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Hurricane Florence (2018) was unique due to its slow storm motion during landfall, causing convective rainbands to produce high amounts of precipitation along the coast of North Carolina. This study focuses on the relationship between precipitation asymmetries and atmospheric stability surrounding the tropical cyclone (TC) during the landfall period of a nearly-stationary TC. Previous research with idealized hurricane simulations suggests that atmospheric stability may vary surrounding a TC during landfall, with the atmosphere destabilizing offshore and stabilizing onshore. However, this finding has not been studied using a realistic approach. Due to Hurricane Florence's slow motion, the storm was situated at the land-ocean boundary for multiple days, providing an ideal opportunity to examine the role of atmospheric stability in modifying hurricane precipitation during landfall. This study uses the Advanced Research Weather Research and Forecasting (WRF-ARW) version 3.6.1 to produce high-resolution simulations to examine the variations in precipitation and atmospheric stability surrounding Hurricane Florence. Precipitation accumulation at different temporal scales was used to determine that asymmetries existed during the landfall period. Observed and model-simulated Convective Available Potential Energy (CAPE) were used to measure stability surrounding the TC. Simulated CAPE indicates that there was a significant difference between stability right- and left-of-track. In addition to a control simulation, two experimental simulations were conducted by modifying the land surface to vary the heat and moisture exchange coefficient (HS) and hold the surface roughness (Z0) constant. By isolating the HS to be more moist or dry, the altered low-level moisture was hypothesized to cause the precipitation and convection distributions to become more symmetrical or asymmetrical, respectively. The results from the experimental simulations showed that the altered land surface affects the relative humidity from the surface to 950 mb, which has an immediate impact on stability off-shore left-of-track. Overall, the precipitation and stability asymmetries were not significantly impacted by the altered near-surface moisture, indicating other physical factors contribute to the asymmetries. The results of this study provide insight into the role of atmospheric instability in generating asymmetrical precipitation distributions in landfalling TCs, which may be particularly important in slow-moving TCs like Hurricane Florence. / Master of Science / Landfalling tropical weather systems such as hurricanes can significantly impact coastal communities due to severe flooding and damaging winds. Hurricane Florence (2018) affected coastal and inland communities in North Carolina and South Carolina when the storm produced a significant amount of precipitation over the coastal region. During landfall, the center of Hurricane Florence moved slowly parallel to the coastline, which creates a suitable time frame to isolate and study the influence of landfall on precipitation asymmetries. Precipitation asymmetry occurs when more rainfall falls on one side of the hurricane; for example, heavier precipitation tends to occur on the right side of a hurricane during the landfall period. Hurricane rainbands that are responsible for producing heavy precipitation form in areas where there is higher moisture near the surface while lighter precipitation forms in areas where there is drier air near the surface. This study focuses on the relationship between land surface moisture and spatial variations of precipitation during the hurricane landfall period by studying observations and model simulations of Hurricane Florence. The model simulation of Hurricane Florence found that more precipitation fell on the right side of the storm, indicating that there was precipitation asymmetry. In order to understand how the precipitation asymmetries form, the model simulation of Hurricane Florence was modified to create two experiments. In the first experiment, the land surface was altered to have a moister land surface, which should cause the hurricane precipitation to be more symmetrical. In the second experiment, the land surface was altered to have a drier land surface, which should cause stronger precipitation asymmetry. However, the results did not match this expectation. Instead, both experiments simulated asymmetrical precipitation with more precipitation falling on the right side of each storm during the landfall period. These results suggest that the modified land surface moisture did not have a significant impact on the formation of precipitation asymmetries. Other factors are therefore suggested to have a more dominant influence on the development of precipitation. Overall, this work can support future studies by ruling out the impact of land surface moisture on a hurricane's precipitation formation during the landfall period.

