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Abstract
Idealized numerical simulations of mesoscale convective systems (MCSs) over a range of instabilities and shears were conducted to examine low-frequency gravity waves generated during initial and mature stages of convection. In all simulations, at initial updraft development a first-order wave was generated by heating extending through the depth of the troposphere. Additional first-order wave modes were generated each time the convective updraft reintensified. Each of these waves stabilized the environment in advance of the system. As precipitation descended below cloud base, and as a stratiform precipitation region developed, second-order wave modes were generated by cooling extending from the midlevels to the surface. These waves destabilized the environment ahead of the system but weakened the 0–5 km shear. Third-order wave modes could be generated by midlevel cooling caused by rear inflow intensification; these wave modes cooled the midlevels destabilizing the environment. The developing stage of each MCS was characterized by a cyclical process: developing updraft, generation of n = 1 wave, increase in precipitation, generation of n = 2 wave, and subsequent environmental destabilization reinvigorating the updraft. After rearward expansion of the stratiform region, the MCSs entered their mature stage and the method of updraft reinvigoration shifted to absorbing discrete convective cells produced in advance of each system. Higher-order wave modes destabilized the environment, making it more favorable to development of these cells and maintenance of the MCS. As initial simulation shear or instability increased, the transition from cyclical wave/updraft development to discrete cell/updraft development occurred more quickly.
Abstract
Idealized numerical simulations of mesoscale convective systems (MCSs) over a range of instabilities and shears were conducted to examine low-frequency gravity waves generated during initial and mature stages of convection. In all simulations, at initial updraft development a first-order wave was generated by heating extending through the depth of the troposphere. Additional first-order wave modes were generated each time the convective updraft reintensified. Each of these waves stabilized the environment in advance of the system. As precipitation descended below cloud base, and as a stratiform precipitation region developed, second-order wave modes were generated by cooling extending from the midlevels to the surface. These waves destabilized the environment ahead of the system but weakened the 0–5 km shear. Third-order wave modes could be generated by midlevel cooling caused by rear inflow intensification; these wave modes cooled the midlevels destabilizing the environment. The developing stage of each MCS was characterized by a cyclical process: developing updraft, generation of n = 1 wave, increase in precipitation, generation of n = 2 wave, and subsequent environmental destabilization reinvigorating the updraft. After rearward expansion of the stratiform region, the MCSs entered their mature stage and the method of updraft reinvigoration shifted to absorbing discrete convective cells produced in advance of each system. Higher-order wave modes destabilized the environment, making it more favorable to development of these cells and maintenance of the MCS. As initial simulation shear or instability increased, the transition from cyclical wave/updraft development to discrete cell/updraft development occurred more quickly.
Abstract
The sensitivity of low-frequency gravity waves generated during the development and mature stages of an MCS to variations in the characteristics of the rimed ice parameterization were tested through idealized numerical simulations over a range of environment shears and instabilities. Latent cooling in the simulations with less dense, graupel-like rimed ice was more concentrated aloft near the melting level, while cooling in simulations with denser, hail-like rimed ice extended from the melting level to the surface. However, the cooling profiles still had significant internal variability across different environments and over each simulation’s duration. Initial wave production during the MCS developing stage was fairly similar in the hail and graupel simulations. During the mature stages, graupel simulations showed stronger perturbations in CAPE due to the cooling and associated wave vertical motion being farther aloft; hail simulations showed stronger perturbations in LFC due to cooling and wave vertical motion being concentrated at lower levels. The differences in the cooling profiles were not uniform enough to produce consistently different higher-order wave modes. However, the initiation of discrete cells ahead of the convective line was found to be highly sensitive to the nature of the prior destabilizing wave. Individual events of discrete propagation were suppressed in some of the graupel simulations due to the higher location of both peak cooling and vertical wave motion. Such results underscore the need to fully characterize MCS microphysical heating profiles and their low-frequency gravity waves to understand their structure and development.
