1. NEXRAD Data Quality 25 August 2000 Briefing Boulder, CO Cathy Kessinger Scott Ellis Joe VanAndel Don Ferraro Jeff Keeler

2. Slide 2 Overview NCAR working with NOAA OSF to improve data quality of WSR-88D AP clutter is significant problem Creates errors in hydrologic algorithms that estimate rainfall from radar Other algorithms are effected, too Leads to errors in interpretation of base data Very important to remove AP clutter

3. Slide 3 Ground clutter due to anomalous propagation degrades the performance of rainfall estimates from radar Currently, it must be detected by operators and clutter filters turned on manually Automation! ReflectivityRadial Velocity Reflectivity AP Clutter Reflectivity Precipitation

4. Slide 4 AP Clutter Mitigation Scheme Automatic clutter filter control Radar Echo Classifier –Uses fuzzy logic techniques –AP Detection Algorithm (APDA) –Precipitation Detection Algorithm (PDA) –Clear Air Detection Algorithm (CADA) –other algorithms, as needed Reflectivity compensation of clutter filter bias Tracking of clutter filtered regions

5. Slide 5 Radar Echo Classifier Uses “fuzzy logic” technique Base data Z, V, W used Derived fields (“features”) are calculated Membership functions are applied to the feature fields, results in “interest” fields Interest fields are weighted and summed Threshold applied, producing final algorithm output

6. Slide 6 Fuzzy logic recognition Feature fields derived from base data Membership function Sum Membership function Membership function w3 w1 w2 REC outputs AP clutter Precipitation Clear air Bright band Sea clutter etc

7. Slide 7 Evaluation of REC Use statistical indices to measure performance of algorithms against “truth” –CSI, POD, FAR computed from 2x2 contingency table For NEXRAD cases, truth defined by human experts (subjective) For S-Pol cases, truth defined by Particle Identification algorithm (objective)

8. Slide 8 Use of S-Pol data for “truth” Advantages: –Independent determination of truth using multi-parameter data –Objective determination of truth (no humans!) –No temporal & spatial differences in Z,V,W fields –Can define ground clutter, precipitation and clear air (from bugs) echoes

9. Slide 9 Hydrometeor identification with polarimetric radar Fuzzy inference engine Rain Snow Hail Graupel Ice crystal SC Liq Water Clutter Z Zdr  dp  hv LDR V,W… Freezing Level Fuzzy logic inference engine

10. Slide 10 PID Algorithm

11. Slide 11 Use of S-Pol data for “truth” 11 February 1999 AP, clear air & precipitation Truth: –green = AP –gold = precipitation –red = clear air ReflectivityRadial Velocity Spectrum WidthTruth

12. Slide 12 AP Detection Algorithm Features derived from base data –Mean radial velocity –Standard deviation of radial velocity –Mean spectrum width –“Texture” of the reflectivity (mean squared difference) –Vertical difference in reflectivity –First 4 are computed over a local area; vertical difference is a gate-to-gate comparison

13. Slide 13 APDA membership functions

14. Slide 14 APDA Data Sets 60 scans of NEXRAD data that were truthed by humans 151 scans of S-Pol data (Brazil) that were truthed with the PID APDA run with 5 features shown in slide 12

15. Slide 15 NCAR S-Pol AP/NP Clutter, Precipitation, Clear air echoes S-Pol movie loops: June 19, 2000 June 22, 2000 Figure shown and movie loops use the 5 features shown in slide 12 for AP clutter

16. Slide 16

17. Slide 17 AP Detection Algorithm 2 reflectivity features added for non-Doppler region –Both computed over a local area (max range = 430 km) –Matthias Steiner “spin” variable Reflectivity difference from gate to gate > threshold Difference > 0, spin > 0; Difference <0, spin <0 Percentage of maximum possible spin changes Sign =100 for “speckled” fields, =0 for pure gradients –Tim O’Bannon “sign” variable Reflectivity difference from gate to gate Accumulate + or -1 depending on sign of difference Sign=0 for “speckled” fields, =+1 for pure gradients –Used in KNQA movie loop (slide 18)

18. Slide 18 NEXRAD AP Clutter, Precipitation, Clear air echoes KNQA movie loop Figure shown uses the five features shown in slide 12 for AP clutter KNQA movie loop uses four reflectivity variables and no Doppler information for AP clutter

19. Slide 19

20. Slide 20 APDA Summary Changes to membership functions and the weighting scheme have improved results, in general Better understanding is needed of the effect on REC algorithm performance that the radar system differences between S-Pol and NEXRAD creates

21. Slide 21 Precipitation Detection Algorithm For FY98, three NEXRAD scans were used to devise a preliminary algorithm For FY98, algorithm detected convective regions of precipitation, not stratiform regions For FY99, algorithm detects both convective and stratiform regions

22. Slide 22 Precipitation Detection Algorithm New features and membership functions used –FY98 used MVE, MSW, TSNR, MDZ, GDZ –FY99 uses SDVE, SDSW, TSNR, MDZ, GDZ The PDA algorithm was run on 42 scans of S-Pol data that covered 4 cases

23. Slide 23 FY99 PDA membership functions -20 a) Standard Deviation of Radial Velocity (SDVE) b) Standard Deviation of Spectrum Width (SDSW) c) Texture of the SNR (TSNR) d) Mean Reflectivity (MDZ) 0 0.5 1.0 50 0 2.050 -5 80 0 2.5 1 0 1 0 1 0 1 0 1 0 1000 100 30 35 e) Vertical Difference of the Reflectivity (GDZ) -20 0 20 -100

24. Slide 24 S-Pol scan with strong convective region CPDA does better in stronger region of convection PDA detects all the precipitation regions while not detecting most of the clutter regions Reflectivity FY99 PDA Truth FY98 CPDA

25. Slide 25 APCAT Performance Curves 42 S-Pol and 60 NEXRAD scans Note improved performance of PDA vs CPDA

26. Slide 26 Clear Air Detection Algorithm 12 S-Pol scans from 1 case used to devise a preliminary algorithm Features used are TVE, MSW, SDSW, MDZ and TSNR

27. Slide 27 FY99 CADA membership functions -30 a) Texture of the Radial Velocity (TVE) b) Mean Spectrum Width (MSW) c) Texture of the SNR (TSNR) d) Mean Reflectivity (MDZ) 150 0 101000 0 3 10-3030 080 0 20 1 0 1 0 1 0 1 0 1 0 1000 30 70 30 e) Standard Deviation of Spectrum Width (SDSW) 0 2 4

28. Slide 28 S-Pol clear air case with low radial velocity values Truth field shows clutter (green), clear air return (red) and small precipitation echoes NE of radar (gold) Reflectivity Spectrum Width Radial Velocity Truth

29. Slide 29 Results shown for case shown on previous slide CADA performs well at detecting the clear air and does not detect most of the clutter return The edges of precipitation echoes are falsely detected CADATruth