In this thesis work, I examine the use and adoption of online communication media by 840 fire and police departments that were affected by the 2012 Hurricane Sandy. I began by exploring how and why these fire and police departments used (or did not use) online media to communicate with the public during Hurricane Sandy. Results show that fire and police departments used online media during Hurricane Sandy to give timely and relevant information to the public about things such as evacuations, damages, weather, and cleanup and to engage in two-way communications with their constituents. In their messages, fire and police departments sought to make the information provided more credible by referencing, rebroadcasting, and recommending other authoritative entities. Though some departments saw online media as a useful and effective means of communication with members of the public, other departments found them difficult to use given the challenging circumstances of Hurricane Sandy such as flooding and power outages. Next, I explore how a large-scale disaster event like Hurricane Sandy affects online media adoption by affected fire and police departments. I found an increase in online activity over Facebook, Twitter, and Nixle by the affected fire and police departments compared to before Hurricane Sandy. However, it is unclear whether this increase in online activity can be attributed to Hurricane Sandy or a natural increase over time.

Ordinary and weighted least squares multiple linear regression techniques were used to derive 720 models predicting Katrina-induced storm damage in cubic foot volume (outside bark) and green weight tons (outside bark). The large number of models was dictated by the use of three damage classes, three product types, and four forest type model strata. These 36 models were then fit and reported across 10 variable sets and variable set combinations for volume and ton units. Along with large model counts, potential independent variables were created using power transforms and interactions. The basis of these variables was field measured plot data, satellite (Landsat TM and ETM+) imagery, and NOAA HWIND wind data variable types. As part of the modeling process, lone variable types as well as two-type and three-type combinations were examined. By deriving models with these varying inputs, model utility is flexible as all independent variable data are not needed in future applications. The large number of potential variables led to the use of forward, sequential, and exhaustive independent variable selection techniques. After variable selection, weighted least squares techniques were often employed using weights of one over the square root of the pre-storm volume or weight of interest. This was generally successful in improving residual variance homogeneity. Finished model fits, as represented by coefficient of determination (R2), surpassed 0.5 in numerous models with values over 0.6 noted in a few cases. Given these models, an analyst is provided with a toolset to aid in risk assessment and disaster recovery should Katrina-like weather events reoccur.

Previous research has suggested that misinterpretations of hurricane track forecasts can lead to errors in estimation of perceived risk. One factor that can be used to understand these errors in judgment of risk perception is called optimistic bias, in which an individual perceives that compared to another person they are at less risk. Thus, the purpose of this study was to examine how risk perceptions of hurricane track forecasts are influenced by the optimistic bias and changes in the forecasts. Students from three coastal universities took a survey regarding hurricane risk from two different track scenarios of a hypothetical hurricane approaching their university. Results indicated that optimism and perceptions of hurricane tracks were not correlated. Regardless of changes in forecast tracks, students perceived the same level of risk by the final forecast. This research has important social implications because hurricane track forecasts are part of the hurricane decision-making process.

We present the results from a user study looking at the ability of observers to mentally integrate wind direction and magnitude over a vector field. The data set chosen for the study is an MM5 (PSU/NCAR Mesoscale Model) simulation of Hurricane Lili over the Gulf of Mexico as it approaches the southeastern United States. Nine observers participated in the study. This study investigates the effect of layering on the observer's ability to detrmine the magnitude and direction of a vector field. We found a tendency for observers to underestimate the magnitude of the vectors and a counter-clockwise bias when determining the average direction of a vector field. We completed an additional study with two observers to try to uncover the source of the counter-clockwise bias. These results have direct implications to atmospheric scientists, but may also be able to be applied to other fields that use 2D vector fields.

The pulse compression scheme implemented on the Imaging Wind and Rain Air-borne Profiler (IWRAP) is described. Developed at the UMASS Microwave Remote Sensing Laboratory (MIRSL), IWRAP is a dual-band (C and Ku) conically scanning Doppler scatterometer designed to map the atmospheric boundary layer wind fields, ocean surface wind fields, and precipitation within tropical cyclones. IWRAP has previously been deployed using a pulsed transmit waveform with a peak transmit power of 80 watts. This limits the average transmit power and sensitivity for the system which affects the more distant range gates (especially at Ku-band). As a result, IWRAP could operate only at lower altitudes (approx. 5000 ft) causing safety concerns and limiting the missions for which it can be deployed. Increasing sensitivity was achieved by converting IWRAP to a pulse compression radar system. Pulse compression is a technique that combines the increased energy of a longer pulse with the high resolution of a short pulse by implementing a frequency modulated (FM) “chirped” transmit waveform. This method requires advanced signal processing, in which the received signal is passed through a filter to compress the pulse on the receiving end. A system with various chirp/filtering schemes as well as a new control system which UMASS has recently developed will be discussed in this thesis.

