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Sensitivity Analysis for Computer Model Projections of Hurricane Losses

Author

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  • Ronald L. Iman
  • Mark E. Johnson
  • Charles C. Watson

Abstract

Projecting losses associated with hurricanes is a complex and difficult undertaking that is fraught with uncertainties. Hurricane Charley, which struck southwest Florida on August 13, 2004, illustrates the uncertainty of forecasting damages from these storms. Due to shifts in the track and the rapid intensification of the storm, real‐time estimates grew from $2 billion to $3 billion in losses late on the 12th to a peak of $50 billion for a brief time as the storm appeared to be headed for the Tampa Bay area. The storm struck the resort areas of Charlotte Harbor and moved across the densely populated central part of the state, with early poststorm estimates in the $28 to $31 billion range, and final estimates converging at $15 billion as the actual intensity at landfall became apparent. The Florida Commission on Hurricane Loss Projection Methodology (FCHLPM) has a great appreciation for the role of computer models in projecting losses from hurricanes. The FCHLPM contracts with a professional team to perform onsite (confidential) audits of computer models developed by several different companies in the United States that seek to have their models approved for use in insurance rate filings in Florida. The team's members represent the fields of actuarial science, computer science, meteorology, statistics, and wind and structural engineering. An important part of the auditing process requires uncertainty and sensitivity analyses to be performed with the applicant's proprietary model. To influence future such analyses, an uncertainty and sensitivity analysis has been completed for loss projections arising from use of a sophisticated computer model based on the Holland wind field. Sensitivity analyses presented in this article utilize standardized regression coefficients to quantify the contribution of the computer input variables to the magnitude of the wind speed.

Suggested Citation

  • Ronald L. Iman & Mark E. Johnson & Charles C. Watson, 2005. "Sensitivity Analysis for Computer Model Projections of Hurricane Losses," Risk Analysis, John Wiley & Sons, vol. 25(5), pages 1277-1297, October.
  • Handle: RePEc:wly:riskan:v:25:y:2005:i:5:p:1277-1297
    DOI: 10.1111/j.1539-6924.2005.00673.x
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    Cited by:

    1. Emanuele Borgonovo, 2006. "Measuring Uncertainty Importance: Investigation and Comparison of Alternative Approaches," Risk Analysis, John Wiley & Sons, vol. 26(5), pages 1349-1361, October.
    2. Isadora Antoniano‐Villalobos & Emanuele Borgonovo & Sumeda Siriwardena, 2018. "Which Parameters Are Important? Differential Importance Under Uncertainty," Risk Analysis, John Wiley & Sons, vol. 38(11), pages 2459-2477, November.
    3. S. Cucurachi & E. Borgonovo & R. Heijungs, 2016. "A Protocol for the Global Sensitivity Analysis of Impact Assessment Models in Life Cycle Assessment," Risk Analysis, John Wiley & Sons, vol. 36(2), pages 357-377, February.
    4. Sinan Xiao & Zhenzhou Lu & Pan Wang, 2018. "Multivariate Global Sensitivity Analysis Based on Distance Components Decomposition," Risk Analysis, John Wiley & Sons, vol. 38(12), pages 2703-2721, December.
    5. Emanuele Borgonovo & Gordon B. Hazen & Elmar Plischke, 2016. "A Common Rationale for Global Sensitivity Measures and Their Estimation," Risk Analysis, John Wiley & Sons, vol. 36(10), pages 1871-1895, October.
    6. Patricia Born & Randy Dumm & Mark E. Johnson, 2023. "Epistemic uncertainty in catastrophe models—A base level examination," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 26(2), pages 247-269, July.
    7. Barry Anderson & Emanuele Borgonovo & Marzio Galeotti & Roberto Roson, 2014. "Uncertainty in Climate Change Modeling: Can Global Sensitivity Analysis Be of Help?," Risk Analysis, John Wiley & Sons, vol. 34(2), pages 271-293, February.
    8. Emanuele Borgonovo, 2008. "Sensitivity Analysis of Model Output with Input Constraints: A Generalized Rationale for Local Methods," Risk Analysis, John Wiley & Sons, vol. 28(3), pages 667-680, June.
    9. Takeda, Satoshi & Kitada, Takanori, 2023. "Importance measure evaluation based on sensitivity coefficient for probabilistic risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    10. Emanuele Borgonovo & William Castaings & Stefano Tarantola, 2011. "Moment Independent Importance Measures: New Results and Analytical Test Cases," Risk Analysis, John Wiley & Sons, vol. 31(3), pages 404-428, March.
    11. Anna Timonina & Stefan Hochrainer‐Stigler & Georg Pflug & Brenden Jongman & Rodrigo Rojas, 2015. "Structured Coupling of Probability Loss Distributions: Assessing Joint Flood Risk in Multiple River Basins," Risk Analysis, John Wiley & Sons, vol. 35(11), pages 2102-2119, November.

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