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Abstract Hail research and forecasting models necessarily involve explicit or implicit—and uncertain—physical assumptions regarding hailstones’ shape, tumbling behavior, fall speed, and thermal energy transfer. Whereas most models assume spherical hailstones, we relax this assumption by using hailstone shape data from field observations to establish empirical size–shape relationships with reasonable degrees of randomness considering hailstones’ natural shape variability, capturing the observed distribution of triaxial ellipsoidal shapes. We also incorporate explicit, random tumbling of individual hailstones during their growth to simulate their free-falling behavior and the resultant changes in cross-sectional area (which affects growth by hydrometeor collection). These physical attributes are incorporated in calculating hailstones’ fall speeds, using either empirical relationships or analytical relationships based on each hailstone’s Best and Reynolds numbers. Options for drag coefficient modification are added to emulate hailstones’ rough surfaces (lobes), which then modifies their thermal energy and vapor exchange with the environment. We investigate how applying these physical assumptions about nonspherical hail to the Penn State hail growth trajectory model, coupled with Cloud Model 1 supercell simulations, impacts hail production and examine the reasons behind the resulting variability in hail statistics. The choice of hailstone size–mass relation and fall speed scheme have the strongest influence on hail sizes. Using nonspherical, tumbling hailstones reduces the number of large hailstones produced. Applying shape-specific thermal energy transfer coefficients subtly increases sizes; the effects of lobes vary depending on the fall speed scheme used. These physical assumptions, although adding complexity to modeling, can be parameterized efficiently and potentially used in microphysics schemes. Significance StatementIn numerical modeling of hailstorms, we usually consider hailstones to be spherical to simplify calculations, but in nature, hailstones generally are not spheres and can be rather lumpy and have spikes. The purpose of this study is to examine how the model result would change when nonspherical hailstone shape is implemented. We examine the relationship between hailstone shape and physical processes during hail growth in effort to explain why these changes occur and offer insights on how nonspherical hailstone shape may be parameterized in bulk microphysics schemes. 

Abstract This is the second of a two-part study that explores the capabilities of a mesoscale atmospheric model to reproduce the near-surface wind fields in hurricanes over land. The Weather Research and Forecasting (WRF) Model is used with two planetary boundary layer parameterizations: the Yonsei University (YSU) and the Mellor–Yamada–Janjić (MYJ) schemes. The first part presented the modeling framework and initial conditions used to produce simulations of Hurricane Wilma (2005) that closely reproduced the track, intensity, and size of its wind field as it passed over South Florida. This part explores how well these simulations can reproduce the winds at fixed points over land by making comparisons with observations from airports and research weather stations. The results show that peak wind speeds are remarkably well reproduced at several locations. Wind directions are evaluated in terms of the inflow angle relative to the storm center, and the simulated inflow angles are generally smaller than observed. Localized peak wind events are associated with vertical vorticity maxima in the boundary layer with horizontal scales of 5–10 km. The boundary layer winds are compared with wind profiles obtained by velocity–azimuth display (VAD) analyses from National Weather Service Doppler radars at Miami and Key West, Florida; results from these comparisons are mixed. Nonetheless the comparisons with surface observations suggest that when short-term hurricane forecasts can sufficiently predict storm track, intensity, and size, they will also be able to provide useful information on extreme winds at locations of interest. 

Abstract Hailstone growth results in a variety of hailstone shapes. These shapes hold implications for modeling of hail processes, hailstone fall behaviors including fall speeds, and remote sensing signatures of hail. This study is an in-depth analysis of natural hailstone shapes, using a large dataset of hailstones collected in the field over a 6-yr period. These data come from manual measurements with digital calipers and three-dimensional infrared laser scans. Hailstones tend to have an ellipsoidal geometry with minor-to-major axis ratios ranging from 0.4 to 0.8, and intermediate-to-major axis ratios between 0.8 and 1.0. These suggest hailstones are better represented as triaxial ellipsoids as opposed to spheres or spheroids, which is commonly assumed. The laser scans allow for precise sphericity measurements, for the first time. Hailstones become increasingly nonspherical with increasing maximum dimension, with a typical range of sphericity values of 0.57 to 0.99. These sphericity values were used to estimate the drag coefficient, which was found to have a typical range of 0.5 to over 0.9. Hailstone maximum dimension tends to be 20%–50% larger than the equivalent-volume spherical diameter. As a step toward understanding and quantifying hailstone shapes, this study may aid in better parameterizations of hail in models and remote sensing hail detection and sizing algorithms. 

Globally relevant and locally devastating, hailstorms produce significant societal impacts; despite this, our understanding of hailstorms and our ability to predict them is still limited. 

Severe (>2.5 cm) hail causes >$5 billion in damage annually in the United States. However, radar sizing of hail remains challenging. Typically, spheroids are used to represent hailstones in radar forward operators and to inform radar hail-sizing algorithms. However, natural hailstones can have irregular shapes and lobes; these details significantly influence the hailstone’s scattering properties. The high-resolution 3D structure of real hailstones was obtained using a laser scanner for hail collected during the 2016–17 Insurance Institute for Business and Home Safety (IBHS) Hail Field Study. Plaster casts of several record hailstones (e.g., Vivian, South Dakota, 2010) were also scanned. The S-band scattering properties of these hailstones were calculated with the discrete dipole approximation (DDA). For comparison, scattering properties of spheroidal approximations of each hailstone (with identical maximum and minimum dimensions and mass) were calculated with the T matrix. The polarimetric radar variables have errors when using spheroids, even for small hail. Spheroids generally have smaller variations in the polarimetric variables than the real hailstones. This increased variability is one reason why the correlation coefficient [Formula: see text] tends to be lower in observations than in forward-simulated cases using spheroids. Backscatter differential phase δ also is found to have large variance, particularly for large hailstones. Irregular hailstones with a thin liquid layer produce enhanced and more variable values for reflectivity factor at horizontal polarization Z_HH, differential reflectivity Z_DR, specific differential phase K_DP, linear depolarization ratio (LDR), and δ compared with dry hailstones; [Formula: see text] is also significantly reduced.