Dual-polarization Weather Radar Research Articles

Principles and applications of dual-polarization weather radar. Part II: Warm- and cold-season applications

Principles and applications of dual-polarization weather radar. Part II: Warm- and cold-season applications

Precipitation Classification and Quantification Using X-Band Dual-Polarization Weather Radar: Application in the Hydrometeorology Testbed

Abstract This paper presents new methods for rainfall estimation from X-band dual-polarization radar observations along with advanced techniques for quality control, hydrometeor classification, and estimation of specific differential phase. Data collected from the Hydrometeorology Testbed (HMT) in orographic terrain of California are used to demonstrate the methodology. The quality control and hydrometeor classification are specifically developed for X-band applications, which use a “fuzzy logic” technique constructed from the magnitude of the copolar correlation coefficient and the texture of differential propagation phase. In addition, an improved specific differential phase retrieval and rainfall estimation method are also applied. The specific differential phase estimation is done for both the melting region and rain region, where it uses a conventional filtering method for the melting region and a self-consistency-based method that distributes the total differential phase consistent with the reflectivity factor for the rain region. Based on the specific differential phase, rainfall estimations were computed using data obtained from the NOAA polarimetric X-band radar for hydrometeorology (HYDROX) and evaluated using HMT rain gauge observations. The results show that the methodology works well at capturing the high-frequency rainfall variations for the events analyzed herein and can be useful for mountainous terrain applications.

Classification and Quantification of Snow Based on Spatial Variability of Radar Reflectivity

In this study, a classification methodology of snow particle types, i.e., crystals, aggregates, rimed snow, and graupel, by using spatial variability of the equivalent radar reflectivity factor is proposed. The methodology is formulated on the basis of the analysis of vertically pointing Doppler radar, scanning dual-polarization weather radar, and supporting surface observations. It is arguedthat by using the proposedsnow-type identification methodology, it is possible to guide the choice of the particular parameters of power law relations of equivalent radar reflectivity factorliquid equivalent snowfall rate. The validity of the classification results are demonstrated by comparing the classification output to Vaisala WXT observations, which can be used to detect presence of high-density particles in snow. The performance of the proposedquantitative snowfall estimation algorithm is illustratedusing an example of the d ata collectedfrom the C-bandoperational Helsinki Vantaa rad ar andgroundinstruments (Vaisala PWD-11, Pluvio).

Threshold Calculation for Coherent Detection in Dual-Polarization Weather Radars

It is customary to censor signals in conventional weather radar using estimates of signal-to-noise ratio (SNR) and/or magnitude of autocorrelation coefficient at lag one. Dual-polarized weather radar provides a pair of highly correlated signals from the two orthogonally polarized returns. A novel censoring technique, previously proposed, sums powers, autocorrelations, and correlation between signals in the two channels and compares the sum to a threshold. In this paper an efficient procedure for calculating such a threshold is proposed.

Leveraging Software-Defined Radio Techniques in Multichannel Digital Weather Radar Receiver Design

This paper describes the instrumentation, design, and implementation of an inexpensive nearly all-digital field-programmable gate-array (FPGA)-based radar receiver useful in a variety of applications including single-/dual-polarization weather radar, sidelobe cancellation, subarray modules for a digital beam-forming phased-array radar, and other applications where a compact, low-power, and low-cost receiver is needed. The design of the receiver includes a minimal analog radio-frequency front-end followed by an analog-to-digital converter utilizing a bandpass sampling technique which allows the FPGA to produce baseband in-phase (I) and quadrature (Q) signals without the use of multipliers or lookup tables.

Recent advances in classification of observations from dual polarization weather radars

It has been a decade since some of the early papers on dual-polarization radar based hydrometeor classification were published. Subsequently this topic has seen rapid advances especially due to adaptation of this technology for operational applications. Since the early papers on hydrometeor classification, additional characterization in terms of echo classification, data quality and hydrometeor identification have been researched, and products produced some distinctly, some with all three procedures combined. This paper provides a summary of the advances since the early papers in this area, especially with an emphasis on how the successful operational applications have influenced this field. Principles of the various types of classification and examples of characterizations such as data quality, echo and hydrometeor classification are also described.

