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. As seen in Figure 2, the r2 value is 0.128, which is an improvement from the r2 value of 0.013 when using the smaller data set (bolded storms in Table 1). The translation speed and duration of hurricane-force winds also showed statistical improvement; however, their r2 values (0.222 and 0.217, respectively) show that both parameters are not an integral part to the MHI. Even when Hurricane Wilma, the outlier in Figure 4, is removed the statistical relationship between the translation speed and damage remains weak. Table 1. Data from all hurricanes that made landfall between 1988 – 2005, including Hurricane Camille (1969). Damage data are normalized quantities from Pielke et. al (2008). Remainder of data is from the extended best track data set. Bolded storm names were those used in the previous development of the MHI completed by Maina (2010) Pmin Rmax R33 Vt Td Year Name MMDDYY Lat Lon Damage ($) (hPa) (n.mi) (n.mi) (m/s) (hr) 1969 Camille 08-18-06 30.7 89.6 909 15 109 6.8 4.5 23,957,867,600 1988 Florence 09-10-00 28.7 89.3 983 N/A 46 6.5 2.0 4,640,241 1989 Chantal 08-01-12 29.5 94.3 984 N/A -99 5.2 N/A 218,804,502 1989 Hugo 09-22-00 31.7 78.8 935 35 144 13.6 2.9 17,483,447,131 1989 Jerry 10-16-00 29.1 95.0 983 N/A -99 7.1 N/A 164,551,638 1991 Bob 08-19-18 41.4 71.4 964 30 23 12.9 0.5 3,066,892,330 1992 Andrew 08-24-06 25.4 79.3 937 15 53 8.7 1.7 54,337,237,494 1995 Erin 08-02-06 27.7 80.4 985 40 -99 6.9 N/A 1,375,861,447 1995 Opal 10-05-00 31.0 86.8 950 25 69 10.7 1.8 6,339,955,773 1996 Bertha 07-13-00 35.0 77.6 993 25 83 8.9 2.6 520,502,973 1996 Fran 09-06-00 33.7 78.0 954 40 127 7.5 4.7 6,168,924,124 1997 Danny 07-18-06 29.2 89.9 992 30 19 2.7 1.9 168,484,613 1998 Bonnie 08-27-00 34.0 77.7 963 45 127 3.1 11.5 1,214,916,672 1998 Earl 09-03-06 30.1 85.7 987 50 83 8.0 2.9 126,232,889 1998 Georges 09-25-12 23.9 81.3 982 30 83 5.6 4.2 3,571,129,627 1999 Bret 08-23-00 26.9 97.4 951 10 35 2.9 3.3 93,900,757 1999 Floyd 09-16-06 33.7 78.0 956 35 111 11.3 2.7 6,787,052,609 1999 Irene 10-15-18 25.1 81.3 986 40 56 4.6 3.3 1,178,217,658 2002 Lili 10-03-12 29.2 92.1 962 10 60 7.6 2.2 1,060,811,848 2003 Claudette 07-15-12 28.3 95.5 982 15 23 5.4 1.2 210,140,802 2003 Isabel 09-18-18 35.1 76.4 958 45 116 9.9 3.3 3,989,771,693 2004 Charley 08-13-18 26.1 82.4 947 10 28 10.3 0.7 16,297,047,080 2004 Frances 09-05-06 27.2 80.2 960 30 109 2.7 11.3 9,648,997,103 2004 Gaston 08-29-12 32.8 79.5 986 15 9 3.6 0.7 141,215,672 2004 Ivan 09-16-06 30.0 87.9 943 20 141 6.3 6.2 15,514,011,620 2004 Jeanne 09-26-00 27.1 79.4 951 25 106 5.4 5.5 7,496,264,391 2005 Cindy 07-06-00 28.5 90.3 992 20 19 6.0 0.9 320,000,000 2005 Dennis 07-10-18 29.9 86.9 942 10 35 7.8 1.2 2,230,000,000 2005 Katrina 08-29-12 29.5 89.6 923 20 139 7.651713 5.0 81,000,000,000 2005 Rita 09-24-06 29.4 93.6 935 20 109 5.558198 5.4 10,000,000,000 2005 Wilma 10-24-12 26.2 81.0 950 30 130 1.497323 24.1 20,600,000,000
Figure 1. Regression plot of storms in Table 1 against total economic damage (in billions) where the coefficient of determination equals 0.410.
