Articles | Volume 23, issue 4
https://doi.org/10.5194/nhess-23-1549-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-23-1549-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Apr 2023
Research article |
| 26 Apr 2023
| 26 Apr 2023
Characteristics of hail hazard in South Africa based on satellite detection of convective storms
Heinz Jürgen Punge
Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Kristopher M. Bedka
NASA Langley Research Center, Science Directorate, Climate Science Branch, Hampton, VA, USA
Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Sarah D. Bang
NASA Marshall Space Flight Center (ST-11), Huntsville, AL, USA
Kyle F. Itterly
Science Systems and Applications Inc., Hampton, VA, USA
Related authors
Antonio Giordani, Michael Kunz, Kristopher M. Bedka, Heinz Jürgen Punge, Tiziana Paccagnella, Valentina Pavan, Ines M. L. Cerenzia, and Silvana Di Sabatino
Nat. Hazards Earth Syst. Sci., 24, 2331–2357, https://doi.org/10.5194/nhess-24-2331-2024, https://doi.org/10.5194/nhess-24-2331-2024, 2024
Short summary
Short summary
To improve the challenging representation of hazardous hailstorms, a proxy for hail frequency based on satellite detections, convective parameters from high-resolution reanalysis, and crowd-sourced reports is tested and presented. Hail likelihood peaks in mid-summer at 15:00 UTC over northern Italy and shows improved agreement with observations compared to previous estimates. By separating ambient signatures based on hail severity, enhanced appropriateness for large-hail occurrence is found.
Katharina Küpfer, Alexandre Tuel, and Michael Kunz
Nat. Hazards Earth Syst. Sci., 25, 2885–2907, https://doi.org/10.5194/nhess-25-2885-2025, https://doi.org/10.5194/nhess-25-2885-2025, 2025
Short summary
Short summary
Using loss data, we assess when and how single and multiple types of meteorological extremes (river floods and heavy rainfall events, windstorms and convective gusts, and hail) are related. We find that the combination of several types of hazards clusters robustly on a seasonal scale, whereas only some single hazard types occur in clusters. This can be associated with higher losses compared to isolated events. We argue for the relevance of jointly considering multiple types of hazards.
Amit Kumar Pandit, Jean-Paul Vernier, Thomas Duncan Fairlie, Kristopher M. Bedka, Melody A. Avery, Harish Gadhavi, Madineni Venkat Ratnam, Sanjeev Dwivedi, Kasimahanthi Amar Jyothi, Frank G. Wienhold, Holger Vömel, Hongyu Liu, Bo Zhang, Buduru Suneel Kumar, Tra Dinh, and Achuthan Jayaraman
Atmos. Chem. Phys., 24, 14209–14238, https://doi.org/10.5194/acp-24-14209-2024, https://doi.org/10.5194/acp-24-14209-2024, 2024
Short summary
Short summary
This study investigates the formation mechanism of a tropopause cirrus cloud layer observed at extremely cold temperatures over Hyderabad in India during the 2017 Asian summer monsoon using balloon-borne sensors. Ice crystals smaller than 50 µm were found in this optically thin cirrus cloud layer. Combined analysis of back trajectories, satellite, and model data revealed that the formation of this layer was influenced by waves and stratospheric hydration induced by typhoon Hato.
Markus Augenstein, Susanna Mohr, and Michael Kunz
EGUsphere, https://doi.org/10.5194/egusphere-2024-2804, https://doi.org/10.5194/egusphere-2024-2804, 2024
Short summary
Short summary
A grid-based analysis of lightning in Europe shows a reduction in thunderstorm activity in many regions. Moving away from a grid-based analysis, a spatio-temporal clustering algorithm was used. The results show a slight trend towards the occurrence of smaller, more separated convective clustered events, suggesting changes in the organization of convective systems. One reason for this could be the increased occurrence of the negative phase of the North Atlantic Oscillation in the last decade.