Abstract
The sensitivity of low-frequency gravity waves generated during the development and mature stages of an MCS to variations in the characteristics of the rimed ice parameterization were tested through idealized numerical simulations over a range of environment shears and instabilities. Latent cooling in the simulations with less dense, graupel-like rimed ice was more concentrated aloft near the melting level, while cooling in simulations with denser, hail-like rimed ice extended from the melting level to the surface. However, the cooling profiles still had significant internal variability across different environments and over each simulation’s duration. Initial wave production during the MCS developing stage was fairly similar in the hail and graupel simulations. During the mature stages, graupel simulations showed stronger perturbations in CAPE due to the cooling and associated wave vertical motion being farther aloft; hail simulations showed stronger perturbations in LFC due to cooling and wave vertical motion being concentrated at lower levels. The differences in the cooling profiles were not uniform enough to produce consistently different higher-order wave modes. However, the initiation of discrete cells ahead of the convective line was found to be highly sensitive to the nature of the prior destabilizing wave. Individual events of discrete propagation were suppressed in some of the graupel simulations due to the higher location of both peak cooling and vertical wave motion. Such results underscore the need to fully characterize MCS microphysical heating profiles and their low-frequency gravity waves to understand their structure and development.
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.
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.
Abstract
This study examines observed mesoscale surface pressure, temperature, and wind features of bow echoes. Bow-echo events in the area of the Oklahoma Mesonet are selected for study to take advantage of high-resolution surface data. Thirty-six cases are identified using 2-km-resolution radar reflectivity data over a 4-yr period (2002–05); their surface features are interrogated using the mesonet data. Distinct surface features usually associated with squall lines, the mesohigh and cold pool, are found to also accompany bow echoes. A common surface pattern preceding bowing is identified. Prior to new bowing development, the mesohigh surges ahead of the convective line while the cold pool remains centered behind it. Surface winds shift to a ground-relative outflow pattern upon arrival of the mesohigh surge. Approximately 30 min later, a new bowing segment forms with its apex slightly to the left (with respect to the direction of system motion) of the mesohigh surge. The cold pool follows the convective line as it bows. This process is termed the “pressure surge–new bowing” cycle, and a conceptual model is presented. In one representative case, the surface signature of a gravity wave, identified through spatial and temporal filtering, is tracked. It is presumed to be generated by deep heating within the convective line. The wave moved at nearly 35 m s−1 and has heretofore been undetected in mesoanalysis studies. Two other distinct features, a sharp pressure rise and temperature drop, were also observed at all mesonet stations affected by the system. Possible explanations for these features in terms of a gravity current, gravity wave, or atmospheric bore are explored.
Abstract
This study examines observed mesoscale surface pressure, temperature, and wind features of bow echoes. Bow-echo events in the area of the Oklahoma Mesonet are selected for study to take advantage of high-resolution surface data. Thirty-six cases are identified using 2-km-resolution radar reflectivity data over a 4-yr period (2002–05); their surface features are interrogated using the mesonet data. Distinct surface features usually associated with squall lines, the mesohigh and cold pool, are found to also accompany bow echoes. A common surface pattern preceding bowing is identified. Prior to new bowing development, the mesohigh surges ahead of the convective line while the cold pool remains centered behind it. Surface winds shift to a ground-relative outflow pattern upon arrival of the mesohigh surge. Approximately 30 min later, a new bowing segment forms with its apex slightly to the left (with respect to the direction of system motion) of the mesohigh surge. The cold pool follows the convective line as it bows. This process is termed the “pressure surge–new bowing” cycle, and a conceptual model is presented. In one representative case, the surface signature of a gravity wave, identified through spatial and temporal filtering, is tracked. It is presumed to be generated by deep heating within the convective line. The wave moved at nearly 35 m s−1 and has heretofore been undetected in mesoanalysis studies. Two other distinct features, a sharp pressure rise and temperature drop, were also observed at all mesonet stations affected by the system. Possible explanations for these features in terms of a gravity current, gravity wave, or atmospheric bore are explored.
