Data Windowing in ORDA

Technical Briefing

Sebastián Torres1,2, Chris Curtis1,2,Rodger Brown2, and Michael Jain2

1Cooperative Institute for Mesoscale Meteorological Studies 2National Severe Storms Laboratory

NEXRAD Technical Advisory Committee MeetingMarch 27, 2007

What is Data Windowing? Unlike legacy RDA, ORDA uses data windows

Time-series samples used to compute base data moments are weighted using a “window”

Data windows available in ORDA are: rectangular (no window)

Equivalent to legacy RDA Hamming von Hann BlackmanM

ore

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essi

ve

Rectangular Hammingvon Hann Blackman

Why Data Windowing? Tapered data windows are usually applied before

processing in the frequency domain (spectral processing) Aggressive windows have lower frequency sidelobes that

help reduce the frequency “leakage” effect In ORDA: GMAP, SZ-2

Tapered data windows reduce the effective antenna beamwidth due to scanning A narrower effective beamwidth results in smaller resolution

volumes In ORDA: Super Resolution

The Effects of Data Windowing

Tapered windows reduce the equivalent number of independent samples available to estimate spectral moments End samples “contribute less” to the estimation process

The more aggressive the data window, the larger the errors of estimates for all base data moments

RectangularWindow

HammingWindow

Standard Error of Moment Estimates for Different Windows vs. Spectrum Width (VCP 11)

Parameters correspond to 1st split cut of VCP 11

M = 17, Ts = 3.1 ms, SNR = 10 dB M = 52, Ts = 1 ms, SNR = 10 dB

Standard Error of Moment Estimates for Different Windows vs. SNR (VCP 11)

Parameters correspond to 1st split cut of VCP 11

M = 17, Ts = 3.1 ms, v = 4 m/s M = 52, Ts = 1 ms, v = 4 m/s

Standard Error of Moment Estimates for Different Windows vs. SNR (VCP 12)

Parameters correspond to 1st split cut of VCP 12

M = 15, Ts = 3.1 ms, v = 4 m/s M = 40, Ts = 1 ms, v = 4 m/s

Relative Error Increase for Different Data Windows

Compared to using a rectangular data window (no window), use of tapered data windows results in higher standard deviation of all base data moments For v = 4 m/s and high SNR

The Hamming window increases the errors by about 30% The von Hann window increases the errors by about 35% The Blackman window increases the errors by about 50%

A Timeline of Data Windowing in the RDA Past (before ORDA)

Rectangular: all legacy RDA algorithms Present (ORDA Builds 8 and 9)

Hamming: default window Blackman: GMAP ground clutter filter von Hann: SZ-2 with overlaid echoes

Near Future (ORDA Builds 10 through 12) Rectangular: proposed default window Blackman: GMAP ground clutter filter von Hann: SZ-2 with overlaid echoes and Super-Resolution

Future (ORDA Build 13 and beyond) Same as Build 12 plus… von Hann: spectral processing?

Windowing would not be necessary in case of unimodal spectra

How does the present system deviates from the past? Compare performance of

Rectangular vs. Hamming

VCP 11 Comparison

Surveillance Doppler Rectangular window Hamming window

Elv (deg) WF PRI M PRI M SD(Z) (dB)* SD(v) (m/s) SD(w) (m/s) SD(Z) (dB)* SD(v) (m/s) SD(w) (m/s)

0.5 CS 1 17 0.59 - - 0.80 - -0.5 CD 5 52 - 0.89 0.73 - 1.20 0.891.45 CS 1 16 0.61 - - 0.83 - -1.45 CD 5 52 - 0.89 0.73 - 1.20 0.892.4 B 1 6 5 41 0.94 0.63 1.01 0.86 1.31 0.84 1.34 1.043.35 B 2 6 5 41 1.04 0.63 1.01 0.86 1.42 0.84 1.34 1.044.3 B 2 6 5 41 1.04 0.63 1.01 0.86 1.42 0.84 1.34 1.045.25 B 3 10 5 41 0.97 0.63 1.01 0.86 1.29 0.84 1.34 1.046.2 B 3 10 5 41 0.97 0.63 1.01 0.86 1.29 0.84 1.34 1.047.5 CD 6 43 0.63 1.01 0.92 0.84 1.34 1.118.7 CD 7 46 0.63 1.01 0.98 0.85 1.34 1.1810 CD 7 46 0.63 1.01 0.98 0.85 1.34 1.1812 CD 7 46 0.63 1.01 0.98 0.85 1.34 1.1814 CD 7 46 0.63 1.01 0.98 0.85 1.34 1.18

16.7 CD 7 46 0.63 1.01 0.98 0.85 1.34 1.1819.5 CD 7 46 0.63 1.01 0.98 0.85 1.34 1.18

f = 2800 MHz, PRI Delta C * Reflectivity errors in the Batch mode are indicated for overlaid / non-overlaid situations.