Cultural landscapes are built over time and reflect the direct interaction between political, economic, social, and environmental factors that affect communities on a daily basis. Many communities maintain a fragile daily balance within these landscapes as they are exposed to hazards and risks such as, lack of access to healthcare and affordable housing options, inadequate public health, and lack of fair wage employment and education. These daily hazards and risks create a fragile balance between sustainability and vulnerability within communities. The destructive power of an acute large scale disturbance, such as a hurricane, can shatter this balance and severe the communities connection to their landscape. Communities that lack the entitlement and access to resources necessary to recover and reconnect to their landscape post-disaster may become displaced from their cultural landscape temporarily or permanently. The void left by displacement post-disaster is often filled by different communities permanently altering the cultural landscape, removing an individual's sense of place. This thesis evaluates the post-hurricane communities of Biloxi, Mississippi and Galveston, Texas, in order to understand the influence of internal and external organizations on the communities' abilities to reconnect to their landscape post-hurricane. The research was done using a mixed method approach that incorporated literature reviews and academic writing reviews, in order to set the framework for site visits to the cities of Biloxi, Mississippi and Galveston, Texas. During the site visits, qualitative data was collected through first-hand observation, photography, and interaction with the various communities and organizations in Biloxi and Galveston. Through this research I gained a better understanding of the paradigms applied to disaster recovery, and the influence of internal and external organizations on the process of reconnecting to the landscape. The purpose of this research was to gain a better understanding of the factors affecting a community's ability to recover, rebuild, and reconnect to the landscape post-hurricane in order to enable a more holistic approach to preparedness and recovery from disaster in other communities in the future. The research suggests that designers, policy makers, community members, and other internal and external organizations must take a pre-emptive approach to the destabilizing effect of hurricanes. By empowering communities to reduce daily risk, and by creating a stronger sense of place and connection to the landscape, communities can decrease vulnerability, increase sustainability, and adapt to the uncertain future brought about by the effects of climate change and coastal development on the destructive power of hurricanes.

This research provides realistic techniques to visualize the process of damage to residential building caused by hurricane force winds. Three methods are implemented to make the visualization useful for educating the public about mitigation measures for their homes. First, the underline physics uses Quick Collision Response Calculation. This is an iterative method, which can tune the accuracy and the performance to calculate collision response between building components. Secondly, the damage process is designed as a Time-scalable Process. By attaching a damage time tag for each building component, the visualization process is treated as a geometry animation allowing users to navigate in the visualization. The detached building components move in response to the wind force that is calculated using qualitative rather than quantitative techniques. The results are acceptable for instructional systems but not for engineering analysis. Quick Damage Prediction is achieved by using a database query instead of using a Monte-Carlo simulation. The database is based on HAZUS® engineering analysis data which gives it validity. A reasoning mechanism based on the definition of the overall building damage in HAZUS® is used to determine the damage state of selected building components including roof cover, roof sheathing, wall, openings and roof-wall connections. Exposure settings of environmental aspects of the simulated environment, such as ocean, trees, cloud and rain are integrated into a scene-graph based graphics engine. Based on the graphics engine and the physics engine, a procedural modeling method is used to efficiently render residential buildings. The resulting program, Hurricane!, is an instructional program for public education useful in schools and museum exhibits.

Responses to a web-based survey following Hurricane Katrina were evaluated. The 3,140 university student respondents were separated into impact groups based on evacuation experience: high-impact (student evacuated), moderate-impact (friend/family evacuated), and low-impact (neither student nor friends or family evacuated). Students’ responses to items evaluating service utilization, services desired, and psychological distress were examined by gender, race, and impact group. Female students rated services as more supportive, and reported a greater desire for services not provided by the university, compared to male students. Compared to Caucasian students, African American students viewed services as more supportive and desired services not already provided by the university. Students in the high-impact group scored higher than the other impact groups on measures assessing symptoms of psychological distress. Overall, the results may be used by universities and other organizations to implement future programs and policies for responding to natural disasters.

This study looks at the changes to NDVI value in the Weeks Bay Reserve following the impact by Hurricane Katrina. Four Landsat images from March 24, 2005 (Pre-Katrina), September 16, 2005/ April 26, 2006 (Post-Katrina) and August 7, 2002 (Control) were classified into different landcover types and run with the NDVI vegetation index. Those images were compared against each other and showed that the September image had a NDVI value drop of 49% and the April image had a 47% drop as compared to the previous March. The emergent vegetation surrounding the shoreline was most susceptible to changes in NDVI value and recovered the slowest of the tested landcover types. Swift tracks, bay areas, and rivers in the study area where tested and showed that the rivers are the most susceptible change in NDVI value and recovered the slowest.

No description available.