This study investigates the implementation of adaptive digital beamforming (DBF) for polarimetric phased array radar (PAR), focusing on the calibration challenges and performance benefits for weather observations. Traditional Fourier-based beamforming methods, while widely used, suffer from fixed and limited spatial resolution and susceptibility to contamination, particularly in the presence of strong reflectivity gradients or clutter. On the other hand, adaptive DBF using the Capon method offers improved data quality by minimizing contamination but requires careful calibration, particularly for maintaining the integrity of polarimetric measurements. This work presents a framework that includes beam pattern calibration and noise estimation to enable accurate polarimetric variable estimation. Simulation results demonstrate the advantages of the Capon method over Fourier processing in terms of resolution and clutter mitigation, while NEXRAD data serve as a basis for more realistic simulations highlighting the method’s practical applicability. These findings provide a critical step toward the application of adaptive DBF in polarimetric phased array weather radar.

Range and velocity ambiguities significantly impact weather radar measurements, particularly dual-polarization variables in simultaneous transmission mode. A key challenge is determining the unambiguous range interval from which returns originate, especially in the presence of overlaid returns that obscure polarimetric estimates. This article presents a novel dual-PRF processing algorithm that blends long- and short-PRT data to recover polarimetric variables in contaminated regions. Two approaches are proposed: designing pulse sequences that inherently produce similar quality of polarimetric variables or applying adaptive range-averaging techniques when pulse sequences cannot be modified. The performance is demonstrated through both time-series simulations and actual radar observations. Results from real data analysis show high consistency between data from the long- and short-PRT sequences, with Pearson correlation coefficients exceeding 0.81 for all polarimetric variables. These advancements improve the operational utility of dual-PRF Batch mode scanning, providing a practical solution for enhanced dual-polarization measurements in weather radar networks.

This addresses errors in [1]. A typographical error appears in several equations. The equation numbers are listed with the corresponding correction.

This study provides the first in-depth examination of the spectral polarimetric characteristics of the ice alignment signatures (IASs) in thunderstorms, emphasizing how aligned ice crystals affect spectral co-cross correlation coefficient ($s\rho _{xp}$), linear depolarization ratio ($sL_{\text{DR}}$), and co-cross differential phase ($s\Phi _{xp}$) using alternate transmission and simultaneous reception (ATSR) mode. IAS is associated with strong electric fields as a precursor to lightning activity and is often manifested by distinct cross-coupling streaks. However, radar observations of these features can be confounded by antenna cross-pol contamination (ACC), which mimics IAS patterns, particularly when strong precipitation cores are present in sidelobe directions. Using simulations that integrate radar antenna patterns, wave propagation, and the microphysics of the hydrometeors, we explore IAS and ACC under varying conditions and demonstrate that, despite some similarities, ACC and IAS exhibit distinguishable spectral and bulk polarimetric characteristics. This work enhances spectral radar analysis, providing robust criteria for differentiating IAS from ACC, and addresses cross-coupling effects by introducing tailored thresholds and phase characteristics. Our findings advance both theoretical radar spectral polarimetry and practical weather radar applications, offering a simulation framework to mitigate misinterpretations and improve severe weather detection.

Pointed herein are differences between turbulence measurements by radars with conventional pencil bean antennas and electronically steering Phased Array Radar (PAR) antennas. Differences are caused by the shape and orientation of the beam cross section on phased array antenna. These depend on the pointing direction and everywhere except at broadside the beam cross section is approximately elliptical. A formalism applicable to pencil beams of circular cross section is extended to elliptic beam cross sections. For lateral dimensions of the radar resolution volume larger than its range extent an approximate formula is suggested for computing the eddy dissipation rate ε. A field of ε1/3indicative of turbulence affecting aircraft and obtained with a digital PAR is presented as well as values along a radial pointing at a 41.5° elevation.

A measurement procedure to determine transmitted differential phase between horizontally and vertically polarized radiation of a dual-polarization radar is presented. It is applicable to radars that transmit and receive Simultaneously Horizontally and Vertically (SHV) polarized waves. The method relies only on power measurements and leverages capabilities of polarimetric radars with independent transmitters per polarization to estimate differential phase on transmission. Measurements using this method by ground and airborne systems are conducted, and results are validated using a software-defined radio that provides true phase measurements. The differential phase estimate produced with the proposed method for the PX-1000 (X-band) radar is -105°, while that produced with the software-defined radio is -104.47° ±4.43°. The proposed method with a simple power sensor achieves a precision of ±2.5°.

