1. 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. 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 2007. BackgroundBackground

2. Advantages of Polarimetric Radar Better rainfall estimates Ability to classify hydrometeor type Better quality control of radar data Better rainfall estimates Ability to classify hydrometeor type Better quality control of radar data

3. New Polarimetric Radar Products Z DR Differential Reflectivity  HV Cross-correlation coefficient  DP Differential phase shift Z DR Differential Reflectivity  HV Cross-correlation coefficient  DP Differential phase shift Base Products Derived Products R (syn) Rainfall estimation using Z H, Z DR, K DP HCAHydrometeor Class- ification Algorithm K DP Specific differential phase shift R (syn) Rainfall estimation using Z H, Z DR, K DP HCAHydrometeor Class- ification Algorithm K DP Specific differential phase shift

4. Joint Polarization Experiment Test engineering design of KOUN Determine quality of KOUN data Collect verification data sets for HCA and several experimental rainfall algorithms Deliver and use KOUN data in WFO Norman Obtain feedback from forecasters Test engineering design of KOUN Determine quality of KOUN data Collect verification data sets for HCA and several experimental rainfall algorithms Deliver and use KOUN data in WFO Norman Obtain feedback from forecasters

5. Data delivery Event driven May 2002 through March 2003 “Intense observation period” 19 April to 13 June 2003 JPOLE Operational Demonstration at NWS Norman, Oklahoma Photos Courtesy: (L) Mike Magsig, CIMMS/WDTB; (R) Steve Kruckenberg, NOAA/NWS Norman, Oklahoma NWS Norman operations area - 9 May 2003 tornadoes “Overall, I think the (operational demonstration) was a wonderful success” – NWS Norman Forecaster

6. Differential Reflectivity (Z DR ) Raindrop shapes in equilibrium

7. Differential Reflectivity (Z DR )

8. Reflectivity (Z H ) Similar reflectivity, different drop size distributions! Hail !!

9. Specific Differential Phase (K DP ) Is influenced by anisotropic hydrometeors In heavy rain: better estimator of rates than Z H Provides a good estimate of liquid water in a rain/hail mixture Indicates the onset of melting With Z H can detect hail Is influenced by anisotropic hydrometeors In heavy rain: better estimator of rates than Z H Provides a good estimate of liquid water in a rain/hail mixture Indicates the onset of melting With Z H can detect hail

10. Specific Differential Phase (K DP ) Reflectivity (Z H ) Heavy rain is apparently mixed with the hail. Specific Differential Phase (K DP )

11. Cross-correlation Coefficient (  HV ) Variety of hydrometeor types Mixture of liquid and frozen hydrometeors Hydrometeors with irregular shape Wide distribution of hydrometeor orientation Presence of large hail Variety of hydrometeor types Mixture of liquid and frozen hydrometeors Hydrometeors with irregular shape Wide distribution of hydrometeor orientation Presence of large hail Decreases in  HV may indicate:

12. Correlation Coefficient (  HV ) Reflectivity (Z H ) SNOW ~0.85-1.00 CLUTTER ~0.5-0.85 CHAFF ~0.2-0.5 Cross-correlation Coefficient (  HV )

13. Hydrometeor Classification Algorithm Partitions in the Z H, Z DR Space into Regions of Hydrometeor Types

14. Hydrometeor Classification Algorithm Weighting Function for Moderate Rain W MR (Z H, Z DR )

15. Hydrometeor classification algorithm Reflectivity before quality control Reflectivity after quality control Hydrometeor Classification Algorithm Hail Light Rain Moderate Rain Heavy Rain Birds/Insects AP Big Drops Misclassification rate 10 dB Misclassification rate ~ 5% when SNR > 5 dB Misclassification rate 10 dB Misclassification rate ~ 5% when SNR > 5 dB

16. Hail Detection JPOLE hail observations: JPOLE hail observations: - 20+ days, 100+ hrs - 20+ days, 100+ hrs Two hail chase cars: Two hail chase cars: - 30 Apr-12 Jun 2003 - 30 Apr-12 Jun 2003 - 5 days, 28 hours - 5 days, 28 hours Supplemental data: Supplemental data: - Storm Data - Storm Data - Public, media, and - Public, media, and spotter reports of spotter reports of hail < 3 / 4 ” diameter hail < 3 / 4 ” diameter

