This document proposes an objective technique to estimate the intensity and predict the formation of tropical cyclones using infrared satellite imagery. As the tropical cyclone develops from an unstructured cloud cluster and intensifies the cloud structures become more axisymmetric around an identified reference point or center. This methodology processes the image gradient to measure the level of symmetry of cloud structures, which characterizes the degree of cloud organization of the tropical cyclone.The center of a cloud system is calculated by projecting and accumulating parallel lines to the gradient vectors. The point where the highest number of line intersections is located pinpoints a common point where the corresponding gradients are directed. This location is used as the center of the system. Next, a procedure that characterizes the departure of the weather system structure from axisymmetry is implemented. The deviation angle of each gradient vector relative to a radial line projected from the center is calculated. The variance of the set of deviation angles enclosed by a circular area around the center describes the axisymmetry of the system, and its behavior through time depicts its dynamics. Results are presented that show the time series of the deviation angle variances is well correlated with the National Hurricane Center best-track estimates.The formation of tropical cyclones is detected by extending the deviation-angle variance technique, it is calculated using every pixel in the scene as the center of the cloud system. Low angle variances indicate structures with high levels of axisimmetry, and these values are compared to a set of thresholds to determine whether a cloud structure can be considered as a vortex. The first detection in a sequence indicates a nascent storm. It was found that 86% of the tropical cyclones during 2004 and 2005 were detected 27 h on average before the National Hurricane Center classified them as tropical storms (33kt).Finally, two procedures to locate the center of a tropical cyclone are compared to the National Hurricane Center best-track center database. Results show that both techniques provide similar accuracy, which increases as the tropical cyclone evolves.

Recent theory on the role of celebrity in a contemporary context emphasizes the unique manner in which celebrity pervades public discourse. This thesis examines the interrelationship between celebrity and disaster theory in order to evaluate the extent to which celebrity had access to public and media discourse about Hurricane Katrina. Attention is also focused on the ways in which celebrity was manifested within this discourse. Social constructionism is employed here as the theoretical lens through which celebrity and disaster merge. With regard to methodology, qualitative elements of Altheide’s (1987) ethnographic content analysis are used to decipher the claims made by and about celebrity within the Katrina news media narrative. In order to address questions of context, Fine’s (1997) adaptation of Smelser’s (1962) value-added model is used to identify some of the structural considerations from which these claims emerge. From this examination, three substantive themes emerge: (1) Gabler’s (1998) celebrity theory offers a suitable approach to the examination of the intersection between celebrity and Hurricane Katrina; (2) Due to the character of this assessment, constructionist applications which consider not only the role of claims-makers but the structural context of claims-making activities provide the most comprehensive framework; (3) The pervasiveness of celebrity in the contemporary context, combined with the dissensus surrounding the Katrina event, allowed celebrities to adopt unique roles within the Katrina narrative. / Thesis (Master, Sociology) -- Queen's University, 2008-09-08 15:45:08.895

Hurricanes cause enormous loss to life and property worldwide. Predicting the damage caused by hurricane and figuring out what factors are responsible for the damage are important. This study utilizes multiple linear regression models to predict a hurricane – induced Texas Windstorm Insurance Association (TWIA) payout or TWIA payout ratio using independent variables that could affect the hurricane intensity, including distance from the coastline, distance from the hurricane track, distance from the landfall center of Hurricane Ike, proportion in floodplain zone (100 year, 500 year, 100-500 year), building area, proportion in island, number of buildings per parcel, and building age. The methodology of this study includes Pearson’s correlation and multiple linear regressions. First, Pearson’s correlation is used to examine whether there are any significant correlations between the dependent and independent variables. For TWIA payout, three independent variables, distance from the coastline, distance from the landfall center, and building area, are correlated to the TWIA payout at the 0.01 level. Distance from the coastline and distance from the landfall center have negative relations with the TWIA payout. The variable, building area, has a positive relation with the TWIA payout. Moreover, the improvement value is correlated to the TWIA payout at the 0.05 level. For TWIA payout ratio, distance from the coastline is correlated to the TWIA payout ratio at the level of 0.01 and distance from the landfall center is correlated to the TWIA payout ratio at the 0.05 level. These two variables have negative relations to the TWIA payout ratio. Multiple linear regressions are applied to predict the TWIA payout and payout ratio. A regression model with an Adjusted R Square of 0.264 is presented to predict the TWIA payout. This model could explain 26.4 percent of the variability in TWIA payout using the variables, distance from coastline and building area. A regression model with an Adjusted R Square of 0.121 is presented to predict the TWIA payout ratio.

