Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2507.06429 |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Inhaltsangabe:
- 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.