Abstract
Numerical simulations of the 13 March 2003 bow echo over Oklahoma are used to evaluate bow echo development and its relationship with gravity wave generation. Multiple fast-moving (with speeds of 30–35 m s−1) gravity waves are generated in association with fluctuations in the first vertical mode of heating in the convective line. The surface impacts of four such waves are observed in Oklahoma Mesonet data during this case. Observations of surface pressure surges ahead of convective lines prior to the bowing process are reproduced; a slower gravity wave produced in the simulation is responsible for a prebowing pressure surge. This slower gravity wave, moving at approximately 11 m s−1, is generated by an increase in low-level microphysical cooling associated with an increase in rear-to-front flow and low-level downdrafts shortly before bowing. The wave moves ahead of the convective line and is manifested at the surface by a positive pressure surge. The pattern of low-level vertical motion associated with this wave, in conjunction with higher-frequency gravity waves generated by multicellularity of the convective line, increases the immediate presystem CAPE by approximately 250 J kg−1 just ahead of the bowing segment of the convective line. Increased presystem CAPE aids convective updraft strength in that segment despite amplified updraft tilt due to a stronger cold pool and surface-based rear-to-front flow, compared to updraft strength in other, nonbowing segments of the convective line.
Abstract
Numerical simulations of the 13 March 2003 bow echo over Oklahoma are used to evaluate bow echo development and its relationship with gravity wave generation. Multiple fast-moving (with speeds of 30–35 m s−1) gravity waves are generated in association with fluctuations in the first vertical mode of heating in the convective line. The surface impacts of four such waves are observed in Oklahoma Mesonet data during this case. Observations of surface pressure surges ahead of convective lines prior to the bowing process are reproduced; a slower gravity wave produced in the simulation is responsible for a prebowing pressure surge. This slower gravity wave, moving at approximately 11 m s−1, is generated by an increase in low-level microphysical cooling associated with an increase in rear-to-front flow and low-level downdrafts shortly before bowing. The wave moves ahead of the convective line and is manifested at the surface by a positive pressure surge. The pattern of low-level vertical motion associated with this wave, in conjunction with higher-frequency gravity waves generated by multicellularity of the convective line, increases the immediate presystem CAPE by approximately 250 J kg−1 just ahead of the bowing segment of the convective line. Increased presystem CAPE aids convective updraft strength in that segment despite amplified updraft tilt due to a stronger cold pool and surface-based rear-to-front flow, compared to updraft strength in other, nonbowing segments of the convective line.
Abstract
The HAILCAST hail growth model has been integrated into the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) Model to predict hail size at the ground. Significant updates to the physics of the hail growth model are added, including variable hail density for both wet and dry growth regimes, an updraft multiplier that parameterizes advection of the hail embryo across an updraft, temperature-dependent ice collection efficiency, mass growth by vapor deposition or condensation, and an improved liquid water shedding threshold. Sample hail trajectories from three different updrafts are presented showing the effects of these physical updates. The updraft multiplier in particular improves the representation of the hail growth by not requiring a hail embryo to be locked in the center of an updraft until it grows large enough to fall. Five weeks of hail diameter forecasts are verified using a maximum expected size of hail (MESH) product. At points where WRF successfully forecasts convection, the forecasted hail size is within 0.5 in. 66% of the time.
Abstract
The HAILCAST hail growth model has been integrated into the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) Model to predict hail size at the ground. Significant updates to the physics of the hail growth model are added, including variable hail density for both wet and dry growth regimes, an updraft multiplier that parameterizes advection of the hail embryo across an updraft, temperature-dependent ice collection efficiency, mass growth by vapor deposition or condensation, and an improved liquid water shedding threshold. Sample hail trajectories from three different updrafts are presented showing the effects of these physical updates. The updraft multiplier in particular improves the representation of the hail growth by not requiring a hail embryo to be locked in the center of an updraft until it grows large enough to fall. Five weeks of hail diameter forecasts are verified using a maximum expected size of hail (MESH) product. At points where WRF successfully forecasts convection, the forecasted hail size is within 0.5 in. 66% of the time.