Figure 2. Regression plot for radius of hurricane-force winds where the coefficient of determination is 0.128.
Figure 3. Regression plot of translation speed where the coefficient of determination is 0.022.
Figure 4. Regression plot for duration of hurricane-force winds where the coefficient of determination is 0.022.
Even with the larger data set, the results from our statistical tests resemble those generated by Maina (2010). We used the data in Table 1 to calculate the coefficient of -1 1/2 determination for MHI = 10(Pmin/ Pmin0) + 1/2(Rh/Rh0) against damage. This resulted in a value of 0.369. Drawing on pressure being the prominent of the four hurricane parameters, the -1 size term was removed so that MHI = 10(Pmin/ Pmin0) . Applying the Pearson‟s r test suggested that the pressure should be the only parameter in the MHI. With an r2 value of 0.418 (Figure 5), we determined that this would be the re-analyzed version of the MHI.
Figure 5. Regression plot of the re-analyzed MHI where the coefficient of determination is 0.418.
a) Sensitivity Analysis
To determine how robust the new form of the MHI is to change, we performed a sensitivity analysis where the coefficients and exponents were changed by ±50%. Results (Table 2) show that pressure remains to be a sensitive parameter; however, the impacts to the index continue to be less than or equal to the percent change.
SOARS®2011, Sandra Maina, 7
Table 2. Sensitivity analysis results reflected through percent change in MHI. The variables a and aa represent the coefficient and exponent, respectively. Year Name a = +/-5 aa = 0.5 aa = 1.5 Year Name a = +/-5 aa = 0.5 aa = 1.5 1969 Camille -49.58% 0.50% 0.85% 1999 Floyd -47.37% 3.06% 5.26% 1988 Florence -46.11% 4.51% 7.79% 1999 Irene -45.97% 4.67% 8.07% 1989 Chantal -46.06% 4.56% 7.88% 2002 Lili -47.09% 3.39% 5.83% 1989 Hugo -48.35% 1.93% 3.30% 2003 Claudette -46.15% 4.46% 7.69% 1989 Jerry -46.11% 4.51% 7.79% 2003 Isabel -47.27% 3.17% 5.45% 1991 Bob -46.99% 3.49% 6.01% 2004 Charley -47.79% 2.58% 4.42% 1992 Andrew -48.26% 2.03% 3.48% 2004 Frances -47.18% 3.28% 5.64% 1995 Erin -46.01% 4.62% 7.97% 2004 Gaston -45.97% 4.67% 8.07% 1995 Opal -47.65% 2.74% 4.70% 2004 Ivan -47.98% 2.36% 4.05% 1996 Bertha -45.64% 5.04% 8.72% 2004 Jeanne -47.60% 2.79% 4.80% 1996 Fran -47.46% 2.96% 5.08% 2005 Cindy -45.69% 4.99% 8.62% 1997 Danny -45.69% 4.99% 8.62% 2005 Dennis -48.02% 2.31% 3.95% 1998 Bonnie -47.04% 3.44% 5.92% 2005 Katrina -48.92% 1.27% 2.17% 1998 Earl -45.92% 4.72% 8.16% 2005 Rita -48.35% 1.93% 3.30% 1998 Georges -46.15% 4.46% 7.69% 2005 Wilma -47.65% 2.74% 4.70% 1999 Bret -47.60% 2.79% 4.80%
With these results, we were able proceed with finding a functional relationship that relates the MHI to the potential damage, in dollars. Using a conventional form of the line of regression in Figure 5, the following two equations translate the MHI output into a damage estimate where the housing unit factor, , is simply a ratio of the actual number of housing units at that time to the mean housing units affected by all storms since 1900 (861,297):
[1]
[2]
b) Evaluation of MHI
The reliability of the new form of the MHI was tested by through calculations of the percent difference between the MHI estimated damage and the actual damage. As seen in Table 3, there is a wide variety of under and over estimations. Storms that affected small towns (e.g. Bret affected a town with 317 housing units) had severely large over estimations. In addition, there were large percent differences for weak land-falling storms (i.e. Category 1 on the SSHWS).
