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  • Author or Editor: Sebastián M. Torres x
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Sebastián M. Torres
and
Dušan S. Zrnić

Abstract

A method for estimation of spectral moments on pulsed weather radars is presented. This scheme operates on oversampled echoes in range; that is, samples of in-phase and quadrature-phase components are collected at a rate several times larger than the reciprocal of the transmitted pulse length. The spectral moments are estimated by suitably combining weighted averages of these oversampled signals in range with usual processing of samples (spaced at the pulse repetition time) at a fixed range location. The weights in range are derived from a whitening transformation; hence, the oversampled signals become uncorrelated and, consequently, the variance of the estimates decreases significantly. Because the estimate errors are inversely proportional to the volume scanning times, it follows that storms can be surveyed much faster than is possible with current processing methods, or equivalently, for the current volume scanning time, accuracy of the estimates can be greatly improved. This significant improvement is achievable at large signal-to-noise ratios.

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Sebastian M. Torres
and
Dušan S. Zrnić

Abstract

A method to reduce errors in estimates of polarimetric variables beyond those achievable with standard estimators is suggested. It consists of oversampling echo signals in range, applying linear transformations to decorrelate these samples, processing in time the sequences at fixed range locations to obtain various second-order moments, averaging in range these moments, and, finally, combining them into polarimetric variables. The polarimetric variables considered are differential reflectivity, differential phase, and the copolar correlation coefficient between the horizontally and vertically polarized echoes. Simulations and analytical formulas confirm a reduction in variance proportional to the number of samples within the pulse compared to standard processing of signals behind a matched filter. This reduction is possible, however, if the signal-to-noise ratios (SNRs) are larger than a critical value. Plots of the critical SNRs for various estimates as functions of Doppler spectrum width and other parameters are provided.

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Pamela L. Heinselman
and
Sebastián M. Torres

Abstract

Since 2007 the advancement of the National Weather Radar Testbed Phased-Array Radar (NWRT PAR) hardware and software capabilities has been supporting the implementation of high-temporal-resolution (∼1 min) sampling. To achieve the increase in computational power and data archiving needs required for high-temporal-resolution sampling, the signal processor was upgraded to a scalable, Linux-based cluster with a distributed computing architecture. The development of electronic adaptive scanning, which can reduce update times by focusing data collection on significant weather, became possible through functionality added to the radar control interface and real-time controller. Signal processing techniques were implemented to address data quality issues, such as artifact removal and range-and-velocity ambiguity mitigation, absent from the NWRT PAR at its installation. The hardware and software advancements described above have made possible the development of conventional and electronic scanning capabilities that achieve high-temporal-resolution sampling. Those scanning capabilities are sector- and elevation-prioritized scanning, beam multiplexing, and electronic adaptive scanning. Each of these capabilities and related sampling trade-offs are explained and demonstrated through short case studies.

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Sebastián M. Torres
and
Dusan S. Zrnic

Abstract

This paper explores ground clutter filtering with a class of cancelers that use regression. Regression filters perform this task in a simple manner, resulting in similar or better performance than the fifth-order elliptic filter implemented in the WSR-88D. Assuming a slowly varying clutter signal, a suitable projection of the composite signal is used to notch a band of frequencies at either side of zero Doppler frequency. The complexity of this procedure is reduced by using a set of orthogonal polynomials. The frequency response of the resulting filter is related to the number of samples in each input block and the maximum order of approximating polynomials. Through simulations, it is demonstrated that the suppression characteristic of this filter is better than that of step-initialized infinite impulse response filters, whereby transients degrade the theoretical frequency response. The performance of regression filters is tested with an actual weather signal, and their efficiency in ground clutter canceling is demonstrated.