Surveillance Doppler Rectangular window

Elv (deg) WF PRI M PRI M SD(Z) (dB)* SD(v) (m/s) SD(w) (m/s)

0.5 CS 1 17 0.59 - -0.5 CD 5 52 - 0.89 0.731.45 CS 1 16 0.61 - -1.45 CD 5 52 - 0.89 0.732.4 B 1 6 5 41 0.94 0.63 1.01 0.863.35 B 2 6 5 41 1.04 0.63 1.01 0.864.3 B 2 6 5 41 1.04 0.63 1.01 0.865.25 B 3 10 5 41 0.97 0.63 1.01 0.866.2 B 3 10 5 41 0.97 0.63 1.01 0.867.5 CD 6 43 0.63 1.01 0.928.7 CD 7 46 0.63 1.01 0.9810 CD 7 46 0.63 1.01 0.9812 CD 7 46 0.63 1.01 0.9814 CD 7 46 0.63 1.01 0.98

16.7 CD 7 46 0.63 1.01 0.9819.5 CD 7 46 0.63 1.01 0.98

Surveillance Doppler Rectangular window

Elv (deg) WF PRI M PRI M SD(Z) (dB)* SD(v) (m/s) SD(w) (m/s)

0.5 CS 1 17 0.59 - -0.5 CD 5 52 - 0.89 0.731.45 CS 1 16 0.61 - -1.45 CD 5 52 - 0.89 0.732.4 B 1 6 5 41 0.94 0.63 1.01 0.863.35 B 2 6 5 41 1.04 0.63 1.01 0.864.3 B 2 6 5 41 1.04 0.63 1.01 0.865.25 B 3 10 5 41 0.97 0.63 1.01 0.866.2 B 3 10 5 41 0.97 0.63 1.01 0.867.5 CD 6 43 0.63 1.01 0.928.7 CD 7 46 0.63 1.01 0.9810 CD 7 46 0.63 1.01 0.9812 CD 7 46 0.63 1.01 0.9814 CD 7 46 0.63 1.01 0.98

16.7 CD 7 46 0.63 1.01 0.9819.5 CD 7 46 0.63 1.01 0.98

NEXRAD Requirements and Historical Data Quality Meeting NEXRAD data quality requirements

Requirements formulated to ensure DQ is maintained within the system Committee established to assess NTR related to base data quality

Matching legacy RDA performance Algorithms and users were accustomed to data with a certain quality

Frequently, legacy RDA exceeded requirements Data quality of ORDA compared to legacy RDA determines the “DQ

Delta” that users and algorithms are experiencing Currently evaluating the operational impact of using a tapered data window

all the time (“apples-to-apples” comparison) Impact may be worse for

FAA’s fully-automated algorithms Data assimilation into numerical forecast models Polarimetric variables

Base Data ComparisonReflectivity (KCRI – 03/19/2006)

Base Data ComparisonReflectivity (KCRI – 03/19/2006)

Rectangularwindow

Base Data ComparisonReflectivity (KCRI – 03/19/2006)

Hammingwindow

Rectangularwindow

Algorithm Performance ComparisonOne-hour Precipitation Accumulation

Courtesy Bob Lee (ROC)

Hammingwindow

Algorithm Performance ComparisonOne-hour Precipitation Accumulation

Courtesy Bob Lee (ROC)