Abstract
This study investigates the sensitivities of mesoscale convective system (MCS) low-frequency gravity waves to changes in the vertical wind and thermodynamic profile through idealized cloud model simulations, highlighting how internal MCS processes impact low-frequency gravity wave generation, propagation, and environmental influence. Spectral analysis is performed on the rates of latent heat release, updraft velocity, and deep-tropospheric descent ahead of the convection as a signal for vertical wavenumber
Abstract
This study investigates the sensitivities of mesoscale convective system (MCS) low-frequency gravity waves to changes in the vertical wind and thermodynamic profile through idealized cloud model simulations, highlighting how internal MCS processes impact low-frequency gravity wave generation, propagation, and environmental influence. Spectral analysis is performed on the rates of latent heat release, updraft velocity, and deep-tropospheric descent ahead of the convection as a signal for vertical wavenumber
Abstract
Shortly after 0600 UTC (midnight local time) 9 June 2020, a convective line produced severe winds across parts of northeast Colorado that caused extensive damage, especially in the town of Akron. High-resolution observations showed gusts exceeding 50 m s−1, accompanied by extremely large pressure fluctuations, including a 5-hPa pressure surge in 19 s immediately following the strongest winds and a 15-hPa pressure drop in the following 3 min. Numerical simulations of this event (using the WRF Model) and with horizontally homogeneous initial conditions (using Cloud Model 1) reveal that the severe winds in this event were associated with gravity wave dynamics. In a very stable postfrontal environment, elevated convection initiated and led to a long-lived gravity wave. Strong low-level vertical wind shear supported the amplification and eventual breaking of this wave, resulting in at least two sequential strong downbursts. This wave-breaking mechanism is different from the usual downburst mechanism associated with negative buoyancy resulting from latent cooling. The model output reproduces key features of the high-resolution observations, including similar convective structures, large temperature and pressure fluctuations, and intense near-surface wind speeds. The findings of this study reveal a series of previously unexplored mesoscale and storm-scale processes that can result in destructive winds.
Significance Statement
Downbursts of intense wind can produce significant damage, as was the case on 9 June 2020 in Akron, Colorado. Past research on downbursts has shown that they occur when raindrops, graupel, and hail in thunderstorms evaporate and melt, cooling the air and causing it to sink rapidly. In this research, we used numerical models of the atmosphere, along with high-resolution observations, to show that the Akron downburst was different. Unlike typical lines of thunderstorms, those responsible for the Akron macroburst produced a wave in the atmosphere, which broke, resulting in rapidly sinking air and severe surface winds.
Abstract
Shortly after 0600 UTC (midnight local time) 9 June 2020, a convective line produced severe winds across parts of northeast Colorado that caused extensive damage, especially in the town of Akron. High-resolution observations showed gusts exceeding 50 m s−1, accompanied by extremely large pressure fluctuations, including a 5-hPa pressure surge in 19 s immediately following the strongest winds and a 15-hPa pressure drop in the following 3 min. Numerical simulations of this event (using the WRF Model) and with horizontally homogeneous initial conditions (using Cloud Model 1) reveal that the severe winds in this event were associated with gravity wave dynamics. In a very stable postfrontal environment, elevated convection initiated and led to a long-lived gravity wave. Strong low-level vertical wind shear supported the amplification and eventual breaking of this wave, resulting in at least two sequential strong downbursts. This wave-breaking mechanism is different from the usual downburst mechanism associated with negative buoyancy resulting from latent cooling. The model output reproduces key features of the high-resolution observations, including similar convective structures, large temperature and pressure fluctuations, and intense near-surface wind speeds. The findings of this study reveal a series of previously unexplored mesoscale and storm-scale processes that can result in destructive winds.
