Thunderstorms that produce surface hail accumulations, sometimes as large as 60 cm in depth, have significantly affected the residents of the Front Range and High Plains of Colorado and Wyoming by creating hazardous road conditions and endangering lives and property. To date, surface hail accumulation is not part of a routine forecasting or monitoring system. Extensive coordinated hail accumulation reports and operational products designed to identify deep hail accumulating storms in real time are lacking. Kalina et al. used dual-polarization WSR-88D radar observations to calculate hail depth and hail accumulations but never validated the algorithm. This study shows how 20 quality-controlled hail depth reports from the hail depth database built by the Colorado Hail Accumulation from Thunderstorms (CHAT) project are being used to validate the Kalina et al. radar-based hail accumulation algorithm for operational application. The validated algorithm shows increased correlations between radar-derived and reported accumulations for hail depth reports not included in the validation. Furthermore, increases in computational efficiency have allowed the improved algorithm to be used operationally. With an improved hail accumulation algorithm, thunderstorms that produce hail accumulations are more frequently detected than previously reported.
In recent years, hail accumulations from thunderstorms have occurred frequently enough to catch the attention of the National Weather Service, the general public, and news agencies. Despite the extreme nature of these thunderstorms, no mechanism is currently in place to obtain adequate reports, measurements, or forecasts of accumulated hail depth. To better identify and forecast hail accumulations, the Colorado Hail Accumulation from Thunderstorms (CHAT) project was initiated in 2016 with the goals of collecting improved and more frequent hail depth reports on the ground as well as studying characteristics of storms that produce hail accumulations in Colorado. A desired outcome of this research is to identify predictors for hail-producing thunderstorms typically occurring along the Colorado Front Range that might be used as operational nowcast products in the future. During the 2016 convective season, we asked amateur meteorologists to send general information, photos, and videos on hail depth using social media. They submitted over 58 reports in Colorado with information on location, time, depth, and areal coverage of hail accumulations. We have analyzed dual-polarization radar and lightning mapping array data from 32 thunderstorms in Colorado, which produced between 0.5 and 50 cm of hail accumulation on the ground, to identify characteristics unique to storms with hail accumulations. This preliminary analysis shows how enhanced in-cloud hail presence and surface accumulation can be tracked throughout the lifetime of a thunderstorm using dual-polarization radar and lightning data, and how hail accumulation events are associated with large in-cloud ice water content, long hailfall duration, or a combination of these.
Thunderstorms that produce hail accumulations at the surface can impact residents by obstructing roadways, closing airports, and causing localized flooding from hail-clogged drainages. These storms have recently gained an increased interest within the scientific community. However, differences that are observable in real time between these storms and storms that produce nonimpactful hail accumulations have yet to be documented. Similarly, the characteristics within a single storm that are useful to quantify or predict hail accumulations are not fully understood. This study uses lightning and dual-polarization radar data to characterize hail accumulations from three storms that occurred on the same day along the Colorado–Wyoming Front Range. Each storm’s characteristics are verified against radar-derived hail accumulation maps and in situ observations. The storms differed in maximum accumulation, either producing 22 cm, 7 cm, or no accumulation. The magnitude of surface hail accumulations is found to be dependent on a combination of in-cloud hail production, storm translation speed, and hailstone melting. The optimal combination for substantial hail accumulations is enhanced in-cloud hail production, slow storm speed, and limited hailstone melting. However, during periods of similar in-cloud hail production, lesser accumulations are derived when storm speed and/or hailstone melting, identified by radar presentation, is sufficiently large. These results will aid forecasters in identifying when hail accumulations are occurring in real time.
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