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Sebastián M. Torres
and
David A. Warde

Abstract

Radar returns from the ground, known as ground clutter, can contaminate weather signals, often resulting in severely biased meteorological estimates. If not removed, these contaminants may artificially inflate quantitative precipitation estimates and obscure polarimetric and Doppler signatures of weather. A ground-clutter filter is typically employed to mitigate this contamination and provide less biased meteorological-variable estimates. This paper introduces a novel adaptive filter based on the autocorrelation spectral density, which is capable of mitigating the adverse effects of ground clutter without unnecessarily degrading the quality of the meteorological data. The so-called Clutter Environment Analysis using Adaptive Processing (CLEAN-AP) filter adjusts its suppression characteristics in real time to match dynamic atmospheric environments and meets Next Generation Weather Radar (NEXRAD) clutter-suppression requirements.

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Sebastián M. Torres
and
Christopher D. Curtis

Abstract

The range-weighting function (RWF) determines how individual scatterer contributions are weighted as a function of range to produce the meteorological data associated with a single resolution volume. The RWF is commonly defined in terms of the transmitter pulse envelope and the receiver filter impulse response, and it determines the radar range resolution. However, the effective RWF also depends on the range-time processing involved in producing estimates of meteorological variables. This is a third contributor to the RWF that has become more significant in recent years as advanced range-time processing techniques have become feasible for real-time implementation on modern radar systems. In this work, a new formulation of the RWF for weather radars that incorporates the impact of signal processing is proposed. Following the derivation based on a general signal processing model, typical scenarios are used to illustrate the variety of RWFs that can result from different range-time signal processing techniques. Finally, the RWF is used to measure range resolution and the range correlation of meteorological data.

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Christopher D. Curtis
and
Sebastián M. Torres

Abstract

This paper describes a real-time implementation of adaptive range oversampling processing on the National Weather Radar Testbed phased-array radar. It is demonstrated that, compared to conventional matched-filter processing, range oversampling can be used to reduce scan update times by a factor of 2 while producing meteorological data with similar quality. Adaptive range oversampling uses moment-specific transformations to minimize the variance of meteorological variable estimates. An efficient algorithm is introduced that allows for seamless integration with other signal processing functions and reduces the computational burden. Through signal processing, a new dimension is added to the traditional trade-off triangle that includes the variance of estimates, spatial coverage, and update time. That is, by trading an increase in computational complexity, data with higher temporal resolution can be collected and the variance of estimates can be improved without affecting the spatial coverage.

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Christopher D. Curtis
and
Sebastián M. Torres

Abstract

One way to reduce the variance of meteorological-variable estimates on weather radars without increasing dwell times is by using range oversampling techniques. Such techniques could significantly improve the estimation of polarimetric variables, which typically require longer dwell times to achieve the desired data quality compared to the single-polarization spectral moments. In this paper, an efficient implementation of adaptive pseudowhitening that was developed for single-polarization radars is extended for dual polarization. Adaptive pseudowhitening maintains the performance of pure whitening at high signal-to-noise ratios and equals or outperforms the digital matched filter at low signal-to-noise ratios. This approach results in improvements for polarimetric-variable estimates that are consistent with the improvements for spectral-moment estimates described in previous work. The performance of the proposed technique is quantified using simulations that show that the variance of polarimetric-variable estimates can be reduced without modifying the scanning strategies. The proposed technique is applied to real weather data to validate the expected improvements that can be realized operationally.

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David Schvartzman
,
Sebastián M. Torres
, and
David Warde

Abstract

Since the dual-polarization upgrade of the Weather Surveillance Radar-1988 Doppler (WSR-88D), the polarimetric variables have become a fundamental tool for better interpretation and forecasting of hazardous weather events. Thus, improving their quality has been an important long-standing effort. In this paper, we introduce the hybrid-scan estimators (HSE), which use the available data in split cuts of operational volume coverage patterns (VCP) to provide better estimates of differential reflectivity, differential phase, and correlation coefficient. The HSE are designed to choose between the data provided by either one of the two scans in split cuts based on their expected statistical performance, resulting in the same or better data quality compared to the conventional estimators. The performance improvement realized with the HSE is characterized with simulations and illustrated with data from WSR-88D. While relatively simple, an operational implementation of the HSE could bring improvements to forecasters’ data interpretation and algorithm performance, both of which rely on dual-polarization radar data.

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