abstract: Severe weather affects many regions of the United States, and has potential to greatly impact many facets of society. This study provides a climatological spatial analysis by county of severe weather warnings issued by the National Weather Service (NWS) between January 1st, 1986 to December 31st, 2017 for the contiguous United States. The severe weather warnings were issued for county-based flash flood, severe thunderstorm, and tornado phenomena issued through the study period and region. Post 2002 severe weather warnings issued by storm warning area were included in this study in the form of county-based warnings simultaneously issued for each affected county. Past studies have researched severe weather warnings issued by the NWS, however these studies are limited in geographic representation, study period, and focused on population bias. A spatial analysis of severe weather warning occurrences by county identify that (a) highest occurrences of flash flood warnings are located in the desert Southwest and Texas, (b) severe thunderstorm warning occurrence is more frequent in Arizona, portions of the Midwest, the South, and the Mid and South Atlantic states, (c) the tornado activity regions of Tornado Alley and Dixie Alley (i.e. Colorado, Kansas, Oklahoma, Arkansas, Texas, Louisiana, Mississippi, Alabama, Tennessee, and Illinois) contained the highest occurrences of tornado warnings, and (d) the highest instances of aggregate warning occurrences are found in the desert Southwest, the Midwest, and the Southern regions of the United States. Generally, severe weather warning “hot spots” tend to be located in those same regions, with greater coverage. This study concludes with a comparison of local maxima and general hot spot regions to expected regions for each phenomenon. Implications of this study are far reaching, including emergency management, and has potential to reduce risk of life. / Dissertation/Thesis / Masters Thesis Geography 2019

Thesis (M.S.)--University of Tennessee, Knoxville, 2009. / Title from title page screen (viewed on Oct. 23, 2009). Thesis advisor: Wayne Clatterbuck. Vita. Includes bibliographical references.

abstract: Recent extreme weather events such the 2020 Nashville, Tennessee tornado and Hurricane Maria highlight the devastating economic losses and loss of life associated with weather-related disasters. Understanding the impacts of extreme weather events is critical to mitigating disaster losses and increasing societal resilience to future events. Geographical approaches are best suited to examine social and ecological factors in extreme weather event impacts because they systematically examine the spatial interactions (e.g., flows, processes, impacts) of the earth’s system and human-environment relationships. The goal of this research is to demonstrate the utility of geographical approaches in assessing social and ecological factors in extreme weather event impacts. The first two papers analyze the social factors in the impact of Hurricane Sandy through the application of social geographical factors. The first paper examines how knowledge disconnect between experts (climatologists, urban planners, civil engineers) and policy-makers contributed to the damaging impacts of Hurricane Sandy. The second paper examines the role of land use suitability as suggested by Ian McHarg in 1969 and unsustainable planning in the impact of Hurricane Sandy. Overlay analyses of storm surge and damage buildings show damage losses would have been significantly reduced had development followed McHarg’s suggested land use suitability. The last two papers examine the utility of Unpiloted Aerial Systems (UASs) technologies and geospatial methods (ecological geographical approaches) in tornado damage surveys. The third paper discusses the benefits, limitations, and procedures of using UASs technologies in tornado damage surveys. The fourth paper examines topographical influences on tornadoes using UAS technologies and geospatial methods (ecological geographical approach). This paper highlights how topography can play a major role in tornado behavior (damage intensity and path deviation) and demonstrates how UASs technologies can be invaluable tools in damage assessments and improving the understanding of severe storm dynamics (e.g., tornadic wind interactions with topography). Overall, the significance of these four papers demonstrates the potential to improve societal resilience to future extreme weather events and mitigate future losses by better understanding the social and ecological components in extreme weather event impacts through geographical approaches. / Dissertation/Thesis / Doctoral Dissertation Geography 2020

The problematics of gigabit optical networks as well as web technologies suitable for a web tool implementation were studied within this thesis. An experimental web application for monitoring GPON frames is developed based on the proposed system architecture. The frontend is implemented using ReactJS and the Tornado web framework is used for backend implementation. Data for analysis are read from the stream using the Kafka platform.

