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Sebastián M. Torres

Abstract

Processing oversampled signals in range with a whitening transformation has been proposed as a means to reduce the variance of meteorological variable estimates on polarimetric Doppler weather radars. However, the original formulation to construct decorrelation transformations does not account for mismatches in the polarimetric channels, which results in abnormally biased polarimetric variable estimates if the two channels are not perfectly matched. This paper extends the initial formulation and demonstrates that, by properly accounting for the differences in the polarimetric channels, it is always possible to produce optimum estimates of all meteorological variables. Simulation analyses based on the reported characteristics of existing polarimetric radars are included to illustrate the performance of the proposed transformations.

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Sebastián M. Torres and David Schvartzman

Abstract

We propose a simulation framework that can be used to design and evaluate the performance of adaptive scanning algorithms on different phased-array weather radar designs. The simulator is proposed as tool to 1) compare the performance of different adaptive scanning algorithms on the same weather event, 2) evaluate the performance of a given adaptive scanning algorithm on several weather events, and 3) evaluate the performance of a given adaptive scanning algorithm on a given weather event using different radar designs. We illustrate the capabilities of the proposed framework to design and evaluate the performance of adaptive algorithms aimed at reducing the update time using adaptive scanning. The example concept of operations is based on a fast low-fidelity surveillance scan and a high-fidelity adaptive scan. The flexibility of the proposed simulation framework is tested using two phased-array-radar designs and three complementary adaptive scanning algorithms: focused observations, beam clustering, and dwell tailoring. Based on a significant weather event observed by an operational NEXRAD radar, our experimental results consist of radar data that were simulated as if the same event had been observed by arbitrary combinations of radar systems and adaptive scanning configurations. Results show that simulated fields of radar data capture the main data-quality impacts from the use of adaptive scanning and can be used to obtain quantitative metrics and for qualitative comparison and evaluation by forecasters. That is, the proposed simulator could provide an effective interface with meteorologists and could support the development of concepts of operations that are based on adaptive scanning to meet the evolutionary observational needs of the U.S. National Weather Service.

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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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Christopher D. Curtis and Sebastián M. Torres

Abstract

Adaptive range oversampling processing can be used either to reduce the variance of radar-variable estimates without increasing scan times or to reduce scan times without increasing the variance of estimates. For example, an implementation of adaptive pseudowhitening on the National Weather Radar Testbed Phased-Array Radar (NWRT PAR) led to a twofold reduction in scan times. Conversely, a proposed implementation of adaptive pseudowhitening the U.S. Next Generation Weather Radar (NEXRAD) network would reduce the variance of dual-polarization estimates while keeping current scan times. However, the original version of adaptive pseudowhitening is not compatible with radar-variable estimators for which an explicit variance expression is not readily available. One such nontraditional estimator is the hybrid spectrum-width estimator, which is currently used in the NEXRAD network. In this paper, an extension of adaptive pseudowhitening is proposed that utilizes lookup tables (rather than analytical solutions based on explicit variance expressions) to obtain range oversampling transformations. After describing this lookup table (LUT) adaptive pseudowhitening technique, its performance is compared to that of the original version of adaptive pseudowhitening using traditional radar-variable estimators. LUT adaptive pseudowhitening is then applied to the hybrid spectrum-width estimator, and simulation results are confirmed with a qualitative analysis of radar data.

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Christopher D. Curtis and Sebastián M. Torres

Abstract

As range-oversampling processing has become more practical for weather radars, implementation issues have become important to ensure the best possible performance. For example, all of the linear transformations that have been utilized for range-oversampling processing directly depend on the normalized range correlation matrix. Hence, accurately measuring the correlation in range time is essential to avoid reflectivity biases and to ensure the expected variance reduction. Although the range correlation should be relatively stable over time, hardware changes and drift due to changing environmental conditions can have measurable effects on the modified pulse. To reliably track changes in the range correlation, an automated real-time method is needed that does not interfere with normal data collection. A method is proposed that uses range-oversampled data from operational radar scans and that works with radar returns from both weather and ground clutter. In this paper, the method is described, tested using simulations, and validated with time series data.

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Sebastián M. Torres and Christopher D. Curtis

Abstract

A fundamental assumption for the application of range-oversampling techniques is that the correlation of oversampled signals in range is accurately known. In this paper, a theoretical framework is derived to quantify the effects of inaccurate range correlation measurements on the performance of such techniques, which include digital matched filtering and those based on decorrelation (whitening) transformations. It is demonstrated that significant reflectivity biases and increased variance of estimates can occur if the range correlation is not accurately measured. Simulations and real data are used to validate the theoretical results and to illustrate the detrimental effects of mismeasurements. Results from this work underline the need for reliable calibration in the context of range-oversampling processing, and they can be used to establish appropriate accuracy requirements for the measurement of the range correlation on modern weather radars.

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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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David Schvartzman, Sebastián Torres, and Tian-You Yu

Abstract

Forecasters often monitor and analyze large amounts of data, especially during severe weather events, which can be overwhelming. Thus, it is important to effectively allocate their finite perceptual and cognitive resources for the most relevant information. This paper introduces a novel analysis tool that quantifies the amount of spatial and temporal information in time series of constant-elevation weather radar reflectivity images. The proposed Weather Radar Spatiotemporal Saliency (WR–STS) is based on the mathematical model of the human attention system (referred to as saliency) adapted to radar reflectivity images and makes use of information theory concepts. It is shown that WR-STS highlights spatially and temporally salient (attention attracting) regions in weather radar reflectivity images, which can be associated with meteorologically important regions. Its skill in highlighting current regions of interest is assessed by analyzing the WR-STS values within regions in which severe weather is likely to strike in the near future as defined by National Weather Service forecasters. The performance of WR-STS is demonstrated for a severe weather case and analyzed for a set of 10 diverse cases. Results support the hypothesis that WR-STS can identify regions with meteorologically important echoes and could assist in discerning fast-changing, highly structured weather echoes during complex severe weather scenarios, ultimately allowing forecasters to focus their attention and spend more time analyzing those regions.

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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

WSR-88D superresolution data are produced with finer range and azimuth sampling and improved azimuthal resolution as a result of a narrower effective antenna beamwidth. These characteristics afford improved detectability of weaker and more distant tornadoes by providing an enhancement of the tornadic vortex signature, which is characterized by a large low-level azimuthal Doppler velocity difference. The effective-beamwidth reduction in superresolution data is achieved by applying a tapered data window to the samples in the dwell time; thus, it comes at the expense of increased variances for all radar-variable estimates. One way to overcome this detrimental effect is through the use of range oversampling processing, which has the potential to reduce the variance of superresolution data to match that of legacy-resolution data without increasing the acquisition time. However, range-oversampling processing typically broadens the radar range weighting function and thus degrades the range resolution. In this work, simulated Doppler velocities for vortexlike fields are used to quantify the effects of range-oversampling processing on the velocity signature of tornadoes when using WSR-88D superresolution data. The analysis shows that the benefits of range-oversampling processing in terms of improved data quality should outweigh the relatively small degradation to the range resolution and thus contribute to the tornado warning decision process by improving forecaster confidence in the radar data.

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