An estimated 10% of the world's population lives in low-lying coastal regions, which are vulnerable to storm surge and waves capable of causing loss of lives and billions of dollars in damage to coastal infrastructure. Among the most vulnerable coastlines are barrier islands, which often act as the first line of defense against storms for the mainland coast. In this dissertation, the physical damage to a developed barrier island (Bay Head, NJ, USA) caused by erosion during Hurricane Sandy (2012) is evaluated using the numerical model, XBeach. Three main objectives of this work are to evaluate the wave-force reducing capabilities of a buried seawall, the effects of bay surge on morphological change and the effectiveness of adaptation strategies to rising sea levels. According to simulation results, a buried seawall located beneath the nourished dunes in Bay Head reduced wave attack by a factor of 1.7 compared to locations without a seawall. The structure also prevented major erosion by blocking bay surge from inundating dunes from the backside, as observed in locations not fronted with a seawall. Altering the timing and magnitude of bay storm surge, the buried seawall continued to protect the island from catastrophic erosion under all conditions except for a substantial increase in bay surge. However, in the absence of a seawall, the morpho- logical response was highly dependent on bay surge levels with respect to ocean side surge. Compared to the damage sustained by the island during Hurricane Sandy, greater erosion was observed on the island for an increase in bay surge magnitude or when peak bay surge occurred after peak ocean surge. Considering sea level rise, which affects bay and ocean surge levels, adaptation strategies were evaluated on the protection afforded to the dune system and backbarrier. Of the sea level rise scenarios and adaptation strategies considered, raising the dune and beach protected the island under moderate rises in sea level, but exacerbated backbarrier erosion for the most extreme scenario. Although an extreme strategy, raising the island is the only option considered that protected the island from catastrophic erosion under low, moderate and extreme sea level rise. / Ph. D.

Pre-disaster damage predictions and post-disaster damage assessments are challenging because they result from complicated interactions between multiple drivers, including exposure to various hazards as well as differing levels of community resiliency. Certain societal characteristics, in particular, can greatly magnify the impact of a natural hazard, however they are frequently ignored in disaster management because they are difficult to incorporate into quantitative analyses. In order to more accurately identify areas of greatest need in the wake of a disaster, both the hazards and the vulnerabilities need to be carefully assessed since they have been shown to be positively correlated with damage patterns. This study evaluated the contribution of eight drivers of structural damage from Hurricane Mar'ia in Puerto Rico, leveraging machine learning algorithms to determine the role that societal factors played. Random Forest and Stochastic Gradient Boosting Trees algorithms analyzed a diverse set of data including wind, flooding, landslide, and vulnerability measures. These data trained models to predict the structural damage caused by Hurricane Mar'ia in Puerto Rico and the importance of each predictive feature was calculated. Results indicate that vulnerability measures are the leading predictors of damage in this case study, followed by wind, flood, and landslide measures. Each predictive variable exhibits unique, often nonlinear, relationships with damage. These results demonstrate that societal-driven vulnerabilities play critical roles in damage pattern analysis and that targeted, pre-disaster mitigation efforts should be enacted to reinforce household resiliency in socioeconomically vulnerable areas. Recovery programs may need to be reworked to focus on the highly impacted vulnerable populations to avoid the persistence, or potential enhancement, of preexisting social inequalities in the wake of a disaster. / Master of Science / Disasters are not entirely natural phenomena. Rather, they occur when natural hazards interact with the man-made environment and negatively impact society. Most risk and impact assessment studies focus on natural hazards (processes beyond human control) and do not incorporate the role of societal circumstances (within human agency). However, it has been shown that certain socioeconomic, demographic, and structural characteristics increase the severity of disaster impacts. These characteristics define the the susceptibility of a community to negative disaster impacts, known as vulnerability. This study quantifies the role of vulnerability via a case study of Hurricane Mar'ia. A variety of statistical modeling, known as machine learning, analyzed flood, wind, and landslide hazards along with the aforementioned vulnerabilities. These variables were correlated with a damage assessment database and the model calculated the strength of each variable's relationship with damage. Results indicate that vulnerability measures exhibit the strongest predictive correlations with the damage caused by Hurricane Mar'ia, followed by wind, flood, and landslide measures, respectively, suggesting that efforts to improve societal equality and improvements to infrastructure in vulnerable areas can mitigate the impacts of future hazardous events. In addition, societal information is critical to include in future risk and impact assessment efforts in order to prioritize areas of greatest need and allocate resources to those who would benefit from them most.

