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.
This study uses a new, unique dataset created by combining multi-Doppler radar wind and reflectivity analysis, diabatic Lagrangian analysis (DLA) retrievals of temperature and water substance, and a complex hail trajectory model to create millions of numerically simulated hail trajectories in the Kingfisher, Oklahoma, supercell on 29 May 2012. The DLA output variables are used to obtain a realistic, 4D depiction of the storm’s thermal and hydrometeor structure as required input to the detailed hail growth trajectory model. Hail embryos are initialized in the hail growth module every 3 min of the radar analysis period (2251–0000 UTC) to produce over 2.7 million hail trajectories. A spatial integration technique considering all trajectories is used to identify locations within the supercell where melted particles and subsevere and severe hailstones reside in their lowest and highest concentrations. It is found that hailstones are more likely to reside for longer periods closer to the downshear updraft within the midlevel mesocyclone in a region of decelerated midlevel mesocyclonic horizontal flow, termed the downshear deceleration zone (DDZ). Additionally, clusters of trajectories are analyzed using a trajectory clustering method. Trajectory clusters show there are many trajectory pathways that result in hailstones ≥ 4.5 cm, including trajectories that begin upshear of the updraft away from ideal growth conditions and trajectories that grow within the DDZ. There are also trajectory clusters with similar shapes that experience widely different environmental and hailstone characteristics along the trajectory.
The purpose of this study is to understand how hail grew in a thunderstorm that was observed by numerous instruments. The observations were input into a hail trajectory model to simulate hail growth. We found a part of the storm near the updraft where hailstones could remain aloft longer and therefore grow larger. Most modeled severe hailstones were found in the storm in this region. However, we also found that there are many different pathways hailstones can take to become large, although there are still some common characteristics among the pathways.
- NSF-PAR ID:
- 10483789
- Publisher / Repository:
- American Meteorological Society
- Date Published:
- Journal Name:
- Monthly Weather Review
- Volume:
- 152
- Issue:
- 1
- ISSN:
- 0027-0644
- Format(s):
- Medium: X Size: p. 245-276
- Size(s):
- ["p. 245-276"]
- Sponsoring Org:
- National Science Foundation
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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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null (Ed.)Abstract A detailed microphysical model of hail growth is developed and applied to idealized numerical simulations of deep convective storms. Hailstone embryos of various sizes and densities may be initialized in and around the simulated convective storm updraft, and then are tracked as they are advected and grow through various microphysical processes. Application to an idealized squall line and supercell storm results in a plausibly realistic distribution of maximum hailstone sizes for each. Simulated hail growth trajectories through idealized supercell storms exhibit many consistencies with previous hail trajectory work that used observed storms. Systematic tests of uncertain model parameters and parameterizations are performed, with results highlighting the sensitivity of hail size distributions to these changes. A set of idealized simulations is performed for supercells in environments with varying vertical wind shear to extend and clarify our prior work. The trajectory calculations reveal that, with increased zonal deep-layer shear, broader updrafts lead to increased residence time and thus larger maximum hail sizes. For cases with increased meridional low-level shear, updraft width is also increased, but hailstone sizes are smaller. This is a result of decreased residence time in the updraft, owing to faster northward flow within the updraft that advects hailstones through the growth region more rapidly. The results suggest that environments leading to weakened horizontal flow within supercell updrafts may lead to larger maximum hailstone sizes.more » « less
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