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Hail trajectory modeling is a popular tool to explore how environment and storm characteristics allow or prohibit large hail growth. However, trajectory models are complex and computationally expensive: storm dynamics relevant to hail growth are inextricably linked such that ``cause’' and ``correlation” are difficult to distinguish. Therefore, we develop a novel hail trajectory model that can be used to untangle hail growth processes. Toward this end, we explore the vertical structure of vertical velocity and liquid water content in updrafts and define analytic functions that approximate the thermodynamic prediction of these quantities. These analytic profiles are used, along with a temporal updraft-pass parameterization to define a 2D updraft (defined in height and time) in which hailstones can grow. Hail growth in this 2D updraft is fully defined by a set of 16 scalar parameters that act as turnable knobs to produce unique hail trajectories. This article is Part I of a series using this modeling framework to explore the nature of hail growth. Here, we define the model and test its ability to produce realistic hail trajectories and hail sizes through a Monte Carlo simulation with physical couplings maintained. The size distribution from 1 billion simulated trajectories is exponential and has maximum hail size of 25.7 cm. Stochasticity in the model’s representation of hail fall speed and cross-sectional area is explored and produces some variability in the resulting hailstone sizes. The model produced and evaluated here will be used in further studies to identify how environment, updraft, and hail embryo characteristics individually impact hail growth. 

This is Part II of a multi-part series exploring the fundamental nature of hail growth through a toy model developed in Part I. The toy model uniquely parameterizes all hail growth processes by single-valued parameters with great computational efficiency. The parameters are uncoupled so that environment, storm, and hail embryo characteristics, and their impact on hail growth, can be studied independently---a task 3D trajectory models cannot perform because of their highly coupled nature. Three Monte Carlo simulations were run to compare hail growth from small and large hail embryos, and coupled and uncoupled model parameters. Hail with maximum dimension ($$D$$) $$\le25.71$$ cm grew in the physically coupled small-embryo simulation, $$D\le33.59$$ cm hail grew in the physically coupled large-embryo simulation, and $$D\le44.97$$ cm hail grew in the uncoupled large-embryo simulation. The largest hailstones from the three Monte Carlo simulations took similar trajectories, accumulating a large proportion of their mass both while suspended and during their fall. Analysis of model parameters corroborate current hail growth theory, indicating three necessary ingredients for large hail: (1) a favorable embryo size and location, (2) a long residence time in a water-rich updraft, and (3) a balance between updraft vertical velocity and hailstone fall speed. The sensitivity of hail size to these parameters is analyzed: a hailstone's potential size is limited by its updraft-pass duration and the available amount of supercooled liquid water, but hail size is most sensitive to the balance between its fall speed and its encountered updraft vertical velocity. 

Abstract Modeled hail trajectories have previously been studied in individual observed supercells or in simulated supercells with similar background environments. To explore the impact of changing updraft structure on hail formation from a different perspective, this study analyzes detailed hail trajectories in a large ensemble of time-averaged supercell-like updrafts. The updrafts are created with an idealized heat source, which allows the systematic investigation of the full range of updraft widths and intensities reported in the literature. The simulations exhibit a dominant hail trajectory pathway with a single ascent and a curved horizontal trace. However, a systematic shift in the trajectories and in their start and end locations is found with increasing updraft intensity and updraft width. Furthermore, wider updrafts but with only moderate intensity provide optimal conditions for the hail of most sizes. The exception is giant hail, which requires both wide and intense updrafts. This result is partially linked to the occurrence of an alternative trajectory pathway characterized by the recycling of hailstones (1–4 cm) in the back-sheared anvil region, which then grew to giant size after reentering the updraft. 

