1. The Generation of Point Forecasts from Polarimetric Radar Data By Mark Alliksaar National Lab for Remote Sensing and Nowcasting Environment Canada

2. Purpose  develop an algorithm that generates nowcasts of precipitation type for individual points (such as airport terminals) from polarimetric radar data  output: short term nowcast (1 to 2 hour range)

3. Overview of Dual Polarized Radar

4. Z dr : An Example of a Dual-Polarized Radar Product -differential reflectivity (Z dr ): ratio of horizontally to vertically polarized return signal - ranges from 0 for perfect sphere to +5dB for large drops Figure 2 from Beard and Chuang ([1987])

5. Other Dual-Polarized Radar Products Z h : horizontal component of reflectivity (i.e. conventional reflectivity) ρ hv : correlation coefficient the return echo from each range gate is made up of a number of distinct return signals, ρ hv is the correlation between the horizontal and vertical signals for each range gate

6. Other Dual-Polarized Radar Products φ dp : differential phase the phase difference between the horizontally polarized signal’s phase ( φ hh ) and the vertically polarized signal’s phase ( φ vv ) K dp : specific differential phase phase shift per unit range, Δ r

7. Melting Layer in the Atmosphere - melting layer represented in conventional radar by bright band - schematic drawing illustrating the various features of a bright band in Z vs. height coordinates - terminal velocities of hydrometeors in the melting layer Figure 1 from Fabry and Zawadzki ([1994]) Figure 8.3 from Rinehart ([2004])

8. Melting Layer in the Atmosphere - melting layer signature in polarimetric radar: - Z h maxima (traditional bright band) - Z dr maxima - ρ hv minima (strongest melting layer signature)

9. parameter (units) drizzlerainsnowmelting snow hailinsectsbirdsground clutter Z h (dbZ)10 to 20 20 to 55 10 to 40 <4540 to 77 -30 to 15 -10 to 25 0 to 97 Z dr (db)00.5 to 4 0 to 3 -0.5 to 0.5 -3 to 10-2 to 3 ~0 K dp (˚/km) 00 to 10 0 to 2 -1 to 1 ++? ρ hv (unitless) <0.9> 0.95 0.8 to >0.9 5 0.9 to 0.95 0.8?0.9?<0.8 LDR (dB) <-34-27 to -34 -13 to - 18 -20 to -10 Table 10.1 from Rinehart [2004] Precipitation types and Polarimetric Parameters

10. iParCA: (interactive Particle Classification Algorithm) developed by Environment Canada developed by Environment Canada takes 6 polarimetric radar products (Z h, Z dr, ρ hv, K dp and standard deviations of Z h and Z dr ) takes 6 polarimetric radar products (Z h, Z dr, ρ hv, K dp and standard deviations of Z h and Z dr ) uses fuzzy logic to decide which type of precipitation is occurring at each range gate uses fuzzy logic to decide which type of precipitation is occurring at each range gate

11. Example of iParCA membership function

12. How iParCA Uses Membership Functions to Determine Particle Type the aggregation value, or score, for each hydrometeor class is calculated using equation below the aggregation value, or score, for each hydrometeor class is calculated using equation below - Q is Quality vector, currently not used (set to 1) - W is weighting function - P i is truth value of membership function - V is polarimetric input variable (e.g. Z h, Z dr, ρ hv or K dp )

13. How iParCA Uses Melting Layer Heights to Modify Aggregate Score - hydrometeor classifications which do not fit conceptual model of melting layer are disallowed

14. How iParCA Uses Melting Layer Heights to Modify Aggregate Score Slant Range Interval Classes permittedClasses forbidden 0 < R < R bb GC/AP, BS, BD, RA, HR, RH DS, WS, CR, GR R bb < R < R b GC/AP, BS, WS, GR, BD, RA, HR, HR DS, CR R b < R < R t GC/AP, BS, DS, WS, GR, BD, HR CR, RA, HR R t < R < R tt GC/AP, BS, DS, WS, CR, GR, BD, RH RA, HR R > R t DS, CR, GR, RHGC/AP, BS, WS, BD, RA, HR

