18.1
High-Resolution Hail Observations: Implications for NWS Warning Operations and Climatological Data

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Friday, 7 November 2014: 9:45 AM
Madison Ballroom (Madison Concourse Hotel)
Scott F. Blair, NOAA/NWS, Pleasant Hill, MO; and D. E. Cavanaugh, J. M. Laflin, J. W. Leighton, K. J. Sanders, and K. L. Ortega

Storm reports remain a critical component of the National Weather Service (NWS) warning decision process by providing ground-truth verification for ongoing weather events, and by helping forecasters calibrate radar signatures to weather phenomena, such as large hail. These storm reports are compiled in a national severe weather climatological database, Storm Data, but unfortunately are many times insufficient for research applications due to the inconsistent and low-resolution nature of the reports.

In an attempt to rectify these limitations, a large observational database consisting of 60 separate severe hail storms (hail diameter ≥ 1 in; ≥ 2.5 cm) between 2011 to present were made by an ongoing field research project, A Hail Spatial and Temporal Observing Network Effort (HailSTONE). Additional high-resolution hail cases from across the United States were obtained from the Severe Hazards Analysis and Verification Experiment (SHAVE) and incorporated into the research. HailSTONE and SHAVE observations provide tremendous spatiotemporal insight into the hail-fall character of convective storms, and allow for several useful comparisons of hail size to the climatological database and NWS warning products.

Maximum diameter hail sizes recorded in Storm Data as well as hail forecasts in NWS warnings and statements differ significantly when compared to the high-resolution observations where consistently larger sizes are noted. In this study, the influence of factors such as storm mode on this size bias will be discussed, as will the potential implications these findings have for the representativeness of the historical hail database and previous hail forecasting techniques derived from those data. The overarching goal of this research will be to identify methods for improving short-fused hail size prediction in NWS warning operations.