SOARS®2011, Sandra Maina, 8
Table 3. Comparison of MHI estimated damage and actual damage. Negative values indicate underestimations of damage. Year Name % Diff Year Name % Diff 1969 Camille 951.09% 1999 Floyd -81.00% 1988 Florence -1066049.91% 1999 Irene -212.40% 1989 Chantal -4655.64% 2002 Lili 3907.25% 1989 Hugo 477.28% 2003 Claudette -13205.13% 1989 Jerry -9947.72% 2003 Isabel 3733.87% 1991 Bob 20.69% 2004 Charley -43.00% 1992 Andrew -64.04% 2004 Frances -28.69% 1995 Erin -748.46% 2004 Gaston -6986.50% 1995 Opal 943.65% 2004 Ivan 285.88% 1996 Bertha -4953.61% 2004 Jeanne 163.04% 1996 Fran 601.64% 2005 Cindy -2090.93% 1997 Danny -12206.90% 2005 Dennis 694.04% 1998 Bonnie 2120.10% 2005 Katrina -66.00% 1998 Earl -27700.02% 2005 Rita 112.83% 1998 Georges -877.72% 2005 Wilma -33.57% 1999 Bret 38396282.37%
The validity of the MHI was also tested against the performance of pre-existing indices that also share the intention of providing more accurate estimates of hurricane damage. Using the data in Table 1 and maximum wind speed values from the TCEBT data set, these indices underwent the same statistical analysis as the MHI to assess their relationship with damage. Table 4 provides the results in addition to the functional forms of each index. The correlation of determination for the MHI (0.418) is comparable to those of the other indices, consolidating the validity of the MHI.
Table 4. Coefficient of determination values for pre-existing indices where V0 and Vmax0 are both 74 mph (33m/s) and R0 is 60mi (96.5km). Index Functional Form r2
Carvill Hurricane Index (Smith) 0.435
Hurricane Intensity Index (Kantha 2006) 0.395
Hurricane Hazard Index (Kantha 2008) 0.358
Saffir Simpson Hurricane Wind Scale N/A 0.326
for Willis Hurricane Index (Owens and Holland 2009) 0.0050 vm > 65, and if vt < 7, vv = 7
SOARS®2011, Sandra Maina, 9
The final evaluation of the MHI involved its application to a storm independent of our data. Maina (2010) performed this analysis with Hurricane Ike where the radius of hurricane- force winds was estimated using data from National Oceanic and Atmospheric Administration (NOAA) –Atlantic Oceanographic and Meteorological Laboratory (AOML) Hurricane Wind Analysis System, H*Wind (Powell et al. 1998). However, accurate size measurements are now available through the TCEBT data set. As a result, we used our larger data set to compare r2 and percent difference values of the former and re-analyzed form of the MHI (Table 5). In light of the limiting factors of both indices, results showed the new version of the index provides more accurate total economic damage estimates.
Table 5. Comparison between re-analyzed and previous MHI of estimated versus actual total economic damage for Hurricane Ike. Ike made landfall September 13, 2008 at 0700UTC with a radius of hurricane- force winds (Rh) of 139km (quadrant average) and central minimum pressure of 951 hPa. Ike caused $29.6 billion in damage. 2 MHI 1/2 1/2 10( -1 MHI Value D (Billion $) D (Billion $) % Diff r N Re-Analyzed 9.46 9.46 13.19 33.26 12.37% 0.418 Previous 0.48 9.46 9.95 37.25 93.97 217.47% 0.369
2.4 Implications and future work of the MHI
The results of the re-analyzed MHI are congruent to the findings of Malmstadt et al. (2009). Assessing only Florida hurricane losses, their hypothesis stated that intensity is the only predictor of potential losses due to hurricane landfalls. They showed that a combination of hurricane size and intensity deteriorates the relationship between damage and hurricane intensity. Consequently, we are able to reinforce the findings of Malmstadt et al. and conclude that minimum central pressure is the best indicator of potential hurricane damage.
