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.

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.

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.

Multistatic radar is a promising option for the low-cost collection of multiple-Doppler weather observations. However, due to the use of low-directivity antennas at the receivers in these systems, they typically suffer from extremely high two-way sidelobe levels compared to monostatic radars. Doppler velocity estimation biases induced by sidelobe contamination have proved to be a significant obstacle to more widespread adoption of this technology. It has been noted in the existing literature that the technique of sidelobe whitening, first developed for use in monostatic systems, has the potential to mitigate this issue. However, the existing sidelobe whitening algorithm is not suitable for use in this application, as it is only capable of achieving whitening in the two-way antenna pattern, whereas an algorithm to be used in the multistatic case must be able to achieve this result in the transmit pattern alone. This article proposes an alternate sidelobe whitening technique based on the method of alternating projections which allows for effective whitening in the one-way antenna pattern. A multistatic weather radar time-series simulator is used in conjunction with numerical weather prediction data to demonstrate the effectiveness of this method in mitigating multiple-Doppler measurement biases.

Practical and accurate estimation of 3-D wind fields is an ongoing challenge in radar meteorology. Multistatic (single transmitter/multiple receivers) radar architectures offer a cost-effective solution for obtaining the multiple Doppler measurements necessary to achieve such estimates. Existing multistatic weather radars, while less costly than comparable monostatic networks, have been constrained by the need for specialized equipment and software to perform frequency and pulse timing synchronization with the transmitting radar. This article describes the implementation of a passive radar network that performs pulse timing and carrier frequency synchronization through measurements of the sidelobe radiation of the transmitting radar. This makes it possible for the receiver modules to be constructed with minimal size and cost, and it allows for their use in coordination with any in-band transmitter capable of recording time-stamped pointing angle information. A prototype network consisting of two passive receivers has been constructed in the Oklahoma City, OK, USA. Weather observations collected using the radiation from a WSR-88D have been used to validate the accuracy of velocity measurements obtained through this technique.

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.

Future weather radar systems will need to provide rapid updates within a flexible multifunctional overall radar network. This naturally leads to the use of electronically scanned phased array antennas. However, the traditional multifaced planar antenna approaches suffer from having radiation patterns that are variant in both beam shape and polarization as a function of electronic scan angle; even with practically challenging angle-dependent polarization correction, this places limitations on how accurately weather can be measured. A cylindrical array with commutated beams, on the other hand, can theoretically provide patterns that are invariant with respect to azimuth scanning with very pure polarizations. This paper summarizes recent measurements of the cylindrical polarimetric phased array radar demonstrator, a system designed to explore the benefits and limitations of a cylindrical array approach to these future weather radar applications.

A radar simulator capable of generating time series data for a polarimetric phased array weather radar has been designed and implemented. The received signals are composed from a high-resolution numerical prediction weather model. Thousands of scattering centers (SCs), each with an independent randomly generated Doppler spectrum, populate the field of view of the radar. The moments of the SC spectra are derived from the numerical weather model, and the SC positions are updated based on the 3-D wind field. In order to accurately emulate the effects of the system-induced cross-polar contamination, the array is modeled using a complete set of dual-polarization radiation patterns. The simulator offers reconfigurable element patterns and positions and access to independent time series data for each element, resulting in easy implementation of any beamforming method. It also allows for arbitrary waveform designs and is able to model the effects of quantization on waveform performance. Simultaneous, alternating, quasi-simultaneous, and pulse-to-pulse phase-coded modes of polarimetric signal transmission have been implemented. This framework allows for realistic emulation of the effects of cross-polar fields on weather observations, as well as the evaluation of possible techniques for the mitigation of those effects.

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.

This letter proposes a design concept that can satisfy the requirements for weather observations with a multifunction polarimetric phased-array radar at about 1-min volume update time while meeting present aviation requirements.

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.

This paper introduces the Spectrum-Time Estimation and Processing (STEP) algorithm developed in the Atmospheric Radar Research Center (ARRC) at the University of Oklahoma (OU). The STEP processing framework integrates three novel algorithms recently developed in ARRC: spectrum clutter identification, bi-Gaussian clutter filtering, and multi-lag moment estimation. The three modules of STEP algorithm fulfill three functions: clutter identification, clutter filtering and noise reduction, respectively. The performance of STEP has been evaluated using simulated data as well as real data collected by the C-band polarimetric research radar OU-Polarimetric Radar for Innovations in Meteorology and Engineering. Results show that STEP algorithm can effectively improve quality of polarimetric weather data in the presence of ground clutter and noise.

This study presents a new identification and mitigation scheme of second trip contamination for pulsed Doppler polarimetric weather radars with the ability of random phase coding. This scheme can be easily implemented in a magnetron radar without any hardware changes. For relatively weak contamination, identification and mitigation are based on a multilag processing method, which uses multiple lags of both the auto- and cross-correlation functions to estimate radar moments. For relatively strong contamination, instantaneous phase variations of horizontal and vertical polarization channels are combined into a simple fuzzy-logic scheme to complete the identification. Data from the C-band OU-PRIME radar are used to demonstrate the effectiveness of the proposed scheme for identification and mitigation of second-trip echoes.

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.

A real-time refractivity retrieval platform for the CASA IP-1 [1] testbed is currently being developed at the University of Oklahoma. From our previous efforts in the 2007-2008 KTLX/KFDR refractivity experiment [2], a software module to produce refractivity products has been developed and is ported over to the IP-1 testbed this year. One of the challenges for refractivity using the IP-1 radars is the use of X-band systems, which results in more rapid phase wrapping across ranges. In this work, a theoretical explanation will be presented to show that X-band is not a limiting factor to refractivity retrieval. Another significant challenge is the use of magnetron-based transmitter, which changes the effective wavelength being applied for measuring the propagation phase. This question remains open but it will be shown that through the use of differential refractivity technique, scan-to-scan refractivity can still be useful in practice.

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 measurement of clear-air turbulence with a Doppler radar is investigated. An autoregressive moving average (ARMA) model is proposed to improve the Doppler spectral width estimates. An iterative algorithm that has its origin in system identification is used for the estimation of the ARMA parameters. By taking advantage of a priori knowledge of the correlation matrix, which arises in the derivation of the governing equations of the ARMA parameters, the ARMA spectral estimate can be improved. This improvement is shown in terms of bias and variance of the spectral width estimate.>