Tornadoes pose a significant danger to human life and structures. Research regarding the effects of tornado-induced loads on residential buildings is in incipient stages and there are no specialized construction standards in place to recommend criteria applicable to structures for withstanding tornadic winds. Three residential house models with different geometries were tested in the Wind-induced Damage Simulator (WDS) built at the University of Ottawa. The WDS is capable of simulating pressures induced by multidirectional and tornadic winds. The peak pressure coefficients were calculated on the walls and roofs of the houses and an analysis was performed on the effects of house model orientation, roof pitch angle, and exposure duration. The peak pressure coefficients were then compared to the NBCC 2015 code to clarify if there were any limitations of the current wind design criteria. It was found that the building orientation did not have a significant effect on pressure coefficient trends and magnitudes on the walls and roofs. For the low roof pitch angle models, it was noticed that the suction on the roof was much greater than the higher roof pitch angle models. An interesting observation was made that found that the leading edge of the walls in the direction of the clockwise tornadic flow were always under greater suction than the trailing edge, which causes a torsional effect on the entire model. When comparing the peak pressure coefficient values to the NBCC 2015 recommended values for the secondary cladding members, it was found that the CpCg stipulated in the code were similar to the experimental tornado Cp’s for the walls. However, the Cp’s on the roof were much greater in the experiments when compared to the NBCC 2015. The CpCg of Zones S and Zone R, which are the edges and central regions of the roof, greatly exceed the minimum values in the NBCC 2015. More experiments for residential house models of different geometries should be conducted in order to propose new tornado-induced pressure coefficients to be used in the design of the structure located in tornado-prone areas.Tornadoes pose a significant danger to human life and structures. Research regarding the effects of tornado-induced loads on residential buildings is in incipient stages and there are no specialized construction standards in place to recommend criteria applicable to structures for withstanding tornadic winds. Three residential house models with different geometries were tested in the Wind-induced Damage Simulator (WDS) built at the University of Ottawa. The WDS is capable of simulating pressures induced by multidirectional and tornadic winds. The peak pressure coefficients were calculated on the walls and roofs of the houses and an analysis was performed on the effects of house model orientation, roof pitch angle, and exposure duration. The peak pressure coefficients were then compared to the NBCC 2015 code to clarify if there were any limitations of the current wind design criteria. It was found that the building orientation did not have a significant effect on pressure coefficient trends and magnitudes on the walls and roofs. For the low roof pitch angle models, it was noticed that the suction on the roof was much greater than the higher roof pitch angle models. An interesting observation was made that found that the leading edge of the walls in the direction of the clockwise tornadic flow were always under greater suction than the trailing edge, which causes a torsional effect on the entire model. When comparing the peak pressure coefficient values to the NBCC 2015 recommended values for the secondary cladding members, it was found that the CpCg stipulated in the code were similar to the experimental tornado Cp’s for the walls. However, the Cp’s on the roof were much greater in the experiments when compared to the NBCC 2015. The CpCg of Zones S and Zone R, which are the edges and central regions of the roof, greatly exceed the minimum values in the NBCC 2015. More experiments for residential house models of different geometries should be conducted in order to propose new tornado-induced pressure coefficients to be used in the design of the structure located in tornado-prone areas.

No description available.

No description available.

Tornadic outbreaks occur annually, causing fatalities and millions of dollars in damage. By improving forecasts, the public can be better equipped to act prior to an event. False alarms (FAs) can hinder the public’s ability (or willingness) to act. As such, a probabilistic FA forecasting scheme would be beneficial to improving public response to outbreaks. Here, a machine learning approach is employed to predict FA likelihood from Storm Prediction Center (SPC) tornado outbreak forecasts. A database of hit and FA outbreak forecasts spanning 2010 – 2020 was developed using historical SPC convective outlooks and the SPC Storm Reports database. Weather Research and Forecasting (WRF) model simulations were done for each outbreak to characterize the underlying meteorological environments. Parameters from these simulations were used to train a support vector machine (SVM) to forecast FAs. Results were encouraging and may result in further applications in severe weather operations.

ropical cyclone-induced tornadoes (TCIT) exacerbate the devastation that landfalling tropical cyclones have on the United States. This research applied machine learning techniques in conjunction with midlatitude severe weather parameters to create an artificial intelligence (AI) capable of predicting TCIT occurrence. Severe weather diagnostic variables were collected at thousands of gridpoints from the North American Regional Reanalysis (NARR) to characterize the environments within tropical cyclones between 1991 and 2011. A support vector machine (SVM) was generated in various configurations to obtain the most effective AI. This approach revealed many parameters that were ineffective at predicting TCITs (primarily those utilizing the effective inflow layer). In addition, the most highly configured AI were capable of predicting TCIT occurrence with a Heidke Skill Score around 0.48.

Digital formats and social networks provide unique opportunities for meteorologists to disseminate weather information to the public, but it comes with a set of challenges. These opportunities and challenges may be enhanced when applied to a younger demographic, which acquires information from different platforms than the traditional sources the older demographics utilize. There is a vast amount of literature that focuses on weather dissemination, weather information sources, and risk perception; however, there is a lack of emphasis on 18 to 24 year olds. The first two parts of this dissertation attempted to fill this lack of knowledge on 18 to 24 year olds by conducting interviews at several college campuses to gain rich knowledge of the daily processes involving weather information and determine their understanding of weather graphics. Participants cited checking the weather forecast pretty frequently but utilized non-traditional sources for the weather forecast. It was also determined that participants lacked an understanding of weather products. The last part of this dissertation attempted to obtain a better understanding of the public’s weather knowledge and self-perceived weather knowledge. This study compared the public to those who actively follow specialty weather pages. In addition, how severity impacts decision-making and confidence in decision-making was evaluated. Followers of specialty weather pages had higher self-perceived and assessed weather knowledge. It was also determined that the public is more likely to adhere to recommendations from meteorologists, and that the correlation between self-perceived weather knowledge and confidence is weak.