As indicated by our wide array of percent differences, there are still limitations to the MHI that need to be addressed to achieve its optimum capability of estimating total economic damage of land-falling hurricanes. Factors such as storm surge susceptibility and bathymetric topography were among the limitations described by Maina (2010). Future work should assess these and other constraints in order to increase the MHI‟s ability to explain the variability in hurricane losses.
While there is much room for improvement to the MHI, we recognized its ambiguity in the perspective of the general public. The MHI is suitable for groups such as emergency managers; however, individuals would benefit more from information that directly aids in decisions such as preparation and evacuation plans. As a result, the remainder of this paper will concentrate on our second goal of this study – to create a spatial damage assessment tool.
SOARS®2011, Sandra Maina, 10
3. Spatial Damage Assessments
3.1. Previous Work
Adaptation of work done by Vickery et al. is an essential part in the development of a spatial map of potential damage distribution across a storm. Their study focused on modeling a hurricane wind field where three distinct steps are detailed. The first step in the process is to estimate the gradient wind, which is equal to the mean wind speed in balance with the system- scale pressure field. Holland (1980) created a simple wind field model involving a formula for the pressure at a distance „r‟ from the center of a hurricane. This formula also includes the Holland B parameter, which defines the maximum wind speed in a hurricane. Since the pressure field drives the wind field, the radial wind profile can be calculated using the equation for gradient wind. The second step involves an adjustment of the gradient wind to a mean surface wind. This is done by using atmospheric boundary layer modeling or by simply using a wind speed reduction factor. Finally, the mean surface wind is adjusted for local terrain conditions and to account for deviations from the mean wind using gust factors.
Expanding upon the hurricane wind field modeling process detailed by Vickery et al, we intend to develop spatial damage assessments by using the radial wind profile to create a spatial map of the forecast wind field along the forecast track of three case hurricanes: Frances, Ike, and Katrina. Subsequently, we will use known relationships, or damage functions, between the wind field and damage to create forecast maps of the relative damage distribution across each storm.
3.2. Holland Wind Field Model In order to estimate the gradient wind field, Holland (1980) first calculates the pressure as a function of the distance from the center of a hurricane using
, [3] where (in hPa) is the central minimum pressure, (in hPa) is the pressure drop from the environmental pressure to the center of the storm, (in km) is the radius if maximum winds and the Holland B parameter is represented by
. [4] To calculate the surface winds, the gradient level winds, given as
, [5] are multiplied by a reduction factor of 0.8. Although using the Holland 1980 model is an effective way of estimating a storm‟s surface winds, there is a strong dependence on accurate information regarding the radius of maximum winds. Consequently, we used the Holland 2010 model which resolves this limitation.
SOARS®2011, Sandra Maina, 11
The Holland 2010 model makes direct calculations of the surface winds (as indicated by the inclusion of s in the subscripts) as a function of radius, thus creating a radial wind profile using
, [6]
-1 where are the maximum surface winds (in m s ), is the Holland B parameter adjusted for surface calculations, and is a varying exponent that ensures that the radial profile passes through the given maximum and external tropical storm force winds. Using the pressure drop at -1 the surface and 1.13 kg m as an approximation of the surface density (for 980 hPa), parameter is estimated by
. [7]
The value of x is dependent on r relative to . If , then x = 0.5. However, if , then
, [8]
where is an iterative value for the exponent x determined by its fit to the external wind observations . Because of this calculation, the Holland 2010 model provides more accurate estimations of the decay of winds compared to the previous 1980 model.
a) Model Output
The Holland 2010 wind model was applied to three storms that had a significant impact upon landfall. The first storm was Hurricane Frances, which made landfall as a Category 2 storm at approximately 0430 UTC on September 5, 2004 near Hutchinson Island, Florida. As the fourth most costly hurricane in Unites States history, the estimated total property damage (excluding economic and agricultural losses) is $9 billion (Beven 2004). Ranked as the third most costly storm in history, Hurricane Ike was is our second case study. On September 13, 2008, it made landfall on Galveston Island, Texas at 0700 UTC as a Category 2 storm causing about $24.9 billion in damage (Berg 2009). The final, most infamous storm on our list is Hurricane Katrina, which made its second landfall near Buras, Louisiana at about 1110 UTC on August 29, 2005. With estimated damage costs of about $81 billion, this storm continues to be the most costly storm in United States history due to the catastrophic effects of its storm surge (Knabb 2004).
