Multiple linear regression was used to develop equations for 12-, 24-, and 36-hour surface wind forecasts for the wind energy site at Goodnoe Hills. Equations were derived separately for warm and cool seasons. The potential predictors included LFM II model output, MOS surface wind forecasts extrapolated from surrounding stations, pressure observations corrected to mean sea level, and two types of climatological variables. Forecasts of wind speed and direction were formulated for an independent sample of predictands and predictors. The forecasts were evaluated using standard methods of forecast verification and the results are summarized in terms of several verification scores. Comparisons of scores were made by season, projection time, and cycle (or preparation) time, and some patterns were evident in the scores with respect to these stratifications. The minimum value of the mean absolute error attained by the forecast system presented here was 5.64 mph for a 12-hour, cool season forecast equation. The minimum value of the root mean square error was 7.57 mph for a 12-hour, warm season forecast equation. Comparison of these results with the results of other statistical wind forecasting studies indicates that the forecast equations for Goodnoe Hills are of comparable accuracy to the equations developed for other wind energy sites. Suggestions for future investigations of statistical wind forecasting are offered as well as recommendations concerning ways of improving the forecasting system described in this study. / Graduation date: 1983

For three small, mountainous catchments in Germany two medium-range forecast systems are compared that predict precipitation for up to 5 days in advance. One system is composed of the global German weather service (DWD) model, GME, which is dynamically downscaled using the COSMO-EU regional model. The other system is an empirical (expanded) downscaling of the ECMWF model IFS. Forecasts are verified against multi-year daily observations, by applying standard skill scores to events of specified intensity. All event classes are skillfully predicted by the empirical system for up to five days lead time. For the available prediction range of one to two days it is superior to the dynamical system.

Pneumonia is the main killer of under-five children worldwide. The developing nations suffer the most. But within such countries, the spatial and temporal distribution of pneumonia cases is not uniform; yet little is known of the spatial and temporal distribution of pneumonia or the factors that might affect spatial and temporal variability. This dissertation explores the causes of spatial and temporal variation in under-five pneumonia morbidity in Tanzania. This study uses an ecological analysis to explore weather, land cover and politics as potential drivers of the observed differences in the distribution of pneumonia. A study is at an ecological level when it examines the population-level health aspects. That is, ecological analyses in health studies evaluate groups of people rather than individuals. The current study found that weather variables such as temperature and atmospheric pressure partially explained pneumonia variance. The strength of weather-pneumonia association varies over space and time in both seasonal elements (temporal factors) and broadly-defined climate zones (spatial factors). For example, the prevalence rate was higher in the regions with bimodal rainfall compared with the regions with unimodal rainfall, with a statistically difference 117.3 (95% confidence interval: 36.6 to 198.0) cases per 100,000. In addition, within the regions (mikoa) with unimodal rainfall regime, however, the rainy season (msimu) had lower rates of pneumonia compared to the dry season (kiangazi). Land use and land cover also were partial drivers of pneumonia. Some land cover types—particularly urban areas and croplands—were associated with high rates of childhood pneumonia. In addition, districts (wilaya) categorized as urban land cover had high rates of pneumonia compared to those categorized as only rural. To determine the associations between politics and pneumonia, this study compared the pneumonia cases in the administrative locations that received less central government funding with those locations that were financially rewarded for voting for the ruling party. The locations with lower funding generally had higher rates of childhood pneumonia. However, it is unclear whether these locations had higher rates of childhood pneumonia because of, or in addition, to their funding gaps. In sum, this dissertation evaluated population-level factors affecting distribution of childhood pneumonia. Like other similarly population-level studies, this dissertation provides an understanding of the coarse-scale dynamics related to childhood pneumonia. By so doing, it contributes to the pneumonia etiology scientific literature. That is, this dissertation contributes to the understanding of within-nation pneumonia distribution in developing nations. It is the first in Tanzania to evaluate the impact of weather, land cover and politics on childhood pneumonia. By evaluating the impact of weather and land cover, this dissertation also provides an example of non socio-economic factors affecting health inequalities. By analyzing a large landmass of two main climatic types, this dissertation also contributes appreciation of non-stationarity of temporal variations of childhood pneumonia, in addition to the commonly-evaluated spatial variations.

