CHAT: The Colorado Hail Accumulation from Thunderstorms Project

Abstract

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.

A Hail Growth Trajectory Model for Exploring the Environmental Controls on Hail Size: Model Physics and Idealized Tests

“…Using reanalysis data, Rädler et al (2018) built a simple statistical model based on instability and deep-layer shear to predict the occurrence of severe hazards, including hail, that was able to reasonably reproduce observed hail climatological features in Europe. In addition, the community is only beginning to recognize different ''flavors'' of hail threats and to identify their distinguishing features on radar displays or from environmental cues, including storms that produce large amounts of small hail (e.g., Kalina et al 2016;Wallace et al 2018;Kumjian et al 2019;Friedrich et al 2019) or those that produce giant or gargantuan hail (Blair et al 2011;Gutierrez and Kumjian 2018;Kumjian et al 2020). Conceptual models for why or how these storms produce these distinct types of hail threats do not exist.…”

Hail: Mechanisms, Monitoring, Forecasting, Damages, Financial Compensation Systems, and Prevention

“…In the case of cumulonimbus clouds (deep convective storms) that produce hail, there are single cell, multicell, MCCs (Mesoscale Convective Cloud systems), and supercells. Among these, hail is frequently generated in supercells [17,20,32]. And during the maturation process, which is a relatively early stage in the development of cumulonimbus clouds, hail within the cloud grows and falls to the ground [33].…”

“…Short-term numerical forecasting of hail events was conducted using a Weather Research and Forecasting (WRF)-HAILCAST model combined with a convection-allowing model [72]. The Advanced Research Weather and Research Forecasting (WRF) model (ARW) [32] was integrated with the HAILCAST hailstone-growth model, producing the WRF-HAILCAST model. When the WRF-HAILCAST model was run at a spatial resolution >4 km (horizontal grid spacing), it reproduced the dominant large-scale circulation and hydrometeorological fields associated with organized storm and convection systems, and generated realistic predictions at grid intervals as fine as 1 km [72].…”

Enlarging the Severe Hail Database in Finland by Using a Radar-Based Hail Detection Algorithm and Email Surveys to Limit Underreporting and Population Biases

“…More recently, several crowd-sourcing studies have begun collecting data on hail. Elmore et al (2014), Barras et al (2019), andFriedrich et al (2019) were all focused on mobile apps and social media sources. Such passive crowd-sourcing tools can be a great addition during hail outbreaks in urban areas, but their performance for isolated convective cells in remote, sparsely populated areas (e.g., much of Finland) may be limited.…”