Technical Interchange Meeting Spring 2008: Status and Accomplishments

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

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

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)

Polarimetric Spectral Density of Z DR

Polarimetric Spectral Analysis Spectral Density of Differential Reflectivity S DR (v k ) = |A h (v k )| 2 /|A v (v k )| 2 Spectral Density of Differential Phase S DP (v k ) = arg{A h *(v k )A v (v k )} Spectral Density of Cross Correlation Coefficient S ρhv (v k ) = Running sum product of three spectral coefficients

Spectral density of Z DR Spectral density of ρ hv Spectral densities of Powers i.e., Doppler Spectra Densities are medians from 20 range locations, 30 to 35 km

Histograms of Pol Variables from weather and clutter

Histograms of pol var obtained from full spectra and from 3 lines centered on 0 Doppler Cold Season Warm Season

Adaptive Clutter Recognition Criterion -Compute the polarimetric variables, Z DR, ρ hv, and δ from the spectra at and near zero Doppler -For SNR>3 dB: Declare clutter If {(Z DR 5 dB) OR (| ρ hv | 20 o )} Otherwise it is not clutter

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

AP cases a) Sep 2007 c) Oct 2007

H Histograms Sep 2007 case

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

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

GMAP applied independently everywhere to H and V channels

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

Probability of Detection

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?

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

180  - power spectral density Range, km Power, dB Power along the radial Range, km Velocity, m s –1 Power spectral density field Power, dB

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

Polarimetric Sea Clutter Algorithm (Ryzhkov et al. paper) This is Spring bonus Fuzzy logic algorithm uses: SD(P), SD(Φ DP ), L DR, Z DR, ρ hv, and V Tested on SPOL data from 2001 (Washington coast)

Scattergrams