As new technology has emerged in the digital era, the public can now choose from a variety of new media from which to get weather information. Weather applications (apps) and social media have emerged as some of the popular new media. This study sought to understand the extent to which these new media are used, how weather apps are perceived, how the news media used Twitter during Hurricane Irma, and how the public engaged with the news media’s tweets. A survey and dataset of tweets were used to evaluate the research questions and hypotheses of this research. The study found that most survey participants used digital sources for weather information, even in severe weather. The weather app was the most used source of all age brackets, though held a stronger majority amongst younger demographics. Numerous relationships were found between weather app usage and gender, smartphone brand and reliance, time of app usage, and app usage frequency. Participants who downloaded a non-standard weather app onto their phone had higher self-perceived weather knowledge and interest. Weather app users perceived their app to be accurate and sometimes inconsistent, which were both found to be correlated to trust. Perceived app accuracy was also moderately correlated with other aspects of the field of meteorology. Respondents indicated that they accounted for uncertainty in a forecast with time and for regional variability of weather when determining if the forecast verified. However, both conclusions will require further research. The final study of this dissertation found that content, frequency, and engagement with news media tweets during Irma fluctuated over the storm’s duration and a relationship was found between content and engagement. Smaller television markets showed less coverage and overall change in coverage and engagement compared to larger markets. Finally, a meteorologist’s tweeting of personal content prior to the storm was found to be weakly correlated with the number of retweets received during the storm.

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Recent technological innovations and the need for increased safety on the world's roads have led to the introduction of In- Vehicle Information Systems (IVIS). These systems will provide navigation and advisory information to drivers while they are driving. One aspect of these systems, In-vehicle Signing Information Systems (ISIS), would provide the warning, regulatory, and advisory information that is currently found on road signs. These systems may be of particular benefit when external elements such as rain, snow, or night driving reduce or eliminate the opportunity for drivers to detect road signs. This study attempts to determine what benefits, if any, are realized by drivers using this system. Fifty-eight drivers operated an instrumented Oldsmobile Aurora under eight conditions. The eight conditions consisted of a daylight-clear weather-ISIS condition, a daylight-clear weather-No ISIS condition, a daylight-rain-ISIS condition, a daylight-rain-No ISIS condition, a night-clear weather-ISIS condition, a night-clear weather-No ISIS condition, a night-rain-ISIS condition, and a night-rain-No ISIS condition. Younger drivers (18-30 years old) and older drivers (65 years or older) took part in this study. Three measures of driver performance were collected along with subjective preference data. Each measure was evaluated in order to determine what impact, if any, weather, time of day, age, and ISIS use had on performance. Subjective data was evaluated to determine driver preference and acceptance of the ISIS display. The results indicated that use of the ISIS display led to reduced speeds and greater reaction distances for all drivers. Evidence was found that seems to indicate that older drivers may receive a greater benefit in complex, unfamiliar, or low visibility situations. Evidence was also found that indicates that all drivers may receive a greater benefit at night for the complex or unfamiliar situations. Subjectively, the majority of the drivers indicated that the ISIS display made them more aware of road sign information. / Master of Science

Changes in weather patterns worldwide and global warming increased the demand for high-performance buildings resilient to climate change. Building Performance Simulation (BPS) is a robust technique to test, assess, and enhance energy efficiency measures and comply with stringent energy codes of buildings. Climate has a considerable impact on the buildings' thermal environment and energy performance; therefore, choosing reliable and accurate weather data is crucial for building performance evaluation and reducing the performance gap. Typical Weather Years (TWYs) have been traditionally used for energy simulation of buildings. Even if detailed energy assessments can be performed using available multi-year weather data, most simulations are carried out using a typical single year. As a result, this fictitious year must accurately estimate the typical multi-year conditions. TWYs are widely used because they accelerate the modeling process and cut down on computation time while generating relatively accurate long-term predictions of building energy performance. However, there is no certainty that a single year can describe the changing climate and year-by-year variations in weather patterns. Nowadays, with increased computational power and higher speeds in calculation processes, it is possible to adopt multi-year weather datasets to fully assess long-term building energy performance and avoid errors and inaccuracies during the preliminary selection procedures. This study aims to investigate the impact of Typical Weather Years and Actual Weather Years (AWYs) on a single-family house and a university building under two opposite climates, Winnipeg (cold) and Catania (hot). First, a single-family house in Winnipeg, Canada, was selected to evaluate how typical weather years affect the energy performance of the building and compare it with AWYs simulation. Two widely used typical weather data, CWEC and TMY, were selected for the simulation. The results were compared with the outcomes of simulation using AWYs derived from the same weather station from 2015 to 2019, which covered the latest climate changes. The results showed that typical weather years could not sufficiently capture the year-by-year variation in weather patterns. The typical weather years overestimated the cooling load while underestimating the heating demands compared to the last five actual weather years. A more extensive study was conducted for more confidence in the findings and understanding of the weather files. The research was expanded by comparing the results of building performance simulation of the single-family house and an institutional building with more complex envelope characteristics belonging to the University of Manitoba under cold (Winnipeg, Canada) and hot (Catania, Italy) climates. Overall, 48 simulations were performed using ten actual weather years from 2010 to 2019 and two TWYs from each climate for both buildings. The results showed that while the TWYs either overestimate or underestimate the cooling and heating demands of both buildings, cooling load predictions were highly overestimated in the heating-dominant climate of Winnipeg, ranging from 10.5% to 82.4% for both buildings by CWEC and TMY weather data. In the cooling-dominant climate of Catania, energy simulations using IWEC and TMY typical weather data highly overestimated the heating loads between 2.8% and 82.4%.