1. Radar Quality Control and Quantitative Precipitation Estimation Intercomparison Project Status Paul Joe Environment Canada Commission of Instruments, Methods and Observations (CIMO) Upper Air and Remote Sensing Technologies (UA&RST)

2. Outline Project Concept The Problem Overview of Data Quality Techniques Pre-RQQI Results Status

3. External Factors

4. Segmenting the DQ Adjustment Algorithms Wind Turbines RLAN RV dilemma Noise Conver sion to “P” Data correction ZDR calibration Ant pol errors; Noise processing Orographic enhancement Recursive issues!

5. Segmenting the DQ Process for Quantitative Precipitation Estimation Remove Artifacts - Cleaned Up 3D volume Estimating Surface/3D Reflectivity Estimating Surface/3D Precipitation Mosaicing Space-Time Estimation Focus on Reflectivity

6. Nowcasting Clear Air Echo as Information

7. Radar DQ is not just about QPE Nowcasting –Non-precipitating echoes/insects –Data Classification Radar Data for NWP –Reflectivity, radial velocity assimilation –VAD Winds

8. Segmenting the DQ Process Remove Artifacts - Cleaned Up 3D volume Estimating Surface/3D Radar Moments Estimating Surface 3D Precipitation (Classification) Mosaicing Space-Time Estimation in 3D Reflectivity Radial Velocity Dual-Polarization

9. Some Examples

10. Every radar has clutter due to environment!

11. Sea Clutter and Ducting

12. Electromagnetic Interference

13. Techniques

14. CAPPI is a classic technique to overcome ground clutter VVO 5o432105o43210 Lines are elevation angles at 1 o spacing, orange is every 5 o.

15. Canada Australia U.S./China VCP21 Whistler Valley Radar 3.0 CAPPI 1.5 CAPPI There are a variety of Scan Strategies (CAPPI Profiles) Make better or drop

16. The elevation angles but nature of weather important for CAPPI 2.5 o 1.5 o 0.5 o PPI’s 1.5km CAPPI

17. Doppler Zero Velocity Notch 1.Doppler Velocity Spectrum Pulse pair (time domain) FFT (frequency domain) 2. Reflectivity statistics Before After

18. Doppler Filtering

20. SNOW RAIN Too much echo removed! However, better than without filtering?

21. Data Processing plus Signal Processing Texture + Fuzzy Logic + Spectral Dixon, Kessinger, Hubbert Data Processing plus Signal Processing FUZZY LOGIC

23. Removal of Anomalous Propagation NONQC QC Liping Liu, CMA

24. The Metric of Success

25. Iso-range “Variance” as an intercomparison Metric Daniel Michelson, SMHI Accumulation – a winter season log (Raingauge-Radar Difference) No blockage Rings of decreasing value Difference increases range! almost

26. Convection Stratiform Snow Vertical Profile of Reflectivity is smoothed as the beam spreads in range Due to Earth curvature and beam propagating above the weather.

27. Variance Metric Similar to before except area of partial blockage contributes to lots of scatter Algorithms that are able to infill data should reduce the variance in the scatter! Michelson

28. Proposed Metric

29. Alternate Metrics Accumulation of Radial Velocity should produce the mean wind for the site. Both look believable, maybe difference is due to different data set length nonQC QC

30. Modality Need a variety of techniques Need a variety of scan strategies Need a variety of data sets that integrate to a uniform pattern Need weather with a variety of artifacts

31. Pilot Study Purpose is to test the assumptions of the project modality -Short data sets for uniformity -Check the interpretation of the metric -Variety of scan strategies, algorithms, etc -Evaluate feasibility

32. Uniform Fields

33. Sample Cases Uniform with local clutter (XLA) Uniform with partial blocking (WVY) Urban Clutter/Niagara Escarpment (WKR) Strong Anomalous Propagation Echo (TJ 2006) Strong AP with Weather (TJ 2007) Sea Clutter (Sydney AU, Kurnell) Sea Clutter / Multi-path AP (Saudi 2002) Convective Weather with Airplane Tracks - One season (TJ Radar 2007)

34. XLA The data accumulates to uniform pattern. Widespread snow. A baseline case. IRIS formatted data. 24 elevation angles. Doppler (dBZT, dBZc, Vr, SPW) at low levels. Range res = 1km or 0.5 km. Az res = 1 or 0.5 degrees.

35. WVY The data accumulates to uniform pattern with an area of blockage. Widespread snow. A baseline case. IRIS formatted data. 24 elevation angles. Doppler (dBZT, dBZc, Vr, SPW) at low levels. Range res = 1km or 0.5 km. Az res = 1 or 0.5 degrees.

36. WKR The data accumulates to uniform pattern with an area of blockage. Widespread snow. Urban (skyscrapers) and small terrain clutter. IRIS formatted data. 24 elevation angles. Doppler (dBZT, dBZc, Vr, SPW) at low levels. Range res = 1km or 0.5 km. Az res = 1 or 0.5 degrees.

37. BSCAN of Z accumulation with no filtering, Doppler and CAPPI CAPPI Doppler No Filtering Azimuth Range [km] 0 100

38. Probability Density Function of Reflectivity as a function of range Raw Doppler CAPPI

39. What length of data sets are needed? Highly Variable More uniform, smoother, more continuous

40. The Techniques Doppler Notching CAPPI 1.5km CAPPI 3.0km Mixed of Doppler Notching and CAPPI Radar Echo Classifier (REC) –Anomalous Propagation –Sea Clutter REC-CMA

41. The Statistic

42. Spread of PDF (at constant range) for various cases and techniques…

45. Status

46. Status and Acknowledgements Kimata, Japan Liu, China Seed, Australia Michelson, Sweden Sempere-Torres, Spain Howard, USA Hubbert, USA Calhieros, Brazil Levizzani, Italy/IPWG Gaussiat, UK/OPERA HUB Donaldson, Canada Data Providers Algorithm Providers Evaluation Team Reviewers

47. Summary On-going Data Providers, Processors identified ODIM_H5 format identified BOM will host and convert data for Data Processors Initial Metric identified Review –Variety of techniques –Variety of scan strategies –Variety of data sets –Weather (e.g. convective, snow) with a variety of artifacts Alternate radial velocity metric