1. Noise is estimated [NEXRAD technical manual] at elevation >20  and scaled. Data with low Signal/Noise are determined and censored (black or white on PPI). The VAD algorithm [Rabin & Zrnic 1979] uses 30 km range for the wind speed and direction computation. The algorithm performs the least square fit on “good” velocities if at least 25 velocities are available. In clear air censoring may result in a very sparsely filled radar display. However, there is no guarantee that the presumably “good” data is good.  180 90 0 -90 -180 v (m s –1 ) 30 20 10 0 -10 -20 -30 v (m s –1 ) 30 20 10 0 -10 -20 -30 180 90 0 -90 -180 30 20 10 0 -10 -20 -30 180 90 0 -90 -180 180 90 0 -90 -180 180 90 0 -90 -180 180 90 0 -90 -180 Z DR 30 20 10 0 -10 30 20 10 0 -10 -20 -30 v (m s –1 ) 30 20 10 0 -10 30 20 10 0 -10 -20 -30 v (m s –1 ) 30 20 10 0 -10 30 20 10 0 -10 -20 -30 v (m s –1 ) 30 20 10 0 -10 30 20 10 0 -10 -20 -30 v (m s –1 ) 30 20 10 0 -10 30 20 10 0 -10 -20 -30 v (m s –1 ) 30 20 10 0 -10 30 20 10 0 -10 -20 -30 v (m s –1 ) 1.0 0.9 0.8 0.7 30 20 10 0 -10 -20 -30 1.0 0.9 0.8 0.7 30 20 10 0 -10 -20 -30 1.0 0.9 0.8 0.7 30 20 10 0 -10 -20 -30 1.0 0.9 0.8 0.7 30 20 10 0 -10 -20 -30 1.0 0.9 0.8 0.7 30 20 10 0 -10 -20 -30 v (m s –1 ) 1.0 0.9 0.8 0.7 30 20 10 0 -10 -20 -30  hv Histograms of Z DR spectral densities @ az. 180  and ranges from 30 to 80 km Histograms of  hv spectral densities @ az. 180  and ranges from 30 to 80 km Velocity (m s -1 ) 6 pm 7 pm8 pm 9 pm 10 pm11 pm 6 pm 7 pm8 pm 9 pm 10 pm11 pm 6 pm 7 pm8 pm 9 pm 10 pm11 pm Velocity (m s -1 ) Birds Insects I Insects II AMS 87 TH ANNUAL MEETING 23 RD CONF. ON IIPS SAN ANTONIO, TEXAS JANUARY 2007 P2.13 AMS 87 TH ANNUAL MEETING 23 RD CONF. ON IIPS SAN ANTONIO, TEXAS JANUARY 2007 Event : Clear air with biological scatterers Nocturnal insects and migrating birds Diurnal passive and active insects, and birds Spectral densities of 2-pol variables (az. 180  ) 6 pm 7 pm8 pm 9 pm10 pm 11 pm 6 pm 7 pm8 pm 9 pm10 pm 11 pm Histograms of Z DR &  spectral densities @ 30  increments of azimuths and ranges from 30 to 80 km Birds Insects I Insects II Birds Insects I Wind Insects II Histograms of  spectral densities @ az. 180  and ranges from 30 to 80 km Reflectivity Z h (dB) Velocity v h (m s –1 ) Differential Reflectivity Z DR (dB) Differential Phase  (  ) Copolar correlation  hv Spectral density of Reflectivity Z h (dB) Spectral density of Differential Reflectivity Z DR (dB) Spectral density of Differential Phase  (  ) Spectral density of Copolar correlation  hv Facts and Problems 5 6 Conclusion We have demonstrated that unreasonable values of polarimetric variables computed with standard techniques are often a result of a mixture of different types of scatterers. The distributions of polarimetric variables in velocity provide a unique way for observing multiple processes in each resolution volume and understanding the values of the resulting polarimetric averages. Presented technique can be used for quality assessment of the spectral moments and polarimetric variables. If there is one sinusoid in the VAD there is one type of dominant scatterers and the moments in PPIs can be trusted. Otherwise, the moment show biased estimates and should be recalculated using more sophisticated (spectral) techniques. Presented technique can be used for the assessment of intrinsic values of polarimetric variables for different scatterer types (not limited to biological scatterers) 8 3 4 ? SPECTRAL DENSITIES OF POLARIMETRIC VARIABLES FOR RETREIVING WINDS AND DETERMINING SCATTERER TYPES Azimuth 2D histograms of Differential Reflectivity Z DR (dB) 2D histograms of Differential Phase  (  ) 2D histograms of Copolar correlation  hv Azimuth 0.7 1  hv 0.7 1  hv -10 20 Z DR -10 20 Z DR -180 180  -180 180  11pm 10pm 9pm 8pm 7pm 6pm Summary We examine range-averaged polarimetric distributions as a function of azimuth and time for a clear air case. The clear air case occurred on September 7, 2004 and was observed with a 10-cm polarimetric weather radar KOUN (Norman OK). These data capture the evolution of the transition process between diurnal and nocturnal scatterers. We construct an ensemble of the two-dimensional (2D) histograms to assess the occurrence of scatterers moving at a certain velocity and exhibiting certain polarimetric properties. Therefore the statistics of the contributing scatterer types is exposed. The echo types are recognized and a novel approach for velocity azimuth display (VAD) analysis from the histograms is presented. The mean flows of different scatterer types are deduced from the clusters with large occurrences and similar polarimetric properties in the 2D histograms of polarimetric density and velocity. We obtain marginal distributions for two dominant types of diurnal and two dominant types of nocturnal scatterers that yield significant new insights into the composite VADs and corresponding PPIs. Presented technique is capable to assess the degree of contamination in a PPI. For two or more sinusoids in a composite VAD standard techniques for moments and polarimetric variable estimation should be replaced with techniques using judicious spectral analyses followed by the appropriate recognition and filtering schemes. 5 3 2 6 7 3 7 1 2 Radar data and weather conditions NOAA/NSSL research S-band dual polarization radar (KOUN) collected time-series data on Sept. 7, 2004 at 11 pm (04 UT). The pulse repetition time is 780  s. The number of samples M is 128. The unambiguous range and velocity are 117 km and 35 m s –1. Elevation is 0.5 degree. Fair weather and a N-NW wind at 5 to 10 m s –1 were recorded on that day. However, the velocities registered by the radar reach 30 m s –1. 7 Deducing degree of contamination by estimating mean dominant flows (~VAD) OK BAD The mean of these sinusoids does not show neither correct velocity nor direction  need spectral analyses GOOD Nocturnal insects and migrating birds Diurnal passive and active insects, and birds Small bias or unbiased  PPIs can be trusted 6 pm 7 pm8 pm 9 pm10 pm 11 pm 6 pm 7 pm8 pm 9 pm10 pm 11 pm 6 pm 7 pm8 pm 9 pm10 pm 11 pm Alternative way to visualize 2D histograms and assess contamination 9 2. Find polarimetric spectral density spectral density for each section for each section 3. Compute 2D histogram 2D histogram for each section for each section 5. Use transparency to expose the helix (~VAD sinusoid) of dominant scatterers 4. Collect the 2D histograms histograms Helix of wind 1. Partition scanned region on sections region on sections Example shows differential phase  spectral density @ 8 pm Reference 2: S. Bachmann and D. Zrnić, “Three Dimensional Display of Polarimetric, Spectral, Azimuthal Attributes of Scatterers,” Trans. On Geosciences and Remote Sensing, submitted Dec. 2006