Quantification of radar QPE performance based on SENSR network design possibilities

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 lays the groundwork for estimating Quantitative Precipitation Estimation (QPE) differences based on network density, array size, and polarimetric bias. These factors create a pareto front of cost-benefit for QPE in a new radar network, and these results will eventually be used to determine appropriate tradeoffs for SENSR requirements. Results of this study are presented in the form of two case examples that quantify errors based on polarimetric bias and elevation, along with a description of eventual application to a national network in upcoming expansion of the work

Object-based Verification of a Prototype Warn-On-Forecast System

An object-based verification methodology for the NSSL Experimental Warn-on-Forecast System for ensembles (NEWS-e) has been developed and applied to 32 cases between December 2015 and June 2017. NEWS-e forecast objects of composite reflectivity and 30-minute rotation tracks of updraft helicity are matched to corresponding objects in Multi-Radar Multi-Sensor data on space and time scales typical of a National Weather Service warning. Object matching allows contingency table-based verification statistics to be used to establish baseline performance metrics for NEWS-e thunderstorm and mesocyclone forecasts. NEWS-e critical Success Index (CSI) scores of reflectivity (updraft helicity) forecasts decrease from approximately 0.7 (0.4) to 0.4 (0.2) over 3 hours of forecast time. CSI scores decrease through the forecast period, indicating that errors have not saturated and skill is retained at 3 hours of forecast time. Lower verification scores for rotation track forecasts are primarily a result of a high frequency bias. Comparison of different system configurations used in 2016 and 2017 show an increase in skill for 2017 reflectivity forecasts, attributable mainly to improvements in the forecast initial condition. A small decrease in skill in 2017 rotation track forecasts is likely a result of sample differences between 2016 and 2017. Although large case-to-case variation is present, evidence is found that NEWS-e forecast skill improves with increasing object size and intensity, as well as in mesoscale environments in which an enhanced or higher risk of severe thunderstorms was forecast