17. Improved Hail Detection and Prediction This storm was producing 5 1 / 4 inch diameter hail! Hydrometeor classification alg. Cross correlation coefficient (  HV ) Horizontal reflectivity (Z H ) Differential reflectivity (Z DR ) Hail

18. More Precise Hail Information Q: Is it hailing at this location? A: NO! Polarimetric Hydrometeor Classification Algorithm WSR-88D Reflectivity

19. Improved Hail Detection and Prediction 3.5 inch diameter hail at Hawley, Oklahoma 30 April 2003 Photo © 2003 Gary Lillie, Hawley, Oklahoma Volunteer Fire Department Result: Polarimetric radar increased forecasters’ precision, lead time, and confidence in hail forecasts, while decreasing false alarms. SCIT-Based HDAPolarimetric HCA POD0.880.94 FAR0.390.08 CSI0.560.86

20. Tornado Debris Detection Reflectivity (Z H )Radial Velocity (V R ) Differential Reflectivity (Z DR ) Correlation coefficient (  HV ) Result: Increased forecaster confidence a damaging tornado was ongoing. Severe weather statements issued with enhanced wording.

21. Wet Microbursts Reflectivity (Z H ) Sp. Differential Phase (K DP ) Differential Reflectivity (Z DR )

22. Beam Attenuation – Looking down a squall line Reflectivity (Z H ) Cross-correlation coefficient (  HV ) Differential Reflectivity (Z DR )

23. Beam Attenuation – Normal to a squall line! Reflectivity (Z H ) Cross-correlation coefficient (  HV ) Differential Reflectivity (Z DR )

24. Flash Flood Warnings & Rainfall Accumulation NWS Flash Flood Guidance National Weather Service Arkansas-Red Basin River Forecast Center Tulsa OK ISSUED 0153 PM CDT FRI OCT 18 2002 1HR 3HR 6HR :COUNTY NAME ==== === ==== ==================== 2.4/ 2.7/ 3.3 :ALFALFA 3.0/ 3.6/ 4.5 :ATOKA 2.6/ 3.3/ 4.5 :BECKHAM 2.3/ 2.7/ 3.5 :BLAINE 2.7/ 3.2/ 4.0 :BRYAN “Traditional” R(Z) estimate Polarimetric R(K DP ) estimate Oklahoma mesonet totals and radar rainfall estimates: 14 UTC 18 October 2002 to 14 UTC 20 October 2002

25. Flash Flood Warnings & Rainfall Accumulation One hour point measurements: Radar estimates vs. gages

26. Flash Flood Warnings & Rainfall Accumulation Spring hail cases Cold season stratiform rain Bias of radar areal rainfall estimates Result: Higher confidence, fewer false alarms for flash flooding during JPOLE

27. KOUN Z H at 8.5 o – 8 May 2003 2149 UTC

28. 4 2 3 1 Z DR = Z H - Z V KOUN Z DR at 8.5 o – 8 May 2003 2149 UTC 20,000 ft 8,500 ft 7,000 ft 15,000 ft 9,000 ft

29. KOUN 4 2 3 1 Z H at 8.5 o – 8 May 2003 2149 UTC 15,000 ft 4 2 3 1 KOUN 20,000 ft 8,500 ft 7,000 ft 15,000 ft 9,000 ft

30. 16.5 o – 8 May 2003 2151 UTC Z DR ZHZH BWER summit22,000 ft

31. 2141 UTC A A Z Z Z DR  HV 2333 UTC B B Z Z Z DR  HV Winter Weather Warnings

32. OKLAHOMA TEXAS KOUN T 1 2 3 4 5 6 7 8 9 N 40 mi 64 km More Lead Time Provided Result: Change in polarimetric radar data prompted forecasts of sleet/snow mixture to be changed to reflect more snow and higher accumulations. Forecaster situation awareness enhanced and lead time increased.

33. Summary – Polarimetric Radar MORE DATA!! EnhanceSituationAwareness OverwhelmForecaster