Preprints
https://doi.org/10.5194/nhess-2023-158
https://doi.org/10.5194/nhess-2023-158
19 Sep 2023
 | 19 Sep 2023
Status: a revised version of this preprint was accepted for the journal NHESS and is expected to appear here in due course.

An open-source radar-based hail damage model for buildings and cars

Timo Schmid, Raphael Portmann, Leonie Villiger, Katharina Schröer, and David N. Bresch

Abstract. Severe hailstorms cause substantial damages to buildings and vehicles, necessitating the quantification of associated risks. Here, we present a novel open-source hail damage model for buildings and cars based on single-polarization radar data and 250’000 geolocated hail damage reports in Switzerland from 2002 to 2021. To this end, we conduct a detailed evaluation of different radar-based hail intensity measures at 1 km resolution and find that the maximum expected severe hail size (MESHS) outperforms the other measures, despite a considerable false alarm ratio. Asset-specific hail damage impact functions for buildings and cars are calibrated based on MESHS and incorporated into the open-source risk modelling platform CLIMADA. The model successfully estimates the correct order of magnitude for the number of building damages in 91 %, their total cost in 77 %, the number of vehicle damages in 74 %, and their total cost in 60 % of over 100 considered large hail events. We found considerable uncertainties in hail damage estimates, which are largely attributable to limitations of radar-based hail detection. Therefore, we explore the usage of crowdsourced hail reports and find substantially improved spatial representation of severe hail for individual events. By highlighting the potential and limitations of radar-based hail size estimates, particularly MESHS, and the utilization of an open-source risk modelling platform, this study represents a significant step towards addressing the gap in risk quantification associated with severe hail events in Switzerland.

Timo Schmid, Raphael Portmann, Leonie Villiger, Katharina Schröer, and David N. Bresch

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2023-158', Anonymous Referee #1, 17 Oct 2023
    • AC1: 'Reply on RC1', Timo Schmid, 07 Dec 2023
  • RC2: 'Comment on nhess-2023-158', Anonymous Referee #2, 23 Oct 2023
    • AC2: 'Reply on RC2', Timo Schmid, 07 Dec 2023

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2023-158', Anonymous Referee #1, 17 Oct 2023
    • AC1: 'Reply on RC1', Timo Schmid, 07 Dec 2023
  • RC2: 'Comment on nhess-2023-158', Anonymous Referee #2, 23 Oct 2023
    • AC2: 'Reply on RC2', Timo Schmid, 07 Dec 2023
Timo Schmid, Raphael Portmann, Leonie Villiger, Katharina Schröer, and David N. Bresch
Timo Schmid, Raphael Portmann, Leonie Villiger, Katharina Schröer, and David N. Bresch

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Short summary
Hailstorms cause large damages to buildings and cars, which motivates a detailed risk assessment. Here, we present a new open-source hail damage model based on radar data in Switzerland. The model successfully estimates the correct order of magnitude of car and building damages for most large hail events over 20 years. However, large uncertainty remains in the geographical distribution of modelled damages, which can be improved for individual events by using crowdsourced hail reports.
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