<p> In this report we explore how the effect of meteorological factors, allergens, and air pollution on respiratory conditions in children using longitudinal data. Our analysis makes use of a dataset from the DAVIS study in southern Ontario. The response variables are children's lower respiratory tract (URT) and upper respiratory tract (URT) scores. The explanatory variables are readings of various meteorological, allergen, and air pollution factors. First we make use of generalized estimating equations to find the main factors that are associated with certain respiratory conditions in children as measured by LRT and URT scores. Then we determine whether there are any interactions between the significant factors associated with LRT /URT scores. Comparisons between case and control groups are made to determine whether children with asthma are more sensitive to any of the changes in meteorological, allergen, and air pollution factors. The analysis results show that the significant factor that is associated with LRT scores for children with asthma is the two-day lag daily average changes in air pressure. On average an increase in air pressure will result in an increase in children's LRT scores. The interaction terms that remained in the final model show some degree of significance but without strong evidence to support it. Children in the case groups are more sensitive to meteorological factors, allergens, and air pollution than the children in control groups. </p> / Thesis / Master of Science (MSc)

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Spatially distributed estimates of meteorological data are becoming increasingly important as inputs to spatially explicit landscape, regional, and global models. Accurate estimates of meteorological values such as temperature, precipitation, and evapotranspiration are required for a number of landscape scale models, including those of regeneration, growth, and mortality in forest ecosystems. Given a set of meteorological data, researchers are confronted with a variety of stochastic and deterministic interpolation methods to estimate meteorological variables at unsampled locations. Depending on the spatial attributes of the data, accuracies may vary widely among different spatial interpolation methods. The choice of spatial interpolator is especially important in mountainous regions where data are sparse and variables may change over short spatial scales. While there have been comparisons of interpolation methods, few research efforts have been directed towards comparing the effectiveness of different spatial interpolators in predicting temperature. Due to the additional effort kriging and cokriging entails, it was decided to compare the effectiveness of kriging and cokriging in estimating mean, maximum, and minimum temperature at unsampled locations with less computationally intensive interpolation techniques such as inverse distance weighted averaging, cubic splining, the fitting of a trend surface, polynomial regression, and the lapse rate method. Eight interpolation techniques (inverse distance squared, optimal inverse distance, cubic splining, trend surface analysis, regression, kriging, cokriging, and the lapse rate method) were compared in their ability to predict temperature at unsampled locations. Temperature data for two regions, two scales (minimum and maximum temperatures) and three temporal scales (10 year mean, seasonal, and daily) were prepared and the eight methods were compared on the basis of bias, MAE, and MSE. In addition, summary statistics of interpolated mean, minimum, and maximum temperatures were recorded to determine how well the interpolated data represented the original temperature values. This dissertation provides evidence that certain apriori data characteristics such as temperature range, temperature variance, and temperature correlation with elevation may influence interpolator choice. The dissertation results also indicate that spatial scale and the relative spatial density and distribution of sampling stations may influence interpolator choice. These results should be of interest to scientists studying global warming. The MAEs associated with interpolation techniques which did not use ancillary information were far greater than the 0.5°F to 1.0°F estimate of global warming over the past 100 years. The use of regression techniques which utilize the relationship between temperature and elevation as ancillary information offers significant improvement over the current inverse distance weighting methods. The dissertation also shows that when station elevations are not representative of regional elevations, bias occurs. In Region 2, stations were underrepresented for higher elevations. Interpolation techniques which did not use elevation as ancillary information were biased 1.0°F to 3.0°F above techniques which used elevation. While it is unclear what the extent of this effect is on a global scale, one would suspect the use of distance weighting techniques would bias global estimates upwards. These dissertation results should also be of interest to scientists who use kriging and cokriging to interpolate irregularly spaced data onto a rectangular grid. The results indicate that when data are isotropic, less subjective methods, such as optimal inverse distance, have lower MAE values. The semivariogram fitting methodology outlined in this dissertation demonstrates how to fit semivariograms iteratively using an indicative goodness of fit (IGF) metric. Semivariogram fitting using an IGF is less subjective and more accurate than traditional fit-by-sight methods. Despite its mathematical elegance, kriging and cokriging did not perform better than many other less computationally intensive methods. In addition, when there is a more intensely sample covariate which is highly correlated, polynomial regression gave far better results than kriging or cokriging. The results of this dissertation should also be of interest to users of geographic information systems (GISs). Because climatic data such as temperature is sampled from an irregular network, a number of interpolation techniques can be used to convert the data to a regular grid for use in visualization, models and GISs. This dissertation shows that the choice of spatial interpolator can influence the resulting data accuracy. In addition, data attributes influence the choice of interpolator. What is dissertation shows, is that through preliminary data analyses, an interpolator may be chosen which yields the most accurate grid for input into a GIS. It should be noted that this dissertation has wider ranging applications beyond the three examples mentioned above. The results should be of interest in any field where point data is interpolated onto a regular grid. Additional application areas include, but are not limited to, medical imaging, scientific visualization, weather forecasting, ecological modeling, forestry, petroleum exploration, and hydrological modeling. / Ph. D.

