Polarimetric Weather Surveillance Radar-1988 Doppler Research Articles

Application of Spectral Polarimetry to a Hailstorm at Low Elevation Angle

AbstractSpectral polarimetry has the potential to be used to study microphysical properties in relation to the dynamics within a radar resolution volume by combining Doppler and polarimetric measurements. The past studies of spectral polarimetry have focused on using radar measurements from higher elevation angles, where both the size sorting from the hydrometeors’ terminal velocities and polarimetric characteristics are maintained. In this work, spectral polarimetry is applied to data from the 0° elevation angle, where polarimetric properties are maximized. Radar data collected by the C-band University of Oklahoma Polarimetric Radar for Innovations in Meteorology and Engineering (OU-PRIME) during a hailstorm event on 24 April 2011 are used in the analysis. The slope of the spectral differential reflectivity exhibits interesting variations across the hail core, which suggests the presence of size sorting of hydrometeors caused by vertical shear in a turbulent environment. A nearby S-band polarimetric Weather Surveillance Radar-1988 Doppler (KOUN) is also used to provide insights into this hailstorm. Moreover, a flexible numerical simulation is developed for this study, in which different types of hydrometeors such as rain and melting hail can be considered individually or as a combination under different sheared and turbulent conditions. The impacts of particle size distribution, shear, turbulence, attenuation, and mixture of rain and melting hail on polarimetric spectral signatures are investigated with the simulated Doppler spectra and spectral differential reflectivity.

Insights into the Evolving Microphysical and Kinematic Structure of Northeastern U.S. Winter Storms from Dual-Polarization Doppler Radar

The recent Weather Surveillance Radar-1988 Doppler (WSR-88D) network upgrade to dual-polarization capabilities allows for bulk characterization of microphysical processes in northeastern U.S. winter storms for the first time. In this study, the quasi-vertical profile (QVP) technique (wherein data from a given elevation angle scan are azimuthally averaged and the range coordinate is converted to height) is extended and applied to polarimetric WSR-88D observations of six Northeast winter storms to survey their evolving, bulk vertical microphysical and kinematic structures. These analyses are supplemented using hourly analyses from the Rapid Refresh (RAP) model. Regions of ascent inferred from QVPs were consistently associated with notable polarimetric signatures, implying planar crystal growth when near −15°C, and riming and secondary ice production at higher temperatures. The heaviest snowfall occurred most often when ascent and enhanced propagation differential phase shift ([Formula: see text]) occurred near −15°C. When available, limited surface observations confirmed heavy snowfall rates and revealed large snow-to-liquid ratios at these times. Other cases revealed sudden, large melting-layer excursions associated with precipitation-type transitions near the surface. RAP analyses failed to capture such complex evolution, demonstrating the added value of dual-polarization radar observations in these scenarios and the potential use of radar data for assessing model performance in real time. These insights are a preliminary step toward better understanding the complex processes in northeastern U.S. winter storms.

Neuro-Fuzzy Gust Front Detection Algorithm With S-Band Polarimetric Radar

A gust front (GF) is the leading edge of the cold outflow from a thunderstorm. The upgrade of the S-band Weather Surveillance Radar-1988 Doppler (WSR-88D) to dual-polarization has been completed recently in the U.S. Therefore, it is timely to exploit the added benefits of polarimetric variables to identify GFs. In this paper, six signatures derived from polarimetric WSR-88D data are developed to characterize GFs, including medium reflectivity, apparent thin line feature in reflectivity, and the motion of reflectivity quantified by a line feature parameter, high differential reflectivity, low copolar cross-correlation coefficient, apparent convergence manifested by the large radial shear, and large standard deviation of differential phase. These signatures are fuzzy in nature, and therefore, a novel neuro-fuzzy GF detection algorithm (NFGDA) is developed using a fuzzy logic inference system, which is optimized by a training process using a neural network. WSR-88D data from 11 cases (totaling 121 volume scans) are used to evaluate the performance of NFGDA and compared to the operational machine intelligent GF algorithm (MIGFA) with single polarization data. The results show that NFGDA can provide improved performance with a higher probability of detection of 92% (versus 78% with MIGFA), lower false alarm ratio of 0% (versus 9%), and higher percentage correct of 93% (versus 74%). Additional length-based scoring schemes show that NFGDA can correctly detect 62% (41% with MIGFA) of the total length of GFs, and minimize falsely detected length to 7% (61%).