Antonio Giordani, Michael Kunz, Kristopher M. Bedka, Heinz Jürgen Punge, Tiziana Paccagnella, Valentina Pavan, Ines M. L. Cerenzia, and Silvana Di Sabatino
Nat. Hazards Earth Syst. Sci., 24, 2331–2357, https://doi.org/10.5194/nhess-24-2331-2024, https://doi.org/10.5194/nhess-24-2331-2024, 2024
Short summary
Short summary
To improve the challenging representation of hazardous hailstorms, a proxy for hail frequency based on satellite detections, convective parameters from high-resolution reanalysis, and crowd-sourced reports is tested and presented. Hail likelihood peaks in mid-summer at 15:00 UTC over northern Italy and shows improved agreement with observations compared to previous estimates. By separating ambient signatures based on hail severity, enhanced appropriateness for large-hail occurrence is found.
Corey E. Clapp, Jessica B. Smith, Kristopher M. Bedka, and James G. Anderson
Atmos. Chem. Phys., 23, 3279–3298, https://doi.org/10.5194/acp-23-3279-2023, https://doi.org/10.5194/acp-23-3279-2023, 2023
Short summary
Short summary
Convection in the Asian monsoon provides an important pathway for the transport of boundary layer and tropospheric air, and potentially pollution and chemically active species, into the stratosphere. We analyzed the distribution of the fastest and deepest convection with geostationary satellite detections for the months of May through October of 2017. We find significant differences in the geographic and monthly distributions of cross-tropopause convection across the Asian monsoon region.
Susanna Mohr, Uwe Ehret, Michael Kunz, Patrick Ludwig, Alberto Caldas-Alvarez, James E. Daniell, Florian Ehmele, Hendrik Feldmann, Mário J. Franca, Christian Gattke, Marie Hundhausen, Peter Knippertz, Katharina Küpfer, Bernhard Mühr, Joaquim G. Pinto, Julian Quinting, Andreas M. Schäfer, Marc Scheibel, Frank Seidel, and Christina Wisotzky
Nat. Hazards Earth Syst. Sci., 23, 525–551, https://doi.org/10.5194/nhess-23-525-2023, https://doi.org/10.5194/nhess-23-525-2023, 2023
Short summary
Short summary
The flood event in July 2021 was one of the most severe disasters in Europe in the last half century. The objective of this two-part study is a multi-disciplinary assessment that examines the complex process interactions in different compartments, from meteorology to hydrological conditions to hydro-morphological processes to impacts on assets and environment. In addition, we address the question of what measures are possible to generate added value to early response management.
Hazel Vernier, Neeraj Rastogi, Hongyu Liu, Amit Kumar Pandit, Kris Bedka, Anil Patel, Madineni Venkat Ratnam, Buduru Suneel Kumar, Bo Zhang, Harish Gadhavi, Frank Wienhold, Gwenael Berthet, and Jean-Paul Vernier
Atmos. Chem. Phys., 22, 12675–12694, https://doi.org/10.5194/acp-22-12675-2022, https://doi.org/10.5194/acp-22-12675-2022, 2022
Short summary
Short summary
The chemical composition of the stratospheric aerosols collected aboard high-altitude balloons above the summer Asian monsoon reveals the presence of nitrate/nitrite. Using numerical simulations and satellite observations, we found that pollution as well as lightning could explain some of our observations.
Kristopher M. Bedka, Amin R. Nehrir, Michael Kavaya, Rory Barton-Grimley, Mark Beaubien, Brian Carroll, James Collins, John Cooney, G. David Emmitt, Steven Greco, Susan Kooi, Tsengdar Lee, Zhaoyan Liu, Sharon Rodier, and Gail Skofronick-Jackson
Atmos. Meas. Tech., 14, 4305–4334, https://doi.org/10.5194/amt-14-4305-2021, https://doi.org/10.5194/amt-14-4305-2021, 2021
Short summary
Short summary
This paper demonstrates the Doppler Aerosol WiNd (DAWN) lidar and High Altitude Lidar Observatory (HALO) measurement capabilities across a range of atmospheric conditions, compares DAWN and HALO measurements with Aeolus satellite Doppler wind lidar to gain an initial perspective of Aeolus performance, and discusses how atmospheric dynamic processes can be resolved and better understood through simultaneous observations of wind, water vapour, and aerosol profile observations.
