Phased-array radar (PAR) is being considered as a candidate for the next generation of weather radars in the United States because it can provide unique capabilities to observe severe and non-severe weather. PAR’s scanning flexibility is key to produce radar data with rapid volumetric updates; one way to achieve this is to use a purposely wider (or spoiled) transmit beam and to form several simultaneous receive beams. One of the key trade-offs of using spoiled transmit beams is an increase in the two-way beam-pattern sidelobes, which can cause increased contamination that biases the estimated radar variables (referred to as “sidelobe contamination”). Adaptive beamforming can be used to mitigate the increased sidelobe contamination from the use of spoiled transmit beams. However, adaptive beamforming methods designed for point targets can result in beam patterns that cannot be calibrated for weather radars because the beam pattern violates key assumptions. In this work, we propose constraints to the conventional Capon adaptive beamforming technique to preserve the shape of the beam-pattern main lobe such that weather radar calibration is the same as for conventional pencil beams. Our simulation results show that the proposed constrained Capon beamforming can mitigate sidelobe contamination while maintaining weather radar calibration.

The rotating phased array radar (RPAR) has the potential to improve the capabilities of the current U.S. Weather Surveillance Radar–1988 Doppler (WSR-88D) operational network and can be more affordable than other candidate phased array radar (PAR) architectures that have been evaluated to replace the WSR-88D. Considering the demanding functional requirements for the future U.S. weather surveillance radar, it is expected that several advanced RPAR scanning techniques will need to be applied simultaneously to achieve them. In this letter, we present the integration of two such RPAR scanning techniques: motion-compensated steering (MCS) and distributed beams (DBs). MCS exploits beam agility to mitigate beam smearing, while DB exploits digital beamforming to reduce the scan time or the standard deviation (SD) of estimates. The integration of these techniques is demonstrated with the National Severe Storms Laboratory’s (NSSL) dual-polarization advanced technology demonstrator (ATD) radar system. Results show that these techniques can be used simultaneously to enhance azimuthal resolution and reduce the SD of estimates without impacting data quality if certain obtainable tradeoff considerations are incorporated in the radar design process.

Important requirements for a future generation of weather surveillance radars include improvements in data quality and more rapid update of volumetric data. Phased array radar (PAR) is a candidate technology capable of providing the required functionality. The rotating PAR (RPAR) is a potential architecture that could improve the capabilities of the current parabolic-reflector-based US Weather Surveillance Radar—1988 Doppler (WSR-88D) operational network and is more affordable than other candidate PAR architectures. However, RPAR concept of operations that support observational needs has to be developed. TheDistributed Beams(DB) technique introduced in this article provides a way to either reduce the scan times or to reduce the variance of radar-variable estimates by azimuthally spoiling the transmit beam while receiving multiple digital beams as the radar rotates in azimuth. Specifically, the rotation speed of the pedestal is derived from the duration of the coherent processing interval (CPI) to produce the desired spatial sampling. This results in beams from subsequent CPIs in approximately the same directions, which increases the number of available data samples for processing. The increased number of available samples can be coherently processed to reduce the variance of estimates. Alternatively, by reducing the number of samples per CPI and increasing the RPAR’s rotation rate, the scan time can be reduced without increasing the variance of estimates. Results presented demonstrate both applications of the DB technique for dual-polarization observations. Given that this technique makes use of spoiled transmit beams, its benefits come at the expense of degraded angular resolution (beamwidth and sidelobe levels), and reduced sensitivity compared with the use of pencil beams. The technique could be implemented as part of an RPAR concept of operations to meet requirements for the future weather surveillance network if certain tradeoffs are accounted for in the radar design process.

