Natural Hazards

, Volume 73, Issue 3, pp 1625–1645 | Cite as

A new physically based stochastic event catalog for hail in Europe

  • H. J. Punge
  • K. M. Bedka
  • M. Kunz
  • A. Werner
Original Paper


Hailstorms represent one of the major sources of damage and insurance loss to residential, commercial, and agricultural assets in several parts of Central Europe. However, there is little knowledge of hail risk across Europe beyond local historical damage reports due to the relative rarity of severe hail events and the lack of uniform detection methods. Here we present a new stochastic catalog of hailstorms for Europe. It is based on satellite observations of overshooting cloud tops (OT) that indicate very strong convective updrafts and hail reports from the European Severe Weather Database (ESWD). Historic hail events are defined based on OT detections from satellite infrared brightness temperatures between 2004 and 2011 for the warm seasons (April–September). The satellite-based historical event properties are complemented by hailstone observations from ESWD to stochastically simulate more than 1 million individual events with an event footprint resolution of 10 km. The final hail event catalog presented in this paper is the first one with a spatial event distribution that is based on a single homogeneous observation source over Europe. Areas of high hail probability or hail risk are found over Central and Southern Europe, including mountainous regions such as the Alps or the Pyrenees. Another region of relatively high hail risk is present over central Eastern Europe.


Hail Climatology Overshooting top Europe 



H.J.P. was funded as part of his Research Fellowship by Willis as part of Willis Research Network (WRN). The Center for Disaster Management and Risk Reduction Technology (CEDIM) supports the research on natural hazards at the Karlsruhe Institute of Technology (KIT) and the German Research Center for Geosciences (GFZ). The authors thank ESWD, European Severe Weather Database ( for providing hail report data. The authors also thank the GOES-R Algorithm Working Group for support in the development of the objective overshooting convective cloud top detection algorithm. The comments of two anonymous reviewers helped to improve this manuscript.


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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • H. J. Punge
    • 1
    • 2
  • K. M. Bedka
    • 3
  • M. Kunz
    • 1
  • A. Werner
    • 4
  1. 1.Institute for Meteorology and Climate ResearchKarlsruhe Institute of TechnologyKarlsruheGermany
  2. 2.Willis Research NetworkWillis Ltd.LondonUK
  3. 3.Science DirectorateNASA Langley Research CenterHamptonUSA
  4. 4.Willis Re GmbH & Co. KGMunichGermany

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