A study was conducted to quantify the relationships between growing season weather conditions and end-use quality of Canada Western Red Spring (CWRS) wheat (Triticum aestivum) grown in producer fields across western Canada (Alberta, Saskatchewan and Manitoba). Grain samples received an official grade at the Canadian Grain Commission and were milled into flour using a Buhler flour. Flour samples underwent an extensive analysis of flour, dough, and breadmaking quality. Daily precipitation amounts and maximum/minimum temperatures were accumulated for each field and then compared to quality data. In this study we also investigated the effectiveness of the Canadian grain grading system’s ability to segregate wheat samples into levels of increasing quality performance and uniformity. We found that for several flour and dough quality analysis, this was often achieved for either quality performance or uniformity, but there was not an improvement in both with an improvement in grade except in bread quality. / October 2006

Generally urban drainage systems are built to protect urban property and control runoff. Moreover, these systems collect the runoff for storage purposes to serve society through sufficient water supply to meet the needs of demand, irrigation, and drainage. Urban environments are exposed to risks of extreme hydrological events. Therefore, urban water systems and their management are critical. Precipitation data are crucial, but may be prone to errors due to the lack of information e.g., short length of records. In this thesis, a Monte Carlo simulation and regional frequency analysis based on L-moments approach were utilized during the research in order to estimate the uncertainty in the Intensity Duration Frequency (IDF) curves by using historical precipitation data from Environment Canada (EC) weather stations and simulating a new series of data through a weather generator (WG) model. The simulations were then disaggregated from daily into hourly data for extraction of the annual maximum precipitation for different durations in hours (1, 2, 6, 10, 12, and 24). Regional frequency analysis was used to form the sites into groups based on homogeneity test results, and the quantile values were computed for various sites and durations with the return periods (T) in years (2, 10, 20, and 100). As a result, the regional frequency analysis was used to estimate the regional quantile values based on L-moment approach. Moreover, the box and whisker plots were utilized to display the results. When the return periods and durations increased, the uncertainty slightly increased. The historical IDF curves of London site falls within the regional simulated IDF curves. Furthermore, 1000 runs have been generated by using the weather generator.

Adverse weather conditions can increase travel risk. Understanding how drivers react to adverse weather, such as rainfall, can aid in the understanding of road safety patterns and traffic operations. This information can in turn be used to improve driver education as well as highway operation through improved signing or the introduction of intelligent highway systems. Hourly rainfall data collected from the Pearson International Airport weather station and City of Toronto traffic data collected at the study site on the Gardiner expressway were used to create event and control pairs. In total, 115 hours with rainfall were matched to control data one week before or after the rainfall event. The traffic sensor at the study site collected speed, volume, and occupancy data at 20-second intervals, which was aggregated to five minutes. In addition, speed deviation and headway data at the 5-minute interval were used for analysis purposes. Two methods were used to test the effects of rainfall on traffic variables and the relationships between them. Matched pair t-tests were used to determine the magnitude of change between event and control conditions for the volume, speed, speed deviation, and headway variables for congested and uncongested traffic conditions. In addition, stepwise multiple linear regression was used to test the effects of rainfall on speed-volume and volume-occupancy relationships. Results of the matched pair t-tests indicated that volumes, speeds, and speed deviations dropped in event conditions, while headways increased slightly. Changes tended to be greater for congested than uncongested conditions. Linear regression results indicated that changes in speed were sensitive to volume conditions, and changes in volume were sensitive to occupancy, although only to a limited extent. Overall, drivers respond to rainfall conditions by reducing both speed and speed deviations, and increasing headway. Reductions in speed are larger in congested conditions, while increases in headway are smaller. Taken in combination, drivers are taking positive steps in order to either maintain or improve safety levels.

Weather data are huge. Traditional visualization techniques are limited to show temperature trends. Pixel-based approaches could be used to visualize the huge amount of weather data and in process show the temperature trends. A prototype using this approach is built to make temperature data more understandable in changing trends. It is implemented using a 2D representation and many popular interaction techniques. It is a lightweight and reusable tool to visualize temperatures.

