Data Windowing in ORDA Technical Briefing Sebastián Torres 1,2, Chris Curtis 1,2, Rodger Brown 2, and Michael Jain 2 1 Cooperative Institute for Mesoscale Meteorological Studies 2 National Severe Storms Laboratory NEXRAD Technical Advisory Committee Meeting March 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 Blackman More aggressive RectangularHamming von HannBlackman

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 Rectangular Window Hamming Window

Standard Error of Moment Estimates for Different Windows vs. Spectrum Width (VCP 11) Parameters correspond to 1 st split cut of VCP 11 M = 17, T s = 3.1 ms, SNR = 10 dB M = 52, T s = 1 ms, SNR = 10 dB

Standard Error of Moment Estimates for Different Windows vs. SNR (VCP 11) Parameters correspond to 1 st split cut of VCP 11 M = 17, T s = 3.1 ms,  v = 4 m/s M = 52, T s = 1 ms,  v = 4 m/s

Standard Error of Moment Estimates for Different Windows vs. SNR (VCP 12) Parameters correspond to 1 st split cut of VCP 12 M = 15, T s = 3.1 ms,  v = 4 m/s M = 40, T s = 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 SurveillanceDoppler Rectangular windowHamming window Elv (deg) WFPRIM MSD(Z) (dB)*SD(v) (m/s)SD(w) (m/s)SD(Z) (dB)*SD(v) (m/s)SD(w) (m/s) 0.5CS CD CS CD B B B B B CD CD CD CD CD CD CD f = 2800 MHz, PRI Delta C * Reflectivity errors in the Batch mode are indicated for overlaid / non-overlaid situations. SurveillanceDoppler Rectangular window Elv (deg) WFPRIM MSD(Z) (dB)*SD(v) (m/s)SD(w) (m/s) 0.5CS CD CS CD B B B B B CD CD CD CD CD CD CD SurveillanceDoppler Rectangular window Elv (deg) WFPRIM MSD(Z) (dB)*SD(v) (m/s)SD(w) (m/s) 0.5CS CD CS CD B B B B B CD CD CD CD CD CD CD

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 Comparison Reflectivity (KCRI – 03/19/2006)

Rectangular window

Base Data Comparison Reflectivity (KCRI – 03/19/2006) Hamming window

Rectangular window Algorithm Performance Comparison One-hour Precipitation Accumulation Courtesy Bob Lee (ROC)

Hamming window Algorithm Performance Comparison One-hour Precipitation Accumulation Courtesy Bob Lee (ROC)

Algorithm Performance Comparison Mesocyclone Detection Algorithm  Rectangular window  Hamming window time Accumulated number of detections

Range Gates with … ClutterOverlaid echoes Clean echoes Split Cuts Surveillance data 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 … ClutterOverlaid echoes Clean echoes Split Cuts Surveillance data Baseline processingBlackman-Default Super ResolutionBlackman-Von Hann Doppler data Baseline processingBlackmanDefault Super ResolutionBlackmanVon Hann SZ-2BlackmanVon HannDefault Batch Cuts Baseline processing for Long-PRT data Rectangular- Baseline processing for Short-PRT data BlackmanDefault Doppler Cuts Baseline processingBlackman-Default Range Gates with … ClutterOverlaid echoes Clean echoes Split Cuts Surveillance data Baseline processingBlackman-Hamming Super ResolutionBlackman-Von Hann Doppler data Baseline processingBlackmanHamming Super ResolutionBlackmanVon Hann SZ-2BlackmanVon HannHamming Batch Cuts Baseline processing for Long-PRT data Rectangular- Baseline processing for Short-PRT data BlackmanHamming Doppler Cuts Baseline processingBlackman-Hamming Range Gates with … ClutterOverlaid echoes Clean echoes Split Cuts Surveillance data Baseline processingBlackman-Rectangular Super ResolutionBlackman-Von Hann Doppler data Baseline processingBlackmanRectangular Super ResolutionBlackmanVon Hann SZ-2BlackmanVon HannRectangular Batch Cuts Baseline processing for Long-PRT data Rectangular- Baseline processing for Short-PRT data BlackmanRectangular Doppler Cuts Baseline processingBlackman-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 fixed Stay tuned!

Back-Up Slides

Relative Standard Error of Moment Estimates for Different Windows vs. Spectrum Width Parameters correspond to 1 st split cut of VCP 11 M = 17, T s = 3.1 ms, SNR = 10 dB M = 52, T s = 1 ms, SNR = 10 dB

Relative Standard Error of Moment Estimates for Different Windows vs. SNR Parameters correspond to 1 st split cut of VCP 11 M = 17, T s = 3.1 ms,  v = 4 m/s M = 52, T s = 1 ms,  v = 4 m/s

Errors with Different Data Windows for VCPs 11, 12, and 21 VCP 11 SD( Z ) (dB) SD( v ) (m/s) SD(  v ) (m/s) Rectangular Hamming Von Hann Blackman VCP 12 SD( Z ) (dB) SD( v ) (m/s) SD(  v ) (m/s) Rectangular Hamming Von Hann Blackman VCP 21 SD( Z ) (dB) SD( v ) (m/s) SD(  v ) (m/s) Rectangular Hamming Von Hann Blackman Errors correspond to the 1 st elevation angle For Z and  v :  v = 4 m/s and SNR = 10 dB For v :  v = 4 m/s and SNR = 8 dB

Relative Errors with Different Data Windows for VCPs 11, 12, and 21 VCP 11 SD( Z )  SD( Z rect ) SD( Z rect ) SD( v )  SD( v rect ) SD( v rect ) SD(  v )  SD(  v rect ) SD(  v rect ) Hamming38%34%23% Von Hann34%36%25% Blackman53%52%44% Errors correspond to the 1 st elevation angle For Z and  v :  v = 4 m/s and SNR = 10 dB For v :  v = 4 m/s and SNR = 8 dB VCP 12 SD( Z )  SD( Z rect ) SD( Z rect ) SD( v )  SD( v rect ) SD( v rect ) SD(  v )  SD(  v rect ) SD(  v rect ) Hamming35%33%20% Von Hann32%34%23% Blackman52%51%42% VCP 21 SD( Z )  SD( Z rect ) SD( Z rect ) SD( v )  SD( v rect ) SD( v rect ) SD(  v )  SD(  v rect ) SD(  v rect ) Hamming35%34%27% Von Hann35%37%31% Blackman52%51%46%