This study investigates the effects of the arctic and subarctic climatic conditions on the built environment, urban housing in particular. The method of research and development of this thesis has been through a literature search coupled with my own working/ design experience in the North (Fairbanks, Alaska) for three years. The thesis is in three parts (chapters 2, 3» and k)» The first part makes a comparison of the climatic conditions in the different northern climatic zones within the state of Alaska, as well as comparing these to more southern climatic zones. The second part (main body of the thesis) investigates the building design responses (solutions) to the varied climatic conditions: solar radiation, temperature, precipitation, wind, and special climatic conditions (humidity/moisture potential, blowing snow, permafrost, and frost heave). This analysis is organized into "planning levels". Four planning levels are established which deal with ^) site layout/circulation patterns, (2) building size, shape, and orientation, activity/space arrangement, and (J) detailing of the building fabric. Using the parameters established in part 2, planning level 1, part 3 illustrates a townsite layout for a specific site, the Willow Site in subarctic Alaska where the new Alaska State Capital i6 to be located. The majority of people who live in the northern urban areas look to the south for their housing styles and designs as well as assess housing quality by "southern standards". Presently there are few ways for people living ln the North to evaluate the quality of housing for that particular climate except through trial and quite often error. This thesis produces an ordered listing of building/housing responses to the northern climates which can be disseminated to the public who can then better assess housing performance and quality for their particular physical environment. The information contained within this thesis would also be of use to professionals in arriving at design decisions for housing/building in northern areas. / Applied Science, Faculty of / Architecture and Landscape Architecture (SALA), School of / Graduate

Thesis: M.S., Massachusetts Institute of Technology, Department of Meteorology, 1977 / Bibliography : leaves 125-126. / M.S. / M.S. Massachusetts Institute of Technology, Department of Meteorology

Includes bibliographical references (pages 139-143). / The central theme of this study concerns the use of classification schemes on weather modification experiments designed to investigate the possibility of increasing rainfall from individual cumuli or cumulus cloud systems. The principal objectives of these experiments are the evaluation of treatment effects and the identification of situations where seeding with artificial ice-nuclei is likely to have positive results. The classification of experimental units into categories that are associated with significantly different physical processes aids the evaluation process and the formulation of seeding strategies in the desired manner. As part of this study a classification scheme, which stratifies convective events on the basis of the synoptic situations which give rise to and maintain the convection, is formulated. In chapter seven and eight this scheme and another scheme presently being employed on a cumulus cloud weather modification experiment are examined statistically. Investigations show that the formulated scheme attains the objectives of classification to a greater degree. Certain attributes of the second scheme, permit the development of a classification procedure whereby the most effective stratification of experimental units can be accomplished.

Records of bovine samples submitted for salmonella cultures at four regional diagnostic laboratories in the state of Virginia were used to investigate the association of weather conditions and the diagnosis of salmonellosis in cattle. Spearman's correlation coefficients were calculated for the correlations between the monthly number of samples positive for salmonella culture and weather parameters: temperature, precipitation, and relative humidity. Significant correlation coefficients between the monthly average temperature and the monthly number of positive samples were found to be negative in one laboratory (rs= -0.38, p=0.03) and positive in one laboratory (rs=0.30, p=0.02). The latter correlation coefficient was found between the monthly average temperature and the monthly number of positive samples the following month. The same laboratories that had significant correlation of the monthly number of positive samples and the monthly average temperature also had significant correlation with the monthly average relative humidity (rs= -0.39, p=0.03 and rs=0.37, p=0.004). The monthly average relative humidity was more highly correlated to the number of positive samples reported in the same month for both laboratories that had significant correlation coefficients. None of the correlations between the monthly precipitation and the monthly number of positive samples were significant (p>0.05). The inconsistent directions of correlation coefficients need to be investigated further to find a reason for the discrepancy between regions of the state. / Master of Science

Commonly experienced bad weather conditions like fog, snow and rain generate pixel intensity changes in images and videos taken in outdoor environment and impair the performance of algorithms in outdoor vision systems. Hence, the impact of bad weather conditions need to be processed to improve the performance of outdoor vision systems. This thesis focuses on three most common weather conditions: fog, snow and rain. Their physical properties are first analyzed. Based on their properties, traditional methods are introduced individually to remove these weather conditions' effect on images or videos. For fog removal, the scattering model is used to describe the fog scene in images and estimate the clear scene radiance from single input images. In this thesis two scenario are discussed, one with videos and the other with single images. The removal of snow and rain in videos is easier than in single images. In videos, temporal and chromatic properties of snow and rain can be used to remove their impact. While in single images, traditional methods with edge preserving filters were discussed. However, there are multiple limitations of traditional methods that are based on physical properties of bad weather conditions. Each of them can only deal with one specific weather condition at a time. In real application scenarios, it is difficult for vision systems to recognize different weather conditions and choose corresponding methods to remove them. Therefore, machine learning methods have advantages compared with traditional methods. In this thesis, Generative Adversarial Network (GAN) is used to remove the effect of these weather conditions. GAN performs the image to image translation instead of analyzing the physical properties of different weather conditions. It gets impressive results to deal with different weather conditions. / Thesis / Master of Applied Science (MASc)

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