When running the model for each storm, the six hourly TCEBT data were interpolated for every hour. Initial comparisons were made of instantaneous times before landfall between the modeled radial wind profile and observations provided by the National Oceanic and Atmospheric Administration (NOAA) –Atlantic Oceanographic and Meteorological Laboratory (AOML) Hurricane Wind Analysis System, (H*Wind, Powell et al. 1998). Figures 6 through 8 contain modeled surface maximum winds (in kts) and H*Wind observations of the maximum one-minute sustained surface winds (in kts) for Hurricanes Frances, Ike, and Katrina, respectively, with the white line representing best track. As seen in each figure, the H*Wind observations are smoother
SOARS®2011, Sandra Maina, 12 and less symmetrical than the modeled winds. The Holland 2010 wind model also estimates higher surface wind speeds than those observed. However, the highest winds for both wind fields are located to the right of the best track line. This continues to be seen for the entire duration of the storms.
a) b)
Figure 6. Instantaneous surface wind fields (kts) for Hurricane Frances on September 4, 2004 at 1800 UTC of a) maximum wind speeds estimated by the Holland 2010 wind model and b) maximum 1-min wind speeds provided by H*Wind analysis.
a) b)
Figure 7. Instantaneous surface wind fields (kts) for Hurricane Ike on September13, 2008 of a)maximum wind speeds estimated by the Holland 2010 wind model at 0000 UTC and b)maximum 1-min wind speeds provided by H*Wind analysis at 0130 UTC.
SOARS®2011, Sandra Maina, 13
a) b)
Figure 8. Instantaneous surface wind fields (kts) for Hurricane Katrina on August 29, 2005 at 0600 UTC of a) maximum wind speeds estimated by the Holland 2010 wind model and b) maximum 1-min wind speeds provided by H*Wind analysis.
Figures 9 though 11 compare the wind swaths (in m/s) for the duration of Frances, Ike, and Katrina, respectively. To concentrate on the surface winds over land, each plot masks out the wind field observed over the open water. Again, the modeled winds are higher by an average of 10 m/s. An explanation of these anomalies is discussed below.
As observed in Figures 8 though 11, there are common similarities and discrepancies between the modeled and H*Wind analyzed winds. The modeled winds repeatedly overestimate the winds yet the maximum winds are consistently observed to the right of the white best track line. In addition, the shape of the modeled wind swath remained quasi-circular relative to the observations. Both differences can be attributed to omitting the component of the Holland 2010 wind model that estimates the maximum winds using data from four quadrants of a storm (i.e. NE, SE, SW, NW). This additional input is made available during post-storm analysis and would create a dependence on unavailable data not suited for real-time application. Therefore, the maximum wind input was an average of the winds from each quadrant. As a result, our model was not able to account for the asymmetries in the wind field. Despite these constraints, radial wind profiles are still used to create spatial damage assessments of relative damage: the amount of potential damage relative to the maximum damage for the select storm.
SOARS®2011, Sandra Maina, 14
Figure 9. Maximum wind speed (m/s) swaths for the entire duration of Hurricane Frances where a) is modeled winds and b) is H*Wind observations.
Figure 10. Maximum wind speed (m/s) swaths for the entire duration of Hurricane Ike where a) is modeled winds and b) is H*Wind observations.
Figure 11. Maximum wind speed (m/s) swaths for the entire duration of Hurricane Katrina where a) is modeled winds and b) is H*Wind observations.
