Enhancing meteorological mobile radar observations through deployment location optimization

Keywords: meteorological mobile radar, GIS, spatial optimization
Session Type: Virtual Paper Abstract
Day: Friday
Session Start / End Time: 2/25/2022 11:20 AM (Eastern Time (US & Canada)) - 2/25/2022 12:40 PM (Eastern Time (US & Canada))
Room: Virtual 17

Abstract

Weather-related injuries have increased by 9% over the last five years, with an 8% increase in the number of weather events. In 2020, a total of 60,714 weather events in the United States lead to 585 deaths and 1,708 injuries according to the data of the National Safety Council. Accurate high-resolution radar observations are critical to improving the prediction and warning of severe weather events and supporting the mission of saving lives and property. In recent years, mobile radars have been shown to provide key observations (e.g., with high spatial and temporal resolution) to improve conceptual modeling of mesoscale severe weather events. Modern dual-polarization radars provide polarimetric measurements that enable quantitative precipitation estimation and hydrometeor classification, used to determine types of scatterers (e.g., rain, hail, snow) in the radar coverage region. The deployment of mobile radar trucks, therefore, becomes an essential research topic to improve the understanding and prediction of severe weather phenomena. However, mobile radar trucks are limited in number and expensive to build. Further, their performance is impacted by local environmental contexts, such as terrain, distance to ground clutter targets (trees, buildings, wind turbines), radar spacing, etc. Both benefits and limitations can be taken into account, bringing to bear on decision making and potential policy associated with mobile radar deployment. This article develops a methodology for locating model radar trucks based on utilizing spatial analytics, including GIS and spatial optimization. Findings and results highlight the potential utility and benefits through the systematic analysis of mobile radar siting.