Novel Tornado Detection Using an Adaptive Neuro-Fuzzy System with S-Band Polarimetric Weather Radar

AbstractTornado debris signatures (TDS) exhibited in polarimetric measurements have the potential to facilitate tornado detection. The upgrade of the network of S-band Weather Surveillance Radar-1988 Doppler (WSR-88D) to dual polarization was completed recently. Therefore, it is timely to develop a tornado detection algorithm that capitalizes on TDS and integrates with other existing signatures observed in the velocity (shear signature) and Doppler spectrum (spectral signature) fields. In this work, the analysis indicates that TDS are not always present with shear and spectral signatures. A neuro-fuzzy tornado detection algorithm (NFTDA) using the Sugeno fuzzy inference system is developed to consider the strength of different tornado signatures that are characterized by operationally available data of differential reflectivity, cross-correlation coefficient, velocity difference, and spectrum width with the goal of reliable and robust detection. The performance is further optimized using a training procedure based on a neural network. The performance of NFTDA is evaluated using polarimetric WSR-88D data from 17 tornadoes with enhanced Fujita (EF) scale ratings ranging from EF-0 to EF-4 and distance from 16 to 133 km to the radar. NFTDA performs well with the probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) of 86%, 11%, and 78%, respectively. Moreover, a computationally efficient method is introduced to analyze the sensitivity of the tornado signatures. It is demonstrated that even though TDS play a less important role than the other two signatures, TDS can help improve the detection, especially during the later stage of a tornado, when the shear and spectral signatures become weaker.

Spatial and Temporal Characteristics of Polarimetric Tornadic Debris Signatures

AbstractNonmeteorological scatter, including debris lofted by tornadoes, may be detected using the polarimetric radar variables. For the 17 months from January 2012 to May 2013, radar data were examined for each tornado reported in the domain of an operational polarimetric Weather Surveillance Radar-1988 Doppler (WSR-88D). Characteristics of the tornadic debris signature (TDS) were recorded when a signature was present. Approximately 16% of all tornadoes reported in Storm Data were associated with a debris signature, and this proportion is shown to vary regionally. Signatures were more frequently seen with tornadoes that were rated higher on the enhanced Fujita (EF) scale, with tornadoes causing higher reported total property damage, with tornadoes that were closer to the radar and thus intercepted by the beam at lower altitude, and associated with tornadoes with greater total pathlength. Tornadic debris signatures were most common in spring, when more strong tornadoes occur, and in autumn, when natural debris is more available. Debris-signature areal extent is shown to increase consistently with EF-scale rating and tornado longevity. Vertical extent of a TDS is shown to be greatest for strong, long-lived tornadoes with large radii of damaging wind. Land cover is also shown to exhibit some control over TDS characteristics—in particular, a large percentage of tornadoes with substantial track over urban land cover exhibited a TDS and do so very quickly after reported tornadogenesis, as compared with tornadoes over other land-cover classifications. TDS characteristics over grassland and cropland tended to be similar.

