Hail forecast evaluations provide important insight into microphysical treatment of rimed ice. In this study we evaluate explicit 0–90-min EnKF-based storm-scale (500-m horizontal grid spacing) hail forecasts for a severe weather event that occurred in Oklahoma on 19 May 2013. Forecast ensembles are run using three different bulk microphysics (MP) schemes: the Milbrandt–Yau double-moment scheme (MY2), the Milbrandt–Yau triple-moment scheme (MY3), and the NSSL variable density-rimed ice double-moment scheme (NSSL). Output from a hydrometeor classification algorithm is used to verify surface hail size forecasts. All three schemes produce forecasts that predict the coverage of severe surface hail with moderate to high skill, but exhibit less skill at predicting significant severe hail coverage. A microphysical budget analysis is conducted to better understand hail growth processes in all three schemes. The NSSL scheme uses two-variable density-rimed ice categories to create large hailstones from dense, wet growth graupel particles; however, it is noted the scheme underestimates the coverage of significant severe hail. Both the MY2 and MY3 schemes produce many small hailstones aloft from unrimed, frozen raindrops; in the melting layer, hailstones become much larger than observations because of the excessive accretion of water. The results of this work highlight the importance of using a MP scheme that realistically models microphysical processes.

Day-ahead (20–22 h) 3-km grid spacing convection-allowing model forecasts are performed for a severe hail event that occurred in Denver, Colorado, on 8 May 2017 using six different multimoment microphysics (MP) schemes including: the Milbrandt–Yau double-moment (MY2), Thompson (THO), NSSL double-moment (NSSL), Morrison double-moment graupel (MOR-G) and hail (MOR-H), and Predicted Particle Properties (P3) schemes. Hail size forecasts diagnosed using the Thompson hail algorithm and storm surrogates predict hail coverage. For this case hail forecasts predict the coverage of hail with a high level of skill but underpredict hail size. The storm surrogate updraft helicity predicts the coverage of severe hail with the most skill for this case. Model data are analyzed to assess the effects of microphysical treatments related to rimed ice. THO uses diagnostic equations to increase the size of graupel within the hail core. MOR-G and MOR-H predict small rimed ice aloft; excessive size sorting and increased fall speeds cause MOR-H to predict more and larger surface hail than MOR-G. The MY2 and NSSL schemes predict large, dense rimed ice particles because both schemes predict separate hail and graupel categories. The NSSL scheme predicts relatively little hail for this case; however, the hail size forecast qualitatively improves when the maximum size of both hail and graupel is considered. The single ice category P3 scheme only predicts dense hail near the surface while above the melting layer large concentrations of low-density ice dominate.

The use of ensembles for numerical weather prediction has become common during the last decade. For global models, the generation of initial-condition perturbations has a number of well-tested methodologies. In ensembles that predict convective storms explicitly (i.e., Δx < 4 km), the generation of physically realistic perturbations is less well posed. This study introduces a technique to generate physically coherent and spatially correlated (PCSC) initial-condition perturbations that are calibrated to the environment. Ensembles of idealized CM1 simulations, initialized with either PCSC perturbations (EXP_PCSC), spatially coherent random perturbations (EXP_3KM), or Gaussian white-noise random perturbations (EXP_WHITE), are run for both a linear convective line of storms and a single "supercell" storm to demonstrate the utility of this new perturbation technique in diverse environments. PCSC perturbations are extracted from high-resolution simulations of boundary-layer turbulence and random perturbations are calibrated to be the same in magnitude as PCSC perturbations.EXP_PCSC simulations spawn turbulence fastest in this study. The simulated turbulence is more robust than in other experiments more than 1 hr into the simulation, because horizontal convective rolls enhance power at the largest scales. Random perturbations are slow to generate turbulence; this problem is exacerbated when the base model state flow is nonturbulent. Due to robust turbulence, EXP_PCSC ensemble spread increases fastest during the first simulation hour and remains largest throughout the remainder of the simulations. Although EXP_PCSC spread is the largest, the sensitivity of convection to the initial perturbations varies at different times in the storm life cycle. Storms appear more sensitive to perturbations added near the time of convective initiation.