Significance Statement
Downbursts of intense wind can produce significant damage, as was the case on 9 June 2020 in Akron, Colorado. Past research on downbursts has shown that they occur when raindrops, graupel, and hail in thunderstorms evaporate and melt, cooling the air and causing it to sink rapidly. In this research, we used numerical models of the atmosphere, along with high-resolution observations, to show that the Akron downburst was different. Unlike typical lines of thunderstorms, those responsible for the Akron macroburst produced a wave in the atmosphere, which broke, resulting in rapidly sinking air and severe surface winds.
Abstract
Shortly after 0600 UTC (midnight MDT) 9 June 2020, a rapidly intensifying and elongating convective system produced a macroburst and extensive damage in the town of Akron on Colorado’s eastern plains. Instantaneous winds were measured as high as 51.12 m s−1 at 2.3 m AGL from an eddy covariance (EC) tower, and a 50.45 m s−1 wind gust from an adjacent 10-m tower became the highest official thunderstorm wind gust ever measured in Colorado. Synoptic-scale storm motion was southerly, but surface winds were northerly in a postfrontal air mass, creating strong vertical wind shear. Extremely high-resolution temporal and spatial observations allow for a unique look at pressure and temperature tendencies accompanying the macroburst and reveal intriguing wave structures in the outflow. At 10-Hz frequency, the EC tower recorded a 5-hPa pressure surge in 19 s immediately following the strongest winds, and a 15-hPa pressure drop in the following 3 min. Surface temperature also rose 1.5°C in less than 1 min, concurrent with the maximum wind gusts, and then fell sharply by 3.5°C in the following minute. Shifting wind direction observations and an NWS damage survey are suggestive of both radial outflow and a gust front passage, and model proximity soundings reveal a well-mixed surface layer topped by a strong inversion and large low-level vertical wind shear. Despite the greatest risk of severe winds forecast to be northeast of Colorado, convection-allowing model forecasts from 6 to 18 h in advance did show similar structures to what occurred, warranting further simulations to investigate the unique mesoscale and misoscale features associated with the macroburst.
Abstract
Shortly after 0600 UTC (midnight MDT) 9 June 2020, a rapidly intensifying and elongating convective system produced a macroburst and extensive damage in the town of Akron on Colorado’s eastern plains. Instantaneous winds were measured as high as 51.12 m s−1 at 2.3 m AGL from an eddy covariance (EC) tower, and a 50.45 m s−1 wind gust from an adjacent 10-m tower became the highest official thunderstorm wind gust ever measured in Colorado. Synoptic-scale storm motion was southerly, but surface winds were northerly in a postfrontal air mass, creating strong vertical wind shear. Extremely high-resolution temporal and spatial observations allow for a unique look at pressure and temperature tendencies accompanying the macroburst and reveal intriguing wave structures in the outflow. At 10-Hz frequency, the EC tower recorded a 5-hPa pressure surge in 19 s immediately following the strongest winds, and a 15-hPa pressure drop in the following 3 min. Surface temperature also rose 1.5°C in less than 1 min, concurrent with the maximum wind gusts, and then fell sharply by 3.5°C in the following minute. Shifting wind direction observations and an NWS damage survey are suggestive of both radial outflow and a gust front passage, and model proximity soundings reveal a well-mixed surface layer topped by a strong inversion and large low-level vertical wind shear. Despite the greatest risk of severe winds forecast to be northeast of Colorado, convection-allowing model forecasts from 6 to 18 h in advance did show similar structures to what occurred, warranting further simulations to investigate the unique mesoscale and misoscale features associated with the macroburst.
Abstract
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.
Significance Statement
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.
Abstract
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.
Significance Statement
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.
