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NEXRAD Data Quality 25 August 2000 Briefing Boulder, CO Cathy Kessinger Scott Ellis Joe VanAndel Don Ferraro Jeff Keeler 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 Slide 2 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 Reflectivity Radial Velocity Reflectivity Precipitation Reflectivity AP Clutter Automation! Slide 3 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 Slide 4 Fuzzy logic recognition Membership function Feature fields derived from base data w1 REC outputs Membership function w2 Membership function w3 Sum AP clutter Precipitation Clear air Bright band Sea clutter etc Slide 6 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) Slide 7 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 Slide 8 Hydrometeor identification with polarimetric radar Z Zdr Fdp rhv LDR V,W… Fuzzy Fuzzy logic inference engine Rain Snow Hail Graupel Ice crystal SC Liq Water Clutter Freezing Level Slide 9 PID Algorithm Echo Type REC Algorithm Ground clutter AP Detection Algorithm Flying insects Clear Air Detection Algorithm Cloud Not used yet Drizzle Not used yet Super-cooled Not used yet liquid droplets Flying birds Not used yet Echo Type REC Algorithm Light, moderate Precipitation & heavy rain Detection Hail Algorithm Rain & hail mixture Graupel & small hail Dry and wet snow Ice crystals Irregular ice crystals Slide 10 Use of S-Pol data for “truth” • 11 February 1999 • AP, clear air & precipitation • Truth: – green = AP – gold = precipitation – red = clear air Reflectivity Spectrum Width Radial Velocity Truth Slide 11 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 Slide 12 1 a) Mean Radial Velocity (MVE) 0 -50 -2.3 0 2.3 50 1 • APDA membership functions b) Mean Spectrum Width (MSW) 0 -30 0 3.2 30 1 c) Texture of SNR (TSNR) 0 0 45 1 1000 d) Standard Deviation of Radial Velocity (SDVE) 0 -30 0 0.7 1 30 e) Vertical Difference of Reflectivity (GDZ) 0 -100 -18 0 100 Slide 13 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 Slide 14 NCAR S-Pol Reflectivity Radial Velocity AP/NP Clutter, Precipitation, Clear air echoes S-Pol movie loops: June 19, 2000 AP Detection Product Objective Truth June 22, 2000 Precipitation Clear Air Figure shown and movie loops use the 5 features shown in slide 12 for AP clutter AP & NP Clutter Slide 15 Statistical Performance of the AP Detection Algorithm S-Pol 151 Scans Pratte and Ecoff Version FY-99 Version Slide 16 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) Slide 17 Reflectivity Radial Velocity AP Detection Product Subjective Truth NEXRAD AP Clutter, Precipitation, Clear air echoes KNQA movie loop AP Clutter 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 Clutter Residue Precipitation Slide 18 Statistical Performance of the AP Detection Algorithm NEXRAD 60 Scans Pratte and Ecoff Version FY-99 Version Slide 19 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 Slide 20 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 Slide 21 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 Slide 22 1 a) Standard Deviation of Radial Velocity (SDVE) 0 0 0.5 50 1.0 1 • FY99 PDA membership functions b) Standard Deviation of Spectrum Width (SDSW) 0 0 2.0 50 1 c) Texture of the SNR (TSNR) 0 0 2.5 1000 30 1 d) Mean Reflectivity (MDZ) 0 -20 35 -5 1 80 e) Vertical Difference of the Reflectivity (GDZ) 0 -100 -20 0 20 100 Slide 23 Reflectivity • 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 FY99 PDA Truth FY98 CPDA Slide 24 APCAT Performance Curves 42 S-Pol and 60 NEXRAD scans Note improved performance of PDA vs CPDA a) FY99 PDA S-Pol b) FY98 CPDA S-Pol c) FY99 PDA NEXRAD d) FY98 CPDA NEXRAD Slide 25 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 Slide 26 1 a) Texture of the Radial Velocity (TVE) 0 0 10 1 • FY99 CADA membership functions 1000 150 b) Mean Spectrum Width (MSW) 0 -30 0 3 10 30 1 c) Texture of the SNR (TSNR) 0 0 70 20 1 1000 d) Mean Reflectivity (MDZ) 0 -30 0 1 30 80 e) Standard Deviation of Spectrum Width (SDSW) 0 0 2 4 30 Slide 27 Reflectivity • 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) Spectrum Width Radial Velocity Truth Slide 28 CADA Truth • 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 Slide 29