Hurricane Hugo struck the coast of South Carolina on September 21, 1989 as a category 4 hurricane on the Saffir-Simpson Scale. Landsat Thematic mapper was utilized to determine the extent of damage experienced at the Santee Experimental Forest (SEF) (a part of Francis Marion National Forest) in South Carolina. Normalized Difference Vegetation Index (NDVI) and the change detection techniques were used to determine initial forest damage and to monitor the recovery over a 22-year period following Hurricane Hugo. According to the results from the NDVI analysis the SEF made a full recovery after a 10-year period. The remote sensing techniques used were effective in identifying the damage as well as the recovery.

Thesis (Ph. D.)--Washington State University, December 2009. / Title from PDF title page (viewed on Dec. 11, 2009). "Department of American Studies." Includes bibliographical references (p. 142-157).

Over the 1951-2009 time period, 47% of all tropical systems in the Atlantic Basin transitioned to post-tropical storms. These storms are capable of producing hurricaneforce winds, torrential, flooding rains and storm surge that floods coastal areas. This study adds to previous climatological work by completing a case-study of Hurricane Ike (2008) and examining how teleconnections such as the El Niño Southern Oscillation (ENSO), the Madden-Julian Oscillation (MJO), the Atlantic Multidecadal Oscillation (AMO) and the Pacific Decadal Oscillation (PDO) contribute to the strength of a transitioning post-tropical storm. T-tests performed show strong statistical relationships between an increase (decrease) in post-tropical storm frequency and warm PDO – La Niña (cold PDO – La Niña), cold PDO – ENSO neutral (warm PDO – ENSO neutral), and warm (cold) AMO conditions. Moreover, nearly significant results were found for the same increase (decrease) and La Niña seasons since (pre) 1980 and for cold (warm) PDO conditions. Modeling the MJO suggests that increased (decreased) relative humidity associated with the wet (dry) phase could increase (decrease) precipitation output from the storm and decrease (increase) forward speed of the storm, decreasing (increasing) wind speeds observed at the surface.

The FPHLM is the only open public hurricane loss evaluation model available for assessment of hazard to insured residential property from hurricanes in Florida. The model consists of three independent components: the atmospheric science component, the vulnerability component and the actuarial component. The atmospheric component simulates thousands of storms, their wind speeds and their decay once on land on the basis of historical hurricane statistics defining wind risk for all residential zip codes in Florida. The focus of the thesis was to analyze atmospheric science component of the Florida Public Hurricane Loss Model, replicate statistical procedures used to model various parameters of atmospheric science component and to validate the model. I establish the distribution for modeling annual hurricane occurrence, choose the best fitting distribution for the radius of maximum winds and compute the expression for the pressure profile parameter Holland B.

Hurricanes have been considered one of the most costly disasters in United State, which lead to both economic loss and human fatalities. Therefore, understanding the characteristics of those who evacuated and of those who did not evacuate have been principal focus of some previous researches related to hurricane evacuation behavior. This research presents two sets of decision-making models for analyzing hurricane evacuation behavior, using two statistical methods: standard logistic model and mixed logistic model.The receipt of evacuation order, elevation, expenditure, the presence of children and elderly people, ownership of a house, and receipt of hurricane warning are found to be extremely important in evacuation decision making. When the mixed logistic model is applied, the rate of concern about hurricane threat is assumed to be random according to normal distribution. Mixed logistic models which account for the heterogeneity of household responses are found to perform better than standard logistic model.