The Analysis and Prediction of Microphysical States and Polarimetric Radar Variables in a Mesoscale Convective System Using Double-Moment Microphysics, Multinetwork Radar Data, and the Ensemble Kalman Filter

Abstract Doppler radar data are assimilated with an ensemble Kalman Filter (EnKF) in combination with a double-moment (DM) microphysics scheme in order to improve the analysis and forecast of microphysical states and precipitation structures within a mesoscale convective system (MCS) that passed over western Oklahoma on 8–9 May 2007. Reflectivity and radial velocity data from five operational Weather Surveillance Radar-1988 Doppler (WSR-88D) S-band radars as well as four experimental Collaborative and Adaptive Sensing of the Atmosphere (CASA) X-band radars are assimilated over a 1-h period using either single-moment (SM) or DM microphysics schemes within the forecast ensemble. Three-hour deterministic forecasts are initialized from the final ensemble mean analyses using a SM or DM scheme, respectively. Polarimetric radar variables are simulated from the analyses and compared with polarimetric WSR-88D observations for verification. EnKF assimilation of radar data using a multimoment microphysics scheme for an MCS case has not previously been documented in the literature. The use of DM microphysics during data assimilation improves simulated polarimetric variables through differentiation of particle size distributions (PSDs) within the stratiform and convective regions. The DM forecast initiated from the DM analysis shows significant qualitative improvement over the assimilation and forecast using SM microphysics in terms of the location and structure of the MCS precipitation. Quantitative precipitation forecasting skills are also improved in the DM forecast. Better handling of the PSDs by the DM scheme is believed to be responsible for the improved prediction of the surface cold pool, a stronger leading convective line, and improved areal extent of stratiform precipitation.

Principles and applications of dual-polarization weather radar. Part I: Description of the polarimetric radar variables

The United States Weather Surveillance Radar-1988 Doppler (WSR-88D) radar network has been upgraded to dual-polarization capabilities, providing operational and research meteorologists with a wealth of new information regarding the types and distributions of hydrometeors within precipitating storms, as well as a means for improved radar data quality. In addition to the conventional moments of reflectivity factor at horizontal polarization (ZH), Doppler velocity (Vr), and Doppler spectrum width (W), the new variables available from upgraded radars are the differential reflectivity (ZDR), differential propagation phase shift (ΦDP), specific differential phase (KDP), and the co-polar correlation coefficient (ρhv or CC). In the first part of this review series, a description of the polarimetric radar variables available from the newly polarimetric WSR-88D radars is provided. An emphasis is made on their physical meaning and interpretation in the context of operational meteorology.

Structures of Bragg Scatter Observed with the Polarimetric WSR-88D

Abstract Enhancements to signal processing and data collection in the dual-polarization Weather Surveillance Radar-1988 Doppler (WSR-88D) to increase its detection capability yield observations of “fine” structures from Bragg scatterers. Several types of the fine structures observed in and above the boundary layer are discussed. These Bragg scatter structures include the top of the convective boundary layer, nonprecipitating clouds, strong convective plumes above the boundary layer, and a layer of weak reflections associated with decaying boundary layer turbulence. A conclusion that data from polarimetric WSR-88Ds can be used to obtain the depth of the convective boundary layer is made.

Polarimetric Signatures above the Melting Layer in Winter Storms: An Observational and Modeling Study

AbstractPolarimetric radar observations above the melting layer in winter storms reveal enhanced differential reflectivity ZDR and specific differential phase shift KDP, collocated with reduced copolar correlation coefficient ρhv; these signatures often appear as isolated “pockets.” High-resolution RHIs and vertical profiles of polarimetric variables were analyzed for a winter storm that occurred in Oklahoma on 27 January 2009, observed with the polarimetric Weather Surveillance Radar-1988 Doppler (WSR-88D) in Norman. The ZDR maximum and ρhv minimum are located within the temperature range between −10° and −15°C, whereas the KDP maximum is located just below the ZDR maximum. These signatures are coincident with reflectivity factor ZH that increases toward the ground. A simple kinematical, one-dimensional, two-moment bulk microphysical model is developed and coupled with electromagnetic scattering calculations to explain the nature of the observed polarimetric signature. The microphysics model includes nucleation, deposition, and aggregation and considers only ice-phase hydrometeors. Vertical profiles of the polarimetric radar variables (ZH, ZDR, KDP, and ρhv) were calculated using the output from the microphysical model. The base model run reproduces the general profile and magnitude of the observed ZH and ρhv and the correct shape (but not magnitude) of ZDR and KDP. Several sensitivity experiments were conducted to determine if the modeled signatures of all variables can match the observed ones. The model was incapable of matching both the observed magnitude and shape of all polarimetric variables, however. This implies that some processes not included in the model (such as secondary ice generation) are important in producing the signature.

