The CARDS System Description and Algorithms

1. The CARDS System Description and Algorithms CAnadian Radar Decision Support Paul Joe Meteorological Service of Canada

2. Outline • Introduction • Requirements / Issues • The CARDS Solution • Algorithms, Products, Functionality • Example of Usage

3. Introduction TITAN = Thunderstorm Initiation, Analysis and Nowcasting (NCAR “free*”) WDSS II = Warning Decision Support System (NSSL “free*” ) CARDS = Canadian Radar Decision Support (EC “free**”) • *Download from web • ** Discuss

4. Introduction • Operational system of the Meteorological Service of Canada • Single radar processing systems for multiple uses • In transition, being integrated with forecaster workstation (NinJo)

5. The Requirements

6. The Severe Warning Challenge • Specificity of information is needed to be effective • Time/duration, Location, Type of Event • Distinguish between severe and non-severe, • And tornadic and non-tornadic thunderstorms. • Looking for the rare event, many types of severe storms • Large forecast area • Work Load, Efficiency 3,000,000 km2

7. High resolution composites

8. Yellow and white = events Green = thunderstorms The Rare Event 100 km Thunderstorm locations and reported severe weather

9. High Level Requirements An expert can… • Recognise patterns • Detect anomalies • Keep the big picture (situational awareness) • Understand the way things work • Relate past, present, and future events • Pick up on very subtle differences • Observe opportunities, able to improvise • Address their own limitations The system design must enable this!

10. Situational Awareness

11. The Canadian Warning Offices > 3,000,000 square km per forecast office

12. Screen Real-estate Issue Poor Efficiency

13. Supporting Mental Models

14. Not an automated answer! Individual algorithms are configured to have high POD but results in high FAR Combination of algorithms: support each other to reduce the FAR create leverage points for further inquiry support use of the conceptual model support expert decision-making Using Algorithm Approch An algorithm searches the data for relevant patterns (spatial or temporal).

15. Enabling Expertise • Can not do anything if only the answer is provided! • This will make anyone dumb! • Self-fulfilling prophesy • Must be able to “access or drill down” to the underlying data

16. Functionality

17. High reflectivity Echo top Shapes Gradients of reflectivity Trends Movement Flair echo/Hail in dual-pol Relationships Updraft Tilt Weak Echo Regions (WER) Bounded WER Location Echotop - Gradient Rotation Divergence Convergence Recall Manual Analysis Process..… We want to mimic this – but quickly

18. Data Access

19. Cell View to access to data/products Cell View Echo Top hail gradient VIL CAPPI’s Time history Automated XSECT

21. Animation to show the functionality and use of cell views

23. Algorithms Approach Not the answer! but … Create “Leverage” Points Support your Conceptual Model Support Decision Making

24. Algorithm • A set of computer procedures or steps • Attempts to match human visual/pattern recognition skills • Software that identifies a feature in the data that represents a meteorological feature (e.g., a thunderstorm cell, a cell track)

25. CAPPI (many) MAXR Height of MAXR EchoTop VIL, Downdraft, Hail Size Reflectivity Gradient PPI’s Radial Velocity Spectral Width Corrected Reflectivity Precipitation Accumulations Composites of various products Interactive Cross-sections Algorithm Ensemble Product Cell Views Storm Cell Identification Table Cell Identification average and max value locations Bounded Weak Echo Region Mesocylone, downburst, gust Cell Properties Echotop, VIL, Hail Size See Product List Automatic Cross-sections Tracking, Simple Nowcast Multi-radar algorithm merge Rank Weight Color Coding Sorted Rank Cross-correlation Tracking Point Forecast Products/Algorithms(configurable)

26. Need for “Leverage” Points AlgorithmsWhere is the rotation/Tornado Vortex Signature? Leverage = “look at me”

27. 2356UTC

28. It is also about relationships!

29. Forecasters need to maintain situational awareness:#1 problem of missed warnings but which cell is the dangerous one? NO NEED FOR SINGLE RADAR PRODUCTS! But…

