A field research campaign, the Hail Spatial and Temporal Observing Network Effort (HailSTONE), was designed to obtain physical high-resolution hail measurements at the ground associated with convective storms to help address several operational challenges that remain unsatisfied through public storm reports. Field phases occurred over a 5-yr period, yielding hail measurements from 73 severe thunderstorms [hail diameter ≥ 1.00 in. (2.54 cm)]. These data provide unprecedented insight into the hailfall character of each storm and afford a baseline to explore the representativeness of the climatological hail database and hail forecasts in NWS warning products. Based upon the full analysis of HailSTONE observations, hail sizes recorded in Storm Data as well as hail size forecasts in NWS warnings frequently underestimated the maximum diameter hailfall occurring at the surface. NWS hail forecasts were generally conservative in size and at least partially calibrated to incoming hail reports. Storm mode played a notable role in determining the potential range of maximum hail size during the life span of each storm. Supercells overwhelmingly produced the largest hail diameters, with smaller maximum hail sizes observed as convection became progressively less organized. Warning forecasters may employ a storm-mode hail size forecast philosophy, in conjunction with other radar-based hail detection techniques, to better anticipate and forecast hail sizes during convective warning episodes.
Airmass boundaries have been documented as a major influence on convective initiation and development, particularly in supercell thunderstorms. Therefore, this study seeks to determine the specific influence of an airmass boundary on supercell development through idealized numerical modeling. To do this, convective initiation is simulated in an environment that represents a case where supercells were observed forming along a preexisting airmass boundary. Three simulations are conducted, which illustrate convective initiation in the warm sector, cool sector, and along the airmass boundary. Deep convection occurs in all simulations; however, a steady-state supercell is only produced in the boundary simulation. Analysis of these results reveals that the airmass boundary supports supercell formation and development by increasing the strength of the updraft, creating and supporting a low-level mesocyclone, and enhancing the gust front. In this study, the airmass boundary is found to have a profound impact on the simulated storm, and is necessary for supercell development and longevity even with an ambient environment that supports supercells.
While mesoscale models have a strong utility in severe convective weather forecasting, errors or biases in these models can hinder their ability to resolve environments conducive to convection, and may alter forecasters' perception of the probability of severe weather. During the 2012 spring convective season, a low bias in both low-level moisture and convective available potential energy (CAPE) forecasts were observed at six locations across the Great Plains in model forecasts from the Rapid Refresh (RAP). These errors may be attributed at least in part to the planetary boundary layer scheme and the assimilation of surface conditions of the RAP, and tend to occur in fairly dry, well-mixed environments. Forecasters should be aware of these errors when determining the likelihood of severe weather in such environments, and should compare RAP solutions to other model output with differing configurations, like the experimental RAP (RAPv2) run at the Earth System Research Laboratory, the High Resolution Rapid Refresh (HRRR), and locally run mesoscale models.
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