Ensemble Kalman Filter Analyses of the 29–30 May 2004 Oklahoma Tornadic Thunderstorm Using One- and Two-Moment Bulk Microphysics Schemes, with Verification against Polarimetric Radar Data

AbstractThe performance of ensemble Kalman filter (EnKF) analysis is investigated for the tornadic supercell on 29–30 May 2004 in Oklahoma using a dual-moment (DM) bulk microphysics scheme in the Advanced Regional Prediction System (ARPS) model. The comparison of results using single-moment (SM) and DM microphysics schemes evaluates the benefits of using one over the other during storm analysis. Observations from a single operational Weather Surveillance Radar-1988 Doppler (WSR-88D) are assimilated and a polarimetric WSR-88D in Norman, Oklahoma (KOUN), is used to assess the quality of the analysis.Analyzed reflectivity and radial velocity in the SM and DM experiments compare favorably with independent radar observations in general. However, simulated polarimetric signatures obtained from analyses using a DM scheme agree significantly better with polarimetric signatures observed by KOUN in terms of the general structure, location, and intensity of the signatures than those generated from analyses using an SM scheme.These results demonstrate for the first time for a real supercell storm that EnKF data assimilation using a numerical model with an adequate microphysics scheme (i.e., a scheme that predicts at least two moments of the hydrometeor size distributions) is capable of producing polarimetric radar signatures similar to those seen in observations without directly assimilating polarimetric data. In such cases, the polarimetric data also serve as completely independent observations for the verification purposes.

Mapping Bragg Scatter with a Polarimetric WSR-88D

Abstract Using a polarimetric Weather Surveillance Radar-1988 Doppler (WSR-88D) radar to distinguish Bragg scatterers from insects and birds in an optically clear atmosphere has the potential to provide information on convective boundary layer depth. Measured median differential reflectivities ZDR of Bragg scatterers lie between −0.08 and 0.06 dB, which supports the hypothesis that the intrinsic ZDR of Bragg scatters is 0 dB. Thus, the intrinsic 0 dB of Bragg scatter can be used for verifying of ZDR radar calibration. Measured copolar correlation coefficients ρhv have distributions peaked at about 0.998–1.0. If insects and birds are spatially separated from Bragg scatterers, the dual-polarization capability of the WSR-88D allows distinguishing echoes from these two types of scatterers since ZDR from biota is significantly larger than 0 dB. In mixtures of Bragg and biota scatter, polarimetric spectral analysis shows differences in portions of the H and V spectra where birds and insects could be contaminating echoes from Bragg scatterers. The values of ZDR ≈ 0 and ρhv ≈ 1 that characterize Bragg scatterers allow coherent summation of signals from the H and V receiver channels and restoration of measurement capability lost as a result of splitting power into H and V channels. Further enhancements to data collection and signal processing allow power measurement, with a standard deviation of about 1 dB, of weak echoes from Bragg scatterers having equivalent reflectivity factors of about −28 dBZ at distance of 10 km from the radar. This level of reflectivity corresponds to a refractive index structure parameter of about 4 × 10−15 m−2/3, a typical magnitude found in maritime air.

Polarimetric Doppler Radar Observations of Kelvin–Helmholtz Waves in a Winter Storm