30. Forecasters must be able to diagnose the salient features to make a warning decision • Severe Storm Features • Large cell with strong elevated reflectivity (MAXR>45 dBZ) • Tall (high echo top) • Hail • Low level Reflectivity gradients under highest echo tops • Weak Echo Region • Hook/Kidney beam shape • Mesocyclones • Downdrafts Codifying the Lemon Technique through Cell Views

31. Some of the Algorithms Hail Downdraft Algorithm Storm Classification Identification and Tracking Ranking Storms

32. CAPPI (many) MAXR EchoTop VIL, WDraft, Hail Size Reflectivity Gradient PPI’s Radial Velocity Spectral Width Corrected Reflectivity Precipitation Accumulations Composites of various products Interactive Cross-sections Algorithm Ensemble Product Cell Views Storm Cell Identification Table Cell Identification average and max value locations Bounded Weak Echo Region Mesocylone, downburst, gust Cell Properties Echotop, VIL, Hail Size See Product List Automatic Cross-sections Tracking, Simple Nowcast Multi-radar algorithm merge Rank Weight Color Coding Sorted Rank Cross-correlation Tracking Point Forecast Products/Algorithms(configurable)

33. The Hail Algorithms Hail Shaft

34. S2K Hail Products • Polarimetric, BOM/MSC, WDSS • BOM/Treloar Empirical Algorithm • Uses height of 50 dBZ echo, VIL and freezing level • WDSS • Uses height diff of freezing level and 45 dBZ top, VIL, hail kinetic energy (fn of dBZ), temperature profile • Probability of severe hail • SHI

35. Hail Size, VIL & Freezing Level

36. Hail Size, Height of 50 dBZ echo and Freezing Level

37. Hail Product(Image and Feature)

38. WDSS HDA Probability of Hail (POH) • Estimate the probability of any size hail associated with a storm • H45 = Height of the 45 dBZ echo AGL (km) • H0 = Height of the melting level AGL (km) -> Δ H Based on data from a Swiss hail suppression experiment

39. HDA Severe Hail Index (SHI) • Vertically Integrated Liquid (VIL) (Emphasis given to lower dBZ) • To remove “hail contamination” • Hailfall Kinetic Energy (E) (Emphasis given to higher dBZ and those dBZ above the melting layer)E = 5 x 10-6 x 100.084Z x W(Z) • W(Z) = 0 for Z < 40 dBZ • W(Z) linearly interpolated for 40 dBZ>Z> 50 dBZ • W(Z) = 1 forZ> 50 dBZ

40. SHI = 0.1WT(Hi) EiHi N i WT(H) HDA Severe Hail Index (SHI) • Weighted by thermodynamic profile • Obtained manually from nearby sounding, or • Obtained automatically from mesoscale model analysis • Greater temporal and spatial resolution • Prob. Of Severe Hail (POSH; dia > 1.9 cm) and Max. Estimated Hail Size (MEHS) derived from SHI (Witt et al. 1998)

41. Hail Shaft Hail algorithm

42. Max Obs Ave S2K ComparisonAverage Hail Size POL CARDS WDSS • Polarimetric, BOM/MSC, WDSS • CARDS/BOM/Treloar Empirical Algorithm • Uses height of 50 dBZ echo, VIL and freezing level • WDSS • Uses height diff of freezing level and 45 dBZ top, VIL, hail kinetic energy (fn of dBZ), temperature profile • Probability of severe hail • SHI • What is the truth? Do you want to just reduce the CSI or do you want high POD? What is the relationship to your forecast product? OBS

43. ComparisonAverage Hail Size C Band Dual Pol S Band CARDS S Band WDSS C Band CARDS C Band CARDS OBS

44. PDF of Hail Size

45. CARDS Hail Size Time SequenceNov 3 Case MAX Ave Harold Brooks

46. WDSS Probability of Hail obs any sever Harold Brooks

47. WDSS Max Hail Size Harold Brooks

48. Severe Wx

49. Severe Wx 45 dBZ 12 10 8 6 4 2 Tornadic Storms Height Ralph Donaldson Reflectivity

50. The Downburst/Gust Potential Algorithm