The rotating phased array radar (RPAR) is an architecture that could improve the capabilities of the current weather surveillance radar—1988 Doppler (WSR-88D) operational network and is likely to be more affordable than other candidate PAR architectures. However, continuous antenna rotation coupled with the need to perform coherent processing of multiple samples results in a degraded effective beamwidth (referred to as beam smearing) compared to architectures based on stationary antennas. The RPAR’s beam agility can be exploited to reduce beam-smearing effects by electronically steering the beam on a pulse-to-pulse basis within the coherent processing interval. That is, the motion of the antenna can be compensated to maintain the beam pointed at the center of resolution volume being sampled. This motion-compensated steering (MCS) could reduce the effects of antenna motion and lead to a reduction in the effective beamwidth. The purpose of this article is to present and demonstrate the MCS technique for a dual-polarization RPAR system. In this article, we provide a formulation for the MCS technique, simulations to quantify its performance in mitigating beam-smearing effects, its impacts on the quality of dual-polarization radar-variable estimates, and a practical implementation on the National Severe Storms Laboratory’s Advanced Technology Demonstrator (ATD) system. Experiments were carried out using two alternative concepts of operations (CONOPS) described in this article. Results show that a system designed with sufficient pointing accuracy can be operated as an RPAR using MCS, and the impact on radar-variable estimates is comparable to that obtained when operating the same system as a stationary PAR.

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.

In an effort to study and characterize scattering mechanisms of debris particles in tornadoes, a numerical polarimetric radar emulator was developed. This paper is primarily motivated by attempts to explain radar observations near tornadoes. One such observation is the regions of negative differential reflectivity, which have been found near tornadoes but they are yet to be explained physically. There are hypotheses that suggest common debris alignment and/or dominant scattering from objects with high radar-cross-section (RCS) values that cause negative ZDR, but they are extremely challenging to verify due to the inherent danger near the vicinity of tornadoes. It is, however, possible to numerically construct the scenes through representative simulations to verify the plausible causes. This serves as our primary motivation to develop the radar emulator. The novel aspects of this paper are the realistic trajectory derivation, which is based on a physical air-drag model, and the representative diversity of RCS contributions from each debris object, developed through realistic polarimetric RCS modeling and anechoic chamber measurements.

Weather radar variables provide useful information about the characteristics and motion of hydrometeors. However, the bulk information may be masked, when the meteorological signal of interest is contaminated by clutter. The dual-polarimetric spectral densities (DPSDs) may unveil additional information about the polarimetric characteristics of the groups of scatterers moving at different Doppler velocities in a given radar resolution volume. Previous DPSD estimation methods required averaging a large number of spectra (obtained from different spatial locations or times), or averaging in frequency to get accurate estimates; though by doing so, the resolution is degraded, and the important features of the meteorological phenomenon may be masked. In an attempt to overcome these limitations, the Bootstrap DPSD estimator is proposed, which allows the estimation of DPSDs from a single dwell with minimal spatial, temporal, or spectral resolution loss. The performance and the limitations of the Bootstrap and conventional DPSD estimators are assessed when identifying signals with different polarimetric signatures of scatterers moving at different radial velocities in the radar volume. The advantages of the Bootstrap DPSD estimator as a tool for the polarimetric spectral analysis are demonstrated with a few examples of polarimetric spectral signatures in data from tornado cases. It is expected that, with the Bootstrap DPSD and the polarimetric spectral analysis, it will be possible to better understand tornado dynamics and their connection to weather radar measurements, as well as to elucidate important scientific questions that motivated this paper.

The National Weather Radar Testbed (NWRT) is maintained and operated by NOAA's 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.

The next generation of weather radars, which may also support other missions, is likely to be based on phased arrays that will utilize simultaneous receive beams to achieve the required update times. Some of the disadvantages of using simultaneous receive beams, such as higher two-way antenna radiation pattern sidelobes, can be mitigated by using adaptive beamforming. A majority of the existing adaptive beamforming algorithms are designed for point targets, and direct application to distributed scatterers (e.g., hydrometeors) can lead to significantly biased estimates of key radar variables. This paper presents an adaptive beamspace processing algorithm specifically designed for weather-surveillance radar applications. Through both simulated and real data, it is shown that the proposed adaptive beamspace processing algorithm can produce accurate and calibrated estimates of radar variables while also automatically rejecting interference signals.

