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Abstract As the existing NEXRAD network nears the end of its life cycle, intense study and planning are underway to design a viable replacement system. Ideally, such a system would offer improved temporal resolution compared to NEXRAD, without a loss in data quality. In this study, scan speedup techniques—such as beam multiplexing (BMX) and radar imaging—are tested to assess their viability in obtaining high-quality rapid updates for a simulated long-range weather radar. The results of this study—which uses a Weather Research and Forecasting (WRF) Model–simulated supercell case—show that BMX generally improves data quality for a given scan time or can provide a speedup factor of 1.69–2.85 compared to NEXRAD while maintaining the same level of data quality. Additionally, radar imaging is shown to improve data quality and/or decrease scan time when selectively used; however, deleterious effects are observed when imaging is used in regions with sharp reflectivity gradients parallel to the beam spoiling direction. Consideration must be given to the subsequent loss of sensitivity and beam broadening. Finally, imaging is shown to have an effect on the radar-derived mesocyclone strength (Δ V ) of a simulated supercell. Because BMX and radar imaging are most easily achieved with an all-digital phased array radar (PAR), these results make a strong argument for the use of all-digital PAR for high-resolution weather observations. It is believed that the results from this study can inform decisions about possible scanning strategies and design of a NEXRAD replacement system for high-resolution weather radar data. 

Abstract Techniques to mitigate analysis errors arising from the nonsimultaneity of data collections typically use advection-correction procedures based on the hypothesis (frozen turbulence) that the analyzed field can be represented as a pattern of unchanging form in horizontal translation. It is more difficult to advection correct the radial velocity than the reflectivity because even if the vector velocity field satisfies this hypothesis, its radial component does not—but that component does satisfy a second-derivative condition. We treat the advection correction of the radial velocity ( υ r ) as a variational problem in which errors in that second-derivative condition are minimized subject to smoothness constraints on spatially variable pattern-translation components ( U , V ). The Euler–Lagrange equations are derived, and an iterative trajectory-based solution is developed in which U , V , and υ r are analyzed together. The analysis code is first verified using analytical data, and then tested using Atmospheric Imaging Radar (AIR) data from a band of heavy rainfall on 4 September 2018 near El Reno, Oklahoma, and a decaying tornado on 27 May 2015 near Canadian, Texas. In both cases, the analyzed υ r field has smaller root-mean-square errors and larger correlation coefficients than in analyses based on persistence, linear time interpolation, or advection correction using constant U and V . As some experimentation is needed to obtain appropriate parameter values, the procedure is more suitable for non-real-time applications than use in an operational setting. In particular, the degree of spatial variability in U and V , and the associated errors in the analyzed υ r field are strongly dependent on a smoothness parameter. 

On 27 May 2015, the Atmospheric Imaging Radar (AIR) collected high-temporal resolution radar observations of an EF-2 tornado near Canadian, Texas. The AIR is a mobile, X-band, imaging radar that uses digital beamforming to collect simultaneous RHI scans while steering mechanically in azimuth to obtain rapid-update weather data. During this deployment, 20°-by-80° (elevation × azimuth) sector volumes were collected every 5.5 s at ranges as close as 6 km. The AIR captured the late-mature and decaying stages of the tornado. Early in the deployment, the tornado had a radius of maximum winds (RMW) of 500 m and exhibited maximum Doppler velocities near 65 m s⁻¹. This study documents the rapid changes associated with the dissipation stages of the tornado. A 10-s resolution time–height investigation of vortex tilt and differential velocity [Formula: see text] is presented and illustrates an instance of upward vortex intensification as well as downward tornado decay. Changes in tornado intensity over periods of less than 30 s coincided with rapid changes in tornado diameter. At least two small-scale vortices were observed being shed from the tornado during a brief weakening period. A persistent layer of vortex tilt was observed near the level of free convection, which separated two layers with contrasting modes of tornado decay. Finally, the vertical cross correlation of vortex intensity reveals that apart from the brief instances of upward vortex intensification and downward decay, tornado intensity was highly correlated throughout the observation period.