Brown, T. M., Pogorzelski, W. H., and Giammanco, I. M.: Evaluating Hail Damage Using Property Insurance Claims Data, Weather Clim. Soc., 7, 197–210,
https://doi.org/10.1175/WCAS-D-15-0011.1, 2015.
a
Brown-Giammanco, T. M., Giammanco, I. M., and Estes, H. E.: New Asphalt Shingle Hail Impact Performance Test Protocol and Damage Assessment, Nat. Hazards Rev., 22, 04021050,
https://doi.org/10.1061/(ASCE)NH.1527-6996.0000509, 2021.
a
Cintineo, J. L., Smith, T. M., Lakshmanan, V., Brooks, H. E., and Ortega, K. L.: An Objective High-Resolution Hail Climatology of the Contiguous United States, Weather Forecast., 27, 1235–1248,
https://doi.org/10.1175/WAF-D-11-00151.1, 2012.
a
Dahl, N. A., Shapiro, A., Potvin, C. K., Theisen, A., Gebauer, J. G., Schenkman, A. D., and Xue, M.: High-resolution, rapid-scan dual-Doppler retrievals of vertical velocity in a simulated supercell, J. Atmos. Ocean. Tech., 36, 1477–1500, 2019. a
Depue, T. K., Kennedy, P. C., and Rutledge, S. A.: Performance of the Hail Differential Reflectivity (HDR) Polarimetric Radar Hail Indicator, J. Appl. Meteorol. Clim., 46, 1290–1301,
https://doi.org/10.1175/JAM2529.1, 2007.
a,
b
Giammanco, I. M., Brown, T. M., Grant, R. G., Dewey, D. L., Hodel, J. D., and Stumpf, R. A.: Evaluating the hardness characteristics of hail through compressive strength measurements, J. Atmos. Ocean. Tech., 32, 2100–2113, 2015. a
Groenemeijer, P., Púčik, T., Tsonevsky, I., and Bechtold, P.: An overview of convective available potential energy and convective inhibition provided by NWP models for operational forecasting, European Centre for Medium-Range Weather Forecasts, Technical Memorandum No. 852,
https://doi.org/10.21957/q392hofrl, 2019.
a
Gunturi, P. and Tippett, M.: Managing severe thunderstorm risk: Impact of ENSO on US tornado and hail frequencies, Willis Re Inc, Tech. rep.,
https://www.columbia.edu/~mkt14/files/WillisRe_Impact_of_ENSO_on_US_Tornado_and_Hail_frequencies_Final.pdf (last access: 1 November 2022), 2017. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., Chiara, G. D., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J. N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049,
https://doi.org/10.1002/QJ.3803, 2020 (data available at:
https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5, last access: 1 May 2022).
a,
b,
c
Hodges Jr., J.: The significance probability of the Smirnov two-sample test, Ark. Mat., 3, 469–486, 1958. a
Hohl, R., Schiesser, H. H., and Aller, D.: Hailfall: the relationship between radar-derived hail kinetic energy and hail damage to buildings, Atmos. Res., 63, 177–207,
https://doi.org/10.1016/S0169-8095(02)00059-5, 2002.
a,
b
Louf, V., Protat, A., Warren, R. A., Collis, S. M., Wolff, D. B., Raunyiar, S., Jakob, C., and Petersen, W. A.: An Integrated Approach to Weather Radar Calibration and Monitoring Using Ground Clutter and Satellite Comparisons, J. Atmos. Ocean. Tech., 36, 17–39,
https://doi.org/10.1175/JTECH-D-18-0007.1, 2019.
a
Lundberg, S. M. and Lee, S.-I.: A Unified Approach to Interpreting Model Predictions, in: Advances in Neural Information Processing Systems 30, edited by: Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., Curran Associates, Inc., 4765–4774, 2017. a
Mobasher, M. E., Adams, B., Mould, J., Hapij, A., and Londono, J. G.: Damage mechanics based analysis of hail impact on metal roofs, Eng. Fract. Mech., 272, 108688,
https://doi.org/10.1016/J.ENGFRACMECH.2022.108688, 2022.
a
Nanni, S., Mezzasalma, P., and Alberoni, P. P.: Detection of hail by means of polarimetric radar data and hailpads: results from four storms, Meteorol. Appl., 7, 121–128,
https://doi.org/10.1017/S135048270000147X, 2000.
a
Ortega, K. L., Krause, J. M., and Ryzhkov, A. V.: Polarimetric Radar Charac
teristics of Melting Hail. Part III: Validation of the Algorithm for Hail Size Discrimination, J. Appl. Meteorol. Clim., 55, 829–848,
https://doi.org/10.1175/JAMC-D-15-0203.1, 2016.
a,
b
Parackal, K. I., Mason, M. S., Henderson, D. J., Smith, D. J., and Ginger, J. D.: Investigation of damage: Brisbane 27 November 2014 severe storm event, 2015 bushfire and natural hazards CRC and AFAC conference, Adelaide, Australia, 1–3 September 2015, ISBN 978-0-9941696-5-5, 2015. a
Richter, H. and Deslandes, R.: The four large hail assessment techniques in severe thunderstorm warning operationsin Australia, 33rd Conference on Radar Meteorology, Cairns, QLD, Australia, 6–10 August 2007 American Meteorological Society (AMS), P5.19,
https://ams.confex.com/ams/pdfpapers/123766.pdf (last access: 8 January 2024), 2007. a
Roebber, P. J.: Visualizing multiple measures of forecast quality, Weather Forecast., 24, 601–608, 2009. a
Ryzhkov, A. V., Kumjian, M. R., Ganson, S. M., and Khain, A. P.: Polarimetric Radar Characteristics of Melting Hail. Part I: Theoretical Simulations Using Spectral Microphysical Modeling, J. Appl. Meteorol. Clim., 52, 2849–2870,
https://doi.org/10.1175/JAMC-D-13-073.1, 2013.
a
Schiesser, H.: Hailfall: the relationship between radar measurements and crop damage, Atmos. Res., 25, 559–582, 1990.
a,
b
Soderholm, J., Louf, V., Brook, J., Protat, A., and Warren, R.: Australian Operational Weather Radar Level 2 Dataset, National Computing Infrastructure [data set],
https://doi.org/10.25914/JJWZ-0F13, 2022.
a,
b
Warren, R. A., Ramsay, H. A., Siems, S. T., Manton, M. J., Peter, J. R., Protat, A., and Pillalamarri, A.: Radar-based climatology of damaging hailstorms in Brisbane and Sydney, Australia, Q. J. Roy. Meteor. Soc., 146, 505–530,
https://doi.org/10.1002/QJ.3693, 2020.
a,
b
Witt, A., Eilts, M. D., Stumpf, G. J., Johnson, J. T., Mitchell, E. D., and Thomas, K. W.: An Enhanced Hail Detection Algorithm for the WSR-88D, Weather Forecast., 13, 286–303, 1998.
a,
b,
c,
d