This research aims to analyze the spatial and temporal characteristics of instrument meteorological conditions (IMC) over the Southern Appalachian Region from 2008 to 2023, utilizing Meteorological Terminal Aerodrome Report (METAR) stations across six USGS-defined physiographical provinces. IMC is defined as visibility less than three statute miles and/or cloud ceiling heights less than 1,000 feet above ground. Percentage of hours reporting IMC are considered on an annual, seasonal, and hourly basis. The greatest increase in IMC over the period occurs in the Piedmont province, and the greatest decrease in the Interior Low Plateaus province. All provinces experience the greatest amount of IMC in wintertime, and the season with the fewest IMC hours varies by province. All provinces see a maximum hour of IMC within three hours following sunrise except summertime in the Interior Low Plateaus, which experiences the highest frequency of IMC within two hours prior to sunset.

The factors involved in calculating Standard Visual Range (SVR) are discussed, and a comparison is made between the visibility reported by airport observers and the photographic slide/ optical densitometer method of calculating SVR. Using slides of Houston, Texas, from the fall and winter of 1988-89, it was found that the altitude at which the contrast measurements are made significantly affects the SVR. Also an index for predicting high and low humidity was developed using the blue/red ratio of the log exposure of the sky, and sun/shade target condition.

Approved for public release, distribution is unlimited / To achieve integration of the Naval Meteorology and Oceanography (METOC) community into the developing FORCEnet environment, transformational innovations must be researched and implemented. Agent based software is an example of technology that can be employed in this way by changing the method by which METOC data is distributed to end-users. This thesis documents the creation and implementation of a software agent that uses Internet connections to retrieve numerical model data, loads this output into array data containers, and then makes it available to the end-user in a machinereadable forecast object format. The impact of the importation of this forecast object into warfare commander command-and-control software is then assessed using the commercially available SEAWAY logistics tool. This assessment highlights the importance of defining the METOC functional requirements for the emerging FORCEnet environment, so that proper interfaces to exchange data freely, and visually depict it, are incorporated during next generation software development. Using these types of agents to automate the generation and delivery of weather parameters could also allow the importation of data into previously insular software, provide reach-back support to the warfighter, and be a means of reducing manpower and budgetary requirements during this time of fiscal constraint. / Lieutenant, United States Navy

QC 351 A7 no. 19 / ABSTRACT The classical properties of an image formed by an optical system looking through a turbulent medium are scintillation, agitation, and blur. A simple but adequate statistical model is obtained describing the pupil function in the presence of random wavefront disturbances in both amplitude and phase. A measure of scintillation is then obtained in the form of a normalized, weighted integral in frequency space containing all the pertinent properties of the optical system. A similar measure of agitation is then developed, and finally a description of the blur is obtained in the form of an equivalent transfer function for the turbulent medium in which the contribution from amplitude variations and the contribution from phase variations are independent.