Adverse weather conditions can increase travel risk. Understanding how drivers react to adverse weather, such as rainfall, can aid in the understanding of road safety patterns and traffic operations. This information can in turn be used to improve driver education as well as highway operation through improved signing or the introduction of intelligent highway systems. Hourly rainfall data collected from the Pearson International Airport weather station and City of Toronto traffic data collected at the study site on the Gardiner expressway were used to create event and control pairs. In total, 115 hours with rainfall were matched to control data one week before or after the rainfall event. The traffic sensor at the study site collected speed, volume, and occupancy data at 20-second intervals, which was aggregated to five minutes. In addition, speed deviation and headway data at the 5-minute interval were used for analysis purposes. Two methods were used to test the effects of rainfall on traffic variables and the relationships between them. Matched pair t-tests were used to determine the magnitude of change between event and control conditions for the volume, speed, speed deviation, and headway variables for congested and uncongested traffic conditions. In addition, stepwise multiple linear regression was used to test the effects of rainfall on speed-volume and volume-occupancy relationships. Results of the matched pair t-tests indicated that volumes, speeds, and speed deviations dropped in event conditions, while headways increased slightly. Changes tended to be greater for congested than uncongested conditions. Linear regression results indicated that changes in speed were sensitive to volume conditions, and changes in volume were sensitive to occupancy, although only to a limited extent. Overall, drivers respond to rainfall conditions by reducing both speed and speed deviations, and increasing headway. Reductions in speed are larger in congested conditions, while increases in headway are smaller. Taken in combination, drivers are taking positive steps in order to either maintain or improve safety levels.

ABSTRACT Seven air quality basins are classified in Taiwan according to landforms¡Bweather conditions and atmosphere diffusion. The data showed that the percentages of fractional number of days that PSI is greater than 100 (unhealthful) are 11.4%, 10.5% and 15.3% in three parts of Southern Taiwan (including Kaohsiung City, Kaohsiung County and Pintung County) in the years of 1999, 2000and 2001. For Kao-Ping area alone, the percentages of unhealthful days are, 12.1% , 10.7%and 8.2% in recent three years. The air pollutants in ambient air in Kao-Ping area are particulate matter (PM10) and ozone (O3). Particulate matter (PM10) is a primary pollutant and derivative aerosol, the mainly were factory stack¡Bmobile vehicles¡Bconstruction site and emission the surface of the earth. O3 is a secondary pollutant that is generated by its precursors such as hydrocarbons (HC) and nitrogen oxides (NOx) through complex photo-chemical reactions. Accordingly, attention has been shifted to the source and control of ozone in recently years in southern part of Taiwan, instead of PM10 alone. In order to improve the air quality, the deteriorated reason and pollution condition must be understood. The purpose of this proposal is thus to identify the O3 trend variation and weather pattern and mixing height. This proposal contains two parts: Collect the meteorological data (including pressure¡Btemperature¡Bwind velocity¡Bwind direction¡Bhumidity and sunshine time) and air quality data. Then we analyze the relationship between meteorological factors and air quality. Analyze the high O3 episodes based on the characteristics of hourly¡Bmonthly and seasonally data and to investigate the effects of mixing height and Ventilation index on air quality in Kao-Ping area.

The southeast United States is prone to severe weather throughout the year. Despite technological advances, some severe weather events occasionally remain unwarned in this part of the country. Past studies examined the relationship between cloud-to-ground (CG) lightning and warm season severe weather episodes. The present study examined the relationship between cool season tornadoes and CG lightning, with a focus over the southeastern United States, where most cool season tornadoes occur. Data from the Storm Prediction Center and National Lightning Detection Network (NLDN) were used to investigate CG lightning properties within 50 km and one hour before tornado touchdown. This was completed over a period of 13 cool seasons from October 1989 through March 2002. Of 3325 tornado events, 2358 contained at least one NLDN-detectable flash. CG lightning attributes of peak current, multiplicity, and flash density compared well with those of prior warm season lightning research. Overall event frequency appeared to be lower than in the warm season. Almost all Central Plains events were accompanied by at least one NLDN-detectable flash. Up to 70% of tornado events near the Gulf of Mexico and Atlantic coasts contained no NLDN-detectable lightning. Although it is not known why this trend was observed, it is speculated that NLDN detection efficiency and/or storm structure differences may play a role in these observations. Warm season studies have correlated tornadoes with predominantly positive (>50% positive CG lightning), or PPCG storms. Gridded maps showed the greatest percentage and highest frequency of cool season PPCG storms across Kansas and Nebraska, with up to 70% of events associated with PPCG lightning. A secondary, albeit lower, frequency maximum extended 1° to 2° inland across Louisiana into North Carolina. This study also subjectively defined a storm with “enhanced” positive cloud-to-ground (EPCG) lightning as one containing >25% positive cloud-to-ground lightning, which corresponds to approximately the 75th percentile of all cool season tornadoes. This has lead to speculation that EPCG criterion may be a better indicator of the possibility of severe weather than the traditional PPCG criterion.