Elody Fluck, Michael Kunz, Peter Geissbuehler, and Stefan P. Ritz
Nat. Hazards Earth Syst. Sci., 21, 683–701, https://doi.org/10.5194/nhess-21-683-2021, https://doi.org/10.5194/nhess-21-683-2021, 2021
Short summary
Short summary
Severe convective storms (SCSs) and the related hail events constitute major atmospheric hazards in parts of Europe. In our study, we identified the regions of France, Germany, Belgium and Luxembourg that were most affected by hail over a 10 year period (2005 to 2014). A cell-tracking algorithm was computed on remote-sensing data to enable the reconstruction of several thousand SCS tracks. The location of hail hotspots will help us understand hail formation and improve hail forecasting.
Benjamin R. Scarino, Kristopher Bedka, Rajendra Bhatt, Konstantin Khlopenkov, David R. Doelling, and William L. Smith Jr.
Atmos. Meas. Tech., 13, 5491–5511, https://doi.org/10.5194/amt-13-5491-2020, https://doi.org/10.5194/amt-13-5491-2020, 2020
Short summary
Short summary
This paper highlights a technique for facilitating anvil cloud detection based on visible observations that relies on comparative analysis with expected cloud reflectance for a given set of angles. A 1-year database of anvil-identified pixels, as determined from IR observations, from several geostationary satellites was used to construct a bidirectional reflectance distribution function model to quantify typical anvil reflectance across almost all expected viewing, solar, and azimuth angles.
Cited articles
Adler, R. F., Markus, M. J., and Fenn, D. D.: Detection of Severe Midwest
Thunderstorms Using Geosynchronous Satellite Data, Mon. Weather Rev., 113,
769–781, https://doi.org/10.1175/1520-0493(1985)113<0769:DOSMTU>2.0.CO;2, 1985. a
Admirat, P., Goyer, G. G., Wojtiw, L., Carte, E. A., Roos, D., and Lozowki, E. P.: A comparative study of hailstorms in Switzerland, Canada and South Africa, J. Climate, 5, 35–51, https://doi.org/10.1002/joc.3370050104, 1985. a, b
Allen, J. T., Tippett, M. K., and Sobel, A. H.: An empirical model relating
U.S. monthly hail occurrence to large-scale meteorological environment, J.
Adv. Model. Earth Syt., 7, 226–243, https://doi.org/10.1002/2014MS000397, 2015. a, b
Allen, J. T., Giammanco, I. M., Kumjian, M. R., Punge, H. J., Zhang, Q.,
Groenemeijer, P., Kunz, M., and Ortega, K.: Undertanding Hail in the Earth
System, Rev. Geophys., 58, e2019RG000665, https://doi.org/10.1029/2019RG000665, 2020. a, b, c
Ayob, N.: Hail nowcasting over the South African Highveld, MS thesis, North-West University, South Africa, https://repository.nwu.ac.za/handle/10394/33836 (last access: 21 April 2023), 2019. a
Bang, S. D. and Cecil, D. J.: Constructing a Multifrequency Passive Microwave
Hail Retrieval and Climatology in the GPM Domain, J. Appl. Meteorol. Clim., 58, 1889–1904, https://doi.org/10.1175/JAMC-D-19-0042.1, 2019. a, b, c, d
Bang, S. D. and Cecil, D. J.: Testing Passive Microwave-Based Hail Retrievals
Using GPM DPR Ku-Band Radar, J. Appl. Meteorol. Clim., 60, 255–271, https://doi.org/10.1175/JAMC-D-20-0129.1, 2021.
a
Bedka, K., Brunner, J., Dworak, R., Feltz, W., Otkin, J., and Greenwald, T.:
Objective Satellite-Based Detection of Overshooting Tops Using Infrared
Window Channel Brightness Temperature Gradients, J. Appl. Meteorol. Clim., 49, 181–202, https://doi.org/10.1175/2009JAMC2286.1, 2010. a, b, c, d
Bedka, K., Allen, J., Punge, H., and Kunz, M.: A Long-Term Overshooting
Convective Cloud Top Detection Database Over Australia Derived From MTSAT
Japanese Advanced Meteorological Imager Observations, J. Appl. Meteorol.
