This paper contains a description of the most recent version of the hydrometeor classification algorithm for polarimetric Weather Surveillance Radar-1988 Doppler (WSR-88D). This version contains several modifications and refinements of the previous echo classification algorithm based on the principles of fuzzy logic. These modifications include the estimation of confidence factors that characterize the possible impacts of all error sources on radar measurements, the assignment of the matrix of weights that characterizes the classification power of each variable with respect to every class of radar echo, and the implementation of a class designation system based on the distance from the radar and the parameters of the melting layer that are determined as functions of azimuth with polarimetric radar measurements. These additions provide considerable flexibility and improve the discrimination between liquid and frozen hydrometeors. The new classification scheme utilizes all available polarimetric variables and discerns 10 different classes of radar echoes. Furthermore, a methodology for the new fuzzy logic classification scheme is discussed and the results are illustrated using polarimetric radar data collected with the Norman, Oklahoma (KOUN), WSR-88D prototype radar during a mesoscale convective system event on 13 May 2005.

As part of the Joint Polarization Experiment (JPOLE), the National Severe Storms Laboratory conducted an operational demonstration of the polarimetric utility of the Norman, Oklahoma (KOUN), Weather Surveillance Radar-1988 Doppler (WSR-88D). The capability of the KOUN radar to estimate rainfall is tested on a large dataset representing different seasons and different types of rain. A dense gauge network—the Agricultural Research Service (ARS) Micronet—is used to validate different polarimetric algorithms for rainfall estimation. One-hour rain totals are estimated from the KOUN radar using conventional and polarimetric algorithms and are compared with hourly accumulations measured by the gauges. Both point and areal rain estimates are examined. A new “synthetic” rainfall algorithm has been developed for rainfall estimation. The use of the synthetic polarimetric algorithm results in significant reduction in the rms errors of hourly rain estimates when compared with the conventional nonpolarimetric relation: 1.7 times for point measurements and 3.7 times for areal rainfall measurements.

Techniques for the absolute calibration of radar reflectivity Z and differential reflectivity ZDR measured with dual-polarization weather radars are examined herein.Calibration of Z is based on the idea of self-consistency among Z, ZDR, and the specific differential phase KDP in rain. Extensive spatial and temporal averaging is used to derive the average values of ZDR and KDP for each 1 dB step in Z. Such averaging substantially reduces the standard error of the KDP estimate so the technique can be used for a wide range of rain intensities, including light rain.In this paper, the performance of different consistency relations is analyzed and a new self-consistency methodology is suggested. The proposed scheme substantially reduces the impact of variability in the drop size distribution and raindrop shape on the quality of the Z calibration. The new calibration technique was tested on a large polarimetric dataset obtained during the Joint Polarization Experiment in Oklahoma and yielded an accuracy of Z calibration within 1 dB.Absolute calibration of ZDR is performed using solar measurements at orthogonal polarizations and polarimetric properties of natural targets like light rain and dry aggregated snow that are probed at high elevation angles. Because vertical sounding is prohibited for operational Weather Surveillance Radar-1988 Doppler (WSR-88D) radars because of mechanical constraints, the existing methodology for ZDR calibration is modified for nonzenith elevation angles. It is shown that the required 0.1–0.2-dB accuracy of the ZDR calibration is potentially achievable.

Polarimetric radars are shown to be capable of tornado detection through the recognition of tornadic debris signatures that are characterized by the anomalously low cross-correlation coefficient ρhv and differential reflectivity ZDR. This capability is demonstrated for three significant tornadic storms that struck the Oklahoma City, Oklahoma, metropolitan area. The first tornadic debris signature, based on the measurements with the National Severe Storms Laboratory’s Cimarron polarimetric radar, was reported for a storm on 3 May 1999. Similar signatures were identified for two significant tornadic events during the Joint Polarization Experiment (JPOLE) in May 2003. The data from these storms were collected with a polarimetric prototype of the Next-Generation Weather Radar (NEXRAD). In addition to a small-scale debris signature, larger-scale polarimetric signatures that might be relevant to tornadogenesis were persistently observed in tornadic supercells. The latter signatures are likely associated with lofted light debris (leaves, grass, dust, etc.) in the inflow region and intense size sorting of hydrometeors in the presence of strong wind shear and circulation.

A novel methodology is introduced for processing and presenting polarimetric data collected by weather surveillance radars. It involves azimuthal averaging of radar reflectivity Z, differential reflectivity Z_DR, cross-correlation coefficient ρ_hv, and differential phase Φ_DP at high antenna elevation, and presenting resulting quasi-vertical profiles (QVPs) in a height-versus-time format. Multiple examples of QVPs retrieved from the data collected by S-, C-, and X-band dual-polarization radars at elevations ranging from 6.4° to 28° illustrate advantages of the QVP technique. The benefits include an ability to examine the temporal evolution of microphysical processes governing precipitation production and to compare polarimetric data obtained from the scanning surveillance weather radars with observations made by vertically looking remote sensors, such as wind profilers, lidars, radiometers, cloud radars, and radars operating on spaceborne and airborne platforms. Continuous monitoring of the melting layer and the layer of dendritic growth with high vertical resolution, and the possible opportunity to discriminate between the processes of snow aggregation and riming, constitute other potential benefits of the suggested methodology.

A new polarimetric melting layer detection algorithm (MLDA) is utilized to estimate the top (melting level) and bottom boundaries of the melting layer and is tailored for operational deployment. Melting layer designations from a polarimetric prototype of the Weather Surveillance Radar-1988 Doppler (WSR-88D) in central Oklahoma are validated using radiosonde and model temperature analysis. It is demonstrated that the MLDA estimates the top of the melting layer with a root-mean-square error of about 200 m within 60 km of the radar. There is evidence that the polarimetric radar might yield better spatial and temporal designation of the melting layer within the storm than that obtained from existing numerical model output and soundings.