Previous work has shown that differential reflectivity ZDR observations from National Weather Service dual-polarization Doppler weather radars (WSR-88Ds) provide accurate estimates of convective boundary layer (CBL) depth when compared with depth estimates from 0000 UTC rawinsonde observations. We extend this work by launching small rawinsondes, called Windsonds, to study ZDR signals throughout the daytime hours. Results show that it can be difficult to identify CBL depth from ZDR alone when biological scatterers are absent. The exploration of other radar variables leads to the use of azimuthal ZDR variance to help in identifying CBL characteristics. A variable that combines both ZDR and azimuthal ZDR variance, called DVar, allows for improved signal identification using the quasi-vertical profile (QVP) method. Furthermore, the QVP channel width is found to be closely tied to the overall entrainment zone (EZ) structure. Results show that the centers and vertical extents of channels of reduced DVar in QVPs correlate well with sounding-observed CBL depth and EZ depth, respectively, across all stages of CBL development and in both clear and cloud-topped CBLs. The QVP approach tends to fail in identifying CBL and EZ depths when the vertical gradient in moisture above the CBL is small. Additionally, we compare the observed EZ depth to various EZ parameterizations and show that the parameterizations generally underestimate EZ depth. We conclude that the ability of WSR-88Ds to sample the CBL should be leveraged to increase our knowledge of CBL properties. Significance Statement: The boundary layer is the lowest layer of Earth’s atmosphere and influences many weather-related phenomena. During the day, sunlight warms the surface and the convective boundary layer (CBL) forms. Even though CBL characteristics are important for accurate weather forecasts, current methods of observing the CBL are severely lacking. This study investigates the potential of using dual-polarization weather radars to expand CBL observations. We also evaluate how well simplified CBL models predict certain CBL characteristics and how they could be improved in the future. 

Convective boundary layer (CBL) depth can be estimated from dual-polarization WSR-88D radars using the product differential reflectivity ZDR because the CBL top is collocated with a local ZDR minimum produced by Bragg scatter at the interface of the CBL and the free troposphere. Quasi-vertical profiles (QVPs) of ZDR are produced for each radar volume scan and profiles from successive times are stitched together to form a time–height plot of ZDR from sunrise to sunset. QVPs of ZDR often show a low-ZDR channel that starts near the ground and rises during the morning and early afternoon, identifying the CBL top. Unfortunately, results show that this channel within the QVP can occasionally be misleading. This motivated creation of a new variable DVar, which combines ZDR with its azimuthal variance and is particularly helpful at identifying the CBL top during the morning hours. Two methods are developed to track the CBL top from QVPs of ZDR and DVar. Although each method has strengths and weaknesses, the best results are found when the two methods are combined using inverse variance weighting. The ability to detect CBL depth from routine WSR-88D radar scans rather than from rawinsondes or lidar instruments would vastly improve our understanding of CBL depth variations in the daytime by increasing the temporal and spatial frequencies of the observations. Significance Statement: The daytime convective boundary layer (CBL) can increase in depth from a few hundred to a few thousand meters between sunrise and sunset and is strongly connected to temperature changes at Earth’s surface. Unfortunately, current observations of CBL depth primarily consist of measurements from twice daily rawinsonde launches at 97 locations across the United States. As a result, CBL depth observations lack fine spatial and temporal resolution and miss the daily cycle of CBL growth. This study seeks to fill the gaps in CBL depth observations by developing an automated method to estimate CBL depth from dual-polarization WSR-88D radar observations with a temporal resolution as fine as 5–10 min. These observations will greatly enhance our ability to observe and monitor CBL depth in real time. 

Abstract. The layered structures inside hailstones provide a direct indication of their shape and properties at various stages during growth. Given the myriadof different trajectories that can exist, and the sensitivity of rime deposit type to environmental conditions, it must be expected that manydifferent perturbations of hailstone properties occur within a single hailstorm; however, some commonalities are likely in the shared early stagesof growth, for hailstones of similar size (especially those that grow along similar trajectories) and final growth near the melting level. Itremains challenging to extract this information from a large sample of hailstones because of the time required to prepare cross sections andaccurately measure individual layers. To reduce the labour and potential errors introduced by manual analysis of hailstones, an automated method formeasuring layers from cross section photographs is introduced and applied to a set of hailstones collected in Melbourne, Australia. This work ismotivated by new hail growth simulation tools that model the growth of layers within individual hailstones, for which accurate measurements ofobserved hailstone cross sections can be applied as validation. A first look at this new type of evaluation for hail growth simulations isdemonstrated. 

Abstract The national upgrade of the operational weather radar network to include polarimetric capabilities has lead to numerous studies focusing on polarimetric radar signatures commonly observed in supercells. One such signature is the horizontal separation of regions of enhanced differential reflectivity ( Z DR ) and specific differential phase ( K DP ) values due to hydrometeor size sorting. Recent observational studies have shown that the orientation of this separation tends to be more perpendicular to storm motion in supercells that produce tornadoes. Although this finding has potential operational utility, the physical relationship between this observed radar signature and tornadic potential is not known. This study uses an ensemble of supercell simulations initialized with tornadic and nontornadic environments to investigate this connection. The tendency for tornadic supercells to have a more perpendicular separation orientation was reproduced, although to a lesser degree. This difference in orientation angles was caused by stronger rearward storm-relative flow in the nontornadic supercells, leading to a rearward shift of precipitation and, therefore, the enhanced K DP region within the supercell. Further, this resulted in an unfavorable rearward shift of the negative buoyancy region, which led to an order of magnitude less baroclinic generation of circulation in the nontornadic simulations compared to tornadic simulations. 