Clim., 57, 937–951, https://doi.org/10.1175/JAMC-D-17-0056.1, 2017. a
Bedka, K., Murillo, E., Homeyer, C. R., Scarino, B., and Mersiovsky, H.: The
Above Anvil Cirrus Plume: An Important Severe Weather Indicator in Visible
and Infrared Satellite Imagery, Weather Forecast., 33, 1159–1181, https://doi.org/10.1175/WAF-D-18-0040.1, 2018a. a
Bedka, K. M., Allen, J. T., Punge, H. J., Kunz, M., and Simanovic, D.: A
long-term overshooting convective cloud-top detection database over Australia
derived from MTSAT Japanese advanced meteorological imager observations, J.
Appl. Meteorol. Clim., 57, 937–951, https://doi.org/10.1175/JAMC-D-17-0056.1, 2018b. a, b, c, d, e
Bedka, K. M.: Overshooting cloud top detections using MSG SEVIRI Infrared
brightness temperatures and their relationship to severe weather over Europe,
Atmos. Res., 99, 175–189, https://doi.org/10.1016/j.atmosres.2010.10.001, 2011. a, b
Bedka, K. M. and Khlopenkov, K.: A probabilistic multispectral pattern
recognition method for detection of overshooting cloud tops using passive
satellite imager observations, J. Appl. Meteorol. Clim., 55, 1983–2005, https://doi.org/10.1175/JAMC-D-15-0249.1, 2016. a, b
Berthet, C., Dessens, J., and Sanchez, J.: Regional and yearly variations of
hail frequency and intensity in France, Atmos. Res., 100, 391–400, https://doi.org/10.1016/j.atmosres.2010.10.008, 2011. a
Carte, A. E.: Features of Transvaal hailstorms, Q. J. Roy. Meteorol. Soc., 92, 290–296, https://doi.org/10.1002/qj.49709239214, 1966. a
Cecil, D. J.: Passive Microwave Brightness Temperatures as Proxies for
Hailstorms, J. Appl. Meteorol. Clim., 48, 1281, https://doi.org/10.1175/2009JAMC2125.1, 2009. a
Changnon Jr., S. A.: The scales of hail, J. Appl. Meteorol., 16, 626–648, https://doi.org/10.1175/1520-0450(1977)016<0626:TSOH>2.0.CO;2, 1977. 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
Cooney, J. W., Bedka, K. M., Bowman, K. P., Khlopenkov, K. V., and Itterly, K.: Comparing Tropopause-Penetrating Convection Identifications Derived from
NEXRAD and GOES over the Contiguous United States, J. Geophys. Res.-Atmos.,
126, e2020JD034319, https://doi.org/10.1029/2020JD034319, 2021. a, b, c
Copernicus: Climate Data Store, https://cds.climate.copernicus.eu/#!/search?text=ERA5&type=dataset, last access: 21 April 2023. a
de Coning, E., Gijben, M., Maseko, B., and van Hemert, L.: Using satellite
data to identify and track intense thunderstorms in South and southern Africa, S. Afr. J. Sci., 111, 1–5, https://doi.org/10.17159/sajs.2015/20140402, 2015. a
Dessens, J., Berthet, C., and Sanchez, J. L.: Change in hailstone size
distributions with an increase in the melting level height, Atmos. Res., 158–159, 245–253, https://doi.org/10.1016/j.atmosres.2014.07.004, 2015. a
Dotzek, N., Groenemeijer, P., Feuerstein, B., and Holzer, A. M.: Overview of
ESSL's severe convective storms research using the European Severe Weather
Database ESWD, Atmos. Res., 93, 575–586, https://doi.org/10.1016/j.atmosres.2008.10.020, 2009. a, b, c
Dowdy, A. J., Soderholm, J., Brook, J., Brown, A., and McGowan, H.: Quantifying hail and lightning risk factors using long-term observations around Australia, J. Geophys. Res.-Atmos., 125, e2020JD033101-T, https://doi.org/10.1029/2020JD033101, 2020. a