SOARS®2011, Sandra Maina, 15
3.3 Damage Functions
An assessment of the work completed by Lin et al (2010) provided the developmental tools needed to determine the relationship between our modeled wind fields and damage. The study involved improving current vulnerability modeling by analyzing the combined effects of debris and pressure damage on various structures. Current models, such as the FEMA HAZUS model and the Florida Public Hurricane Loss Protection (FPHLP) focus on the effects of pressure damage to individual structures. Alternatively, the integrated vulnerability model created by Lin et al. (2010) allows for more accurate damage predictions in areas that have structures within a small vicinity (e.g. residential developments) because of the consideration of windborne debris.
This vulnerability model is quite complex in nature and involves “micro” damage assessment to structures. For our purpose of creating spatial maps (a “macro” assessment) we adopted the methodology of Lin et al (2008) to parameterize the relationship between wind and damage. In the development of the model, it was suggested to use either a time series or instantaneous wind damage analysis to predict debris damage. For both approaches, the effect of wind direction variation was considered by using the maximum wind speed for every 15 degrees of direction change for the whole duration of a storm. In our study, we correlated the relative damage to the average maximum wind speed in every 45 degrees of direction change.
a) Relative Damage Spatial Assessments for Hurricanes Frances, Ike, and Katrina
In the assessment maps, relative damage is expressed as a scale from 0 to 1 with increments of tenths where 1 represents the maximum potential damage. As previously stated, the relative damage is a measurement of the amount of potential damage relative to the maximum damage assessed for each storm. Figure 12 provides the relative damage maps for Frances, Ike, and Katrina, respectively. There is no monetary value for these damage amounts due to time constraints; however, the spatial maps are valuable for assessing the degree of potential damage in regions that are in a storm‟s paths.
SOARS®2011, Sandra Maina, 16
Figure 12. Spatial Relative Damage maps for Hurricane a) Frances, b) Ike, and c) Katrina. In the scale, 1 represents the maximum amount of potential damage unique to the storm.
As seen in Figure 12, the relative damage is divided about either side of the best track line, with the greatest damage occurring on the right. This is due to the wind field pattern within a storm. Figure 13 illustrates the various wind directions that various locations along the coast may experience. As a storm translates forward, Point C only experiences winds in two directions (easterly and westerly) resulting in low average winds over the 8 directional segments and creating the path of minimal damage in the middle of the damage swath. Points A and E solely experience northerly and southerly winds, respectively, causing moderate damage within the storm‟s path. Point B bears the effects of winds from all directions; however, Point D is exposed to the additional effects of the forward translation of the storm. Thus, the greatest amount of damage is seen to the right of the best track line.
SOARS®2011, Sandra Maina, 17
Figure 13. Demonstration of the various wind directions and speeds coastal locations may experience during a hurricane translating northward towards land.
b) Evaluation of spatial damage maps
In order to assess the validity of the relative damage maps, each storm was compared to the damage statistics and/or assessment maps provided in post-storm damage reports.
1) HURRICANE FRANCES
According to the Florida Department of Environmental Protection (2004), the counties that suffered the greatest impacts of Hurricane Frances were located north of the landfall. The top five counties that experienced substantial structural damage (within the coastal building zone) are Brevard, St. Lucie, Indian River, Volusia, and Martin. After locating these counties using Figure 14, analysis of the swath of predicted damage (Figure 12a) confirms this statistic by estimating these counties to withstand 80%-90% of the maximum amount of possible damage for Hurricane Frances.
SOARS®2011, Sandra Maina, 18
Figure14. County Map of Florida provided by censusfinder.com
2) HURRICANE IKE
To evaluate the spatial maps for hurricane Ike, post-storm damage assessment maps were made available through the Northern Rockies Fires Use Management Team (FUMT). Figure 15 provides a damage assessment map prepared on Wednesday September 17, 2008. There is a significant resemblance between the area that suffered moderate damage (pink) in Figure 15 and 90% relative damage (maroon) in Figure 12b. Similar to the results for Hurricane Frances, the damage spatial map proved to be a valid resource of designating which counties and/or cities lay in the path of eminent danger.
SOARS®2011, Sandra Maina, 19
Figure 15. Damage assessment map for Hurricane Ike provided by the Northern Rockies Fires Use Management Team (FUMT) where the greatest amount of damage occurred within the red region.
