Wind velocities approximated via velocity azimuth display (VAD) have been found to be contaminated by birds and enabled by insects. However, the widely used VAD wind profile (VWP) does not account for taxa, largely due to the challenge of distinguishing bird from insect echoes. This problem has been addressed by the recently developed bird-insect ridge classifier (BIRC). Hence, this work proposes an intelligent VAD (IVAD) that leverages BIRC to improve clear-air wind estimates by generating three new products, including the insects-birds ratio, bird-only VAD, and insect-only VAD. These products are analyzed for one-month periods containing nocturnal and diurnal bird migration. Wind bias is used as the evaluation metric, defined as the deviation of the predicted VAD from reference wind measurements obtained from the rapid refresh (RAP) model. Results show an inverse relationship between biases and the insects-birds ratio, such that increasing (decreasing) bird (insect) population was accompanied by larger biases. Furthermore, contaminated VADs showed improvements when insects-only signals were used instead of all biological echoes. We recommend that these products can be incorporated into the VWP. First, the insects-birds ratio can be used to identify whether a given height is bird dominated, mixed, or insect dominated. For the mixed case, improved wind estimates can be obtained from insect-only VAD. Otherwise, bird-only VADs can be obtained from bird-dominated heights, while insect-only VADs are obtained from insect-dominated heights. The former can be used to track birds while the latter tracks insects and the wind.

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.

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.

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.

A gust front (GF) is the leading edge of the cold outflow from a thunderstorm. The upgrade of the S-band Weather Surveillance Radar-1988 Doppler (WSR-88D) to dual-polarization has been completed recently in the U.S. Therefore, it is timely to exploit the added benefits of polarimetric variables to identify GFs. In this paper, six signatures derived from polarimetric WSR-88D data are developed to characterize GFs, including medium reflectivity, apparent thin line feature in reflectivity, and the motion of reflectivity quantified by a line feature parameter, high differential reflectivity, low copolar cross-correlation coefficient, apparent convergence manifested by the large radial shear, and large standard deviation of differential phase. These signatures are fuzzy in nature, and therefore, a novel neuro-fuzzy GF detection algorithm (NFGDA) is developed using a fuzzy logic inference system, which is optimized by a training process using a neural network. WSR-88D data from 11 cases (totaling 121 volume scans) are used to evaluate the performance of NFGDA and compared to the operational machine intelligent GF algorithm (MIGFA) with single polarization data. The results show that NFGDA can provide improved performance with a higher probability of detection of 92% (versus 78% with MIGFA), lower false alarm ratio of 0% (versus 9%), and higher percentage correct of 93% (versus 74%). Additional length-based scoring schemes show that NFGDA can correctly detect 62% (41% with MIGFA) of the total length of GFs, and minimize falsely detected length to 7% (61%).

An intelligent wheelchair is devised, which is controlled by a coordinated mechanism based on a brain-computer interface (BCI) and speech recognition. By performing appropriate activities, users can navigate the wheelchair with four steering behaviors (start, stop, turn left, and turn right). Five healthy subjects participated in an indoor experiment. The results demonstrate the efficiency of the coordinated control mechanism with satisfactory path and time optimality ratios, and show that speech recognition is a fast and accurate supplement for BCI-based control systems. The proposed intelligent wheelchair is especially suitable for patients suffering from paralysis (especially those with aphasia) who can learn to pronounce only a single sound (e.g., ‘ah’).

Radar-derived refractivity from stationary ground targets can be used as a proxy of near-surface moisture field and has the potential to improve the forecast of convection initiation. Refractivity retrieval was originally developed for a single radar and was recently extended for a network of radars by solving a constrained least squares (CLS) minimization. In practice, the number of high-quality ground returns can be often limited, and consequently, the retrieval problem becomes ill-conditioned. In this paper, an emerging technology of compressive sensing (CS) is proposed to estimate the refractivity field using a network of radars. It has been shown that CS can provide an optimal solution for the underdetermined inverse problem under certain conditions and has been applied to different fields such as magnetic resonance imaging, radar imaging, etc. In this paper, a CS framework is developed to solve the inversion. The feasibility of CS for refractivity retrieval using single and multiple radars is demonstrated using simulations, where the model refractivity fields were obtained from the Advanced Regional Prediction System. The root-mean-squared error was introduced to quantify the performance of the retrieval. The performance of CS was assessed statistically and compared to the CLS estimates for various amounts of measurement errors, numbers of radars, and model refractivity fields. Our preliminary results have shown that CS can consistently provide relatively robust and high-quality estimates of the refractivity field.

An image segmentation algorithm using an alternating erosion/dilation technique called strong point analysis (SPA) is introduced for general-purpose feature detection. The ability to associate and group pixels with the salient features of an image allows computers to consider images not as an array of values but as a collection of objects. This enables other algorithms to perform advanced tasks, such as tracking an object in a time series of images. The qualitative needs for proper tracking of storm cells in radar images are discussed. To test SPA for those qualities, radar reflectivity images from three S-band weather radars were used. The algorithm is demonstrated to identify features fairly consistently over a time series of images, as well as exhibiting well-behaved changes to its output with respect to changes to the algorithm's input parameters.

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.

In this paper, an investigation of the potential of rapid refractivity retrieval is presented. The retrieval technique utilizes radar phase measurements of ground clutter to derive near-surface refractivity, which has been commonly used as a proxy for humidity, given its close relation to vapor pressure. Surface humidity is an important meteorological parameter and has been known to play an important role in convective initiation. In this paper, the refractivity retrieval technique is exploited by using smaller numbers of samples for phase calculation, which is a fundamental process in refractivity retrieval. The impetus for this paper is to explore the possibility of rapid refractivity retrieval by exploiting the rapid beam-steering capability of a phased-array radar. Using the National Weather Radar Testbed in Norman, OK, a 64-pulse per radial raw-data set was collected for conventional refractivity processing. Then, subsets of the 64 samples were extracted to emulate shorter dwell periods and the corresponding more rapid experiments. The test cases that were considered are 2, 4, 8, 16, and 32 samples. Refractivity fields retrieved using smaller numbers of samples are compared against the reference field, which was obtained using the entire 64-sample data set. It will be shown that, statistically, significant refractivity fields can be obtained from as short as a two-sample dwell.

The research Weather Surveillance Radar-1988 Doppler locally operated by the National Severe Storms Laboratory in Norman, OK, has the unique capability of collecting massive volumes of Level I time-series data over many hours, which provides a rich environment for evaluating our new postprocessing algorithms. In this letter, an approach of identifying tornado vortices in Doppler spectra is proposed and investigated using eigen analysis, cluster estimation, and fuzzy logic technique.