Dyson, L. L., Pienaar, N., Smit, A., and Kijko, A.: An ERA-Interim HAILCAST
hail climatology for southern Africa, Int. J. Climatol., 41, 262–277, https://doi.org/10.1002/joc.6619, 2020. a, b, c
Eccel, E., Cau, P., Riemann-Campe, K., and Biasioli, F.: Quantitative hail
monitoring in an alpine area: 35-year climatology and links with atmospheric
variables, Int. J. Climatol., 32, 503–517, 2012. a
ESWD: European Severe Weather Database, https://eswd.eu/, last access: 21 April 2023. a
EUMETSAT: EUMETSAT Data Centre, https://www.eumetsat.int/eumetsat-data-centre, last access: 21 April 2023. a
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S.,
Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf, D.: The shuttle radar topography mission, Rev. Geophys., 45, RG2004, https://doi.org/10.1029/2005RG000183, 2007. a
Feng, Z., Leung, L. R., Liu, N., Wang, J., Houze Jr., R. A., Li, J., Hardin,
J. C., Chen, D., and Guo, J.: A Global High-Resolution Mesoscale Convective
System Database Using Satellite-Derived Cloud Tops, Surface Precipitation,
and Tracking, J. Geophys. Res.-Atmos., 126, e2020JD034202, https://doi.org/10.1029/2020JD034202, 2021. a
Fluck, E., Kunz, M., Geissbuehler, P., and Ritz, S. P.: Radar-based assessment of hail frequency in Europe, Nat. Hazards Earth Syst. Sci., 21, 683–701, https://doi.org/10.5194/nhess-21-683-2021, 2021. a, b, c
Garstang, M., Kelbe, B. E., Emmitt, G. D., and London, W. B.: Generation of
Convective Storms over the Escarpment of Northeastern South Africa, Mon. Weather Rev., 115, 429–443, https://doi.org/10.1175/1520-0493(1987)115<0429:GOCSOT>2.0.CO;2, 1987. a
Giaiotti, D., Nordio, S., and Stel, F.: The climatology of hail in the plain of Friuli Venezia Giulia, Atmos. Res., 67–68, 247–259, https://doi.org/10.1016/S0169-8095(03)00084-X, 2003. a
Grieser, J. and Hill, M.: How to Express Hail Intensity-Modeling the Hailstone Size Distribution, J. Appl. Meteorol. Clim., 58, 2329–2345,
https://doi.org/10.1175/JAMC-D-18-0334.1, 2019. a, b, c
Heikenfeld, M., Marinescu, P. J., Christensen, M., Watson-Parris, D., Senf, F., van den Heever, S. C., and Stier, P.: tobac 1.2: towards a flexible framework for tracking and analysis of clouds in diverse datasets, Geosci. Model Dev., 12, 4551–4570, https://doi.org/10.5194/gmd-12-4551-2019, 2019. a
Held, G.: Hail frequency in the Pretoria–Witwatersrand area during 1962 to 1972, Pure Appl. Geophys., 112, 765–776, https://doi.org/10.1007/BF00876951, 1974. a, b
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., De Chiara, G., 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. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Junghänel, T., Brendel, C., Winterrath, T., and Walter, A.: Towards a
radar-and observation-based hail climatology for Germany, Meteorol. Z., 25,
435–445, https://doi.org/10.1127/metz/2016/0734, 2016. a
Kunz, M., Blahak, U., Handwerker, J., Schmidberger, M., Punge, H. J., Mohr, S., Fluck, E., and Bedka, K. M.: The severe hailstorm in southwest Germany on
28 July 2013: Characteristics, impacts and meteorological conditions, Q. J. Roy. Meteorol. Soc., 144, 231–250, https://doi.org/10.1002/qj.3197, 2018. a, b