Algorithm Performance ComparisonMesocyclone Detection Algorithm

Rectangular windowHamming window

time

Acc

umul

ated

num

ber

of d

etec

tions

20%20% 20%20%20%20%

Range Gates with …

Clutter Overlaid echoesClean echoes

Split Cuts

Surveillancedata

Baseline processing

Super Resolution

Doppler data

Baseline processing

Super Resolution

SZ-2

Batch Cuts

Baseline processing for Long-PRT data

Baseline processing for Short-PRT data

Doppler Cuts Baseline processing

Range Gates with …

Clutter Overlaid echoesClean echoes

Split Cuts

Surveillancedata

Baseline processing Blackman - Default

Super Resolution Blackman - Von Hann

Doppler data

Baseline processing Blackman Default Default

Super Resolution Blackman Von Hann Von Hann

SZ-2 Blackman Von Hann Default

Batch Cuts

Baseline processing for Long-PRT data Rectangular - Rectangular

Baseline processing for Short-PRT data Blackman Default Default

Doppler Cuts Baseline processing Blackman - Default

Range Gates with …

Clutter Overlaid echoesClean echoes

Split Cuts

Surveillancedata

Baseline processing Blackman - Hamming

Super Resolution Blackman - Von Hann

Doppler data

Baseline processing Blackman Hamming Hamming

Super Resolution Blackman Von Hann Von Hann

SZ-2 Blackman Von Hann Hamming

Batch Cuts

Baseline processing for Long-PRT data Rectangular - Rectangular

Baseline processing for Short-PRT data Blackman Hamming Hamming

Doppler Cuts Baseline processing Blackman - Hamming

Range Gates with …

Clutter Overlaid echoesClean echoes

Split Cuts

Surveillancedata

Baseline processing Blackman - Rectangular

Super Resolution Blackman - Von Hann

Dopplerdata

Baseline processing Blackman Rectangular Rectangular

Super Resolution Blackman Von Hann Von Hann

SZ-2 Blackman Von Hann Rectangular

Batch Cuts

Baseline processing for Long-PRT data Rectangular - Rectangular

Baseline processing for Short-PRT data Blackman Rectangular Rectangular

Doppler Cuts Baseline processing Blackman - Rectangular

Near-Future Impact of Changing the Default Data Window in ORDA

Conclusions Hamming window increases errors of all base data moments

Compared to legacy RDA current data has ~30% larger errors VCPs that met NEXRAD DQ technical requirements with legacy RDA no

longer meet them with ORDA Is this lower data quality operationally acceptable?

Working on “apples-to-apples” quantitative analyses Preliminary results indicate there can be performance differences in certain

products Users always demand better data quality and faster updates

Back in 2003, one of the TAC’s recommendations was to… “Produce the best quality data possible from the WSR-88D throughout the remainder of its service life.” e.g., recommendation for super resolution followed this criterion (recombination)

Recommendations ORDA should operate with a rectangular window

whenever possible Making this change is straightforward, low-cost, and would not change the

computational complexity of ORDA algorithms Historically, requirements have been relaxed only if significant operational

benefits were realized Clutter filtering → Uncontaminated data VCP 12 → Faster updates SZ-2 → Recovery of overlaid echoes (reduced obscuration) Super Resolution → Improved detection of weather features

Operating with the Hamming window does not bring any significant operational benefits However, before using the rectangular window as the default window,

we recommend the ORDA spectrum width estimator be fixedStay tuned!

Back-Up Slides

Relative Standard Error of Moment Estimates for Different Windows vs. Spectrum Width

Parameters correspond to 1st split cut of VCP 11

M = 17, Ts = 3.1 ms, SNR = 10 dB M = 52, Ts = 1 ms, SNR = 10 dB

Relative Standard Error of Moment Estimates for Different Windows vs. SNR

Parameters correspond to 1st split cut of VCP 11

M = 17, Ts = 3.1 ms, v = 4 m/s M = 52, Ts = 1 ms, v = 4 m/s

Errors with Different Data Windows for VCPs 11, 12, and 21

VCP 11SD(Z) (dB)

SD(v) (m/s)

SD(v) (m/s)

Rectangular 0.58 0.89 0.73

Hamming 0.80 1.19 0.90

Von Hann 0.78 1.21 0.91

Blackman 0.89 1.35 1.05

VCP 12SD(Z) (dB)

SD(v) (m/s)

SD(v) (m/s)

Rectangular 0.62 1.02 0.88

Hamming 0.84 1.36 1.06

Von Hann 0.82 1.37 1.08

Blackman 0.94 1.54 1.25

VCP 21SD(Z) (dB)

SD(v) (m/s)

SD(v) (m/s)

Rectangular 0.46 0.68 0.52

Hamming 0.62 0.91 0.66

Von Hann 0.62 0.93 0.68

Blackman 0.70 1.03 0.76

Errors correspond to the 1st elevation angleFor Z and v: v = 4 m/s and SNR = 10 dBFor v: v = 4 m/s and SNR = 8 dB

Relative Errors with Different Data Windows for VCPs 11, 12, and 21

VCP 11SD(Z) SD(Zrect)

SD(Zrect)

SD(v) SD(vrect)

SD(vrect)

SD(v) SD(v rect)

SD(v rect)

Hamming 38% 34% 23%

Von Hann 34% 36% 25%

Blackman 53% 52% 44%

Errors correspond to the 1st elevation angleFor Z and v: v = 4 m/s and SNR = 10 dBFor v: v = 4 m/s and SNR = 8 dB

VCP 12SD(Z) SD(Zrect)

SD(Zrect)

SD(v) SD(vrect)

SD(vrect)

SD(v) SD(v rect)

SD(v rect)

Hamming 35% 33% 20%

Von Hann 32% 34% 23%

Blackman 52% 51% 42%

VCP 21SD(Z) SD(Zrect)

SD(Zrect)

SD(v) SD(vrect)

SD(vrect)

SD(v) SD(v rect)

SD(v rect)

Hamming 35% 34% 27%

Von Hann 35% 37% 31%

Blackman 52% 51% 46%