NEXRAD Data Quality 25 August 2000 Briefing Boulder, CO

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NEXRAD Data Quality25 August 2000
BriefingBoulder, CO

• Cathy Kessinger
• Scott Ellis
• Joe VanAndel
• Don Ferraro
• Jeff Keeler

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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

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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!
Reflectivity
Radial Velocity
Reflectivity Precipitation
Reflectivity AP Clutter
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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

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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

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

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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

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Hydrometeor identification with polarimetric
radar

Z Zdr Fdp rhv LDR V,W Freezing Level
Rain Snow Hail Graupel Ice crystal SC Liq
Water Clutter
Fuzzy inference engine
Fuzzy logic inference engine
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PID Algorithm
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Use of S-Pol data for truth
Reflectivity
Radial Velocity

• 11 February 1999
• AP, clear air precipitation
• Truth
• green AP
• gold precipitation
• red clear air

Spectrum Width
Truth
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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

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• APDA membership functions

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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

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• 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
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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 gt
  threshold
• Difference gt 0, spin gt 0 Difference lt0, spin lt0
• Percentage of maximum possible spin changes
• Sign 100 for speckled fields, 0 for pure
  gradients
• Tim OBannon sign variable
• Reflectivity difference from gate to gate
• Accumulate or -1 depending on sign of
  difference
• Sign0 for speckled fields, 1 for pure
  gradients
• Used in KNQA movie loop (slide 18)

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• 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
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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

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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

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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

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• FY99 PDA membership functions

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Reflectivity
Truth

• 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
FY98 CPDA
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APCAT Performance Curves
42 S-Pol and 60 NEXRAD scans Note
improved performance of PDA vs CPDA
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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

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• FY99 CADA membership functions

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Reflectivity
Radial Velocity

• 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
Truth
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• 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