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Radar Data Quality Control

Radar Data Quality Control . Kevin L. Manross CIMMS/NSSL 18 October 2011. Importance of Radar QC. Radar data assimilation: “Garbage in – garbage out… on steroids”

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Radar Data Quality Control

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  1. Radar Data Quality Control Kevin L. Manross CIMMS/NSSL 18 October 2011

  2. Importance of Radar QC • Radar data assimilation: “Garbage in – garbage out… on steroids” • “Any source of data bias will cause bias in the resulting analysis, even if it is localized in space. For example, poorly removed clutter causes problems when assimilating radial velocity, as these cause errors in the obtained winds that persist in time and may ultimately falsely trigger instabilities…” Fabry (2011 Radar Conference) WoF Storm-scale Radar Data Assimilation Workshop

  3. A Word About Radar Quality Control Techniques… Two general categories • Operate on spectral data at the Radar Data Acquisition (RDA) step • (timeseries” or “Level I”) • Examples • Notch Filter • Clutter Mitigation Decision (CMD) Algorithm • Gaussian Model Adaptive Processing (GMAP) • Staggered Pulse Repetition Time (SPRT) • Phase coding • Many others • Operate on “products” (Reflectivity, Velocity, Correlation Coefficient, etc.) after the Radar Product Generation (RPG) step • This is where our focus will be WoF Storm-scale Radar Data Assimilation Workshop

  4. Radar QC Techniques Involved WoF Storm-scale Radar Data Assimilation Workshop

  5. Cases • VORTEX2 Cases • Manually QC’ed WoF Storm-scale Radar Data Assimilation Workshop

  6. Scoring Methodology • Match radar data gate-by-gate • Mainly “on vs off”, but some value differences particularly in Velocity. • ‘Estimate’ grid – ‘Target’ grid • Basic Contingency Table • Broken down between Reflectivity techniques and Velocity • For Reflectivity, only the Doppler cut was considered in the split-cut tilts (00.50, 00.90, 01.30, 01.45 deg. elev.). This was a consideration driven by the use of SOLOii • Velocity grids are thresholded by common Reflectivity, so we only count velocity errors Still a ‘work in progress’ for quantified repeatable results WoF Storm-scale Radar Data Assimilation Workshop

  7. Scoring Methodology - Example - = KCYS UNEDITED (20090605-234255Z 0.5 DEG) KCYS “TRUTH” (20090605-234255Z 0.5 DEG) KCYS UN-TRUTH (20090605-234255Z 0.5 DEG) - = Unedited “Truth” “Difference” (Hit) (Miss) (False Alarm) (Estimate) (Target) 20090605 KCYS “Unedited-Truth” WoF Storm-scale Radar Data Assimilation Workshop

  8. Scoring Methodology - Example - = KPUX CREM (20090611-005523Z 0.5 DEG) KPUX QCNN (20090611-005523Z 0.5 DEG) KPUX CREM-QCNN (20090611-005523Z 0.5 DEG) (Estimate) (Target) (Hit) (Miss) (False Alarm) 20090611 KPUX “CREM-QCNN” WoF Storm-scale Radar Data Assimilation Workshop

  9. Scoring Methodology - Example - = KCYS VELOCITY (20090605-234435Z 1.8 DEG) KCYS LEG DEAL (20090605-234435Z 1.8 DEG) KCYS VEL-LEG (20090605-234435Z 1.8 DEG) (Estimate) (Target) (Hit) (Miss) (False Alarm) 20090605 KCYS “VELOCITY-LEGACY” WoF Storm-scale Radar Data Assimilation Workshop

  10. Quantitative Results WoF Storm-scale Radar Data Assimilation Workshop

  11. Conclusions • REFLECTIVITY • QCNN removes a lot of clutter, though much of it is rather weak (< 20 dBZ). • Still misses a non-trivial number of strong ground clutter returns • Struggles with biologicals • CREM technique seems to offer little improvement on QCNN • Will pursue “tweaking” parameters • Aggressive RDA clutter techniques (CMD) clean much before product generation? • Need to implement AR-VAD (next on list) • Very aggressive • Bad for forecasters, but good for algorithm/assimilation (Fabry 2011) WoF Storm-scale Radar Data Assimilation Workshop

  12. Conclusions • VELOCITY • Legacy does surprisingly well when given frequent soundings (I.e., ourly RUC 20 analyses) • Still suffers from failures in strong shear and at anvil level • 2D Dealiasing shows improvement over Legacy WoF Storm-scale Radar Data Assimilation Workshop

  13. Conclusions • Should have quantitative results soon • Misclassifying and removing good echo is rare • Techniques shown run in realtime • Students have improved to 3.5 to 4 hours per Volume Scan • At worst, these techniques should provide a good initial clean up before manual QC WoF Storm-scale Radar Data Assimilation Workshop

  14. Considerations and Future Improvements • For Past and Near Term Data • Implement AR-VAD • Investigate additional “post product generation” techniques • Near and far range future data • DUAL-POL • Very good at identifying ground clutter andbiologicals • Upcoming RDA improvements WoF Storm-scale Radar Data Assimilation Workshop

  15. Questions? Please feel free to contact me: kevin.manross@noaa.gov WoF Storm-scale Radar Data Assimilation Workshop

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