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.

Phased array radars (PARs) are attractive in weather surveillance primarily because of their capability to electronically steer. When combined with the recently developed beam multiplexing (BMX) technique, these radars can obtain very rapid update scans that are useful in monitoring severe weather. A consequence is that the small number of contiguous samples of the time series obtained can be a challenge for temporal/spectral filters used for clutter mitigation. As a result, the accurate extraction of weather signals can become the limiting performance barrier for PARs that employ BMX in clutter-dominated scattering fields. By exploiting the spatial correlation of the auxiliary channel signals, the effect of clutter contamination can be reduced in these conditions. In this paper, three spatial filtering techniques that used low-gain auxiliary receive channels are presented. The effect of clutter mitigation was studied using numerical simulations of a tornadic environment for changes in signal-to-noise ratio, clutter-to-signal ratio, number of time series samples, varying clutter spectral widths, and maximum weight constraints. Since such data are not currently available from a horizontally pointed phased array weather radar, experimental validation was applied to an existing data set from the turbulent eddy profiler, which is a vertically pointed PAR. Although preliminary, the results show promise for clutter mitigation with extremely short nonuniform sampling.

Several mechanisms are currently provided to alleviate effects of range overlaid echoes and velocity aliasing in the Weather Surveillance Radar-1988 Doppler (WSR-88D). However, due to limitations in these techniques, observation of severe weather phenomena is significantly impaired. This work presents results from a multi-year study at the National Severe Storms Laboratory dealing with methods to mitigate the effects of range and velocity ambiguities in the WSR-88D.

Pseudowhitening of oversampled signals in range is proposed as a method to improve the performance of spectral moment and polarimetric variable estimators on weather surveillance radars. In an attempt to overcome the noise sensitivity of the whitening transformation, a solution based on the minimum mean-square-error criterion is considered first; however, this transformation is less practical than whitening because it requires knowledge of the signal-to-noise ratio at every range location. Pseudowhitening techniques are introduced as practical solutions that achieve a suboptimal compromise between variance reduction and noise sensitivity. Based on regularization methods for the solution of ill-conditioned problems, two pseudowhitening schemes are proposed: the clipped singular value decomposition transformation and the sharpening filter. By comparing their statistical performance with theoretical minimum bounds, it is shown that pseudowhitening-based estimators are almost optimal under practical conditions. Estimators based on pseudowhitening techniques avoid the pitfalls of their whitening-transformation-based counterparts and lead to more accurate radar products and/or rapid data acquisition for a much wider range of signal-to-noise ratios.

A method for estimation of Doppler 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 taken at a rate several times larger than the reciprocal of the transmitted pulse length. The aforementioned radar variables are estimated by suitably combining weighted averages of these oversampled signals in range with the usual processing of samples (spaced at pulse repetition time) at a fixed range location. The weights in range are chosen such that the oversampled signals become uncorrelated and consequently the variance of the estimates decreases significantly. Because the estimates' errors are inversely proportional to the volume scanning times, it follows that storms can be surveyed much faster than it is possible with current processing methods, or equivalently, for the current volume scanning time, the accuracy of the estimates can be greatly improved.

A fully adaptive infinite impulse response notch filter in cascade form is proposed to detect and track multiple time-varying frequencies in additive white noise. Based on transformations for digital filters in the frequency domain, the filter results in a minimal number of parameters. In addition, a simple adaptive algorithm with good tracking and convergence properties is obtained by using all-pass filters and truncating the gradient. Computer simulations are included to verify the competitive performance of this filter under a wide range of conditions. From this analysis, we conclude that our new design is computationally simple, achieves rapid convergence, and is consequently a good choice in many non-stationary environments.