Mobile radar platforms designed for observation of severe local storms have consistently pushed the boundaries of spatial and temporal resolution in order to allow for detailed analysis of storm structure and evolution. Digital beamforming, or radar imaging, is a technique that is similar in nature to a photograwphic camera, where data samples from different spaces at the same range are collected simultaneously. This allows for rapid volumetric update rates compared to radars that scan with a single narrow beam. The Atmospheric Imaging Radar (AIR) is a mobile X-band (3.14-cm wavelength) imaging weather radar that transmits a vertical, 20° fan beam and uses a 36-element receive array to form instantaneous range–height indicators (RHIs) with a native beamwidth of 1° × 1°. Rotation in azimuth allows for 20° × 90° volumetric updates in under 6 s, while advanced pulse compression techniques achieve 37.5-m range resolution. The AIR has been operational since 2012 and has collected data on tornadoes and supercells at ranges as close as 6 km, resulting in high spatial and temporal resolution observations of severe local storms. The use of atmospheric imaging is exploited to detail rapidly evolving phenomena that are difficult to observe with traditional scanning weather radars.

In this study, data collected by the Atmospheric Imaging Radar (AIR) are analyzed in conjunction with WSR-88D data (KFDR) for a tornado near Tipton, Oklahoma, on 16 May 2015. The analysis presented herein utilizes PPIs from both radars, polarimetric data from KFDR, time–height plots from the AIR, and a ground-based velocity track display (GBVTD) analysis. This study is novel in that it uses high-resolution mobile radar data (update time of 6–7 s) in tandem with polarimetric data from KFDR in order to identify possible areas of debris, including a debris ring contained within the outer vortex circulation. Leveraging the high spatiotemporal resolution of the AIR with the polarimetric capability of KFDR leads to analysis of reflectivity distributions, debris lofting, kinematic changes, and oscillations in tornado intensity during a portion of the mature stage of the tornado, with a particular focus on the relationship between changes in the reflectivity field and dynamical changes around the tornado. Debris is lofted in a high-reflectivity concentric ring of increasing radius and height around the tornado over several minutes, within the outer weak-echo hole (WEH). Simultaneously, debris lofting and asymmetric reflectivity distribution around the WEH coincide with changes in vortex tilt on multiple occasions. In one instance, hydrometeor fallout appears to precede a possible descending reflectivity core. Using the GBVTD results, near-surface convergence intensifies at the same time and location as when the debris ring is lofted. Additionally, strengthening of the tornado via multiple modes of vertical evolution (i.e., bottom-up intensification over time vs simultaneous intensification throughout the lowest few hundred meters) is observed.

Phased-array radar (PAR) technology can potentially provide high quality clear-air radial velocity observations at a high spatiotemporal resolution, usually ∼1 min or less. These observations are hypothesized to partially fill the gaps in current operational observing systems with relatively coarse-resolution surface mesonet observations and the lack of high-resolution upper-air observations especially in planetary boundary layer. In this study, observing system simulation experiments (OSSEs) are conducted to investigate the potential value of assimilating PAR observations of clear-air radial velocity to improve the forecast of convection initiation (CI) along small-scale boundary-layer convergence zones. Both surface-based and elevated CIs driven by meso-γ-scale boundary-layer convergence are tested. An ensemble Kalman filter method is used to assimilate synthetic surface mesonet observations and PAR clear-air radial velocity observations. Results show that assimilating only surface mesonet observations fails to predict either surface-based or elevated CI processes. Assimilating clear-air radial velocity observations in addition to surface mesonet observations can capture both surface-based and elevated CI processes successfully. Such an improvement benefits from the better analyses of boundary-layer convergence, resulting from the assimilation of clear-air radial velocity observations. Additional improvement is observed with more frequent assimilation. Assimilating clear-air radial velocity observations only from the one radar results in analysis biases of cross-beam winds and CI location biases, and assimilating additional radial velocity observations from the second radar at an appropriate position can reduce these biases, while sacrificing the CI timing. These results suggest the potential of assimilating clear-air radial velocity observations from PAR to improve the forecast of CI processes along boundary-layer convergence zones.