Improvement of hurricane risk perceptions: re-analysis of a hurricane damage index and development of spatial damage assessments Sandra N. Maina Academic Affiliation, Fall 2011: Senior, Florida Institute of Technology SOARS® Summer 2011 Science Research Mentor: James Done, Cindy Bruyere Writing and Communication Mentor: Cody Phillips ABSTRACT Implementation of potential damage assessment tools in hurricane-prone areas is an essential aspect of communicating accurate hurricane risk information. Several indices currently exist (e.g., Saffir-Simpson Hurricane Wind Scale) that effectively communicate storm strength; however, they are unable to quantify potential damage. The recently developed Maina Hurricane Index (MHI) addressed this deficiency and used a hurricane‟s central minimum pressure and radius of hurricane-force winds to create damage forecasts. The MHI was developed by analyzing data from 13 historical storms – a small sample size limited by the availability of storm size data. This work revisited the MHI using a larger data set, and results showed that central pressure is the best indicator of potential damage. By creating a single damage value per storm, the MHI aids in the decision-making of government and private agencies such as emergency managers, yet it does not convey storm impacts on individuals within a community. In response, this study also developed a methodology to generate spatial damage assessments that illustrate the relative damage distribution across a storm‟s path. A swath of maximum winds was first generated using a parametric wind field model, then translated to damage using a function based on wind speed and a measure of the directional change in wind. Through the ability to forecast both net damage of land falling storms and the distribution of damage across the storm‟s path, individual perceptions of hurricane risk can be unified to make way for safer preparation and evacuation decisions. The Significant Opportunities in Atmospheric Research and Science (SOARS) Program is managed by the University Corporation for Atmospheric Research (UCAR) with support from participating universities. SOARS is funded by the National Science Foundation, the National Oceanic and Atmospheric Administration (NOAA) Climate Program Office, the NOAA Oceans and Human Health Initiative, the Center for Multi-Scale Modeling of Atmospheric Processes at Colorado State University, and the Cooperative Institute for Research in Environmental Sciences. 1. Introduction During hurricane season, there is a need for effectively communicating storm risk and expected damage to the public. Forecasts of total potential damage are useful for groups such as insurance companies, emergency managers, and state governments. However, most of the general public is not interested in understanding and interpreting the meteorology and probabilities of storm damage. Instead, individuals are interested in the personal storm impact to their home and family. Unfortunately, there is no current system of quantifying damage for the individual. Therefore, the purpose of this study is to create a personal damage index via a spatial map of potential damage distribution. Such information can be used in hurricane preparation and evacuation decisions made by individuals A study conducted by Zhang et al. (2007) found that many people perform their own evaluation of hurricane risk when making evacuation decisions, a demonstration of the need for effective communication of hurricane forecasts and risk. Through a survey of 120 people affected by Hurricane Rita on the Texas Gulf Coast, Zhang et al. (2007) investigated perception of hurricane risk, evacuation decisions, and use of hurricane forecasts. It was shown that most people made preparation and/or evacuation decisions based on televised broadcasts of hurricane forecasts from the National Hurricane Center (NHC). Those who received information from the television were aware of the evolution of the hurricane and its relevant threat. In the United States, the NHC is the main source of the hurricane forecasts, where the Saffir-Simpson Hurricane Wind Scale (SSHWS) is used to convey a storm‟s forecast strength to the public. Zhang et al. (2007) showed that the public generally understands the information the SSHWS provides; however, the recognition and understanding of hurricane-related risks such as storm surge and flooding varies among individuals. As a result, people create their own risk perceptions resulting in self-made evacuation decisions, despite federal orders. Previous work by Maina (2010) concentrated on effective communication of hurricane risk to federal groups, for example, by initially questioning the accuracy of the SSHWS and ultimately developing a damage potential index, the Maina Hurricane Index (MHI). While the SSWHS uses one-minute sustained wind speeds to determine potential storm damage, the MHI uses a storm‟s minimum central pressure and radius of hurricane-force winds. The index also incorporates the number of housing units within an affected area