15. Example of iParCA output Legend: RH: Rain/Hail HR: Heavy Rain RA: Rain BD: Big Drops CR: Snow crystals WS: Wet Snow DS: Dry Snow GR: Graupel BS: Biological/Chaff GC: Ground Clutter UN: unknown Legend: RH: Rain/Hail HR: Heavy Rain RA: Rain BD: Big Drops GR: Graupel CR: Snow crystals WS: Wet Snow DS: Dry Snow BS: Biological/Chaff GC: Ground Clutter UN: unknown

16. CAPPI reflectivity image Corresponding CAPPI reflectivity image

17. Ingredients of Algorithm iParCA (Interactive Particle Classification Algorithm) iParCA (Interactive Particle Classification Algorithm) –particle identification algorithm using dual polarized radar products Cross Correlation Tracker software (CC3) Cross Correlation Tracker software (CC3) –determines motion of radar echoes by examining correlation between subsequent radar images Point Forecast algorithm (PF3) Point Forecast algorithm (PF3) –generates 2 hr nowcast from CC3 output

18. Cross-Correlation Tracker (CC3) developed by Norman Donaldson of EC developed by Norman Donaldson of EC based on early version of MAPLE algorithm (McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation) developed by Bellon and Austin based on early version of MAPLE algorithm (McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation) developed by Bellon and Austin compares two radar images and determines the offset with the maximum correlation between two images compares two radar images and determines the offset with the maximum correlation between two images offset are used to determine the motion field offset are used to determine the motion field

19. Germann and Zawadzki (2002)

20. Point Forecast Algorithm (PF3) - issues 2 hour forecast for a number of selected points in radar field-of-view - motion field input from Cross-Correlation Tracker (CC3) - iParCA field advected using CC3 motions

21. Block Diagram of Algorithm - radar advection algorithm cannot use polarimetric output to derive motion field because polarimetric images are PPI not CAPPI

22. Algorithm Behaviour in a Steady State Atmosphere: the Vertical Effects Problem - how steady state melting layer is depicted in PPI iParCA image - at point A, given echo motion filed depicted, algorithm will predict snow – wet snow – rain – wet snow - snow - this erroneous result is caused by PPI scan interacting with melting layer

23. Algorithm Behaviour in Steady and Non-Steady State Atmospheres - ρ hv image of a steady state atmosphere - notice: ρ hv minima is circular and centered on radar - ρ hv image of a dynamic atmosphere - notice: ρ hv minima is oblong and not centered on radar

24. Algorithm Behaviour in a Non-steady State Atmosphere - cold fronts are steeper than radar beam used by algorithm (0.3˚) - therefore radar beam will intersect cold front resulting in oblong melting layer in iParCA image - warm front not necessarily steeper than radar beam

25. Algorithm Behaviour in a Non-steady State Atmosphere - warm fronts not necessarily steeper than radar beam used by algorithm (0.3˚) - therefore radar beam might not intersect cold front -if radar beam does not intersect warm front, then melting layer of advecting warm front will appear as a slowly expanding circular melting layer centered on radar

26. Methodology for Testing Algorithm point forecasts generated for YYZ (Pearson Airport) from WKR (King City) radar data point forecasts generated for YYZ (Pearson Airport) from WKR (King City) radar data 2 hr nowcast compared to corresponding surface observations from YYZ 2 hr nowcast compared to corresponding surface observations from YYZ performance measures calculated: performance measures calculated: –Average correct forecast –Probability of Detection (POD) –False Alarm Ratio (FAR) –Critical Success Index (CSI) algorithm run for CONVOL and POLPPI radar scans algorithm run for CONVOL and POLPPI radar scans

27. Methodology: - standard performance scores used to rate algorithm performance

28. - CONVOL radar scan -5 minute volume scan - 24 angles measured (0.3˚ to 24.6˚) -Antenna rotation 6 RPM -Most conventional radar products (CAPPI dBZ, etc.) generated from CONVOL -Maximum unambiguous range 250 km -No Doppler filtering - POLPPI radar scan -Antenna speed 1 RPM -Doppler filtering -Only 1 antenna angle (0.3˚) -Maximum unambiguous range 114 km.

