Matching Items (18)
Description
The characteristics of the wintertime 500hPa height surface, the level of non-divergence and used for identifying/observing synoptic-scale features (ridges and troughs), and their impact on precipitation are of significance to forecasters, natural resource managers and planners across the southwestern United States. For this study, I evaluated the location of the 500hPa mean Pacific ridge axis over the winter for the period of 1948/49 to 2011/12 and derived the mean ridge axis in terms of location (longitude) and intensity (geopotential meters) from the NCEP/NCAR Reanalysis dataset. After deriving a mean ridge axis climatology and analyzing its behavior over time, I correlated mean location and intensity values to observed wintertime precipitation in select U.S. Climate Divisions in Arizona, Colorado, Nevada, Utah and New Mexico. This resulted in two findings. First specific to the 500hPa ridge behavior, the ridge has been moving eastward and also has been intensifying through time. Second, results involving correlation tests between mean ridge location and intensity indicate precipitation across the selected Southwest Climate Divisions are strongly related to mean ridge intensity slightly more than ridge location. The relationships between mean ridge axis and observed precipitation also are negative, indicating an increase of one of the ridge parameters (i.e. continued eastward movement or intensification) lead to drier winter seasons across the Southwest. Increased understanding of relationships between upper-level ridging and observed wintertime precipitation aids in natural resource planning for an already arid region that relies heavily on winter precipitation.
ContributorsNolte, Jessica Marie (Author) / Cerveny, Randall S. (Thesis advisor) / Selover, Nancy J. (Committee member) / Brazel, Anthony J. (Committee member) / Arizona State University (Publisher)
Created2013
Description
Isentropic analysis is a type of analysis that is based on using the concept of potential temperatures, the adiabatically established temperature at 1000 hPa. In the 1930s and 1940s this type of analysis proved to be valuable in indicating areas of increased moisture content and locations experiencing flow up or down adiabatic surfaces. However, in the early 1950s, this type of analysis faded out of use and not until the twenty-first century have some researchers started once again to examine the usefulness of isentropic analysis. One aspect in which isentropic analysis could be practical, based on prior research, is in severe weather situations, due to its ability to easily show adiabatic motion and moisture. As a result, I analyzed monthly climatological isentropic surfaces to identify distinct patterns associated with tornado occurrences for specific regions and months across the contiguous United States. I collected tornado reports from 1974 through 2009 to create tornado regions for each month across the contiguous United States and corresponding upper air data for the same time period. I then separated these upper air data into tornado and non-tornado days for specific regions and conducted synoptic and statistical analyses to establish differences between the two. Finally, I compared those results with analyses of individual case studies for each defined region using independent data from 2009 through 2010. On tornado days distinct patterns can be identified on the isentropic surface: (1) the average isentropic surface lowered on tornado days indicating a trough across the region, (2) a corresponding increase in moisture content occurred across the tornado region, and (3) wind shifted in such a manner to produce flow up the isentropic trough indicating uplift. When comparing the climatological results with the case studies, the isentropic pattern for the case studies in general was more pronounced compared to the climatological pattern; however, this would be expected as when creating the average the pattern/conditions will be smoothed. These findings begin to bridge the large gap in literature, show the usefulness of isentropic analysis in monthly and daily use and serve as catalysts to create a finer resolution database in isentropic coordinates.
ContributorsPace, Matthew Brandon (Author) / Cerveny, Randall S. (Thesis advisor) / Selover, Nancy J (Committee member) / Brazel, Anthony J. (Committee member) / Arizona State University (Publisher)
Created2012
Description
Fossil fuel CO2 (FFCO2) emissions are recognized as the dominant greenhouse gas driving climate change (Enting et. al., 1995; Conway et al., 1994; Francey et al., 1995; Bousquet et. al., 1999). Transportation is a major component of FFCO2 emissions, especially in urban areas. An improved understanding of on-road FFCO2 emission at high spatial resolution is essential to both carbon science and mitigation policy. Though considerable research has been accomplished within a few high-income portions of the planet such as the United States and Western Europe, little work has attempted to comprehensively quantify high-resolution on-road FFCO2 emissions globally. Key questions for such a global quantification are: (1) What are the driving factors for on-road FFCO2 emissions? (2) How robust are the relationships? and (3) How do on-road FFCO2 emissions vary with urban form at fine spatial scales?
