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Igor R. Ivić

Abstract

Estimates of copolar correlation coefficient |ρ hv(0)| are one of the essential products generated by polarimetric weather radars because they are used to discriminate among different scatterer types. In theory, estimates of |ρ hv(0)| take values between zero and one. But, statistical errors may cause the estimates to take values that are outside this interval, in which case they are deemed unusable. This effect is exacerbated if the noise contamination is significant. In addition, even valid |ρ hv(0)| estimates can introduce excessive errors in echo classification if not sufficiently accurate and precise. Consequently, it is vital to produce the |ρ hv(0)| fields populated with estimates of the acceptable accuracy as well as precision and with a minimal number of invalid estimates. To improve |ρ hv(0)| estimation, a simple hybrid technique which produces estimates by combining the outputs of the two previously proposed estimators and the conventional one is presented herein. The technique generates estimates with reduced bias compared to previously proposed estimators. Bias reduction results in an increased number of valid estimates, which translates into improved |ρ hv(0)| fields.

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Igor R. Ivić

Abstract

One of the main challenges to the use of phased array radar for weather observations is the implementation of dual polarization with acceptable levels of cross-polar fields induced by the antenna. For example, to achieve acceptable differential reflectivity (Z DR) bias (e.g., less than 0.1 dB) using simultaneous transmission and reception of H and V polarized waves, the isolation between coaxial cross-polar and copolar beams needs to be in excess of 50 dB. Because such isolation cannot be achieved at an affordable price by antenna hardware, additional methods are required to attain supplementary isolation of orthogonal channels. One such option is time multiplexing. Herein, this approach is evaluated from the statistical aspect, whereby the depolarization caused by the radar hardware is accounted for in this study. An evaluation is conducted using theoretical analysis as well as simulated and time series data from a weather radar. The main criteria for evaluation are the bias and standard deviation of differential reflectivity estimates. The results indicate that the implementation of the time-multiplexing method has the capability to significantly improve upon the radar intrinsic cross-polar isolation. However, it is demonstrated herein that the reflectivity gradients in range adversely affect the efficacy of the method and that the standard deviation of estimates can significantly increase as a result of the time-multiplexing application.

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Igor R. Ivić

Abstract

A weather surveillance radar antenna intercepts thermal radiation from various sources, including the ground, the sun, the sky, and precipitation. In the radar receiver, this external radiation produces noise that adds to the receiver internal noise and results in the system noise power varying with the antenna position. If these variations are not captured, they translate into erroneous signal powers because these are computed via subtraction of noise power measurements from the overall power estimates. This may lead to biased meteorological variables at low to moderate signal-to-noise ratios if those are computed using signal power estimates. In dual-polarization radars, this problem is even more pronounced, particularly for correlation coefficient estimates that use noise power measurements from both the horizontal and vertical channels. An alternative is to use estimators that eliminate the need for noise corrections but require sufficient correlation of signals in sample time, which limits their applicability. Therefore, when the use of the latter is inappropriate, the quality of correlation coefficient estimates can be improved by computing them using sufficiently accurate noise powers measured at each antenna position. An effective technique that estimates the noise powers in real time at each scan direction and in parallel with weather data collection has been proposed. Herein, the impacts of such a technique on the estimation of the correlation coefficient are investigated. The results indicate that the use of more accurate noise power estimates can significantly reduce the bias of correlation coefficient estimates, thus visibly improving the correlation coefficient fields. This is expected because the correlation coefficient is computed using noise power measurements from both the horizontal and vertical channels.

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Igor R. Ivić

Abstract

One of the main challenges of using phased array radar for weather observations is the implementation of dual polarization with acceptable errors of polarimetric variable estimates. This is because the differences between the copolar antenna patterns at the horizontal and vertical polarizations, as well as cross-polar fields, can introduce unacceptable measurement biases, as the main beam is electronically steered away from the principal planes. Because the sufficient cross-polar isolation is difficult to achieve by the phased array antenna hardware and because the copolar as well as cross-polar patterns inevitably vary with each beam position, it is crucial to properly evaluate errors of estimates due to radiation patterns. Herein, a method that combines the measured or simulated radiation patterns and simulated time series is introduced. The method is suited for phased array and parabolic antennas, and it allows for evaluation of radiation-pattern-induced polarimetric variable biases and standard deviations specific to the antenna used to produce the patterns. The method can be used either as an alternative to a well-established approach using analytical derivations or as a tool for cross validation of the bias computations. For standard deviation evaluation in the presence of antenna cross-polar fields, the analytical approach becomes overly complex, which inexorably leads to the introduction of numerous approximations to obtain the results. These approximations inevitably compromise the accuracy of such computations. The method proposed herein avoids such approximations and therefore provides a valuable tool for accurate assessment of polarimetric measurement precision.