Abstract Kelvin–Helmholtz waves were observed by the Twin Lakes, Oklahoma (KTLX), Weather Surveillance Radar-1988 Doppler (WSR-88D); the Norman, Oklahoma (KOUN), polarimetric WSR-88D; and the polarimetric Collaborative Adaptive Sensing of the Atmosphere (CASA) radars on 30 November 2006 during a winter storm in central Oklahoma. The life cycle and structure of the waves are analyzed from the radar data, and the nearby atmospheric conditions are examined. The initial perturbations associated with the waves are first evident only in the radars’ radial velocity fields. As the waves mature, perturbations become discernable in the reflectivity factor Z and spectrum width (SW) fields of both radars, and in the differential reflectivity Zdr and, to a lesser extent, the cross-correlation coefficient ρhv fields of KOUN. As the waves break and begin to dissipate, the perturbations subside. A dual-Doppler analysis is synthesized to examine the kinematic structure of the waves and to relate the polarimetric observations to the kinematics. It is determined that Z and Zdr are enhanced in regions of upward motion (wave crests), and ρhv is reduced in the same vicinity and near the base of the wave circulations. Vertical velocity perturbations transport horizontal momentum upward and downward, inducing horizontal wind perturbations that are approximately 90° out of phase and downstream from their corresponding vertical velocity perturbations. Perturbations in Z, Zdr, and ρhv are observed in the vicinity of wave crests while SW perturbations occur predominately in and just upstream from wave troughs. It is determined that perturbations in the polarimetric variables are a result of the waves modifying local precipitation microphysics. Perturbations in Z and Zdr are hypothesized to be the result of columnar ice crystal generation whereas those in ρhv likely result from the mixing of ice crystals of various shapes and sizes. Perturbations in SW are a result of turbulent motions likely associated with wave breaking and downward advection of a strong shear layer.

Cross Validation of Spaceborne Radar and Ground Polarimetric Radar Aided by Polarimetric Echo Classification of Hydrometeor Types

AbstractGround-based polarimetric weather radar is arguably the most powerful validation tool that provides physical insight into the development and interpretation of spaceborne weather radar algorithms and observations. This study aims to compare and resolve discrepancies in hydrometeor retrievals and reflectivity observations between the NOAA/National Severe Storm Laboratory “proof of concept” KOUN polarimetric Weather Surveillance Radar-1988 Doppler (WSR-88D) and the spaceborne precipitation radar (PR) on board NASA’s Tropical Rainfall Measuring Mission (TRMM) platform. An intercomparison of PR and KOUN melting-layer heights retrieved from 2 to 5 km MSL shows a high correlation coefficient of 0.88 with relative bias of 5.9%. A resolution volume–matching technique is used to compare simultaneous TRMM PR and KOUN reflectivity observations. The comparisons reveal an overall bias of <0.2% between PR and KOUN. The bias is hypothesized to be from non-Rayleigh scattering effects and/or errors in attenuation correction procedures applied to Ku-band PR measurements. By comparing reflectivity with respect to different hydrometeor types (as determined by KOUN’s hydrometeor classification algorithm), it is found that the bias is from echoes that are classified as rain–hail mixture, wet snow, graupel, and heavy rain. These results agree with expectations from backscattering calculations at Ku and S bands, but with the notable exception of dry snow. Comparison of vertical reflectivity profiles shows that PR suffers significant attenuation at lower altitudes, especially in convective rain and in the melting layer. The attenuation correction performs very well for both stratiform and convective rain, however. In light of the imminent upgrade of the U.S. national weather radar network to include polarimetric capabilities, the findings in this study will potentially serve as the basis for nationwide validation of space-based precipitation products and also invite synergistic development of coordinated space–ground multisensor precipitation products.

Modeling, Error Analysis, and Evaluation of Dual-Polarization Variables Obtained from Simultaneous Horizontal and Vertical Polarization Transmit Radar. Part II: Experimental Data

Abstract In this second article in a two-part work, the biases of weather radar polarimetric variables from simultaneous horizontally and vertically transmit (SHV) data are investigated. The biases are caused by cross coupling of the simultaneously transmitted vertical (V) and horizontal (H) electric fields. There are two primary causes of cross coupling: 1) the nonzero mean canting angle of the propagation medium (e.g., canted ice crystals) and 2) antenna polarization errors. Given herein are experimental data illustrating both bias sources. In Part I, a model is developed and used to quantify cross coupling and its impact on polarization measurements. Here, in Part II, experimental data from the National Center for Atmospheric Research’s (NCAR’s) S-band dual-polarimetric Doppler radar (S-Pol) and the National Severe Storms Laboratory’s polarimetric Weather Surveillance Radar-1988 Doppler (WSR-88D), KOUN, are used to illustrate biases in differential reflectivity (Zdr). The S-Pol data are unique: both SHV data and fast alternating H and V transmit (FHV) data are gathered in close time proximity, and thus the FHV data provide “truth” for the SHV data. Specifically, the SHV Zdr bias in rain caused by antenna polarization errors is clearly demonstrated by the data. This has not been shown previously in the literature.

