标题： 从雷达到风险：构建奥地利高分辨率冰雹数据库并通过将分布神经网络整合到元统计框架中来估计风险 显示英文标题

标题： From Radar to Risk: Building a High-Resolution Hail Database for Austria And Estimating Risk Through the Integration of Distributional Neural Networks into the Metastatistical Framework

摘要： 这项研究对奥地利冰雹气候学的理解做出了重要贡献。 首先，它引入了一个全面的冰雹大小数据库，该数据库基于2009年至2022年的三维雷达数据，并通过约5000份经过验证的冰雹报告进行校准。 该数据库为描述冰雹的短期气候提供了基础，并提供了用于估算具有更高空间分辨率和质量的冰雹风险图的数据。 其次，该研究使时空元极值分布（TMEVD）能够在一个1公里×1公里的高分辨率网格上呈现出长达30年的重现水平。 关键进展包括对TMEVD的适应，现在它包含了大气输入变量，以在数据稀疏区域进行稳健估计。 此外，本文提出了一种新的方法论，利用分布神经网络，并通过创新的样本加权进行定制，以高效处理与建模分布参数相关的计算需求和复杂性。 这些贡献共同为未来的研究和风险评估提供了宝贵的资源。 显示英文摘要

摘要： This study makes significant contributions to the understanding of hail climatology in Austria. First, it introduces a comprehensive database of hailstone sizes, constructed from three-dimensional radar data spanning 2009 to 2022 and calibrated by approximately 5000 verified hail reports. The database serves as foundation for describing the short-term climatology of hail and provides the data necessary for estimating hail risk maps with enhanced spatial resolution and quality. Second, the study enables the spatio-temporal metastitical extreme value distribution (TMEVD) to feature return levels of up to 30 years on a high-resolution grid of 1km x 1km. Key advancements include the adaptation of the TMEVD, which now incorporates atmospheric input variables for robust estimations in data-sparse regions. Additionally, this paper presents a novel methodological approach that utilizes a distributional neural network, tailored with innovative sample weighting to efficiently handle the increased computational demands and complexities associated with modeling the distribution parameters. Together, these contributions provide a valuable resource for future research and risk assessment.