Iterative Bayesian Retrieval of Hydrometeor Content From X-Band Polarimetric Weather Radar

Dual-polarized weather radars are capable to detect and identify different classes of hydrometeors, within stratiform and convective storms, exploiting polarimetric diversity. Among the various techniques, a model-supervised Bayesian method for hydrometeor classification, tuned for S- and X-band polarimetric weather radars, can be effectively applied. Once the hydrometeor class is estimated, the retrieval of their water content can also be statistically carried out. However, the critical issue of X-band radar data processing, and in general of any attenuating wavelength active system, is the intervening path attenuation, which is usually not negligible. Any approach aimed at estimating hydrometeor water content should be able to tackle, at the same time, path attenuation correction, hydrometeor classification uncertainty, and retrieval errors. An integrated iterative Bayesian radar algorithm (IBRA) scheme, based on the availability of the differential phase measurement, is presented in this paper and tested during the International H2O Project experiment in Oklahoma in 2002. During the latter campaign, two dual-polarized radars, at S- and X-bands, were deployed and jointly operated with closely matched scanning strategies, giving the opportunity to perform experimental comparisons between coincident measurements at different frequencies. Results of the IBRA technique at X-band are discussed, and the impact of path attenuation correction is quantitatively analyzed by comparing hydrometeor classifications and estimates with those obtained at S-band. The overall results in terms of error budget show a significant improvement with respect to the performance with no path attenuation correction.

The Use of Coherency to Improve Signal Detection in Dual-Polarization Weather Radars

Abstract Currently, signal detection and censoring in operational weather radars is performed by using thresholds of the estimated signal-to-noise ratio (SNR) and/or the magnitude of the autocorrelation coefficient at the first temporal lag. The growing popularity of polarimetric radars prompts the quest for improved detection schemes that take advantage of the signals from the two orthogonally polarized electric fields. A hybrid approach is developed based on the sum of the cross-correlation estimates as well as the powers and autocorrelations from each of the dual-polarization returns. The hypothesis that “signal is present” is accepted if the sum exceeds a predetermined threshold; otherwise, the data are considered to represent noise and are censored. The threshold is determined by the acceptable rate of false detections that is less than or equal to a preset value. The scheme is evaluated both in simulations and through implementation on time series data collected by the research weather surveillance radar (KOUN) in Norman, Oklahoma.

POTENTIAL ROLE OF DUAL-POLARIZATION RADAR IN THE VALIDATION OF SATELLITE PRECIPITATION MEASUREMENTS

Dual-polarization weather radars have evolved significantly in the last three decades culminating in operational deployment by the National Weather Service. In addition to operational applications in the weather service, dual-polarization radars have shown significant potential in contributing to the research fields of ground-based remote sensing of rainfall microphysics, the study of precipitation evolution, and hydrometeor classification. Microphysical characterization of precipitation and quantitative precipitation estimation are important applications that are critical in the validation of satellite-borne precipitation measurements and also serve as valuable tools in algorithm development. This paper presents the important role played by dual-polarization radar in validating spaceborne precipitation measurements. Examples of raindrop size distribution retrievals and hydrometeor-type classification are discussed. The quantitative precipitation estimation is a product of direct relevance to spaceborne o...

Whitening Dual-Polarized Weather Radar Signals With a Hermitian Transformation

Oversampling weather radar signals in range and then whitening these signals has been shown to improve the accuracy of spectral moments. For dual-polarized radar, the polarimetric variables depend upon information gleaned from the cross correlation of the different received signals. Theoretical improvements to the polarimetric variables have been provided to date, but experimental evidence of improvements through whitening has been limited. This paper provides an analysis of the effects of whitening on the estimated cross correlation along with experimental results of whitening applied to polarimetric variables. Different whitening transformations based solely on covariance matrix inversions will be shown to affect the copolar correlation of the whitened data. A Hermitian symmetric whitening transformation will be shown to produce better estimates of polarimetric variables obtained from whitened data than the original whitening transformation defined for use with range oversampling.