Kunz, M., Wandel, J., Fluck, E., Baumstark, S., Mohr, S., and Schemm, S.: Ambient conditions prevailing during hail events in central Europe, Nat. Hazards Earth Syst. Sci., 20, 1867–1887, https://doi.org/10.5194/nhess-20-1867-2020, 2020. a
Leigh, R. and Kuhnel, I.: Hailstorm loss modelling and risk assessment in the
Sydney region, Australia, Nat. Hazards, 24, 171–185, https://doi.org/10.1023/A:1011855801345, 2001. a
Lin, Y. and Kumjian, M. R.: Influences of CAPE on hail production in simulated supercell storms, J. Atmos. Sci., 79, 179–204, https://doi.org/10.1175/JAS-D-21-0054.1, 2022. a
Loftus, A., Cotton, W., and Carrió, G.: A triple-moment hail bulk
microphysics scheme. Part I: Description and initial evaluation, Atmos. Res., 149, 35–57, https://doi.org/10.1016/j.atmosres.2014.05.013, 2014. a
Mardia, K. V. and Zemroch, P. J.: Algorithm AS 86: The Von Mises Distribution
Function, J. Roy. Stat. Soc. C., 24, 268–272, https://doi.org/10.2307/2346578, 1975. a
Marion, G. R., Trapp, R. J., and Nesbitt, S. W.: Using Overshooting Top Area to Discriminate Potential for Large, Intense Tornadoes, Geophys. Res. Lett., 46, 12520–12526, https://doi.org/10.1029/2019GL084099, 2019. a, b
Markowski, P. and Richardson, Y.: Mesoscale meteorology in midlatitudes, Wiley, Blackwell, ISBN 9780470742136, https://doi.org/10.1002/9780470682104, 2010. a
Melcón, P., Merino, A., Sánchez, J. L., López, L., and Hermida, L.: Satellite remote sensing of hailstorms in France, Atmos. Res., 182,
221–231, https://doi.org/10.1016/j.atmosres.2016.08.001, 2016. a
Mohr, S., Wandel, J., Lenggenhager, S., and Martius, O.: Relationship between atmospheric blocking and warm-season thunderstorms over western and central Europe, Q. J. Roy. Meteorol. Soc., 145, 3040–3056, https://doi.org/10.1002/qj.3603, 2019. a
Mohr, S., Wilhelm, J., Wandel, J., Kunz, M., Portmann, R., Punge, H. J.,
Schmidberger, M., Quinting, J. F., and Grams, C. M.: The role of large-scale
dynamics in an exceptional sequence ofsevere thunderstorms in Europe
May–June 2018, Weather Clim. Dynam., 1, 325–348, https://doi.org/10.5194/wcd-1-325-2020, 2020. a
Mroz, K., Battaglia, A., Lang, T. J., Cecil, D. J., Tanelli, S., and Tridon,
F.: Hail-Detection Algorithm for the GPM Core Observatory Satellite Sensors, J. Appl. Meteorol. Clim., 56, 1939–1957, https://doi.org/10.1175/JAMC-D-16-0368.1, 2017. a
Munich RE: Severe convective storms and hail – Icy cricket balls from above,
http://www.munichre.com/australia/australia-natural-hazards/australia-storm/hailstorm/index.html,
last access: 25 November 2015. a
Murillo, E. M. and Homeyer, C. R.: Severe hail fall and hailstorm detection
using remote sensing observations, J. Appl. Meteorol. Clim., 58, 947–970, https://doi.org/10.1175/JAMC-D-18-0247.1, 2019. a, b
Murillo, E. M., Homeyer, C. R., and Allen, J. T.: A 23-Year Severe Hail
Climatology Using GridRad MESH Observations, Mon. Weather Rev., 149, 945–958, https://doi.org/10.1175/MWR-D-20-0178.1, 2021. a
Ni, X., Liu, C., Cecil, D. J., and Zhang, Q.: On the Detection of Hail Using
Satellite Passive Microwave Radiometers and Precipitation Radar, J. Appl.
