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.
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The Lightning and Dual-Polarization Radar Characteristics of Three Hail-Accumulating Thunderstorms
Abstract 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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- Award ID(s):
- 1661583
- NSF-PAR ID:
- 10180335
- Date Published:
- Journal Name:
- Weather and Forecasting
- Volume:
- 35
- Issue:
- 4
- ISSN:
- 0882-8156
- Page Range / eLocation ID:
- 1583 to 1603
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Lasting updrafts are necessary to produce severe hail; conventional wisdom suggests that extremely large hailstones require updrafts of commensurate strength. Because updraft strength is largely controlled by convective available potential energy (CAPE), one would expect environments with larger CAPE to be conducive to storms producing larger hail. By systematically varying CAPE in a horizontally homogeneous initial environment, we simulate hail production in high-shear, high-instability supercell storms using Cloud Model 1 and a detailed 3D hail growth trajectory model. Our results suggest that CAPE modulates the updraft’s strength, width, and horizontal wind field, as well as the liquid water content along hailstones’ trajectories, all of which have a significant impact on final hail sizes. In particular, hail sizes are maximized for intermediate CAPE values in the range we examined. Results show a non-monotonic relationship between the hailstones’ residence time and CAPE due to changes to the updraft wind field. The ratio of updraft area to southerly wind speed within the updraft serves as a proxy for residence time. Storms in environments with large CAPE may produce smaller hail because the in-updraft horizontal wind speeds become too great, and hailstones are prematurely ejected out of the optimal growth region. Liquid water content (LWC) along favorable hailstone pathways also exhibits peak values for intermediate CAPE values, owing to the horizontal displacement across the midlevel updraft of moist inflow air from differing source levels. In other words, larger CAPE does not equal larger hail, and storm-structural nuances must be examined.
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null (Ed.)Abstract Storms that produce gargantuan hail (defined here as ≥ 6 inches or 15 cm in maximum dimension), although seemingly rare, can cause extensive damage to property and infrastructure, and cause injury or even death to humans and animals. Currently, we are limited in our ability to accurately predict gargantuan hail and detect gargantuan hail on radar. In this study, we analyze the environments and radar characteristics of gargantuan hail-producing storms to define the parameter space of environments in which gargantuan hail occurs, and compare environmental parameters and radar signatures in these storms to storms producing other sizes of hail. We find that traditionally used environmental parameters used for severe storms prediction, such as most unstable convective available potential energy (MUCAPE) and 0–6 km vertical wind shear, display considerable overlap between gargantuan hail-producing storm environments and those that produce smaller hail. There is a slight tendency for larger MUCAPE values for gargantuan hail cases, however. Additionally, gargantuan hail-producing storms seem to have larger low-level storm-relative winds and larger updraft widths than those storms producing smaller hail, implying updrafts less diluted by entrainment and perhaps maximizing the liquid water content available for hail growth. Moreover, radar reflectivity or products derived from it are not different from cases of smaller hail sizes. However, inferred mesocyclonic rotational velocities within the hail growth region of storms that produce gargantuan hail are significantly stronger than the rotational velocities found for smaller hail categories.more » « less
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Abstract Recent advances in hail trajectory modeling regularly produce datasets containing millions of hail trajectories. Because hail growth within a storm cannot be entirely separated from the structure of the trajectories producing it, a method to condense the multidimensionality of the trajectory information into a discrete number of features analyzable by humans is necessary. This article presents a three-dimensional trajectory clustering technique that is designed to group trajectories that have similar updraft-relative structures and orientations. The new technique is an application of a two-dimensional method common in the data mining field. Hail trajectories (or “parent” trajectories) are partitioned into segments before they are clustered using a modified version of the density-based spatial applications with noise (DBSCAN) method. Parent trajectories with segments that are members of at least two common clusters are then grouped into parent trajectory clusters before output. This multistep method has several advantages. Hail trajectories with structural similarities along only portions of their length, e.g., sourced from different locations around the updraft before converging to a common pathway, can still be grouped. However, the physical information inherent in the full length of the trajectory is retained, unlike methods that cluster trajectory segments alone. The conversion of trajectories to an updraft-relative space also allows trajectories separated in time to be clustered. Once the final output trajectory clusters are identified, a method for calculating a representative trajectory for each cluster is proposed. Cluster distributions of hailstone and environmental characteristics at each time step in the representative trajectory can also be calculated.
Significance Statement To understand how a storm produces large hail, we need to understand the paths that hailstones take in a storm when growing. We can simulate these paths using computer models. However, the millions of hailstones in a simulated storm create millions of paths, which is hard to analyze. This article describes a machine learning method that groups together hailstone paths based on how similar their three-dimensional structures look. It will let hail scientists analyze hailstone pathways in storms more easily, and therefore better understand how hail growth happens.
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Hail-bearing storms produce substantial socioeconomic impacts each year, yet challenges remain in forecasting the type of hail threat supported by a given environment and in using radar to estimate hail sizes more accurately. One class of hail threat is storms producing large accumulations of small hail (SPLASH). This paper presents an analysis of the environments and polarimetric radar characteristics of such storms. Thirteen SPLASH events were selected to encompass a broad range of geographic regions and times of year. Rapid Refresh model output was used to characterize the mesoscale environments associated with each case. This analysis reveals that a range of environments can support SPLASH cases; however, some commonalities included large precipitable water (exceeding that day’s climatological 90th-percentile values), CAPE < 2500 J kg−1, weak storm-relative wind speeds (<10 m s−1) in the lowest few kilometers of the troposphere, and a weak component of the storm-relative flow orthogonal to the 0–6-km shear vector. Most of the storms were weak supercells that featured distinctive S-band radar signatures, including compact (<200 km2) regions of reflectivity factor > 60 dB Z, significant differential attenuation evident as negative differential reflectivity extending downrange of the hail core, and anomalously large specific differential phase KDP. The KDPvalues often approached or exceeded the operational color scale’s upper limit (10.7° km−1); reprocessing the level-II data revealed KDP>17° km−1, the highest documented in precipitation at S band. Electromagnetic scattering calculations using the T-matrix method confirm that large quantities of small melting hail mixed with heavy rain can plausibly explain the observed radar signatures.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1175/WAF-D-19-0224.1