29. Methodology: Cases Examined 1. Synoptic snow 2. Synoptic rain with high melting layer 3. Synoptic rain with low melting layer 4. Wet snow (i.e. melting layer at surface) 5. Changing precipitation phase 1.warm front cases 2.cold front cases

30. Results for Synoptic Snow Cases CONVOL nowcasts slightly higher CSI than POLPPI CONVOL nowcasts slightly higher CSI than POLPPI FAR’s generally very high due to ground clutter FAR’s generally very high due to ground clutter CONVOL handles ground clutter better than POLPPI CONVOL handles ground clutter better than POLPPI –CONVOL ground clutter filtering: Fast Fourier Transform –POLPPI ground clutter filtering: Pulse Pair but with Doppler filtering

31. Ground Clutter with Snow iParCA based on CONVOL misidentifies clutter as snow iParCA based on CONVOL misidentifies clutter as snow result: increased FAR in first hour for onset-of- precipitation cases result: increased FAR in first hour for onset-of- precipitation cases

32. Algorithm vs. Persistence in Synoptic Snow Cases algorithm superior in 2 hr nowcasts algorithm superior in 2 hr nowcasts persistence superior in first hour of nowcast persistence superior in first hour of nowcast

33. Results for Synoptic Rain Cases CONVOL and POLPPI: CONVOL and POLPPI: –significantly higher POD with snow –much lower FAR with rain –end result: CSI lower for rain than snow CONVOL outperformed POLPPI on both rain and snow CONVOL outperformed POLPPI on both rain and snow

34. Algorithm vs. Persistence in Synoptic Rain Cases algorithm superior in 2 hr nowcasts algorithm superior in 2 hr nowcasts persistence superior in first hour of nowcast persistence superior in first hour of nowcast

35. Results from Low Freezing Level Cases algorithm performance scores poor algorithm performance scores poor –persistence beat algorithm for 1 and 2 hr nowcasts principal cause: phase misclassification –liquid precipitation misforecast as mixed phase or solid –removal of significant phase misclassification cases resulted in significant improvement

36. Results from Wet Snow Cases algorithm performance scores poor algorithm performance scores poor –persistence beat algorithm for 1 and 2 hr nowcasts principal cause: principal cause: –all cases were selected for significant occurrence of mixed phase precipitation (R-S- or RW-SW-) in observations –iParCA extremely reluctant to identify wet snow –ice pellet problem: 3 cases had ice pellets in verification obs no membership functions for ice pellets no membership functions for ice pellets –removal of significant phase misclassification cases resulted in significant improvement

37. Results from Warm Front Cases algorithm POD scores poor, FAR very good algorithm POD scores poor, FAR very good –persistence (barely) beat algorithm for 1 and 2 hr nowcasts principal causes principal causes –significant phase misclassification in 6 of 9 cases –wet snow problem: wet snow present in verification obs of 4 cases removal of significant phase misclassification cases resulted in significant improvement removal of significant phase misclassification cases resulted in significant improvement

38. Results from Cold Front Cases algorithm performance scores poor algorithm performance scores poor principal causes principal causes –significant phase misclassification in 7 of 9 cases –wet snow problem: wet snow present in verification obs of 7 cases removal of significant phase misclassification cases resulted in significant improvement removal of significant phase misclassification cases resulted in significant improvement

39. Melting Layer Determination Problem - ideal melting layer- typical melting layer depicted by iParCA - wet snow (dark blue) indicates melting layer but wet snow never observed in practice - Why?