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Igor R. Ivić, Reino Keränen, and Dušan S. Zrnić

Abstract

In Doppler weather radars, signals may exhibit coherency in sample time, whereas noise does not. Additionally, in dual-polarized radars, samples of precipitation echo obtained in the two orthogonally polarized channels are substantially more correlated than samples of noise. Therefore, estimates of auto- and cross correlations can be used individually, collectively, and/or with power measurements to enhance detection of precipitation signals, compared to the approach that uses only power estimates from one channel. A possible advantage of using only estimates of coherency for signal detection is that the detector’s performance is less sensitive to errors in noise power measurements. Hence, censoring is more likely to produce desired false alarm rates even if nonnegligible uncertainties are present in the noise power estimates. In this work these aspects are considered using real data from weather radars. Three novel censoring approaches are evaluated and compared to the censoring approach that uses only estimates of signal and noise powers. The first approach uses only cross-correlation measurements, and the second approach combines these with the lag-1 autocorrelation estimates. The third approach utilizes all estimates as in the previous two approaches in combination with power measurements from the horizontal and the vertical channels. Herein, it is shown that, when more accurate measurements of noise powers are available, the third approach produces the highest detection rates followed by the second and the first approaches. Also, it is corroborated that the first and the second approaches exhibit less sensitivity to inaccurate system noise power measurements than the third one.

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Igor R. Ivić, Dušan S. Zrnić, and Tian-You Yu

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.

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Igor R. Ivić, Dušan S. Zrnić, and Sebastián M. Torres

Abstract

Demonstration of a method for improved Doppler spectral moment estimation is made on NOAA's research and development Weather Surveillance Radar-1988 Doppler (WSR-88D) in Norman, Oklahoma. Time series data have been recorded using a commercial processor and digital receiver whereby the sampling frequency is several times larger than the reciprocal of the transmitted pulse width. The in-phase and quadrature-phase components of oversampled weather signals are used to estimate the first three spectral moments by suitably combining weighted averages in range with usual processing at fixed range locations. The weights are chosen in such a manner that the resulting signals become uncorrelated. Consequently, the variance of estimates decreases significantly as is verified by this experiment.

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Igor R. Ivić, Christopher Curtis, and Sebastián M. Torres

Abstract

A radar antenna intercepts thermal radiation from various sources including the ground, the sun, the sky, precipitation, and man-made radiators. In the radar receiver, this external radiation produces noise that constructively adds to the receiver internal noise and results in the overall system noise. Consequently, the system noise power is dependent on the antenna position and needs to be estimated accurately. Inaccurate noise power measurements may lead to reduction of coverage if the noise power is overestimated or to radar data images cluttered by noise speckles if the noise power is underestimated. Moreover, when an erroneous noise power is used at low-to-moderate signal-to-noise ratios, estimators can produce biased meteorological variables. Therefore, to obtain the best quality of radar products, it is desirable to compute meteorological variables using the noise power measured at each antenna position. In this paper, an effective method is proposed to estimate the noise power in real time from measured powers at each radial. The technique uses a set of criteria to detect radar range resolution volumes that do not contain weather signals and uses those to estimate the noise power. The algorithm is evaluated using both simulated and real time series data; results show that the proposed technique accurately produces estimates of the system noise power. An operational implementation of this technique is expected to significantly improve the quality of weather radar products with a relatively small computational burden.

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Igor R. Ivić, Jane C. Krause, Olen E. Boydstun, Amy E. Daniel, Alan D. Free, and Walter D. Zittel

Abstract

A radar antenna intercepts thermal radiation from various sources, including the ground, the sun, the sky, precipitation, and man-made radiators. In the radar receiver, this external radiation produces noise that constructively adds to the receiver internal noise and results in the overall system noise. Consequently, the system noise power is dependent on the antenna position and needs to be estimated accurately. Inaccurate noise power measurements may lead to a reduction of coverage if the noise power is overestimated or to radar data images cluttered by noise speckles if the noise power is underestimated. Moreover, when an erroneous noise power is used at low to moderate signal-to-noise ratios, estimators can produce biased meteorological variables. Therefore, to obtain the best quality of radar products, it is desirable to compute meteorological variables using the noise power measured at each antenna position. An effective technique that achieves this by estimating the noise power in real time from measured powers at each scan direction and in parallel with weather data collection has been proposed. Herein, the effects of such radial-based noise power estimation on spectral moment estimates are investigated.

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Mark Weber, Kurt Hondl, Nusrat Yussouf, Youngsun Jung, Derek Stratman, Bryan Putnam, Xuguang Wang, Terry Schuur, Charles Kuster, Yixin Wen, Juanzhen Sun, Jeff Keeler, Zhuming Ying, John Cho, James Kurdzo, Sebastian Torres, Chris Curtis, David Schvartzman, Jami Boettcher, Feng Nai, Henry Thomas, Dusan Zrnić, Igor Ivić, Djordje Mirković, Caleb Fulton, Jorge Salazar, Guifu Zhang, Robert Palmer, Mark Yeary, Kevin Cooley, Michael Istok, and Mark Vincent

Abstract

This article summarizes research and risk reduction that will inform acquisition decisions regarding NOAA’s future national operational weather radar network. A key alternative being evaluated is polarimetric phased-array radar (PAR). Research indicates PAR can plausibly achieve fast, adaptive volumetric scanning, with associated benefits for severe-weather warning performance. We assess these benefits using storm observations and analyses, observing system simulation experiments, and real radar-data assimilation studies. Changes in the number and/or locations of radars in the future network could improve coverage at low altitude. Analysis of benefits that might be so realized indicates the possibility for additional improvement in severe-weather and flash-flood warning performance, with associated reduction in casualties. Simulations are used to evaluate techniques for rapid volumetric scanning and assess data quality characteristics of PAR. Finally, we describe progress in developing methods to compensate for polarimetric variable estimate biases introduced by electronic beam-steering. A research-to-operations (R2O) strategy for the PAR alternative for the WSR-88D replacement network is presented.

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