David A. Warde

The Autocorrelation Spectral Density for Doppler-Weather-Radar Signal Analysis

Time-domain autocovariance processing is widely accepted as a computationally efficient method to estimate the first three spectral moments of Doppler weather radar signals (i.e., mean signal power, mean Doppler velocity, and spectrum width). However, when signals with different frequency content (e.g., ground clutter) contaminate the weather signal, spectral processing using the periodogram estimator of the power spectral density (PSD) is the preferred tool of analysis. After spectral processing (i.e., filtering), a PSD-based autocorrelation estimator is typically employed to produce unbiased estimates of the weather-signal spectral moments. However, the PSD does not convey explicit phase information, which has the potential to aid in the spectral analysis of radar signals. In this paper, the autocorrelation spectral density (ASD) is introduced for spectral analysis of weather-radar signals as a generalization of the classical PSD, and an ASD-based autocorrelation estimator is proposed to produce unbiased estimates of the weather-signal spectral moments. A significant advantage of the ASD over the PSD is that it provides explicit phase information that can be exploited to identify and remove certain types of contaminant signals. Thus, the ASD provides an alternative means for spectral analysis, which can lead to improved quality of meteorological data from weather radars.

Ground Clutter Mitigation for Weather Radars Using the Autocorrelation Spectral Density

AbstractRadar 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.

Staggered-PRT Sequences for Doppler Weather Radars. Part I: Spectral Analysis Using the Autocorrelation Spectral Density

AbstractThe autocorrelation spectral density (ASD) was introduced as a generalization of the classical periodogram-based power spectral density (PSD) and as an alternative tool for spectral analysis of uniformly sampled weather radar signals. In this paper, the ASD is applied to staggered pulse repetition time (PRT) sequences and is related to both the PSD and the ASD of the underlying uniform-PRT sequence. An unbiased autocorrelation estimator based on the ASD is introduced for use with staggered-PRT sequences when spectral processing is required. Finally, the strengths and limitations of the ASD for spectral analysis of staggered-PRT sequences are illustrated using simulated and real data.

A demonstration of adaptive weather-surveillance capabilities on the national weather radar testbed phased-array radar

This paper describes the latest adaptive scanning capabilities of the National Weather Radar Testbed Phased-Array Radar located in Norman, OK. Focused observations, tailored observations, and the required scheduling algorithms are introduced, and their performance is illustrated with real-data examples. It is demonstrated that adaptive scanning for weather radars has the potential to reduce revisit times and to provide meteorological data that can aid in the forecasters warning-decision process.

Adaptive-Weather-Surveillance and Multifunction Capabilities of the National Weather Radar Testbed Phased Array Radar

The National Weather Radar Testbed (NWRT) is maintained and operated by NOAAs National Severe Storm Laboratory in Norman, OK, USA. It is a phased array radar (PAR) that was established to evaluate the potential to perform aircraft and weather surveillance with a single, multifunction radar. The NWRT PAR is also being used to demonstrate advanced weather-surveillance concepts that are well suited to the unique capabilities offered by phased arrays. This paper provides an overview of the adaptive-weather-surveillance and multifunction capabilities of this system.

Staggered-PRT Sequences for Doppler Weather Radars. Part II: Ground Clutter Mitigation on the NEXRAD Network Using the CLEAN-AP Filter

AbstractThe staggered–pulse repetition time (SPRT) technique has been shown to be effective at mitigating range and velocity ambiguities; however, mitigation of ground clutter contamination for SPRT sequences has proven to be more challenging. Using the properties of the autocorrelation spectral density, the Clutter Environment Analysis using Adaptive Processing (CLEAN-AP) filter is extended to SPRT sequences for its implementation on the U.S. Next Generation Weather Radar (NEXRAD) network. The performance of the CLEAN-AP filter for SPRT sequences is characterized and illustrated with simulations and real data. The study shows that the proposed ground clutter filter meets NEXRAD operational performance requirements for ground clutter mitigation.

Spectrum Width Estimation Using Matched Autocorrelations

The matched-autocorrelation spectrum-width estimator is introduced; statistics are derived and compared to those of the conventional estimator. It is demonstrated that the proposed estimator exhibits improved performance for narrow spectrum widths without increased computational complexity.