This study used urban form/socio-economic data combined with self-reported on-road FFCO2 emissions for a sample of global cities to estimate relationships within a multivariate regression framework based on an adjusted STIRPAT model. The on-road high-resolution (whole-city) regression FFCO2 model robustness was evaluated by introducing artificial error, conducting cross-validation, and assessing relationship sensitivity under various model specifications. Results indicated that fuel economy, vehicle ownership, road density and population density were statistically significant factors that correlate with on-road FFCO2 emissions. Of these four variables, fuel economy and vehicle ownership had the most robust relationships.
A second regression model was constructed to examine the relationship between global on-road FFCO2 emissions and urban form factors (described by population
ii
density, road density, and distance to activity centers) at sub-city spatial scales (1 km2). Results showed that: 1) Road density is the most significant (p<2.66e-037) predictor of on-road FFCO2 emissions at the 1 km2 spatial scale; 2) The correlation between population density and on-road FFCO2 emissions for interstates/freeways varies little by city type. For arterials, on-road FFCO2 emissions show a stronger relationship to population density in clustered cities (slope = 0.24) than dispersed cities (slope = 0.13). FFCO2 3) The distance to activity centers has a significant positive relationship with on-road FFCO2 emission for the interstate and freeway toad types, but an insignificant relationship with the arterial road type.
This study used urban form/socio-economic data combined with self-reported on-road FFCO2 emissions for a sample of global cities to estimate relationships within a multivariate regression framework based on an adjusted STIRPAT model. The on-road high-resolution (whole-city) regression FFCO2 model robustness was evaluated by introducing artificial error, conducting cross-validation, and assessing relationship sensitivity under various model specifications. Results indicated that fuel economy, vehicle ownership, road density and population density were statistically significant factors that correlate with on-road FFCO2 emissions. Of these four variables, fuel economy and vehicle ownership had the most robust relationships.
A second regression model was constructed to examine the relationship between global on-road FFCO2 emissions and urban form factors (described by population
ii
density, road density, and distance to activity centers) at sub-city spatial scales (1 km2). Results showed that: 1) Road density is the most significant (p<2.66e-037) predictor of on-road FFCO2 emissions at the 1 km2 spatial scale; 2) The correlation between population density and on-road FFCO2 emissions for interstates/freeways varies little by city type. For arterials, on-road FFCO2 emissions show a stronger relationship to population density in clustered cities (slope = 0.24) than dispersed cities (slope = 0.13). FFCO2 3) The distance to activity centers has a significant positive relationship with on-road FFCO2 emission for the interstate and freeway toad types, but an insignificant relationship with the arterial road type.
ContributorsSong, Yang (Author) / Gurney, Kevin (Thesis advisor) / Kuby, Michael (Committee member) / Golub, Aaron (Committee member) / Chester, Mikhail (Committee member) / Selover, Nancy (Committee member) / Arizona State University (Publisher)
Created2018
Description
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.
ContributorsLawhorn, Brandon (Author) / Cerveny, Randall S. (Thesis advisor) / Balling, Robert C. (Committee member) / Vose, Russel S (Committee member) / Krahenbuhl, Daniel (Committee member) / Arizona State University (Publisher)
Created2019
Description
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.
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.
ContributorsMalloy, Jonny William (Author) / Cerveny, Randall S. (Thesis advisor) / Selover, Nancy J (Committee member) / Brazel, Anthony J. (Committee member) / Balling, Robert C. (Committee member) / Arizona State University (Publisher)
Created2019
Description
Water resource systems have provided vital support to transformative growth in the Southwest United States; and for more than a century the Salt River Project (SRP) has served as a model of success among multipurpose federal reclamation projects, currently delivering approximately 40% of water demand in the metropolitan Phoenix area. Drought concerns have sensitized water management to risks posed by natural variability and forthcoming climate change.
Full simulations originating in climate modeling have been the conventional approach to impacts assessment. But, once debatable climate projections are applied to hydrologic models challenged to accurately represent the region’s arid hydrology, the range of possible scenarios enlarges as uncertainties propagate through sequential levels of modeling complexity. Numerous issues render future projections frustratingly uncertain, leading many researchers to conclude it will be some decades before hydroclimatic modeling can provide specific and useful information to water management.
Alternatively, this research investigation inverts the standard approach to vulnerability assessment and begins with characterization of the threatened system, proceeding backwards to the uncertain climate future. Thorough statistical analysis of historical watershed climate and runoff enabled development of (a) a stochastic simulation methodology for net basin supply (NBS) that renders the entire range of droughts, and (b) hydrologic sensitivities to temperature and precipitation changes. An operations simulation model was developed for assessing the SRP reservoir system’s cumulative response to inflow variability and change. After analysis of the current system’s drought response, a set of climate change forecasts for the balance of this century were developed and translated through hydrologic sensitivities to drive alternative NBS time series assessed by reservoir operations modeling.
