Return periods capture the essence of uncertainty in extreme meteorological phenomena (storm surge, wave, and wind) associated with hurricanes. Unfortunately, a detailed historical record of hurricane activity in the Atlantic Basin is only slightly more than one hundred years, making return period estimation of "storm-of-the-century" effects problematic. The purpose of this paper is to describe statistical methodology to address the problem of return period estimation. Initially conceived in a project for the Organization of American States (Montego Bay, Jamaica), the methodology was subsequently refined and expanded in the context of various consulting projects and a large contract with the Division of Community Affairs for the state of Florida involving hazard mitigation. Output from our work includes both maximum likelihood estimates of extremes and their associated uncertainties. Hence, our approach can provide rationale assessments of the uncertainty that is more technically sophisticated than the commonplace but extremely conservative method of inflating the Saffir-Simpson category.
In addition to outlining the methodology, the presentation will demonstrate applications to the full North Atlantic basin with GIS-based output products and extensive validation/verification tests. An additional benefit of the approach is that it affords us the opportunity to assess directly the impacts of El Nino and la Nina by partitioning the historical hurricane record appropriately. Other phenomena outside the Atlantic Basin presumed to impact hurricane frequency and intensity are characterized according to their impacts on the return period estimates and corresponding uncertainty. Overall, the methodology offers a sounder statistical basis than the conventional scan radius method.