Thesis (Ph.D.)--Boston University / One of the more powerful and consistent tools available to nature is the phenomenon of alternate freezing and thawing. Mechanically, extraordinary pressures may be involved because of the density differential existing between the liquid and the solid phases of water. Physiologically, there is the availability or non-availability of water to sustain growth. Despite this, catastrophic changes are not to be expected. On the other hand, such a powerful tool must leave its imprint in one manner or another upon the natural landscape. In most arctic and highland areas the imprint is directly discernible. In more moderate climes the imprint is indirectly applied principally as a limiting parameter within an aggregate of generally favorable conditions. The phenomenon of freeze-thaw is a climatic parameter but not a climatic element. Unlike the elements, there is a definite threshold involved; that is, 32 degrees Fahrenheit or 0 degrees Centigrade. At this threshold water may exist in either the liquid or solid state but by the addition or subtraction of heat it can change from one state to the other without a gain or loss in temperature. In the natural environment the terms are not quite so precise. Time for the process to take place, impurities in the water, and the variation of temperature regimes among the many nooks and crannies of the landscape point to the necessity of relaxing the temperature threshold. In this study the zone of 34 degrees F and 28 degrees F is used. Conditions favorable for a thaw are thought to occur when the temperature rises through the zone and conditions favorable for a freeze when the temperature drops through this zone [TRUNCATED]

Thesis (Ph.D.)--University of Oklahoma, 1987. / Bibliography: leaves 213-219.

Thesis (M.S.)--Wisconsin. / Includes bibliographical references (leaves 87-91).

abstract / Mechanical Engineering / Doctoral / Doctor of Philosophy

Department of Defense (DoD) climatology products rely mainly on long term means (LTMs) of climate system variables. In this project, we have demonstrated that climatologies based on LTMs can be substantially improved using modern data and methods, especially by accounting for climate variations. We analyzed, and identified mechanisms for, enhanced (suppressed) autumn precipitation in the Horn of Africa (HOA) during El Nino (La Nina) events. El Nino (La Nina) precipitation anomalies were associated with anomalously warm (cool) western Indian Ocean sea surface temperatures, and with anomalously onshore (offshore) moisture transports in the HOA. These transport anomalies supported anomalously strong (weak) precipitation over the HOA. To improve climatological support for DoD operations, we developed and tested a six-step smart climatology process. We applied this process in the context of a notional, unclassified non-combatant evacuation operation (NEO) set in the HOA during autumn of an El Nino year. Using this process, we translated our scientific and operational findings into warfighter impacts. The smart climatology process we have developed is readily adaptable to other regions, seasons, climate variations, and military operations. We have provided a detailed description of our smart climatology process to facilitate its use by DoD agencies.

This dissertation develops multivariate statistical models for seasonal forecasting and downscaling of climate variables. In the case of seasonal climate forecasting, where record lengths are typically short and signal-to-noise ratios are low, particularly at long lead-times, forecast models must be robust against noise. To this end, two models are developed. Robust nonlinear canonical correlation analysis, which introduces robust cost functions to an existing model architecture, is outlined in Chapter 2. Nonlinear principal predictor analysis, the nonlinear extension of principal predictor analysis, a linear model of intermediate complexity between multivariate regression and canonical correlation analysis, is developed in Chapter 3. In the case of climate downscaling, the goal is to predict values of weather elements observed at local or regional scales from the synoptic-scale atmospheric circulation, usually for the purpose of generating climate scenarios from Global Climate Models. In this context, models must not only be accurate in terms of traditional model verification statistics, but they must also be able to replicate statistical properties of the historical observations. When downscaling series observed at multiple sites, correctly specifying relationships between sites is of key concern. Three models are developed for multi-site downscaling. Chapter 4 introduces nonlinear analog predictor analysis, a hybrid model that couples a neural network to an analog model. The neural network maps the original predictors to a lower-dimensional space such that predictions from the analog model are improved. Multivariate ridge regression with negative values of the ridge parameters is introduced in Chapter 5 as a means of performing expanded downscaling, which is a linear model that constrains the covariance matrix of model predictions to match that of observations. The expanded Bernoulli-gamma density network, a nonlinear probabilistic extension of expanded downscaling, is introduced in Chapter 6 for multi-site precipitation downscaling. The single-site model is extended by allowing multiple predictands and by adopting the expanded downscaling covariance constraint.

This dissertation develops multivariate statistical models for seasonal forecasting and downscaling of climate variables. In the case of seasonal climate forecasting, where record lengths are typically short and signal-to-noise ratios are low, particularly at long lead-times, forecast models must be robust against noise. To this end, two models are developed. Robust nonlinear canonical correlation analysis, which introduces robust cost functions to an existing model architecture, is outlined in Chapter 2. Nonlinear principal predictor analysis, the nonlinear extension of principal predictor analysis, a linear model of intermediate complexity between multivariate regression and canonical correlation analysis, is developed in Chapter 3. In the case of climate downscaling, the goal is to predict values of weather elements observed at local or regional scales from the synoptic-scale atmospheric circulation, usually for the purpose of generating climate scenarios from Global Climate Models. In this context, models must not only be accurate in terms of traditional model verification statistics, but they must also be able to replicate statistical properties of the historical observations. When downscaling series observed at multiple sites, correctly specifying relationships between sites is of key concern. Three models are developed for multi-site downscaling. Chapter 4 introduces nonlinear analog predictor analysis, a hybrid model that couples a neural network to an analog model. The neural network maps the original predictors to a lower-dimensional space such that predictions from the analog model are improved. Multivariate ridge regression with negative values of the ridge parameters is introduced in Chapter 5 as a means of performing expanded downscaling, which is a linear model that constrains the covariance matrix of model predictions to match that of observations. The expanded Bernoulli-gamma density network, a nonlinear probabilistic extension of expanded downscaling, is introduced in Chapter 6 for multi-site precipitation downscaling. The single-site model is extended by allowing multiple predictands and by adopting the expanded downscaling covariance constraint.

Thesis (Ed.D.)--Teachers College, Columbia University, 1963. / Typescript; issued also on microfilm. Sponsor: James P. Matthai. Dissertation Committee: Margaret Lindsey. Includes bibliographical references (leaves 206-219).

Thesis--Wisconsin. / Vita. Includes bibliographical references (leaves 162-166).

Thesis (M.S.)--University of Wisconsin--Madison, 1961. / Typescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaf 19).