Hail, Wind, Ground-Truth, and What We Can Learn

Severe thunderstorms bring hail and wind damage, but due to the fact that supercells are mesoscale, the current ground-truth infrastructure does a poor job of tracking and predicting where damage occured. As such, we are mostly reliant on radar and spotter reports to identify damaged areas. The current stations in the Automated Surface Observing System (ASOS) network are unable to detect and report on hail, one of the main perils in supercell storms. Spotter reports only provide size estimates of hail, but the damage potential of a hailstorm is dependent on the velocity, composition, impact angle, and frequency of hail. As a result, damage assessment is tied to hail size reports, but varies greatly from storm to storm. Understory Weather has developed a weather station that measures the force and direction of every single hailstone that hits it. To ensure that hailstones are not missed, the stations take samples at over 3kHz, providing a high temporal resolution to use while reconstructing a storm's progress. The stations also measure wind, temperature, humidity, rain, and pressure. By measuring the force that is applied to the station by hail and wind with such high temporal resolution, the stations are able to construct a much more accurate picture of the damaging power of a given storm. Hailstorms occur predominantly in mesoscale systems; therefore a weather station that detects hail deployed on a synoptic scale would only be incremental improvement on the current infrastructure. Most mesonets are also too widely spaced to capture the erratic fall of hail during storms. During the 2015 storm season, Understory Weather piloted micronets in Kansas City and Dallas, covering the highest population centers of these metro areas with stations spaced every one to five kilometers. By blanketing a metro area with these stations, we can get a better picture of how mesoscale systems affect a city, accurately determine where hail fell, and assess the potential damage from the storm. When deploying these micronets, network design is paramount. Sites were selected based mostly on the population of the coverage area and the distance from other stations. Data from the micronets is combined in real-time, providing a current picture of the conditions in those metro areas. Due to the density of the micronets, clear and accurate geospatial information can be assembled in real-time. The geospatial data has already been used by broadcast meteorologists and private sector catastrophe teams to pinpoint where storms affected metro areas as a storm occurs.