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.

Monsoon hazards routinely affect the community, economy, and environment of the American Southwest. A common link for hazard development during the North American Monsoon concerns the interplay between temperature, moisture, and wind in the vertical atmosphere controlled by an unstable monsoon circulation. This dissertation investigates vertical atmospheric patterns using in-situ sounding data, specifically, 1) environments favorable for severe hail on the Colorado Plateau, 2) significant parameters distinguishing unhealthy versus healthy ozone days in Phoenix, Arizona, and 3) vertical profile alignments associated with distinct ranges in ozone concentrations observed in Phoenix having defined health impacts.

The first study (published in the Journal of the Arizona-Nevada Academy of Science) determines significant variables on Flagstaff, Arizona 12Z rawinsonde data (1996-2009) found on severe hail days on the Colorado Plateau. Severe hail is related to greater sub-300 hectopascals (hPa) moisture, a warmer atmospheric column, lighter above surface wind speeds, more southerly to southeasterly oriented winds throughout the vertical (except at the 700 hPa pressure level), and higher geopotential heights.

The second study (published in Atmospheric Environment) employs principal component, linear discriminant, and synoptic composite analyses using Phoenix, Arizona rawinsonde data (2006-2016) to identify common monsoon patterns affecting ozone accumulation in the Phoenix metropolitan area. Unhealthy ozone occurs with amplified high-pressure ridging over the Four Corners region, 500 hPa heights often exceeding 5910 meters, surface afternoon temperatures typically over 40°C, lighter wind speeds in the planetary boundary layer under four ms-1, and persistent light easterly flow between 700-500 hPa countering the daytime mountain-valley circulation.

The final study (under revision in Weather and Forecasting) assesses composite atmospheric sounding analysis to forecast Air Quality Index ozone classifications of Good, Moderate, and collectively categories exceeding the U.S. EPA 2015 standard. The analysis, using Phoenix 12Z rawinsonde data (2006-2017), identifies the existence of “pollutant dispersion windows” for ozone accumulation and dispersal in Phoenix.

Ultimately, monsoon hazards result from a complex and evolving vertical atmosphere. This dissertation demonstrates the viability using available in-situ vertical upper-air data to anticipate recurring atmospheric states contributing to specific hazards. These results will improve monsoon hazard prediction in an effort to protect public and infrastructure.

Gnamma pit is an Australian aboriginal term for weathering pit. A mix of weathering and aeolian processes controls the formation of gnamma pits. There is a potential to utilize gnamma as an indicator of paleowind intensity because gnamma growth is promoted by the removal of particles from gnamma pits by wind, a process referred to as deflation. Wind tunnel tests determining the wind velocity threshold of deflation over a range of pit dimensions and particles sizes are conducted. Computational fluid dynamics (CFD) modeling utilizing the Re-Normalisation Group (RNG) K-Epsilon turbulence closure is used to investigate the distribution of wall shear stress and turbulent kinetic energy. An empirical equation is proposed to estimate shear stress as a function of the wind velocity and pit depth dimensions. With this equation and Shields Diagram, the wind velocity threshold for evacuating particles in the pit can be estimated by measuring the pit depth ratio and particle size. It is expected that the pit would continue to grow until this threshold is reached. The wind speed deflation threshold is smaller in the wind tunnel than predicted by the CFD and Shields diagram model. This discrepancy may be explained by the large turbulent kinetic energy in the gnamma pit as predicted by the CFD model as compared to the flat bed experiments used to define the Shields diagram. An empirical regression equation of the wind tunnel data is developed to estimate paleowind maximums.

Many previous investigators highlight the importance of snowfall to the water supply of the western United States (US). Consequently, the variability of snowpack, snowmelt, and snowfall has been studied extensively. Snow level (the elevation that rainfall transitions to snowfall) directly influences the spatial extent of snowfall and has received little attention in the climate literature. In this study, the relationships between snow level and El Niño-Southern Oscillation (ENSO) as well as Pacific Decadal Oscillation (PDO) are established. The contributions of ENSO/PDO to observed multi-decadal trends are analyzed for the last ~80 years. Snowfall elevations are quantified using three methods: (1) empirically, based on precipitation type from weather stations at a range of elevations; (2) theoretically, from wet-bulb zero heights; (3) theoretically, from measures of thickness and temperature. Statistically significant (p < 0.05) results consistent between the three datasets suggest snow levels are highest during El Niño events. This signal is particularly apparent over the coastal regions and the increased snow levels may be a result of frequent maritime flow into the western US during El Niño events. The El Niño signal weakens with distance from the Pacific Ocean and the Southern Rockies display decreased snow level elevations, likely due to maritime air masses within the mid-latitude cyclones following enhanced meridional flow transitioning to continental air masses. The modulation of these results by PDO suggest that this El Niño signal is amplified (dampened) during the cold (warm) phase of the PDO particularly over Southern California. Additionally, over the coastal states, the La Niña signal during the cold PDO is similar to the general El Niño signal. This PDO signal is likely due to more zonal (meridional) flow throughout winter during the cold (warm) PDO from the weakening (strengthening) of the Aleutian low in the North Pacific. Significant trend results indicate widespread increases in snow level across the western US. These trends span changes in PDO phase and trends with ENSO/PDO variability removed are significantly positive. These results suggest that the wide spread increases in snow level are not well explained by these sea surface temperature oscillations.