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.
Remote sensing has demonstrated to be an instrumental tool in monitoring land changes as a result of anthropogenic change or natural disasters. Most disaster studies have focused on large-scale events with few analyzing small-scale disasters such as tornadoes. These studies have only provided a damage assessment perspective with the continued need to assess reconstruction. This study attempts to fill that void by examining recovery from the 1999 Moore, Oklahoma Tornado utilizing Landsat TM and ETM+ imagery. Recovery was assessed for 2000, 2001 and 2002 using spectral enhancements (vegetative and urban indices and a combination of the two), a recovery index and different statistical thresholds. Classification accuracy assessments were performed to determine the precision of recovery and select the best results. This analysis proved that medium resolution imagery could be used in conjunction with geospatial techniques to capture recovery. The new indices, Shortwave Infrared Index (SWIRI) and Coupled Vegetation and Urban Index (CVUI), developed for disaster management, were the most effective at discerning reconstruction using the 1.5 standard deviation threshold. Recovery rates for F-scale damages revealed that the most incredibly damaged areas associated with an F5 rating were the slowest to recover, while the lesser damaged areas associated with F1-F3 ratings were the quickest to rebuild. These findings were consistent for 2000, 2001 and 2002 also exposing that complete recovery was never attained in any of the F-scale damage zones by 2002. This study illustrates the significance the biophysical impact has on recovery as well as the effectiveness of using medium resolution imagery such as Landsat in future research.