Statistically significant changes in key metrics were found for climate change forecasts, but the risk of reservoir depletion was found to remain zero. System outcomes fall within ranges to which water management is capable of responding. Actions taken to address natural variability are likely to be the same considered for climate change adaptation. This research approach provides specific risk assessments per unambiguous methods grounded in observational evidence in contrast to the uncertain projections thus far prepared for the region.
Full simulations originating in climate modeling have been the conventional approach to impacts assessment. But, once debatable climate projections are applied to hydrologic models challenged to accurately represent the region’s arid hydrology, the range of possible scenarios enlarges as uncertainties propagate through sequential levels of modeling complexity. Numerous issues render future projections frustratingly uncertain, leading many researchers to conclude it will be some decades before hydroclimatic modeling can provide specific and useful information to water management.
Alternatively, this research investigation inverts the standard approach to vulnerability assessment and begins with characterization of the threatened system, proceeding backwards to the uncertain climate future. Thorough statistical analysis of historical watershed climate and runoff enabled development of (a) a stochastic simulation methodology for net basin supply (NBS) that renders the entire range of droughts, and (b) hydrologic sensitivities to temperature and precipitation changes. An operations simulation model was developed for assessing the SRP reservoir system’s cumulative response to inflow variability and change. After analysis of the current system’s drought response, a set of climate change forecasts for the balance of this century were developed and translated through hydrologic sensitivities to drive alternative NBS time series assessed by reservoir operations modeling.
Statistically significant changes in key metrics were found for climate change forecasts, but the risk of reservoir depletion was found to remain zero. System outcomes fall within ranges to which water management is capable of responding. Actions taken to address natural variability are likely to be the same considered for climate change adaptation. This research approach provides specific risk assessments per unambiguous methods grounded in observational evidence in contrast to the uncertain projections thus far prepared for the region.
ContributorsMurphy, Kevin W (Author) / Cerveny, Randall S. (Thesis advisor) / Balling, Jr., Robert C. (Committee member) / Ellis, Andrew W. (Committee member) / Skindlov, Jon A. (Committee member) / Arizona State University (Publisher)
Created2016
Description
ABSTRACT
Famine is the result of a complex set of environmental and social factors. Climate conditions are established as environmental factors contributing to famine occurrence, often through teleconnective patterns. This dissertation is designed to investigate the combined influence on world famine patterns of teleconnections, specifically the North Atlantic Oscillation (NAO), Southern Oscillation (SO), Pacific Decadal Oscillation (PDO), Atlantic Multidecadal Oscillation (AMO), or regional climate variations such as the South Asian Summer Monsoon (SASM). The investigation is three regional case studies of famine patterns specifically, Egypt, the British Isles, and India.
The first study (published in Holocene) employs the results of a Principal Component Analysis (PCA) yielding a SO-NAO eigenvector to predict major Egyptian famines between AD 1049-1921. The SO-NAO eigenvector (1) successfully discriminates between the 5-10 years preceding a famine and the other years, (2) predicts eight of ten major famines, and (3) correctly identifies fifty out of eighty events (63%) of food availability decline leading up to major famines.
The second study investigates the impact of the NAO, PDO, SO, and AMO on 63 British Isle famines between AD 1049 and 1914 attributed to climate causes in historical texts. Stepwise Regression Analysis demonstrates that the 5-year lagged NAO is the primary teleconnective influence on famine patterns; it successfully discriminates 73.8% of weather-related famines in the British Isles from 1049 to 1914.
The final study identifies the aggregated influence of the NAO, SO, PDO, and SASM on 70 Indian famines from AD 1049 to 1955. PCA results in a NAO-SOI vector and SASM vector that predicts famine conditions with a positive NAO and negative SO, distinct from the secondary SASM influence. The NAO-famine relationship is consistently the strongest; 181 of 220 (82%) of all famines occurred during positive NAO years.
Ultimately, the causes of famine are complex and involve many factors including societal and climatic. This dissertation demonstrates that climate teleconnections impact famine patterns and often the aggregates of multiple climate variables hold the most significant climatic impact. These results will increase the understanding of famine patterns and will help to better allocate resources to alleviate future famines.