Meteorol. Clim., 56, 2693–2709, https://doi.org/10.1175/JAMC-D-17-0065.1, 2017. a
Nisi, L., Hering, A., Germann, U., and Martius, O.: A 15-year hail streak
climatology for the Alpine region, Q. J. Roy. Meteorol. Soc., 144,
1429–1449, https://doi.org/10.1002/qj.3286, 2018. a, b, c
NOAA: Comprehensive Large Array-Data Stewardship System (CLASS), https://www.avl.class.noaa.gov/saa/products/welcome;jsessionid=BDF491314248B818A20B53DFF3EC97B5, last access: 21 April 2023. a
Olivier, J.: Hail in the Transvaal – some geographical and climatological
aspects, PhD thesis, Rand Afrikaans University, https://ujcontent.uj.ac.za/esploro/outputs/graduate/Hail-in-the-Transvaal--some/9912217207691#file-0 (last access: 21 April 2023), 1990. a
Palencia, C., Castro, A., Giaiotti, D., Stel, F., Vinet, F., and Fraile, R.:
Hailpad-based research: A bibliometric review, Atmos. Res., 93, 664–670,
2009. a
Perry, A.: Severe hailstorm at Grahamstown in relation to convective weather
hazards in South Africa, Weather, 50, 211–214, https://doi.org/10.1002/j.1477-8696.1995.tb06110.x, 1995. a
Petty, G. W. and Bennartz, R.: Field-of-view characteristics and resolution matching for the Global Precipitation Measurement (GPM) Microwave Imager (GMI), Atmos. Meas. Tech., 10, 745–758, https://doi.org/10.5194/amt-10-745-2017, 2017. a
Powell, C. L. and Burger, R. P.: The severe Gauteng hailstorms of 28 November 2013, in: Proc. 30th Ann. Conf. South African, Soc. Atmos. Sci.,
1–2 October 2014, Potchefstroom, South Africa, 54–57, http://hdl.handle.net/10394/16144 (last access: 21 April 2023), 2014. a
Prein, A. F. and Holland, G. J.: Global estimates of damaging hail hazard, Weather Clim. Ext., 22, 10–23, https://doi.org/10.1016/j.wace.2018.10.004, 2018. a, b, c
Punge, H. and Kunz, M.: Hail observations and hailstorm characteristics in
Europe: A review, Atmos. Res., 176, 159–184, https://doi.org/10.1016/j.atmosres.2016.02.012, 2016. a, b, c
Punge, H. J., Bedka, K. M., Kunz, M., and Reinbold, A.: Hail frequency
estimation across Europe based on a combination of overshooting top detections and the ERA-INTERIM reanalysis, Atmos. Res., 198, 34–43,
https://doi.org/10.1016/j.atmosres.2017.07.025, 2017. a, b
Púčik, T., Castellano, C., Groenemeijer, P., Kühne, T., Rädler, A. T., Antonescu, B., and Faust, E.: Large Hail Incidence and Its Economic and Societal Impacts across Europe, Mon. Weather Rev., 147, 3901–3916, https://doi.org/10.1175/MWR-D-19-0204.1, 2019. a, b
Puskeiler, M., Kunz, M., and Schmidberger, M.: Hail statistics for Germany
derived from single-polarization radar data, Atmos. Res., 178–179, 459–470, https://doi.org/10.1016/j.atmosres.2016.04.014, 2016. a, b
Rädler, A. T., Groenemeijer, P., Faust, E., and Sausen, R.: Detecting
severe weather trends using an Additive Regressive Convective Hazard Model (AR-CHaMo), J. Appl. Meteorol. Clim., 57, 569–587,
https://doi.org/10.1175/JAMC-D-17-0132.1, 2018. a, b
Reges, H. W., Doesken, N., Turner, J., Newman, N., Bergantino, A., and
Schwalbe, Z.: CoCoRaHS: The evolution and accomplishments of a volunteer
rain gauge network, B. Am. Meteorol. Soc., 97, 1831–1846,
https://doi.org/10.1175/BAMS-D-14-00213.1, 2016. a
Sánchez, J., Gil-Robles, B., Dessens, J., Martin, E., Lopez, L., Marcos,
J., Berthet, C., Fernández, J., and García-Ortega, E.: Characterization of hailstone size spectra in hailpad networks in France,
Spain, and Argentina, Atmos. Res., 93, 641–654, https://doi.org/10.1016/j.atmosres.2008.09.033, 2009. a
Sandmæl, T. N., Homeyer, C. R., Bedka, K. M., Apke, J. M., Mecikalski, J. R., and Khlopenkov, K.: Evaluating the Ability of Remote Sensing Observations to Identify Significantly Severe and Potentially Tornadic Storms, J. Appl. Meteorol. Clim., 58, 2569–2590, https://doi.org/10.1175/JAMC-D-18-0241.1, 2019. a
Scarino, B. R., Bedka, K., Bhatt, R., Khlopenkov, K., Doelling, D. R., and Smith Jr., W. L.: A kernel-driven BRDF model to inform satellite-derived visible anvil cloud detection, Atmos. Meas. Tech., 13, 5491–5511, https://doi.org/10.5194/amt-13-5491-2020, 2020. a
Schmetz, J., Pili, P., Tjemkes, S., Just, D., Kerkmann, J., Rota, S., and
Ratier, A.: An introduction to Meteosat second generation (MSG), B. Am. Meteorol. Soc., 83, 977–992, https://doi.org/10.1175/1520-0477(2002)083<0977:AITMSG>2.3.CO;2, 2002. a
Schmidberger, M.: Hagelgefährdung und Hagelrisiko in Deutschland basierend
auf einer Kombination von Radardaten und Versicherungsdaten, PhD thesis,
Institute of Meteorology and Climate Research, KIT – Karlsruhe Institute of
Technology, 263 pp., https://publikationen.bibliothek.kit.edu/1000086012 (last access: 21 April 2023), 2018.
