Technical Interchange Meeting

1. Technical Interchange Meeting Spring 2008: Status and Accomplishments

2. TASKS • Task 1. Support Deployment of SZ-2 RV Ambiguity Mitigation Algorithms • To support the ROC in upgrading the SZ-2 RV Ambiguity Mitigation algorithm • To discuss any anomalies in the SZ-2 algorithm encountered by the ROC at operational sites

3. TASKS • Task 2. Support implementation of Staggered PRT • Determine operational scanning strategy and if any additional PRFs are required • Update AEL to allow any PRT ratio • Analyze other PRT ratios • Support implementation of the Clutter Spectrum Filter • Support validation and verification of Staggered PRT Clutter Spectrum Filter • Task 3. Spectrum width improvements • Provide an algorithm that includes the spectrum width improvements

4. TASKS • Task 4. Spectral Processing • Provide information on NSSL’s spectral processing • Spectral processing for dual polarization variables (Bachmann and Zrnic paper, and Bachmann’s PhD) • Ground clutter identification using Polarimetric Spectral densities (Melnikov and Zrnic paper Radar Conf. Australia and report to be put on NSSL’s WEB)

5. Polarimetric Spectral Density of ZDR

6. Polarimetric Spectral Analysis • Spectral Density of Differential Reflectivity SDR (vk) = |Ah(vk)|2 /|Av(vk)|2 • Spectral Density of Differential Phase SDP(vk) = arg{Ah*(vk)Av(vk)} • Spectral Density of Cross Correlation Coefficient Sρhv(vk) = Running sum product of three spectral coefficients

7. Spectral density of ZDR Spectral density of ρhv Spectral densities of Powers i.e., Doppler Spectra Densities are medians from 20 range locations, 30 to 35 km

8. Histograms of PolVariables fromweather and clutter

9. Histogramsof pol varobtained from full spectra and from 3 linescentered on 0Doppler Warm Season Cold Season

10. Adaptive Clutter Recognition Criterion • Compute the polarimetric variables, ZDR, ρhv, and • δfrom the spectra at and near zero Doppler • -For SNR>3 dB: • Declare clutter • If {(ZDR < -2 dB) OR (ZDR > 5 dB) OR (|ρhv|<0.8) OR (δ > 20o)} • Otherwise it is not clutter

11. Adaptive Generation of Clutter Map using Dual Polarization Spectra of ground clutter and weather at Horizontal and Vertical polarization Filtered spectra for the decision Notch filter

12. AP casesa) Sep 2007c) Oct 2007

13. HistogramsSep 2007 case H

14. Clutter identified with the algorithm (red) in a field of weather echoes (aqua marine)

15. Distribution of Clutter to Noise Ratio in the H and V channels

16. GMAPapplied independentlyeverywhereto H and Vchannels

17. Adaptive GMAP applied equally to both channels according to the spectral recognition (Serbian oven baked bread) alg acting on the H channel

18. Probability of Detection

19. Performance • Simulations • Add time series data from clutter and weather and apply the classification criterion • Probability of false alarm ~ 5% • Probability of detection ~ 90 % • Addition of coherency threshold (NCAR) could improve the performance?

20. Polarimetric Spectral Analysis Separates Insects from BirdsBachmann and Zrnic 2007Bachmann’s PhD (NSSL,WEB)

21. 35 0 -35 Velocity, m s–1 0 20 40 60 80 100 Range, km Radial @ 180 - power spectral density Power along the radial Power, dB Range, km Power spectral density field Power, dB

22. Spectral density of , Zdr,  Average spectral densities for ranges from 30 to 70 km for each radial of PPI to get Spectral VADs N NE E SE S SW W NW N Power, dB Zdr, dB Birds Insects  , degree Azimuth, degree

23. Polarimetric Sea Clutter Algorithm (Ryzhkov et al. paper) • This is Spring bonus • Fuzzy logic algorithm uses: SD(P), SD(ΦDP), LDR, ZDR, ρhv, and V • Tested on SPOL data from 2001 (Washington coast)

24. Scattergrams