A Comparison of Scan Speedup Strategies and Their Effect on Rapid-Scan Weather Radar Data Quality

As the existing NEXRAD radar network nears the end of its lifecycle, 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 the attendant loss in data quality typically associated with faster scan rates. In this study, scan speedup techniques—such as beam multiplexing (BMX) and beam spoiling (i.e., imaging)—are tested to assess their viability in obtaining high-quality rapid updates for a weather radar system with a large-domain weather radar simulator. The radar simulator in this study uses Weather Research and Forecasting (WRF) model data to emulate radar moment output. This has the advantage of simulating realistic radar returns for a variety of meteorological phenomena.

The results of this study show that in most scenarios, BMX improves data quality for a given scan time, and can be used in tandem with other scan speedup methods to achieve rapid updates with acceptable data quality. In one supercell case, the implementation of BMX produces a result where 86.4% of retrieved radial velocity values are “acceptable” (SD(v) < 1 m s^-1), while only 24.0% of retrieved radial velocities are “acceptable” for traditional data collection modes (i.e., contiguous pulse sampling). Additionally, beam spoiling is shown to improve data quality for a given scan time, with minimal effects on RMSE of radar variables when used in areas without strong gradients in radar reflectivity factor. Because BMX and beam spoiling are most easily achieved with one all-digital phased array radar (PAR) such as Horus (Palmer et al. 2019), 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 help inform decisions about the design and possible scanning strategies of a NEXRAD replacement system for high-resolution weather radar data.