Radar Quality Control and Quantitative Precipitation Estimation Intercomparison Project Status

1. Radar Quality Control and Quantitative Precipitation Estimation Intercomparison ProjectStatus 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 Process for Quantitative Precipitation Estimation Remove Artifacts - Cleaned Up 3D volume Focus on Reflectivity Estimating Surface/3D Reflectivity Estimating Surface/3D Precipitation Mosaicing Space-Time Estimation

5. NowcastingClear Air Echo as Information

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

7. Every radar has clutter due to environment!

8. Sea Clutter and Ducting

9. Electromagnetic Interference

10. Techniques

11. CAPPI is a classic technique to overcome ground clutter 5o 4 3 2 1 0 Lines are elevation angles at 1o spacing, orange is every 5o. VVO

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

13. 2.5o 1.5o 0.5o The elevation angles but nature of weather important for CAPPI 1.5km CAPPI PPI’s

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

15. Doppler Filtering

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

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

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

21. The Metric of Success

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

23. 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.

24. 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

25. Proposed Metric

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

27. 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

28. 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

29. Uniform Fields

30. 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)

31. 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.

32. 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.

33. 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.

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

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

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

37. 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

38. The Statistic

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

42. Status

43. 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

44. 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