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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.06429 |
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| _version_ | 1866909681091346432 |
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| author | Ehrensperger, Gregor Meyer, Vera Katharina Falkensteiner, Marc-André Hell, Tobias |
| author_facet | Ehrensperger, Gregor Meyer, Vera Katharina Falkensteiner, Marc-André Hell, Tobias |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_06429 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | 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 Ehrensperger, Gregor Meyer, Vera Katharina Falkensteiner, Marc-André Hell, Tobias Methodology 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. |
| title | 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 |
| topic | Methodology |
| url | https://arxiv.org/abs/2507.06429 |