Filtering problems in high dimensional geophysical applications often require spatially continuous models to interpolate spatially and temporally sparse data. Many applications in numerical weather and ocean state prediction are concerned with tracking and assessing the uncertainty in the position of large scale vorticity features, such as storm fronts, jets streams, and hurricanes. Quantifying the amplitude variance in these features is complicated by the fact that both height and lateral perturbations in the feature geometry are represented in the same covariance estimate. However, when there are sufficient observations to detect feature information like spatial gradients, the positions of these features can be used to further constrain the filter, as long as the statistical model (cost function) has provisions for both height perturbations and lateral displacements. Several authors since the 1990s have proposed various formalisms for the simultaneous modeling of position and amplitude errors, and the typical approaches to computing the generalized solutions in these applications are variational or direct optimization. The ensemble Kalman filter is often employed in large scale nonlinear filtering problems, but its predication on Gaussian statistics causes its estimators suffer from analysis deflation or collapse, as well as the usual curse of dimensionality in high dimensional Monte Carlo simulations. Moreover, there is no theoretical guarantee of the performance of the ensemble Kalman filter with nonlinear models. Particle filters which employ importance sampling to focus attention on the important regions of the likelihood have shown promise in recent studies on the control of particle size. Consider an ensemble forecast of a system with prominent feature information. The correction of displacements in these features, by pushing them into better agreement with observations, is an application of importance sampling, and Monte Carlo methods, including particle filters, and possibly the ensemble Kalman filter as well, are well suited to applications of feature displacement correction. In the present work, we show that the ensemble Kalman filter performs well in problems where large features are displaced both in amplitude and position, as long as it is used on a statistical model which includes both function height and local position displacement in the model state. In a toy model, we characterize the performance-degrading effect that untracked displacements have on filters when large features are present. We then employ tools from classical physics and fluid dynamics to statistically model displacements by area-preserving coordinate transformations. These maps preserve the area of contours in the displaced function, and using strain measures from continuum mechanics, we regularize the statistics on these maps to ensure they model smooth, feature-preserving displacements. The position correction techniques are incorporated into the statistical model, and this modified ensemble Kalman filter is tested on a system of vortices driven by a stochastically forced barotropic vorticity equation. We find that when the position correction term is included in the statistical model, the modified filter provides estimates which exhibit substantial reduction in analysis error variance, using a much smaller ensemble than what is required when the position correction term is removed from the model.

Following growing public awareness of the danger from hurricanes and tremendous demands for analysis of loss, many researchers have conducted studies to develop hurricane damage analysis methods. Although researchers have identified the significant indicators, there currently is no comprehensive research for identifying the relationship among the vulnerabilities, natural disasters, and economic losses associated with individual buildings. To address this lack of research, this study will identify vulnerabilities and hurricane indicators, develop metrics to measure the influence of economic losses from hurricanes, and visualize the spatial distribution of vulnerability to evaluate overall hurricane damage. This paper has utilized the Geographic Information System (GIS) to facilitate collecting and managing data, and has combined vulnerability factors to assess the financial losses suffered by Texas coastal counties. A multiple linear regression method has been applied to develop hurricane economic damage predicting models. To reflect the pecuniary loss, insured loss payment was used as the dependent variable to predict the actual financial damage and ratio. Geographical vulnerability indicators, built environment vulnerability indicators, and hurricane indicators were all used as independent variables. Accordingly, the models and findings may possibly provide vital references for government agencies, emergency planners, and insurance companies hoping to predict hurricane damage.

Thesis (Ph. D.)--University of Hawaii at Manoa, 1995. / Includes bibliographical references. / Microfiche. / xiv, 153 leaves, bound ill., map 29 cm

In October of 1998 the category 5 storm, Hurricane Mitch, struck Nicaragua, leaving in its wake mass destruction and devastation. Numerous aid agencies and social organisations poured funds into the country to assist in emergency disaster relief efforts, and to rebuild the lives of those who lost their homes and livelihoods (damnificados). El Mirador in the city of Matagalpa is one example of the many communities built with aid monies after Hurricane Mitch. This thesis uses qualitative data constructed from in-depth interviews with participants (community members in El Mirador) to understand the level of successful community development that has been achieved, the ability for longer term sustainability as a result of community development strategies, and the areas in which community development has failed. Through an examination of the relationship the community has with the NGO the Communal Movement, the question of long term sustainability becomes important. The most telling indicator (that development practice is unsustainable) is the unproductive coping mechanisms of community members as aid and social organisations withdraw leaving members with ineffective social networks and at times uncooperative behaviour. Added into this is the arrival of new members into the community, and squatters, who have only added to the feelings of segregation already apparent, as a 'them and us' mentality develops. This study provides a detailed case specific analysis of community development through disaster relief efforts. It highlights some of the consistent, broad inefficiencies as well as more location and situation specific difficulties of community development. Moreover, it adds to the growing body of literature researching how disaster relief can become more effective and sustainable in the longer term.

Thesis (M.Arch)--Kent State University, 2007. / Title from PDF t.p. (viewed Nov. 14, 2007). Advisor: Charles L. Harker. Keywords: transfer of development rights, Lower Ninth neighborhood, Central City neighborhood. Includes bibliographical references (p. 45).

Thesis (M.S.)--Texas State University-San Marcos, 2008. / Vita. Includes bibliographical references (leaves 115-131). Also available on microfilm.

Thesis (M.A.I.S.)--Oregon State University, 2008. / Printout. Includes bibliographical references (leaves 85-91). Also available on the World Wide Web.

Thesis (Ed.D.)--Duquesne University, 2007. / Title from document title page. Abstract included in electronic submission form. Includes bibliographical references (p. 85-90).

Thesis (Ph.D.)--Baylor University, 2008. / Includes bibliographical references (p. 211-219).

Thesis (M.S.)--Florida State University, 2004. / Advisor: Dr. T.N. Krishnamurti, Florida State University, College of Arts and Sciences, Dept. of Meteorology. Title and description from dissertation home page (Jan. 20, 2005). Includes bibliographical references.