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Dusan Zrnic and David Schvartzman

Abstract

We review cubic phase codes for mitigating ambiguities in range and velocity before introducing two specific codes. The two have periodicities of 5 and 7 samples for both the transmitted and the modulation code sequences. The short periods are suitable for generating codes of arbitrary length starting with about 15. We abbreviate the two codes with L5 and L7 and describe generation of the codes starting with kernels (i.e., minimum length sequences which repeat to generate the codes of desired lengths). The L5 modulation code produces 5 spectral replicas of the coded signal and the L7 produces 7. We apply the L7 code to a sinusoid and reveal spectra of the modulated signals from several ambiguous range intervals. Through simulation, we show application to weather-like signals and construct examples whereby two weather signals and ground clutter are overlaid. Using theory, we define the operating region of the codes in the signal parameter space. The region covers a wide range of overlaid returned powers and spectrum widths; it is obtained from simulations involving the L codes and the SZ(8/64) code. The technique is effective in distinguishing the returns from two trip regions separated by no more than L-2 ambiguous range intervals and reconstructing the corresponding spectral moments. The L5 and L7 codes protect from trip returns, up to 5th and 7th making them suitable for short wavelength (3 and 5 cm) radars as their PRTs must be relatively short to accommodate the expected spread of velocities in storms.

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Dusan Zrnić and David Schvartzman

Abstract

We review cubic phase codes for mitigating ambiguities in range and velocity before introducing two specific codes. The two have periodicities of 5 and 7 samples for both the transmitted and the modulation code sequences. The short periods are suitable for generating codes of arbitrary length starting with about 15. We abbreviate the two codes with L5 and L7 and describe generation of the codes starting with kernels (i.e., minimum length sequences that repeat to generate the codes of desired lengths). The L5 modulation code produces 5 spectral replicas of the coded signal and the L7 produces 7. We apply the L7 code to a sinusoid and reveal spectra of the modulated signals from several ambiguous range intervals. Through simulation, we show application to weatherlike signals and construct examples whereby two weather signals and ground clutter are overlaid. Using theory, we define the operating region of the codes in the signal parameter space. The region covers a wide range of overlaid returned powers and spectrum widths; it is obtained from simulations involving the L codes and the SZ(8/64) code. The technique is effective in distinguishing the returns from two trip regions separated by no more than L − 2 ambiguous range intervals and reconstructing the corresponding spectral moments. The L5 and L7 codes protect from trip returns up to the fifth and seventh, making them suitable for short-wavelength (3 and 5 cm) radars as their PRTs must be relatively short to accommodate the expected spread of velocities in storms.

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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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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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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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Feng Nai, Jami Boettcher, Christopher Curtis, David Schvartzman, and Sebastián Torres

Abstract

To fulfill the evolving observational needs of the National Weather Service (NWS), future weather radar systems will have to meet demanding requirements. Designing such systems will likely involve trade-offs between system cost and operational performance. A potential cost driver for future weather radars that could cause significant data-quality impacts on forecasters is the required angular resolution and sidelobe performance, which are mainly dictated by the antenna radiation pattern. Typical antenna radiation patterns can be characterized by the width of the main lobe and their sidelobe levels, which are traditionally measured across the azimuthal and elevation dimensions. In this work, we study the impact of increasing sidelobe levels on NWS forecasters’ data interpretation during warning operations. The resulting impact model can be used by decision-makers to better understand the cost–benefit trade-offs inherent in any radar system design.

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Sebastián M. Torres, Christopher D. Curtis, and David Schvartzman

Abstract

With more weather radars relying on low-power solid-state transmitters, pulse compression has become a necessary tool for achieving the sensitivity and range resolution that are typically required for weather observations. While pulse compression is well understood in the context of point-target radar applications, the design of pulse compression waveforms for weather radars is challenging because requirements for these types of systems traditionally assume the use of high-power transmitters and short conventional pulses. In this work, Weather Surveillance Radar-1988 Doppler (WSR-88D) antenna pattern requirements are used to illustrate how suitable requirements can be formulated for the radar range weighting function (RWF), which is determined by the transmitted waveform and any range-time signal processing. These new requirements set bounds on the RWF range sidelobes, which are unavoidable with pulse compression waveforms. Whereas nonlinear frequency modulation schemes are effective at reducing RWF sidelobes, they usually require a larger transmission bandwidth, which is a precious commodity. An optimization framework is proposed to obtain minimum-bandwidth pulse compression waveforms that meet the new RWF requirements while taking into account the effects of any range-time signal processing. Whereas pulse compression is used to meet sensitivity and range-resolution requirements, range-time signal processing may be needed to meet data-quality and/or update-time requirements. The optimization framework is tailored for three processing scenarios and corresponding pulse compression waveforms are produced for each. Simulations of weather data are used to illustrate the performance of these waveforms.

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