3) HURRICANE KATRINA
The combined effects of strong winds and flooding from Hurricane Katrina resulting from levee and floodwall breaching caused extensive devastation especially in Louisiana. The extensive amount of damage in New Orleans and neighboring cities was not shown in our relative assessment map (Figure 16b), yet it was able to recognize the coastal areas that suffered from catastrophic damage as designated by FEMA (seen in Figure 16a). In addition, these regions are located to the right of the storm track beyond the area of minimal relative damage (green and yellow areas) seen in Figure 16b. As previously stated, our model does not accommodate for the effects of storm surge and flooding. Nevertheless, the estimated spatial damage provides an insight to the extent of damage it may have caused without the failure of levees and floodwalls.
SOARS®2011, Sandra Maina, 20
a)
b)
Figure 16. Hurricane Katrina spatial damage assessments of a) FEMA-based flood and damage assessments provided by the Congressional Cartography Program and b) predicted relative damage.
SOARS®2011, Sandra Maina, 21
4. Summary We have developed two damage assessment tools for land-falling hurricanes by 1) revising a hurricane damage index and 2) creating spatial damage maps for relative damage. Both serve to improve communication of accurate hurricane risk information to government agencies such as emergency managers, as well as individuals in a community. The Maina Hurricane Index (MHI) tends to the need of large groups by quantifying the amount of potential damage for a hurricane through a single value. On the other hand, the spatial damage maps provide visual assessments of the damage distribution across the storm, addressing the personal impacts of land-falling hurricanes. The re-analysis of the MHI includes a larger data set, allowing for more robust correlations between the various hurricane parameters (i.e. central minimum pressure, radius of hurricane-force winds, translation speed, and duration of winds) and total economic damage of each storm. Statistical analysis indicated that pressure is the best indicator of potential damage, making the central minimum pressure the only parameter represented in the MHI which deviates from its previous form. Corresponding to the limitations of Maina (2010), future work would include incorporating factors such as storm surge susceptibility and building infrastructure to produce more accurate damage predictions. While the MHI provides a monetary damage estimate value, the spatial damage maps we developed provide measurements of the degree of potential damage through relative damage estimates. Methodology involved generating swaths of maximum wind using the Holland 2010 wind field model. Following this, the wind swaths were translated to relative damage by using a known relationship between damage and the wind speed and directional variability of the wind. Wind swaths and relative damage maps were generated for three historically damaging storms: Frances (2005), Ike (2008), and Katrina (2005). Comparisons of their wind swaths to H*Wind observations showed similarities in the location of the highest winds – to the right of the storm track. The relative damage maps were also analyzed against storm reports and post-storm assessment maps. For each storm, our estimated spatial maps prove to be good indicators of where heavy damage occurred. To increase the validity of these maps, future work should include improvements to the modeled wind swaths to incorporate asymmetries of the wind. This should be done by increasing the amount of input without increasing the dependence of data not readily available for real-time application. We intend to apply both damage assessment tools to the 2011 Atlantic Hurricane Season. Further extensions of our work include incorporating societal impacts to assess how the public would respond and utilize a spatial damage map. Methodology presented by Zhang et al serves as a precedent to investigating public perceptions where we ultimately wish to assist in unifying individual perceptions of hurricane risk, allowing the public to make safer preparation and evacuation decisions.
SOARS®2011, Sandra Maina, 22
ACKNOWLEDGMENTS
This research would not be possible without the generosity of the UCAR/SOARS staff allowing me to participate in a second year of SOARS. This summer, I have been able to build upon the skills and knowledge gained from last year, making me feel more confident as a growing researcher. This expansion of my knowledge and research experience falls in the hand of my science mentor, James Done. I greatly appreciate the research and computing assistance from Cindy Bruyere throughout the summer. In addition, my writing mentor Cody Phillips helped me greatly improve my writing skills, and for that I am very grateful. Finally, I would like to thank NESL‟s Mesoscale & Microscale Meteorology for supporting this research.
SOARS®2011, Sandra Maina, 23
REFERENCES
Berg, R., 2009: Tropical Cyclone Report Hurricane Ike. National Hurricane Center,
SOARS®2011, Sandra Maina, 24