Classification of Ground Clutter and Anomalous Propagation Using Dual-Polarization Weather Radar

This paper presents the results of a study designed to classify weather radar clutter echoes obtained from ground-based dual-polarization weather radar systems. The clutter signals are due to ground clutter, sea clutter, and anomalous propagation echoes, which represent sources of error in quantitative radar rainfall estimation. Fuzzy and Bayes classifiers are evaluated as an alternative approach to traditional polarimetric-based methods. Both systems were trained and validated by using C-band dual- polarization radar measurements, and a novel technique is proposed to calculate the texture function to mitigate against the edge effects at the boundaries of precipitation regions. A methodology is presented to extract the membership functions and conditional probability density functions to train the classifiers. The critical success index indicates that the Bayes classifier has, on average, a slightly better performance than the fuzzy classifiers. However, when optimal weighting was applied, the fuzzy classifier gave one of the best performances. The classifiers are sufficiently robust to be used when only single-polarization radar measurements are available.

Phase Coding for Range Ambiguity Mitigation in Dual-Polarized Doppler Weather Radars

Abstract This paper evaluates the retrieval of polarimetric variables when phase-coded waveforms are employed to suppress range overlaid echoes. A phase-coded waveform tags transmitted pulses with a phase code and then decodes the received signal to separate the overlaid echoes. Two methods suggested for separating overlaid echoes use random and systematic phase-coding techniques. In this paper, random phase and systematic phase-coded waveforms are evaluated for dual-polarized operation. The random phased-coded and systematic phase-coded waveforms are known to provide fairly good estimates of the Doppler spectral moments. This paper presents results at S band to quantify the performance of phase-coded waveform in retrieving polarimetric variables. It is shown that the polarimetric variables for both strong and weak trip echoes are estimated with acceptable accuracy.

Depolarization in Ice Crystals and Its Effect on Radar Polarimetric Measurements

Abstract Simultaneous transmission and reception of horizontally and vertically polarized waves is a preferable choice technique for dual-polarization weather radar. One of the consequences of such a choice is possible cross-coupling between orthogonally polarized waves. Cross-coupling depends on depolarizing properties of propagation media, and it is usually negligible in rain because the net mean canting angle of raindrops is close to zero. Snow crystals at the tops of thunderstorm clouds are often canted in the presence of strong electric fields and produce noticeable cross-coupling between radar signals at horizontal and vertical polarizations if both signals are transmitted and received simultaneously. As a result, peculiar-looking radial signatures of differential reflectivity ZDR and differential phase ΦDP are commonly observed in the crystal regions of thunderstorms. The paper presents examples of strong depolarization in oriented crystals from the data collected by the polarimetric prototype of the Weather Surveillance Radar-1988 Doppler (WSR-88D) and a theoretical model that explains the results of measurements. It is shown that the sign and magnitude of the ZDR and ΦDP signatures strongly depend on the orientation of crystals and a system differential phase on transmission.

Polarimetric Weather Radar Retrieval of Raindrop Size Distribution by Means of a Regularized Artificial Neural Network

The raindrop size distribution (RSD) is a critical factor in estimating rain intensity using advanced dual-polarized weather radars. A new neural-network algorithm to estimate the RSD from S-band dual-polarized radar measurements is presented. The corresponding rain rates are then computed assuming a commonly used raindrop diameter speed relationship. Numerical simulations are used to investigate the efficiency and accuracy of this method. A stochastic model based on disdrometer measurements is used to generate realistic range profiles of the RSD parameters, while a T-matrix solution technique is adopted to compute the corresponding polarimetric variables. The error analysis, which is performed in order to evaluate the expected errors of this method, shows an improvement with respect to other methodologies described in the literature. A further sensitivity evaluation shows that the proposed technique performs fairly well even for low specific differential phase-shift values