Famine is the result of a complex set of environmental and social factors. Climate conditions are established as environmental factors contributing to famine occurrence, often through teleconnective patterns. This dissertation is designed to investigate the combined influence on world famine patterns of teleconnections, specifically the North Atlantic Oscillation (NAO), Southern Oscillation (SO), Pacific Decadal Oscillation (PDO), Atlantic Multidecadal Oscillation (AMO), or regional climate variations such as the South Asian Summer Monsoon (SASM). The investigation is three regional case studies of famine patterns specifically, Egypt, the British Isles, and India.
The first study (published in Holocene) employs the results of a Principal Component Analysis (PCA) yielding a SO-NAO eigenvector to predict major Egyptian famines between AD 1049-1921. The SO-NAO eigenvector (1) successfully discriminates between the 5-10 years preceding a famine and the other years, (2) predicts eight of ten major famines, and (3) correctly identifies fifty out of eighty events (63%) of food availability decline leading up to major famines.
The second study investigates the impact of the NAO, PDO, SO, and AMO on 63 British Isle famines between AD 1049 and 1914 attributed to climate causes in historical texts. Stepwise Regression Analysis demonstrates that the 5-year lagged NAO is the primary teleconnective influence on famine patterns; it successfully discriminates 73.8% of weather-related famines in the British Isles from 1049 to 1914.
The final study identifies the aggregated influence of the NAO, SO, PDO, and SASM on 70 Indian famines from AD 1049 to 1955. PCA results in a NAO-SOI vector and SASM vector that predicts famine conditions with a positive NAO and negative SO, distinct from the secondary SASM influence. The NAO-famine relationship is consistently the strongest; 181 of 220 (82%) of all famines occurred during positive NAO years.
Ultimately, the causes of famine are complex and involve many factors including societal and climatic. This dissertation demonstrates that climate teleconnections impact famine patterns and often the aggregates of multiple climate variables hold the most significant climatic impact. These results will increase the understanding of famine patterns and will help to better allocate resources to alleviate future famines.
ContributorsSantoro, Michael Melton (Author) / Cerveny, Randall S. (Thesis advisor) / McHugh, Kevin (Committee member) / Brazel, Anthony (Committee member) / Balling Jr., Robert C. (Committee member) / Arizona State University (Publisher)
Created2017
Description
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.
ContributorsWagner, Melissa A (Author) / Cerveny, Randall S. (Thesis advisor) / Myint, Soe W. (Thesis advisor) / Wentz, Elizabeth (Committee member) / Brazel, Anthony J. (Committee member) / Arizona State University (Publisher)
Created2011
Description
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.
ContributorsSvoma, Bohumil V (Author) / Cerveny, Randall S. (Thesis advisor) / Balling, Robert C. (Committee member) / Ellis, Andrew W. (Committee member) / Arizona State University (Publisher)
Created2011
Description
While there are many elements to consider when determining one's risk of heat or cold stress, acclimation could prove to be an important factor to consider. Individuals who are participating in more strenuous activities, while being at a lower risk, will still feel the impacts of acclimation to an extreme climate. To evaluate acclimation in strenuous conditions, I collected finishing times from six different marathon races: the New York City Marathon (New York City, New York), Equinox Marathon (Fairbanks, Alaska), California International Marathon (Sacramento, California), LIVESTRONG Austin Marathon (Austin, Texas), Cincinnati Flying Pig Marathon (Cincinnati, Ohio), and the Ocala Marathon (Ocala, Florida). Additionally, I collected meteorological variables for each race day and the five days leading up to the race (baseline). I tested these values against the finishing times for the local runners, those from the race state, and visitors, those from other locations. Effects of local acclimation could be evaluated by comparing finishing times of local runners to the change between the race day and baseline weather conditions. Locals experienced a significant impact on finishing times for large changes between race day and the baseline conditions for humidity variables, dew point temperature, vapor pressure, relative humidity, and temperature based variables such as the heat index, temperature and the saturation vapor pressure. Wind speed and pressure values also marked a change in performance, however; pressure was determined to be a larger psychological factor than acclimation factor. The locals also demonstrated an acclimation effect as performance improved when conditions were similar on race day to baseline conditions for the three larger races. Humidity variables had the largest impact on runners when those values increased from training and acclimation values; however increased wind speed appeared to offset increased humidity values. These findings support previous acclimation research stating warm wet conditions are more difficult to acclimate to than warm dry conditions. This research while primarily pertaining to those participating physically demanding activities may also be applied to other large scale events such as festivals, fairs, or concerts.
ContributorsDeBiasse, Kimberly Michelle (Author) / Cerveny, Randall S. (Thesis advisor) / Brazel, Anthony (Committee member) / Selover, Nancy (Committee member) / Arizona State University (Publisher)
Created2011