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Tropical cyclone hazard assessment using model-based track simulation

Author

Listed:
  • Jonas Rumpf
  • Helga Weindl
  • Peter Höppe
  • Ernst Rauch
  • Volker Schmidt

Abstract

A method is introduced for assessing the probabilities and intensities of tropical cyclones at landfall and applied to data from the North Atlantic. First, a recently developed model for the basin-wide Monte-Carlo simulation of tropical cyclone tracks is enhanced and transferred to the North Atlantic basin. Subsequently, a large number of synthetic tracks is generated by means of an implementation of this model. This synthetic data is far more comprehensive than the available historical data, while exhibiting the same basic characteristics. It, thus, creates a more sound basis for assessing landfall probabilities than previously available, especially in areas with a low historical landfall frequency. Copyright Springer Science+Business Media B.V. 2009

Suggested Citation

  • Jonas Rumpf & Helga Weindl & Peter Höppe & Ernst Rauch & Volker Schmidt, 2009. "Tropical cyclone hazard assessment using model-based track simulation," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 48(3), pages 383-398, March.
    • Handle: RePEc:spr:nathaz:v:48:y:2009:i:3:p:383-398
    DOI: 10.1007/s11069-008-9268-9
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    References listed on IDEAS

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    1. Jonas Rumpf & Helga Weindl & Peter Höppe & Ernst Rauch & Volker Schmidt, 2007. "Stochastic modelling of tropical cyclone tracks," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 66(3), pages 475-490, December.
    2. Iman, Ronald L. & Johnson, Mark E. & Watson, Charles C., 2006. "Statistical Aspects of Forecasting and Planning for Hurricanes," The American Statistician, American Statistical Association, vol. 60, pages 105-121, May.
    3. Baddeley, Adrian & Turner, Rolf, 2005. "spatstat: An R Package for Analyzing Spatial Point Patterns," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 12(i06).
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    Cited by:

    1. Ashley C. Freeman & Walker S. Ashley, 2017. "Changes in the US hurricane disaster landscape: the relationship between risk and exposure," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 88(2), pages 659-682, September.
    2. H. Poulos, 2010. "Spatially explicit mapping of hurricane risk in New England, USA using ArcGIS," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 54(3), pages 1015-1023, September.
    3. Jiayang Zhang & Yangbo Chen, 2019. "Risk Assessment of Flood Disaster Induced by Typhoon Rainstorms in Guangdong Province, China," Sustainability, MDPI, vol. 11(10), pages 1-20, May.
    4. Yu Chen & Zhongdong Duan, 2018. "Impact of ENSO on typhoon wind hazard in the coast of southeast China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 92(3), pages 1717-1731, July.
    5. Björn Kriesche & Helga Weindl & Anselm Smolka & Volker Schmidt, 2014. "Stochastic simulation model for tropical cyclone tracks, with special emphasis on landfall behavior," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 73(2), pages 335-353, September.

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