Modeling, Error Analysis, and Evaluation of Dual-Polarization Variables Obtained from Simultaneous Horizontal and Vertical Polarization Transmit Radar. Part I: Modeling and Antenna Errors

Abstract In this two-part paper the biases of polarimetric variables from simultaneous horizontally and vertically transmitted (SHV) data are investigated. Here, in Part I, a radar-scattering model is developed and antenna polarization errors are investigated and estimated. In Part II, experimental data from the National Center for Atmospheric Research S-band dual-polarization Doppler radar (S-Pol) and the National Severe Storms Laboratory polarimetric Weather Surveillance Radar-1988 Doppler (WSR-88D) radar, KOUN, are used to illustrate biases in differential reflectivity (Zdr). The biases in the SHV polarimetric variables are caused by cross coupling of the horizontally (H) and vertically (V) polarized signals. The cross coupling is caused by the following two primary sources: 1) the nonzero mean canting angle of the propagation medium and 2) antenna polarization errors. The biases are strong functions of the differential propagation phase (ϕdp) and the phase difference between the H and V transmitted field components. The radar-scattering model developed here allows for the evaluation of biases caused by cross coupling as a function of ϕdp, with the transmission phase difference as a parameter. Also, antenna polarization errors are estimated using solar scan measurements in combination with estimates of the radar system’s linear depolarization ratio (LDR) measurement limit. Plots are given that show expected biases in SHV Zdr for various values of the LDR system’s limit.

The Hydrometeor Classification Algorithm for the Polarimetric WSR-88D: Description and Application to an MCS

Abstract 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.

Polarimetric Signatures in Supercell Thunderstorms

Abstract Data from polarimetric radars offer remarkable insight into the microphysics of convective storms. Numerous tornadic and nontornadic supercell thunderstorms have been observed by the research polarimetric Weather Surveillance Radar-1988 Doppler (WSR-88D) in Norman, Oklahoma (KOUN); additional storm data come from the Enterprise Electronics Corporation “Sidpol” C-band polarimetric radar in Enterprise, Alabama, as well as the King City C-band polarimetric radar in Ontario, Canada. A number of distinctive polarimetric signatures are repeatedly found in each of these storms. The forward-flank downdraft (FFD) is characterized by a signature of hail observed as near-zero ZDR and high ZHH. In addition, a shallow region of very high ZDR is found consistently on the southern edge of the FFD, called the ZDR “arc.” The ZDR and KDP columns and midlevel “rings” of enhanced ZDR and depressed ρHV are usually observed in the vicinity of the main rotating updraft and in the rear-flank downdraft (RFD). Tornado touchdown is associated with a well-pronounced polarimetric debris signature. Similar polarimetric features in supercell thunderstorms have been reported in other studies. The data considered here are taken from both S- and C-band radars from different geographic locations and during different seasons. The consistent presence of these features may be indicative of fundamental processes intrinsic to supercell storms. Hypotheses on the origins, as well as microphysical and dynamical interpretations of these signatures, are presented. Implications about storm morphology for operational applications are suggested.