Polarization isolation requirements for linear dual-polarization weather Radar in simultaneous transmission mode of operation

A dual-polarization radar system operating in simultaneous transmission mode of both horizontal and vertical polarization states is a viable implementation if only copolar measurements are needed. The simultaneous transmission of horizontal and vertical polarizations results in an arbitrary elliptical polarization state transmitted, whereas the reception states in horizontal and vertical polarizations are neither copolar nor cross-polar to the transmitted state. Because of this, it is often referred as the hybrid mode. Previous studies have shown that the hybrid mode in the linear horizontal and vertical polarization basis is capable of providing measurements similar to the alternate transmission mode for most measurement conditions. These findings are based on the assumption of perfect sensing systems. This paper presents the results of radar system limitations on hybrid mode measurements that in turn are converted to system requirements. It is shown that the polarization purity requirement is more stringent for the hybrid mode compared to the alternate mode of operation

Rainfall estimation and ground clutter rejection with dual polarization weather radar

Abstract. Conventional radars, used for atmospheric remote sensing, usually operate at a single polarization and frequency to estimate storm parameters such as rainfallrate and water content. Because of the high variability of the drop size distribution conventional radars do not succeed in obtaining detailed information because they just use horizontal reflectivity. The potentiality of the dual-polarized weather radar is investigated, in order to reject the ground-clutter, using differential reflectivity. In this light, a radar meteorology campaign was conducted over the city of Rome (Italy), collecting measurements by the polarimetric Doppler radar Polar 55C and by a raingauge network. The goodness of the results is tested by comparison of radar rainfall estimates with raingauges rainfall measurements.

Hydrometeor classification from dual-polarized weather radar: extending fuzzy logic from S-band to C-band data

Abstract. A model-based fuzzy classification method for C-band polarimetric radar data, named Fuzzy Radar Algorithm for Hydrometeor Classification at C-band (FRAHCC), is presented. Membership functions are designed for best fitting simulation data at C-band, and they are derived for ten different hydrometeor classes by means of a scattering model, based on T-Matrix numerical method. The fuzzy logic classification technique uses a reduced set of polarimetric observables, i.e. copolar reflectivity and differential reflectivity, and it is finally applied to data coming from radar sites located in Gattatico and S. Pietro Capofiume in North Italy. The final purpose is to show qualitative accuracy improvements with respect to the use of a set of ten bidimensional MBFs, previously adopted and well suited to S-band data but not to C-band data.

Calibration Issues of Dual-Polarization Radar Measurements

Abstract 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.

Calibration of Dual-Polarization Radar in the Presence of Partial Beam Blockage

Abstract In the presence of partial beam blockage (PBB), weather radar measurements can experience significant bias that directly compromises the accuracy of the hydrologic applications. Techniques for the calibration of the radar reflectivity factor Z and differential reflectivity ZDR, measured with dual-polarization weather radars in the presence of partial beam obstruction, are examined in this paper. The proposed ZDR calibration technique utilizes radar measurements of ZDR in light rain and dry aggregated snow at unblocked and blocked elevations. This calibration technique was tested for the National Severe Storms Laboratory’s (NSSL’s) Cimarron radar that suffers from PBB, and a polarimetric prototype of the Weather Surveillance Radar-1988 Doppler (WSR-88D) that does not experience PBB. Results indicate that the ZDR bias that is associated with PBB can be calibrated with an accuracy of 0.2–0.3 dB, provided that the dataset is sufficiently large. Calibration of Z in the presence of PBB is based on the idea of self-consistency among Z, ZDR, and the specific differential phase KDP in rain. The self-consistency calibration of Z from the Cimarron radar is performed following an area–time integral method. Integration is partitioned into small azimuthal sectors to assess the azimuthal modulation of the Z bias. The suggested technique is validated by direct comparisons of reflectivity factors that are measured by the Cimarron radar and the unobstructed operational WSR-88D radar. It is shown that the azimuthal modulation of Z that is caused by PBB is well captured, and the accuracy of the Z calibration is within 2–3 dB.