Dependence of US hurricane economic loss on maximum wind speed and storm size
By Alice R. Zhai, Jonathan H. Jiang. 2014
138 citations recorded by the source corpus.
Many empirical hurricane economic loss models consider only wind speed and neglect storm size. These models may be inadequate in accurately predicting the losses of super-sized storms, such as Hurricane Sandy in 2012. In this study, we examined the dependences of normalized US hurricane loss on both wind speed and storm size for 73 tropical cyclones that made landfall in the US from 1988 through 2012. A multi-variate least squares regression is used to construct a hurricane loss model using both wind speed and size as predictors. Using maximum wind speed and size together captures more variance of losses than using wind speed or size alone. It is found that normalized hurricane loss (L) approximately follows a power law relation with maximum wind speed (V max) and size (R), L = 10c V maxa R b , with c determining an overall scaling factor and the exponents a and b generally ranging between 4–12 and 2–4 respectively. Both a and b tend to increase with stronger wind speed. Hurricane Sandy’s size was about three times of the average size of all hurricanes analyzed. Based on the bi-variate regression model that explains the most variance for hurricanes, Hurricane Sandy’s loss would be approximately 20 times smaller if its size were of the average size with maximum wind speed unchanged. It is important to revise conventional empirical hurricane loss models that are only dependent on maximum wind speed to include both maximum wind speed and size as predictors.
Subfields: Atmospheric Science, Environmental Engineering
View Paper: https://www.semanticscholar.org/paper/126c65453f796e5a7d686a7de80d37a78386f5ac
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Citations in the corpus
- Resolving Tropical Cyclone Intensity in Models (2018, background): “Numerous studies have shown that the damage produced by a tropical cyclone, including storm surge, wave damage, and inland flooding, depends both on the radial extent of strong winds and the maximum wind value (Done et al., 2015; Irish et al., 2008; Powell & Reinhold, 2007; Zhai & Jiang, 2014).”