Articles | Volume 17, issue 2
https://doi.org/10.5194/amt-17-407-2024
https://doi.org/10.5194/amt-17-407-2024
Research article
 | 
19 Jan 2024
Research article |  | 19 Jan 2024

Radar and environment-based hail damage estimates using machine learning

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

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Cited articles

Allen, J. T. and Tippett, M. K.: The Characteristics of United States Hail Reports: 1955–2014, E-Journal of Severe Storms Meteorology, 10, 1–31, https://doi.org/10.55599/EJSSM.V10I3.60, 2015. a
Blong, R.: Residential building damage and natural perils: Australian examples and issues, Build. Res. Inf., 32, 379–390, https://doi.org/10.1080/0961321042000221007, 2007. a
Brook, J. P., Protat, A., Soderholm, J., Carlin, J. T., McGowan, H., and Warren, R. A.: HailTrack–Improving Radar-Based Hailfall Estimates by Modeling Hail Trajectories, J. Appl. Meteorol. Clim., 60, 237–254, https://doi.org/10.1175/JAMC-D-20-0087.1, 2021. a, b, c, d
Brook, J. P., Protat, A., Soderholm, J. S., Warren, R. A., and McGowan, H.: A Variational Interpolation Method for Gridding Weather Radar Data, J. Atmos. Ocean. Tech., 39, 1633–1654, https://doi.org/10.1175/JTECH-D-22-0015.1, 2022. a, b
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Short summary
The paper addresses the crucial topic of hail damage quantification using radar observations. We propose a new radar-derived hail product that utilizes 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 regard.
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