This study makes contributions to the understanding of multi-year hail statistics in Austria. First, it introduces a database of daily maximum hailstone sizes, constructed from three-dimensional radar data spanning 2009 to 2022 and adjusted to best align with approximately 4000 quality-controlled hail reports. The database serves as a foundation for examining the short-term characteristics of hail and provides the data necessary for estimating hail hazard maps with enhanced spatial resolution and quality. Second, the study enables the spatio-temporal metastatistical extreme value distribution (STMEVD) to estimate return levels of up to 30 years on a high-resolution grid of 1 km × 1 km across an approximately 617 km × 358 km (E-W, N-S) domain centered on Austria. Key advancements include the adaptation of the STMEVD, which now incorporates atmospheric input variables – such as CAPE and lightning amplitude – for robust estimations in data-sparse regions. Additionally, this paper presents a novel methodological approach that utilizes a Distributional Neural Network, tailored with an innovative sample weighting function to efficiently handle the increased computational demands and complexities of estimating the Weibull parameters required to compute the STMEVD. The resulting hail size and frequency maps indicate an orographic influence. While small hail frequently occurs along the Alpine foothills, large hail occurs predominantly in the adjacent plains where atmospheric instability is higher. Hail hotspots are found in the transition from the Alpine foothills to the Graz Basin, the northern Alpine foreland spanning southern Germany and Austria, and the northern Waldviertel toward the Czech Republic. The return levels appear to reflect that shift with elevated return levels in these areas. Together, these contributions provide a valuable resource for hail hazard assessment and establish the first high-resolution benchmark for future hail research in Austria.
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Ehrensperger et al. (2026) studied this question.
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