QPE Performance Benefits for Radar Network Design

In 2016, the FAA, NOAA, DoD, and DHS initiated a feasibility study for a Spectrum Efficient National Surveillance Radar (SENSR). The goal is to assess approaches for vacating the 1.3- to 1.35-GHz radio frequency band currently allocated to FAA/DoD long-range radars so that this band can be auctioned for commercial use. As part of this goal, the participating agencies have developed preliminary performance requirements that not only assume minimum capabilities based on legacy radars, but also recognize the need for enhancements in future radar networks. The relatively low density of the legacy radar networks, especially the WSR-88D network, had led to the goal of enhancing low-altitude weather coverage. With multiple design metrics and network possibilities still available to the SENSR agencies, the benefits of low-altitude coverage must be assessed quantitatively. This study takes a regression approach to modeling Quantitative Precipitation Estimation (QPE) differences based on network density, array size, and polarimetric bias. Thousands of cases are processed using both the R(A) and R(Z,ZDR) QPE methods and are compared against Automated Surface Observing System (ASOS) rain gauge data. Absolute QPE errors are quantified based on beam height, cross-azimuth resolution, added polarimetric bias, and the observed rainfall rate. The collected data are used to construct a support vector regression model that is applied to the current WSR-88D network for holistic error quantification. Average, 90th, and 99th percentile rainfall rates derived from an ASOS-based climatology are used for a CONUS-wide analysis of QPE errors. These errors are then re-quantified using proposed SENSR network density scenarios with additional radars that provide enhanced weather variable estimate capabilities. This study also analyzes the potential for non-polarimetric rainfall estimates from “gap-filler” radars, allowing for a full analysis of possible SENSR network designs.