Abstract The discovery of a polarimetric radar signature indicative of hydrometeor refreezing has shown promise in its utility to identify periods of ice pellet production. Uniquely characterized well below the melting layer by locally enhanced values of differential reflectivity ( Z DR ) within a layer of decreasing radar reflectivity factor at horizontal polarization ( Z H ), the signature has been documented in cases where hydrometeors were completely melted prior to refreezing. However, polarimetric radar features associated with the refreezing of partially melted hydrometeors have not been examined as rigorously in either an observational or microphysical modeling framework. Here, polarimetric radar data—including vertically pointing Doppler spectral data from the Ka-band Scanning Polarimetric Radar (KASPR)—are analyzed for an ice pellets and rain mixture event where the ice pellets formed via the refreezing of partially melted hydrometeors. Observations show that no such distinct localized Z DR enhancement is present, and that values instead decrease directly beneath enhanced values associated with melting. A simplified, explicit bin microphysical model is then developed to simulate the refreezing of partially melted hydrometeors, and coupled to a polarimetric radar forward operator to examine the impacts of such refreezing on simulated radar variables. Simulated vertical profiles of polarimetric radar variables and Doppler spectra have similar features to observations, and confirm that a Z DR enhancement is not produced. This suggests the possibility of two distinct polarimetric features of hydrometeor refreezing: ones associated with refreezing of completely melted hydrometeors, and those associated with refreezing of partially melted hydrometeors. Significance Statement There exist two pathways for the formation of ice pellets: refreezing of fully melted hydrometeors, and refreezing of partially melted hydrometeors. A polarimetric radar signature indicative of fully melted hydrometeor refreezing has been extensively documented in the past, yet no study has documented the refreezing of partially melted hydrometeors. Here, observations and idealized modeling simulations are presented to show different polarimetric radar features associated with partially melted hydrometeor refreezing. The distinction in polarimetric features may be beneficial to identifying layers of supercooled liquid drops within transitional winter storms. 

Abstract During the early morning hours of 5 November 2018, a mature mesoscale convective system (MCS) propagated discretely over the second-most populous province of Argentina, Córdoba Province, during the Remote Sensing of Electrification, Lightning, and Mesoscale/Microscale Processes with Adaptive Ground Observations–Cloud, Aerosol, and Complex Terrain Interactions (RELAMPAGO–CACTI) joint field campaigns. Storm behavior was modified by the Sierras de Córdoba, a north–south-oriented regional mountain chain located in the western side of the province. Here, we present observational evidence of the discrete propagation event and the impact of the mountains on the associated physical processes. As the mature MCS moved northeastward and approached the windward side of the mountains, isolated convective cells developed downstream in the mountain lee, 20–50 km ahead of the main convective line. Cells were initiated by an undular bore, which formed as the MCS cold pool moved over the mountain ridge and perturbed the leeside nocturnal, low-level stable layer. The field of isolated cells organized into a new MCS, which continued to move northeastward, while the parent storm decayed as it traversed the mountains. Only the southern portion of the storm propagated discretely, due to variability in mountain height along the chain. In the north, taller mountain peaks prevented the MCS cold pool from moving over the terrain and perturbing the stable layer. Consequently, no bore was generated, and no discrete propagation occurred in this region. To the south, the MCS cold pool was able to traverse the lower-relief mountains, and the discrete propagation was successful. 

Abstract Quasi-linear convective systems (QLCSs) are responsible for approximately a quarter of all tornado events in the U.S., but no field campaigns have focused specifically on collecting data to understand QLCS tornadogenesis. The Propagation, Evolution, and Rotation in Linear System (PERiLS) project was the first observational study of tornadoes associated with QLCSs ever undertaken. Participants were drawn from more than 10 universities, laboratories, and institutes, with over 100 students participating in field activities. The PERiLS field phases spanned two years, late winters and early springs of 2022 and 2023, to increase the probability of intercepting significant tornadic QLCS events in a range of large-scale and local environments. The field phases of PERiLS collected data in nine tornadic and nontornadic QLCSs with unprecedented detail and diversity of measurements. The design and execution of the PERiLS field phase and preliminary data and ongoing analyses are shown.