Autocorrelation and Cross-Correlation Estimators of Polarimetric Variables

Abstract Herein are proposed novel estimators of differential reflectivity ZDR and correlation coefficient ρhv between horizontally and vertically polarized echoes. The estimators use autocorrelations and cross correlations of the returned signals to avoid bias by omnipresent but varying white noise. These estimators are considered for implementation on the future polarimetric Weather Surveillance Radar-1988 Doppler (WSR-88D) network. On the current network the reflectivity factor is measured at signal-to-noise ratios (SNRs) as low as 2 dB and the same threshold is expected to hold for the polarimetric variables. At such low SNR and all the way up to SNR = 15 dB, the conventional estimators of differential reflectivity and the copolar correlation coefficient are prone to errors due to uncertainties in noise levels caused by instability of radar devices, thermal radiations of precipitation and the ground, and wideband radiation of electrically active clouds. Noise variations at SNR less than 15 dB can bias the estimates beyond apparatus accuracy. For brevity the authors refer to the estimators of ZDR and ρhv free from noise bias as the “1-lag estimators” because these are derived from 1-lag correlations. The estimators are quite robust and the only weak assumption for validity is that spectral widths of signals from vertically and horizontally polarized returns are equal. This assumption is verified on radar data. Radar observations demonstrate the validity of these estimator and lower sensitivity to interference signals than the conventional algorithms.

Electrical and Polarimetric Radar Observations of a Multicell Storm in TELEX

Abstract On 28–29 June 2004 a multicellular thunderstorm west of Oklahoma City, Oklahoma, was probed as part of the Thunderstorm Electrification and Lightning Experiment field program. This study makes use of radar observations from the Norman, Oklahoma, polarimetric Weather Surveillance Radar-1988 Doppler, three-dimensional lightning mapping data from the Oklahoma Lightning Mapping Array (LMA), and balloon-borne vector electric field meter (EFM) measurements. The storm had a low flash rate (30 flashes in 40 min). Four charge regions were inferred from a combination of LMA and EFM data. Lower positive charge near 4 km and midlevel negative charge from 4.5 to 6 km MSL (from 0° to −6.5°C) were generated in and adjacent to a vigorous updraft pulse. Further midlevel negative charge from 4.5 to 6 km MSL and upper positive charge from 6 to 8 km (from −6.5° to −19°C) were generated later in quantity sufficient to initiate lightning as the updraft decayed. A negative screening layer was present near the storm top (8.5 km MSL, −25°C). Initial lightning flashes were between lower positive and midlevel negative charge and started occurring shortly after a cell began lofting hydrometeors into the mixed phase region, where graupel was formed. A leader from the storm’s first flash avoided a region where polarimetric radar suggested wet growth and the resultant absence of noninductive charging of those hydrometeors. Initiation locations of later flashes that propagated into the upper positive charge tracked the descending location of a polarimetric signature of graupel. As the storm decayed, electric fields greater than 160 kV m−1 exceeded the minimum threshold for lightning initiation suggested by the hypothesized runaway breakdown process at 5.5 km MSL, but lightning did not occur. The small spatial extent (≈100 m) of the large electric field may not have been sufficient to allow runaway breakdown to fully develop and initiate lightning.

The Joint Polarization Experiment: Polarimetric Radar in Forecasting and Warning Decision Making

Abstract To test the utility and added value of polarimetric radar products in an operational environment, data from the Norman, Oklahoma (KOUN), polarimetric Weather Surveillance Radar-1988 Doppler (WSR-88D) were delivered to the National Weather Service Weather Forecast Office (WFO) in Norman as part of the Joint Polarization Experiment (JPOLE). KOUN polarimetric base data and algorithms were used at the WFO during the decision-making and forecasting processes for severe convection, flash floods, and winter storms. The delivery included conventional WSR-88D radar products, base polarimetric radar variables, a polarimetric hydrometeor classification algorithm, and experimental polarimetric quantitative precipitation estimation algorithms. The JPOLE data collection, delivery, and operational demonstration are described, with examples of several forecast and warning decision-making successes. Polarimetric data aided WFO forecasters during several periods of heavy rain, numerous large-hail-producing thunderstorms, tornadic and nontornadic supercell thunderstorms, and a major winter storm. Upcoming opportunities and challenges associated with the emergence of polarimetric radar data in the operational community are also described.