1. National Lab for Remote Sensing and Nowcasting Dual Polarization Radar and Rainfall Nowcasting by Mark Alliksaar

2. National Lab for Remote Sensing and Nowcasting Dual Polarization can potentially improve rainfall nowcasting in three ways: 1. Radar attenuation can be corrected using the polarimetric parameter Φ dp. This improves rain rate estimates and QPE derived from reflectivity factor Z. 2. Rain rate and QPE can also be derived directly from K dp instead of Z. 3. Hail identification. In conventional QPE estimates, hail contamination is always a possibility in convective situations.

3. National Lab for Remote Sensing and Nowcasting Rain rate estimation from K dp R is rain rate in mm/hr b,c are empirical constants R derived from K dp is more accurate because K dp not subject to attenuation

4. National Lab for Remote Sensing and Nowcasting Attenuation correction using Φ dp ΔZ is attenuation correction r is range along radial α is an empirical constant

5. Cloud Physics & Severe Weather Research Division King City August 2, 2005 (day of Air France accident at YYZ)

6. Cloud Physics & Severe Weather Research Division King City Corrected Radar Reflectivity 0.4° PPI

7. Cloud Physics & Severe Weather Research Division King City Enhanced View near YYZ Corrected Radar ReflectivityRadar Reflectivity

8. Cloud Physics & Severe Weather Research Division King City Attenuation Calculation (Radial 200.5°) Z φ dp Z corr Cloud Boundary

9. Cloud Physics & Severe Weather Research Division King City Validation with Buffalo NEXRAD Frequency Histograms of Reflectivity near YYZ

10. Cloud Physics & Severe Weather Research Division King City August 19, 2005 (flash flood event in North Toronto)

11. Cloud Physics & Severe Weather Research Division King City Radar Reflectivity Corrected Reflectivity Z CORR Reflectivity Z

12. Cloud Physics & Severe Weather Research Division King City One Hour Precipitation Accumulation (Z) Enhanced View in North Toronto Rain Accumulation (Z)

13. Cloud Physics & Severe Weather Research Division King City One Hour Precipitation Accumulation (Zc) Enhanced View in North Toronto Rain Accumulation (Z CORR )

14. Cloud Physics & Severe Weather Research Division King City Improved QPE Using Zcorr (Location near MSC HQ in Downsview)

15. Cloud Physics & Severe Weather Research Division King City Summer Applications: Hail Detection

16. National Lab for Remote Sensing and Nowcasting iParCA (interactive Particle Classification Algorithm) developed by Environment Canada, King City research group input: 6 polarimetric radar products (Z h, Z dr, ρ HV, K dp as well as standard deviations of Z h and Z dr ) output: hydrometeor type at each range gate determined by fuzzy logic routines

17. National Lab for Remote Sensing and Nowcasting an example of a fuzzy logic membership function for moderate rain

18. National Lab for Remote Sensing and Nowcasting iParCA GUI interface

19. Cloud Physics & Severe Weather Research Division King City Comparison of URP and iParCA Hail Algorithms URP Hail Algorithm: Related to storm structure Based on vertical integration of cell’s reflectivity profile Disadvantages: –Difficult to quantify –Exact hail location not specified iParCA Hail Algorithm: Measurements directly related to hail properties iParCA Fuzzy Logic Thresholds: Z : 50 – 75 dBZ Z DR : 0 – 1 dB ρ HV :0.80 – 0.90 φ DP : abrupt changes

20. Cloud Physics & Severe Weather Research Division King City Grimsby Hailstorm July 23 rd 2008 – 0140 Z Radar ReflectivityEnhanced View

21. Cloud Physics & Severe Weather Research Division King City Grimsby Hailstorm July 23 rd 2008 – 0140 Z ρ HV φ dp VIL

22. Cloud Physics & Severe Weather Research Division King City Grimsby Hailstorm July 23 rd 2008 – 0140 Z Location201/94202/97.5 Z corr 46.18 (39.35) 50.42 (49.21) Z DRcorr 1.050.14 ρ HV 0.930.82 1 1 2 2 Hail Pixel Map  : iParCA  : URP

23. Cloud Physics & Severe Weather Research Division King City Grimsby Hailstorm July 23 rd 2008 – 0230 Z Hail Pixel Map  : iParCA  : URP

24. Cloud Physics & Severe Weather Research Division King City Detection Statistics of URP vs iParCA Hail Algorithms 74 cells examined for 21 days during the summers of 2005- 2008. Cases were selected by meteorologist M. Leduc targeting those cells which may contain high impact weather based on reflectivity patterns. iParCAURP Hit6351 Miss416 False Alarm53 Correct Negative24 Total Skill Scores 74 CSI8873 Bias1.5-19.4 POD9476 FAR76

25. Cloud Physics & Severe Weather Research Division King City Summary Statistics of URP vs iParCA Hail Algorithms 30 cells on 10 of the study days were examined in depth to assess the physical reasoning for the differences in the algorithm performance. In 21/30 cells iParCA was subjectively determined to be better in terms of the quality of the information Reasons for iParCA superiority: –Geometry/Timing 4 cases –Attenuation Correction 5 cases –Dual Polarization Discrimination11 cases –Location 5 cases iParCA Hail Product superior for 70% of cells studied

26. National Lab for Remote Sensing and Nowcasting Questions?