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Paul Joe and Alan Seed Environment Canada Centre for Australian Weather and Climate Research

The Radar Quality Control and Quantitative Precipitation Estimation Intercomparison Project RQQI (pronounced Ricky). Paul Joe and Alan Seed Environment Canada Centre for Australian Weather and Climate Research.

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Paul Joe and Alan Seed Environment Canada Centre for Australian Weather and Climate Research

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  1. The Radar Quality Control and Quantitative Precipitation Estimation Intercomparison ProjectRQQI(pronounced Ricky) Paul Joe and Alan SeedEnvironment CanadaCentre for Australian Weather and Climate Research Acknowledgement: Sempere-Torres, Keenan, Kitchen, Zhang, Sireci, Donaldson, Alberoni, Dixon, Hubbert, Balducci, Seo, Liu, Kimata, Seltmann, Levizzani, Haase, Kimata, Liu, Koistinen, Michelson, Holleman, Beerkhuis, Vaisala, Gematronix, Howard et al

  2. Outline • Why now? Applications and Science Trends • The Promise • The Problem • The Processing • The Project • Metrics • Data • Modality • Summary

  3. Why Now?

  4. Qualitative – understanding, severe weather, patterns Local applications Instrument level quality control Quantitative hydrology NWP Data Assimilation Climate Regional Exchange Global quality control Progress in the Use of Weather Radar Before/NowNow/Emerging See Sireci, Turkish Met Service Poster

  5. Local Applications: Severe WeatherUnderstanding – Qualitative Use

  6. Local Application: Hydrological Application for Flash Flooding Sempere-Torres

  7. Regional:Radar Assimilation and NWP Reflectivity Assimilation Weygandt et al, 2009

  8. Global: Precipitation Assimilation and NWP/ECMWF Lopez and Bauer, 2008

  9. Climate Applications

  10. Why Radar? Chris Kidd Climate, Weather, Global CPC Station List

  11. 10,000 gauges, 15 cm diameter, fit inside half of the center circle at Royal Bafokeng Stadium, South Africa Sampling Royal Bafokeng Stadium, Rustenberg World Cup 2010

  12. Representativeness Habib and Krajewski, 2001 10,000 gauges, 10km correlation length, 1.6x10-5 % of Earth’s surface

  13. The Promise

  14. The Potential: Radar-Raingauge Trace Rain Rate [mm/h]

  15. almost Almost A Perfect Radar! Accumulation – a winter season log (Raingauge-Radar Difference) Difference increases range! No blockage Rings of decreasing value Michelson, SMHI

  16. Vertical Profiles of Reflectivity • Beam smooths the data AND • Overshoots the weather Explains increasing radar-raingauge difference with range Joss-Waldvogel

  17. No correction VPR correction FMI, Koistinen

  18. The Problem

  19. Problem: The Environment No echo Over report Under report Under report under report Over report No echo Over report Over report Under report

  20. Bright Band Bright Band RLAN Challenger Sea Clutter

  21. Insects and BugsClear Air Echoes Insects, bad for QPE but good for winds, NWP One man’s garbage is another man’s treasure!

  22. Severe Attenuation Flare Artifact Signature of Hail Peter Visser SAWS One man’s garbage is another man’s treasure!

  23. The Processing Electromagnetics to Essential Climate Variable

  24. Starting Point of QPE ProcessingCalibrated and maintained radar. Radar WMO Turkey Training Course A complex instrument but if maintained is stable to about 1-2 dB cf ~100 dB. Note TRMM spaceborne radar is stable to 0.5 dB.

  25. Conceptual QPE Radar Software ChainAdjustments from EM to QPE • 1st RQQI Workshop • Ground clutter and anomalous prop • Calibration/Bias Adjustment • VPR correction

  26. The Project

  27. RQQI • A variety of adjustments are needed to convert radar measurements to precipitation estimates • Various methods are available for each adjustment and dependent on the radar features • A series of inter-comparison workshops to quantify the quality of these methods for quantitative precipitation estimation globally • The first workshop will focus on clutter removal, “calibration” or bias adjustment, (Vertical Profile Reflectivity Correction?)

  28. Doppler Filtering

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

  30. Combination of Signal and Data Processing Removal of Ground Clutter and Anomalous Propagation Echoes NONQC QC Liping Liu, CMA; Hubbert, Kessinger, Dixon NCAR

  31. Relative Metrics • Metrics • “truth” is hard to define or non-existent. • result of corrections will cause the spatial and temporal statistical properties of the echoes in the clutter affected areas to be the same as those from the areas that are not affected by clutter • UNIFORMITY, CONTINUITY AND SMOOTHNESS. • Temporal and spatial correlation of reflectivity • higher correlations between the clutter corrected and adjacent clutter free areas • improvement may be offset by added noise coming from detection and infilling • Probability Distribution Function of reflectivity • The single point statistics for the in-filled data in a clutter affected area should be the same as that for a neighbouring non-clutter area.

  32. Reflectivity Continuity Metric Highly Variable More uniform, smoother, more continuous

  33. Radial Velocity Accumulation

  34. 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

  35. Data Sets and Modality • No Weather • urban clutter (hard), • rural clutter (silos, soft forests), • mountain top- microclutter • valley radar-hard clutter • intense AP • mild anomalous propagation • intense sea clutter [Saudi Arabia] • mild sea clutter [Australia] • Weather • convective weather • low-topped thunderstorms • wide spread weather • convective, low topped and wide spread cases with overlapping radars • Modality • Short data sets in a variety of situations • Some synthetic data sets considered • Run algorithms and accumulate data • Independent analysis of results • Workshop to present algorithms, results

  36. Deliverables • Test Data Sets • Alan Seed, BOM • Workshop – Hydrological Applications of Weather Radar • Malcom Kitchen, UKMO • Documentation • A better and documented understanding of the relative performance of an algorithm for a particular radar and situation • A better and documented understanding of the balance and relative merits of identifying and mitigating the effects of clutter during the signal processing or data processing components of the QPE system. • A better and documented understanding of the optimal volume scanning strategy to mitigate the effects of clutter in a QPE system. • A legacy of well documented algorithms and possibly code. • Best Practices and quality description

  37. Inter-comparison Expert Panel • Yoshihisa Kimata, JMA, Japan • Liping Liu, CAMS/CMA, China • Alan Seed, CAWCR, Australia • Daniel Sempere-Torres, GRAHI, Spain • Vincenzo Levizzani, WCRP/WGRN • Dixon or Hubbert, NCAR, USA • OPERA • NOAA

  38. Summary and Challenges • Many steps in processing – electromagnetics to essential climate variable, first workshop to address the most basic issues (TBD, ICE) • RQQI’s goal is to inter-compare different algorithms for radar quality control with a focus on QPE applications • Instrument and data analysis systems intertwined • Descriptive, quantitative data quality concepts needed • What about mixed data analysis systems? • Ultimately, the goal is to develop a method to assess the overall quality of precipitation products from radars globally • Processing -> quality or some generic tests

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