1. Rainfall Monitioring Using Dual-polarization Radars Alexander Ryzhkov National Severe Storms Laboratory / University of Oklahoma, USA

2. Polarimetric weather radar has enormous potential for - Accurate rainfall estimation - Classification of different types of radar echo including - hail detection, - rain / snow discrimination, - identification of nonmeteorological scatterers (ground /clutter / AP, insects, birds, chaff, etc.), - tornado detection - Data quality improvement - Microphysical parametrization of the storm-scale numerical models Benefits of a dual-polarization weather radar

3. Upcoming polarimetric upgrade of the NEXRAD radars Based on the results of two decades of research studies and the demonstration project referred to as Joint POLarization Experiment (JPOLE), the US National Weather Service made a decision to add polarimetric capability to all existing operational WSR-88D radars starting in 2008 – 2009. Similar upgrade is planned by national weather services of Canada and several European countries.

4. One-polarization Radar

5. Dual-polarization Radar

6. Polarimetric radar variables 1.Differential reflectivity Z DR 2.Total differential phase Φ DP 3.Specific differential phase K DP 4.Cross-correlation coefficient ρ hv

7. Differential reflectivity Z DR ZvZv ZhZh Z DR is a measure of the median raindrop diameter Z DR is efficient for discrimination between rain and snow Z DR depends on the particle size, shape, orientation, and density

8. time H H V V Φ DP Differential phase Φ DP Φ DP is not affected by radar miscalibration, attenuation, and partial beam blockage

9. Specific differential phase K DP - radial derivative of differential phase b = 0.75 – 0.85 K DP is less affected by DSD variations at higher end of the raindrop spectrum than Z K DP is less affected by the presence of hail than Z K DP is immune to radar miscalibration, attenuation, and partial beam blockage K DP can be used for calibration of Z according to consistency between Z, Z DR, and K DP in rain

10. Cross-correlation coefficient ρ hv H and V are complex voltages and P h and P v are powers of radar signals at orthogonal polarizations ρ hv is an important parameter for data quality assessment and classification of radar echoes ρ hv is high (close to 1) for rain and dry snow, moderately low for hail and wet snow in the melting layer, and very low for nonmeteorological scatterers (ground clutter /AP, biological scatterers, chaff, and tornado debris)

11. Radar echo classification and data quality control Accurate rainfall estimation is contingent upon reliable classification of radar echoes and unbiased measurements of key radar variables Ten classes of radar echo will be identified with the classification algorithm implemented on the polarimetric prototype of the WSR-88D radar 1.Ground clutter / AP 2.Biological scatterers 3.“Big” drops 4.Light and moderate rain 5.Heavy rain 6.Rain / hail 7.Graupel 8.Wet snow (melting layer) 9.Dry snow 10. Snow crystals

12. INSECTS BIRDS Data Quality: Identification & Filtering of Nonmeteorological Echo AP – Ground Clutter / Anomalous Propagation BS – Biological Scatterers (insects, birds) RA – Rain Classification Legend 99% of nonweather echo is correctly identified if SNR > 10 dB

13. Hydrometeor Classification: Hail Detection 14 May 2003 Classification Legend HA – Hail / Rain HR – Heavy Rain MR – Moderate Rain LR – Light Rain BD – ‘Big Drops’ BS – Biological Scatterers AP – Ground Clutter/ Anomalous Propagation

14. Hail Detection: A Summary of Validation during JPOLE Hail Detection Statistics - Conventional Hail Detection Algorithm POD=88%, FAR=39%, CSI=0.56 - Polarimetric Hail Detection Algorithm POD=100%, FAR=11%, CSI=0.89 Conventional method provides probability of hail in a storm, whereas polarimetric algorithm determines location of hail within the storm

15. Bright band detection. January 5, 2005. El = 5.18º

16. Bright band detection. January 5, 2005. El = 0.43º

17. A tower Partial beam blockage of the radar beam

18. B B A A R(Z) R(K DP ) 30 km Lake Texoma Lake Texoma Data Quality: Partial Beam Blockage 48-hour rain accumulation map from A.conventional R(Z) relation B.polarimetric R(K DP ) relation 18 – 20 October 2002

19. Data Quality: Correction of Radar Reflectivity for Attenuation

20. KTLX KOUN CIM KINX KFDR KVNX KOUN KOUN 100 km range ring ARS rain gauge micronet Polarimetric radars WSR-88D radars EVAC rain gauge piconet Mesonet Gages JPOLE Instrumentation and Dataset 98 events have been observed during JPOLE 24 rain events (50 hours) are validated with the ARS micronet (42 gages) 22 rain events (83 hours) are validated with the Mesonet (108 gages)

21. Point Estimates Polarimetric Rainfall Estimation Areal Estimates

22. Spring hail cases Cold season stratiform rain The bias in areal rain rates estimated from radar using conventional and polarimetric algorithms Polarimetric Rainfall Estimation

23. The Quality of Rainfall Estimation as a Function of Range “Cold season” events“Warm season” events

24. Sensitivity of rainfall estimate to DSD variations

25. Conventional and polarimetric rainfall estimation algorithms have been validated using 108 Oklahoma Mesonet and 42 ARS Micronet gages during JPOLE. The polarimetric algorithm outperforms the conventional one in terms of bias and RMS error. The RMS error of the one- hour total estimate is reduced 1.7 times for point measurements and 3.7 times for areal rainfall estimates. Most significant improvement is achieved in areal rainfall estimation and in measurements of heavy precipitation (often mixed with hail). The polarimetric method is more robust with respect to radar calibration errors, beam blockage, attenuation, DSD variations, and presence of hail than the conventional R(Z) method. Polarimetric Rainfall Estimation: Summary