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1 
Freezethaw phenomenon as a climatological parameterWilliams, Llewelyn January 1960 (has links)
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 nonavailability 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 freezethaw 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]

2 
Optimal sampling to provide userspecific climate information /Panturat, Suwanna, January 1987 (has links)
Thesis (Ph.D.)University of Oklahoma, 1987. / Bibliography: leaves 213219.

3 
On spectra and coherence of tropical climate anomaliesKaczmarczyk, Edward Bruce. January 1978 (has links)
Thesis (M.S.)Wisconsin. / Includes bibliographical references (leaves 8791).

4 
Exploring cityscale thermal and wind environmentsWang, Xiaoxue, 王霄雪 January 2015 (has links)
abstract / Mechanical Engineering / Doctoral / Doctor of Philosophy

5 
The impacts of climate variations on military operations in the Horn of AfricaLaJoie, Mark R. 03 1900 (has links)
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 sixstep smart climatology process. We applied this process in the context of a notional, unclassified noncombatant 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.

6 
Multivariate statistical models for seasonal climate prediction and climate downscalingCannon, Alex Jason 05 1900 (has links)
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 signaltonoise ratios are low, particularly at long leadtimes, 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 synopticscale 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 multisite 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 lowerdimensional 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 Bernoulligamma density network, a nonlinear probabilistic extension of expanded downscaling, is introduced in Chapter 6 for multisite precipitation downscaling. The singlesite model is extended by allowing multiple predictands and by adopting the expanded downscaling covariance constraint.

7 
Multivariate statistical models for seasonal climate prediction and climate downscalingCannon, Alex Jason 05 1900 (has links)
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 signaltonoise ratios are low, particularly at long leadtimes, 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 synopticscale 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 multisite 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 lowerdimensional 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 Bernoulligamma density network, a nonlinear probabilistic extension of expanded downscaling, is introduced in Chapter 6 for multisite precipitation downscaling. The singlesite model is extended by allowing multiple predictands and by adopting the expanded downscaling covariance constraint.

8 
Teaching concepts of climate in a program of geography at the college level.Trevaskis, Graham Arthur. January 1963 (has links)
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 206219).

9 
A climatonomic study of the North American interior plainsHall, Leonard Frazier, January 1977 (has links)
ThesisWisconsin. / Vita. Includes bibliographical references (leaves 162166).

10 
Thermal characteristics of bogs and their value as climatic indicatorsLettau, Bernhard. January 1961 (has links)
Thesis (M.S.)University of WisconsinMadison, 1961. / Typescript. eContent providerneutral record in process. Description based on print version record. Includes bibliographical references (leaf 19).

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