Accurate radar observations are critical to improving the prediction and warning of severe weather events and supporting the mission of the National Weather Service in protecting lives and property. Mobile radars have been demonstrated to enhance the modeling of severe weather events by providing high-resolution observations. The deployment of mobile radar vehicles, therefore, becomes an essential research topic for improving the understanding and prediction of extreme weather phenomena. Even though many articles have been published on research using mobile radar observations, determining a mobile radar location is typically based on the intuition, experience, and judgment of knowledgeable practitioners. This proof-of-concept article provides the first quantitative approach using data-driven analysis and rigorous logic to determine the best possible location to deploy the mobile radar vehicle. Designed for field use, a framework consisting of digital information, geographic information systems (GIS), and spatial optimization was developed to delineate feasible areas, eliminate inferior solutions, and identify suitable locations. Findings and results highlight the potential utility and benefit of systematically analyzing mobile radar siting. This rational, transparent, and replicable decision-making process can significantly aid human meteorological observation.

Progressive pulse compression (PPC) was introduced to mitigate the need for a fill pulse in pulse-compression-based radar systems. It provides a method for recovering signals in the blind-range region created by the transmission of relatively long pulses. However, the initial implementation of PPC has limitations that need to be addressed for it to be more useful for meteorological applications. The proposed updated algorithm, named herein PPC+, brings significant improvements to mitigate these limitations. The methodology of PPC+ is similar to that of PPC, except that it uses a set of improved pulse compression filters. The improved compression filters are designed based on an amplitude modulation approach and are generated by multiplying the original filter by a range-dependent window. The window can be divided into two sections, the first part has a number of nulled samples used for mitigating the main lobe migration, and the remaining portion is a number of tapered samples to alleviate the “shoulder” effect from range sidelobes. Also, in contrast to PPC, the calibration factor used in PPC+ is further tuned to account for the tapering used in the improved compression filters. The PPC+ technique has been tested using data collected with PX-1000, a polarimetric X-band transportable solid-state radar system designed and operated by the Advanced Radar Research Center (ARRC) at The University of Oklahoma, and it is implemented and operational on that system (data available athttps://radarhub.arrc.ou.edu). This technique has also been implemented on Horus, a fully digital phased array radar recently completed at the ARRC.

Pulsed-Doppler radars are susceptible to range-velocity ambiguities inherent from using a uniform train of electromagnetic pulses to sample the atmosphere. Ambiguities arise due to the well-known Doppler dilemma, where increasing the pulse repetition time (PRT) increases the maximum unambiguous range but decreases the maximum unambiguous velocity and vice versa. Demands on any of the techniques to simultaneously mitigate these range-and-velocity ambiguities increase to a point of breakdown when they are needed most, that is, in the presence of widespread outbreaks of severe weather with convective storm over large areas. In this article, we present an algorithm to increase the region of valid Doppler velocities recovered when multi-PRT scans are used. The velocity recovery and dealiasing (VRAD) algorithm blends data from different scans and uses simple dealiasing techniques to mitigate regions with obscured velocity estimates. Data from the operational WSR-88D network are used to demonstrate the algorithm. On average, the algorithm is able to increase valid velocity estimates by 25.67% in conventional scans (i.e., non-phase coded) and 12.42% in phase coded scans.

Observations of weather phenomenon by polarimetric pulsed-Doppler weather radars are employed worldwide to monitor impending severe storms, flash-floods, and other weather related public hazards. The basis for processing received meteorological signals from pulsed-radar waveforms relies on stochastic processes where the accurate estimation of radar variables from received signals in additive white noise is essential for meaningful interpretation of weather phenomena and algorithm-derived products. For polarimetric weather radars, these estimates are calculated from signal correlations in time and across the horizontal and vertical polarization channels. Conventional estimators only use 1 or 2 signal correlation time-lags and may not utilize all the available information intrinsic in the received signals. Weather-variable estimates could benefit from the use of all intrinsic characteristics in the received data; accordingly, more complex estimators use multiple lags to extract additional information. However, not all estimates are improved by the use of more lags; in fact, improvement in estimates depends on signal characteristics and requires that the additional correlation lags provide new information. In this article, we derive and examine general multi-lag estimators for reflectivity, differential reflectivity, polarimetric cross-correlation coefficient, and Doppler spectrum width. We compare the performance of these proposed estimators against conventional estimators using Monte-Carlo simulations on different meteorological signal characteristics to find estimators that can improve the quality of certain radar-variable estimates.

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.