storms – Much cleaner data displays More on the rainfall accumulation aspect: – Tomorrow at 9:15 am

15. National Weather Association 31 st Annual Meeting 17 October 2006 Cleveland, Ohio Questions? Kevin.Scharfenberg@noaa.gov Questions? Kevin.Scharfenberg@noaa.gov Thank you for listening!