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The United States hail observation dataset maintained and updated annually by the Storm Prediction Center is one of the largest currently available worldwide and spans the period 1955-present. Despite its length, climatology of this dataset is nontrivial because of numerous characteristics that are nonmeteorological in origin. Here, the main features and limitations of the dataset are explored, including the implications of an increasing frequency in the time series, approaches to spatial smoothing of observations, and the sources that contribute to the hail dataset. Despite these problems, using limited temporal windows, spatial binning and judicious application of smoothing techniques reveals important characteristics of the hail dataset. The annual and diurnal cycles are found to be sensitive to the spatial shift northwards of observations and increasing report frequency in the Southeast. Hail days, in contrast to hail reports, show no national trend over the last 25 y. Regional and local influences on hail reporting are identified stemming from verification procedures and contributions from local officials. The change in the definition of severe hail size from 0.75 in (1.9 cm) to 1.00 in (2.5 cm) in 2010 has a particularly clear signature in the report statistics. The contribution of storm chasers and source of report factors beyond population to the hail dataset is also explored, and the difficulty in removing these changes discussed. The overall findings highlight the limitations and nonmeteorological features present in hail observations. Adding visual and descriptive metadata has the potential to improve the hail reporting process.
Allen et al. (Tue,) studied this question.