to predict a storm‟s monetary damage anywhere along the coastal continental United States. One of the limitations of the MHI, however, was the lack of storm size data. Fortunately, a new data source has been discovered that resolves this problem, increasing the data set to 31 storms. As a result, the first portion of this study involves the validity of the MHI by re-evaluating the index using the new data set. The MHI addresses the need of disseminating accurate information to groups such as state governments but overlooks the public interest of personal impacts. Using the methods of hurricane wind field modeling presented by Vickery et al 2009, the approach used to develop the MHI can be adapted in the development of a spatial map of potential damage distribution across a storm. There is a three-step process outlined by Vickery et al. to create a hurricane wind field model. This process and its expansion to create the spatial map will be detailed later in the paper as the second portion of our study. 2. Re-Analysis of the MHI 2.1. Previous Findings Development of the MHI set the foundation for communicating hurricane risk through quantification of damage from land-falling storms. Maina (2010) acknowledged the importance of central minimum pressure and recognized the need to account for storm size resulting in MHI -1 1/2 = 10(Pmin/ Pmin0) + 1/2(Rh/Rh0) , where Rh is the radius of hurricane-force winds and Pmin0 and Rh0 are reference values (taken as 900hPa and 150km, respectively). Existing indices, such as the SSHWS, provide effective ways to communicate a storm‟s strength, but do not accurately measure a hurricane‟s potential damage. In fact, the MHI is shown to explain a larger portion of the variance in total economic damage for 13 recent US landfalling storms compared to the SSHWS and five other existing indices (e.g. Kantha 2006; Owens and Holland 2009; Smith 2006). Future work, described by Maina (2010), included addressing and resolving the limitations that arose in the development of the MHI. Factors such as storm surge, bathymetric topography, and infrastructure of structures were neglected due to their complex nature. Our re- analysis of the MHI continues not to address these issues. However, we are able resolve the small sample size by increasing the number of storms from to 31. The insufficient amount of information on storm size has been compromised through a new best track data set – the Tropical Cyclone Extended Best Track Data Set (TCEBT data set). 2.2. Tropical Cyclone Extended Best Track Data Set The extended best track data set recently became available online, providing information about all tropical depressions, storms, and hurricanes for the years 1988 – 2008. The data for storms in the Atlantic can be found at ftp://rammftp.cira.colostate.edu/demaria/ebtrk/ebtrk 19_atlc.txt. The following are the data used in the re-analysis of the MHI: date, time (UTC), latitude, longitude, central minimum pressure (hPa), radius of maximum winds (n.mi), and the radius of 34kt, 50kt, and 64kt (18 ms-1 , 26 ms-1 , and 33 ms-1 , respectively) winds to the NE, SE, NW, and SW. Out of the 100+ storms in the data set through the year 2005, 30 storms made landfall. Hurricane Camille, a landfalling storm in 1969, was also added to our data set because of its inclusion in the development of the MHI. Using the TCEBT data set, the relevance of the following hurricane parameters was examined: pressure, size, translation speed, and duration of winds. The size of the storms was calculated by averaging the radius from the four storm quadrants. The translation speed involved calculations with the latitude and longitude, and duration of winds is the radius of hurricane- force winds divided by translation speed. All the data needed in the re-analysis is shown in Table 1. Following the methodology of Maina (2010), the damage data for each storm was again adapted from Pielke et al. (2008) where the total economic damage of storms was normalized using the Collins/Lowe methodology. This involved adjusting hurricane losses with respect to changes in inflation, wealth, and number of housing units. The total damage was also divided by the average number of housing units affected by all storms since 1900 (861,287) to account for spatial variability. 2.3. Results Using the data provided in Table 1, we assessed the statistical significance between the four hurricane parameters and total economic damage. Using the Pearson‟s r test, we calculated the correlation of determination at a confidence interval of 95%. Regression plots (Figures 1 though 4) of each parameter against the damage, in billions, showed improved yet similar statistical results produced by Maina (2010). There was a strong statistical significance between central minimum pressure and damage, as indicated by the coefficient of determination (r2) of 0.410. Again, the radii of tropical storm force winds (18 ms-1 and 26 ms-1) were not statistically relevant, establishing the radius of hurricane-force winds (33 ms-1) is best indicator of storm size.