Preprints
https://doi.org/10.5194/amt-2023-161
https://doi.org/10.5194/amt-2023-161
31 Jul 2023
 | 31 Jul 2023
Status: a revised version of this preprint was accepted for the journal AMT and is expected to appear here in due course.

Radar and Environment-based Hail Damage Estimates using Machine Learning

Luis Ackermann, Joshua Soderholm, Alain Protat, Rhys Whitley, Lisa Ye, and Nina Ridder

Abstract. Large hail events are typically infrequent, with significant time gaps between occurrences at specific locations. However, when these events do happen, they can cause rapid and substantial economic losses within a matter of minutes. Therefore, it is crucial to have the ability to accurately observe and understand hail phenomena to improve the mitigation of this impact. While in-situ observations are accurate, they are limited in number for an individual storm. Weather radars, on the other hand, provide a larger observation footprint, but current radar-derived hail size estimates exhibit low accuracy due to horizontal advection of hailstones as they fall, the variability of hail size distributions (HSD), complex scattering and attenuation, and mixed hydrometeor types. In this paper, we propose a new radar-derived hail product that is developed using a large dataset of hail damage insurance claims and radar observations. We use these datasets coupled with environmental information to calculate a Hail Damage Estimate (HDE) using a deep neural network approach aiming to quantify hail impact, with a critical success index of 0.88 and a coefficient of determination against observed damage of 0.78. Furthermore, we compared HDE to a popular hail size product (MESH), allowing us to identify meteorological conditions that are associated with biases on MESH. Environments with relatively low specific humidity, high CAPE and CIN, low wind speeds aloft and southerly winds at ground are associated with a negative MESH bias, potentially due to differences in HSD or mixed hydrometeors. In contrast, environments with low CAPE, high CIN, and relatively high specific humidity aloft are associated with a positive MESH bias.

Luis Ackermann et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Referee Comment on amt-2023-161', Tanya Brown-Giammanco, 28 Aug 2023
    • AC1: 'Reply on RC1', Luis Ackermann, 03 Oct 2023
  • RC2: 'Comment on amt-2023-161', Anonymous Referee #2, 29 Aug 2023
    • AC2: 'Reply on RC2', Luis Ackermann, 03 Oct 2023

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Referee Comment on amt-2023-161', Tanya Brown-Giammanco, 28 Aug 2023
    • AC1: 'Reply on RC1', Luis Ackermann, 03 Oct 2023
  • RC2: 'Comment on amt-2023-161', Anonymous Referee #2, 29 Aug 2023
    • AC2: 'Reply on RC2', Luis Ackermann, 03 Oct 2023

Luis Ackermann et al.

Luis Ackermann et al.

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Short summary
The manuscript addresses the crucial topic of hail damage quantification using radar observations. We propose a new radar-derived hail product that utilises a large dataset of insurance hail damage claims and radar observations. A deep neural network was employed, trained with local meteorological variables and the radar observations, to better quantify hail damage. Key meteorological variables were identified to have the most predictive capability in this regards.