a, b, c, d
Seifert, A. and Beheng, K. D.: A two-moment cloud microphysics parameterization for mixed-phase clouds. Part 1: Model description, Meteorol.
Atmos. Phys., 92, 45–66, https://doi.org/10.1007/s00703-005-0112-4, 2006. a
Sioutas, M., Meaden, T., and Webb, J. D.: Hail frequency, distribution and
intensity in Northern Greece, Atmos. Res., 93, 526–533, https://doi.org/10.1016/j.atmosres.2008.09.023, 2009. a
Smith, H., Wingfied, M., and Coutinho, T.: The role of latent Sphaeropsis
sapinea infections in post-hail associated die-back of Pinus patula, Forest
Ecol. Manage., 164, 177–184, https://doi.org/10.1016/S0378-1127(01)00610-7, 2002. a
Stout, G., Blackmer, R., and Wilk, K.: Hail studies in Illinois relating to
cloud physics, Physics of Precipitation, Geophys. Monogr., 5, 369–381, https://doi.org/10.1029/GM005p0369, 1960. a
Taszarek, M., Allen, J., Púčik, T., Groenemeijer, P., Czernecki, B.,
Kolendowicz, L., Lagouvardos, K., Kotroni, V., and Schulz, W.: A climatology
of thunderstorms across Europe from a synthesis of multiple data sources, J. Climate, 32, 1813–1837, https://doi.org/10.1175/JCLI-D-18-0372.1, 2019. a
Taszarek, M., Allen, J. T., Púčik, T., Hoogewind, K. A., and Brooks, H. E.: Severe convective storms across Europe and the United States. Part 2: ERA5 environments associated with lightning, large hail, severe wind and
tornadoes, J. Climate, 33, 10263–10286, https://doi.org/10.1175/JCLI-D-20-0346.1, 2020. a, b
University of Oklahoma: 3-D Gridded NEXRAD WSR-88D Radar Data, http://gridrad.org/data.html, last access: 21 April 2023. a
Wellmann, C., Barrett, A. I., Johnson, J. S., Kunz, M., Vogel, B., Carslaw,
K. S., and Hoose, C.: Comparing the impact of environmental conditions and
microphysics on the forecast uncertainty of deep convective clouds and hail,
Atmos. Chem. Phys., 20, 2201–2219, https://doi.org/10.5194/acp-20-2201-2020, 2020. a
Wilhelm, J., Mohr, S., Punge, H. J., Mühr, B., Schmidberger, M., Daniell,
J. E., Bedka, K. M., and Kunz, M.: Severe thunderstorms with large hail across Germany in June 2019, Weather, 76, 228–237, https://doi.org/10.1002/wea.3886, 2021. a
Wingfield, M. J. and Swart, W. J.: Integrated management of forest tree
diseases in South Africa, Forest Ecol. Manage., 65, 11–16,
https://doi.org/10.1016/0378-1127(94)90253-4, 1994. a
Zinner, T., Forster, C., de Coning, E., and Betz, H.-D.: Validation of the Meteosat storm detection and nowcasting system Cb-TRAM with lightning network data – Europe and South Africa, Atmos. Meas. Tech., 6, 1567–1583, https://doi.org/10.5194/amt-6-1567-2013, 2013. a
Short summary
We have estimated the probability of hail events in South Africa using a combination of satellite observations, reanalysis, and insurance claims data. It is found that hail is mainly concentrated in the southeast. Multivariate stochastic modeling of event characteristics, such as multiple events per day or track dimensions, provides an event catalogue for 25 000 years. This can be used to estimate hail risk for return periods of 200 years, as required by insurance companies.
We have estimated the probability of hail events in South Africa using a combination of...
Altmetrics
Final-revised paper
Preprint