40. Melting Layer Determination Problem - examination of all cold and warm front cases (18 in total) revealed that iParCA detected melting layer in only 4 cases - in 11 of these 14 cases, melting layer signature was present on polarimetric products ( ρ hv minima) - in 12 of the 14 cases where ρ hv minima was evident, minima was oblong and not centered on radar, indicating that temperature advection was detectable by iParCA Conclusion: melting layer detection in iParCA needs improvement

41. Wet Snow Problem iParCA image from low freezing level case iParCA image from low freezing level case melting layer is clearly indicated, however few echoes are identified as wet snow (dark blue) even though mixed phase precipitation must be present melting layer is clearly indicated, however few echoes are identified as wet snow (dark blue) even though mixed phase precipitation must be present - why is iParCA biased against wet snow?

42. Wet Snow Problem: Possible Causes 1. Bias Problem: iParCA biased against wet snow in Southern Ontario climactic regime 2. Melting Layer Problem: poor melting layer height determination (see figure left) 3. melting layer too low to be detectable

43. Wet Snow Problem: Investigations iParCA determined melting layer compared to actual melting layer (as determined by ACARS soundings) iParCA determined melting layer compared to actual melting layer (as determined by ACARS soundings) out of 32 mixed precipitation and low freezing level cases: out of 32 mixed precipitation and low freezing level cases: freezing level was 500 m ASL or lower in 14 cases freezing level was 500 m ASL or lower in 14 cases iParCA determined freezing level heights were misplaced from reality in 6 cases iParCA determined freezing level heights were misplaced from reality in 6 cases iParCA determined freezing level heights overlapped reality in 10 cases iParCA determined freezing level heights overlapped reality in 10 cases bias problem in iParCA against wet snow? bias problem in iParCA against wet snow?

44. Wet Snow Problem: Findings subject individual points in melting layer of low melting layer from misplaced-melting-layer and correctly-placed- melting-layer cases to manual analysis subject individual points in melting layer of low melting layer from misplaced-melting-layer and correctly-placed- melting-layer cases to manual analysis objective: isolate possible bias in membership functions from errors in melting layer placement objective: isolate possible bias in membership functions from errors in melting layer placement result: aggregate score of wet snow in actual melting layer lower than other hydrometeor types result: aggregate score of wet snow in actual melting layer lower than other hydrometeor types therefore bias against wet snow is real therefore bias against wet snow is real

45. Wet Snow Problem: Findings how serious is bias? how serious is bias? 0% of cases where wet snow is in verification obs does algorithm forecast wet snow 0% of cases where wet snow is in verification obs does algorithm forecast wet snow 91% of cases where wet snow is in verification obs algorithm forecasts frozen precipitation 91% of cases where wet snow is in verification obs algorithm forecasts frozen precipitation remainder of cases result in liquid precipitation remainder of cases result in liquid precipitation

46. Conclusions Summary of Algorithm Weaknesses Discovered - poor ground clutter filtering - range problem - melting layer problem - wet snow problem - ice pellet problem - vertical effects problem

47. Conclusions Recommended Improvements to Algorithm Components 1.implement WKR_MELT melting layer detection algorithm 2.implement Q vector into iParCA 3.implement convective/stratiform differentiation algorithm 4.tune wet snow membership function to Southern Ontario 5.develop ice pellet membership functions 6.Better ground clutter filtering scheme 7.Superior radar nowcasting software such as VET instead of simple correlation technique used in Cross Correlation Tracker

48. Conclusions iParCA based on HCA developed by Park et al (2009) for NWS HCA with melting layer detection and Q vector HCA with melting layer detection and no Q vector HCA with no melting layer detection and no Q vector - last version of HCA most similar to iParCA both in result and configuration - fails to resolve melting layer in similar fashion to iParCA - implement Q vector to enable melting layer detection

49. Acknowledgements I wish to acknowledge the assistance of the King City Research Group, which is part of the Cloud Physics and Severe Weather Research Section. Particularly, I would like to thank Dr. Norman Donaldson for his technical assistance in constructing the algorithm. I also wish to acknowledge Dr. David Hudak and Sudesh Boodoo for their expert advice on polarimetric radar, iParCA and melting layer detection. I also wish to acknowledge my MSc supervisory committee, which consists of Dr. Peter Taylor and Dr. George Isaac.

50. Questions?