Coastal storm surges from hurricanes are one of the most costly natural disasters in the United States (US). Current research arguably indicates a mean sea-level (MSL) increase due to global warming, as well as an increase in damages caused by hurricanes under climate change. The objectives of this research are: 1) to develop a framework that integrates Geographical Information Systems (GIS) with hurricane storm surge numerical models; 2) to quantify the uncertainty derived from coastal land cover spatial data on hurricane storm surge; and 3) to investigate the potential impacts of SLR changes on land cover to hurricane storm surge and coastal damages. Numerical analysis is an important tool for predicting and simulating storm surges for coastal structure design, planning and disaster mitigation. Here we proposed a framework to integrate Geographical Information Systems (GIS) with computational fluid dynamic (CFD) models used to simulate hurricane storm surge. The geodatamodel "Arc StormSurge" is designed to store geospatial information for hurricane storm surge modeling and GIS tools are designed to integrate the high performance computing (HPC) input and output files to GIS; pre-process geospatial data and post-process model results, thereby, streamlining the delineation of coastal flood maps. Georeferenced information of land cover is used to define the frictional drag at the sea bottom and to infer modifications to the momentum transmitted to the water column by the winds. We investigated uncertainties in the surge response arising from land cover for Texas central bays considering several land cover datasets. The uncertainties were quantified based on the mean maximum surge response and inundated area extent. Considering projected SLR, wetland composition and spatial distribution are also expected to change with coastal environmental conditions. Our results showed that wetland degradation by SLR increased the mean maximum surge for coastal bays. Direct damage to buildings and businesses was also significantly increased by the loss of wetlands due to SLR. Here, we demonstrated the importance of considering the effects of land cover and SLR to hurricane storm surge simulations for coastal structure design, floodplain delineation or coastal planning.

Thesis (M.S.)--University of Hawaii at Manoa, 2007. / Includes bibliographical references (leaves 98-102). / xi, 102 leaves, bound ill. (some col.) 29 cm

Thesis (M.S.)--University of Missouri-Columbia, 2008. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on August 25, 2008) Includes bibliographical references.

Dissertation (Ph.D.)--Auburn University,2009. / Abstract. Vita. Includes bibliographic references (p.196-206).

The purposes of this study were to evaluate the shoreline dynamics and environmental change of St. Catherines Island shoreline through the application of an updated shoreline model (1859-2017). Efforts were completed to document and quantify the impacts to the nearshore environments of the island from Hurricane Matthew (07-08 October 2016). This was accomplished through the measurement of Net Shoreline Movement (NSM) that was performed along the shoreface at 200-meter spacings by using aerial imagery and ground-collected GPS data. The Hurricane Matthew NSM data along with the short-term shoreline rates were used to calculate the years of change along the shoreline in response to the storm, indicating that the storm represented an average of 3.7 years of average erosion. A spatial analysis of impacts conducted along the shoreline revealed major habitat losses within the study area of 66.5 acres and the limited accretion of 3.7 acres.

Hurricane Wilma: A Love Story is a coming-of-age memoir about the two years the narrator spent rebuilding his hurricane-damaged condo in Miami Beach in order to provide a home for the woman he would eventually marry. The torturous rebuilding process forced the narrator to confront his deepest insecurities, to overcome a lifelong mother dependency and to assume adult responsibility. He learns to accept and even love the imperfections and particularities of his apartment, just as he does those of his girlfriend. The writing style aspires to the elegance of Tobias Wolff’s This Boy’s Life, the integrity of William Finnegan’s Crossing the Line, the irreverence of Carl Hiaasen’s Basket Case and the insight of Calvin Trillin’s Remembering Denny. The memoir is a tale of growing up despite oneself, of a young journalist who comes to learn, through a series of missteps and misadventures, the true meaning of home.

Traffic from major hurricane evacuations is known to cause severe gridlocks on evacuation routes. Better prediction of the expected amount of evacuation traffic is needed to improve the decision-making process for the required evacuation routes and possible deployment of special traffic operations, such as contraflow. The objective of this dissertation is to develop prediction models to predict the number of daily trips and the evacuation distance during a hurricane evacuation. Two data sets from the surveys of the evacuees from Hurricanes Katrina and Ivan were used in the models' development. The data sets included detailed information on the evacuees, including their evacuation days, evacuation distance, distance to the hurricane location, and their associated socioeconomic characteristics, including gender, age, race, household size, rental status, income, and education level. Three prediction models were developed. The evacuation trip and rate models were developed using logistic regression. Together, they were used to predict the number of daily trips generated before hurricane landfall. These daily predictions allowed for more detailed planning over the traditional models, which predicted the total number of trips generated from an entire evacuation. A third model developed attempted to predict the evacuation distance using Geographically Weighted Regression (GWR), which was able to account for the spatial variations found among the different evacuation areas, in terms of impacts from the model predictors. All three models were developed using the survey data set from Hurricane Katrina and then evaluated using the survey data set from Hurricane Ivan. All of the models developed provided logical results. The logistic models showed that larger households with people under age six were more likely to evacuate than smaller households. The GWR-based evacuation distance model showed that the household with children under age six, income, and proximity of household to hurricane path, all had an impact on the evacuation distances. While the models were found to provide logical results, it was recognized that they were calibrated and evaluated with relatively limited survey data. The models can be refined with additional data from future hurricane surveys, including additional variables, such as the time of day of the evacuation.