A numerical procedure based upon a boundary integral method for gravity wave making problems is studied in the time domain. The free-surface boundary conditions are combined and expressed in a Lagrangian notation to follow the free-surface particle's motion in time. The corresponding material derivative term is approximated by a finite difference expression, and the velocity terms are extrapolated in time for the completion of the formulations. The fluid-body intersection position at the free surface is predicted by an interpolation function that requires information from both the free surface and the submerged surface conditions. Solutions corresponding to a linear free-surface condition and to a non-linear free-surface condition are obtained at small time increment values. Numerical modelling of surface wave problems is studied in two dimensions and in three dimensions. Comparisons are made to linear analytical solutions as well as to published experimental results. Good agreement between the numerical solutions and measured values is found. For the modelling of a three dimensional wave diffraction problem, results at high wave amplitude are restricted because of the use of quadrilateral elements. The near cylinder region of the free surface is not considered to be well represented because of the coarse element size. Wave forces calculated on the vertical cylinder are found to be affected by the modelled tank length. When the simulated wave length is comparable to the wave tank's dimension, numerical results are found to be less than the experimental measurements. However, when the wave length is shorter than the tank's length, solutions are obtained with very good precision. / Applied Science, Faculty of / Mechanical Engineering, Department of / Graduate

The Caribbean is a complex region that heavily relies on its seasonal rainfall cycle for its economic and societal needs. This makes the Caribbean especially susceptible to hydro-meteorological disasters (e.g., droughts and floods), and other weather/climate risks. Therefore, effectively predicting the Caribbean rainfall cycle is valuable for the region. The efficacy of predicting the Caribbean rainfall cycle is largely dependent on effectively characterizing the climate dynamics of the region. However, the dynamical processes and climate drivers that shape the seasonal cycle are not fully understood, as previous observational studies show inconsistent findings as to what mechanisms influence the mean state and variability of the cycle. These inconsistencies can be attributed to the limitations previous studies have when investigating the Caribbean rainfall cycle, such as using monthly or longer resolutions in the data or analysis that often mask the seasonal transitions and regional differences of rainfall, and investigating the Caribbean under a basin-wide lens rather than a sub-regional lens. This inhibits the ability to accurately calculate and predict subseasonal-to-seasonal (S2S) rainfall characteristics in the region. To address these limitations and inconsistencies, the research in this thesis examines the seasonal climatology, variability, and characteristics of the Caribbean rainfall cycle under a sub-regional and temporally fine lens in order to investigate the prediction of the cycle. Regional variations and dynamical processes of the Caribbean annual rainfall cycle are assessed using (1) a principal component analysis across Caribbean stations using daily observed precipitation data; and, (2) a moisture budget analysis. The results show that the seasonal cycle of rainfall in the Caribbean hinges on three main facilitators of moisture convergence: the Atlantic Intertropical Convergence Zone (ITCZ), the Eastern Pacific ITCZ, and the North Atlantic Subtropical High (NASH). A warm body of sea-surface temperatures (SSTs) in the Caribbean basin known as the Atlantic Warm Pool (AWP) and a low-level jet centered at 925hPa over the Caribbean Sea known as the Caribbean Low-Level Jet (CLLJ) modify the extent of moisture provided by these main facilitators. The interactions of these dynamical processes are responsible for shaping the seasonal components of the annual rainfall cycle: The Winter Dry Season (WDS; mid-November to April); the Early-Rainy Season (ERS; mid-April to mid-June); an intermittent relatively dry period known as the mid-summer drought, (MSD; mid-June to late August), and the Late-Rainy Season (LRS; late August to late November). Five geographical sub-regions are identified in the Caribbean Islands, each with its unique set of dynamical processes, and consequently, its unique pattern of rainfall distribution throughout the rainy season: Northwestern Caribbean, the Western Caribbean, the Central Caribbean, the Central and Southern Lesser Antilles, and Trinidad and Tobago and Guianas. Convergence by sub-monthly transients contributes little to Caribbean rainfall. The wettest and driest Caribbean ERS and LRS years’ are then explored by conducting the following: (1) a spatial composite of rainfall using the daily rainfall data; and, (2) spatial composites of SSTs, sea-level pressure (SLP), and mean flow moisture convergence and transports using monthly data. The ERS and LRS are impacted in distinctly different ways by two different, and largely independent, large-scale phenomena, external to the region: a SLP dipole mode of variability in the North Atlantic known as the North Atlantic Oscillation (NAO), and the El Nino Southern Oscillation (ENSO). Dry ERS years are associated with a persistent dipole of cold and warm SSTs over the Caribbean Sea and Gulf of Mexico, respectively, that are caused by a preceding positive NAO state. This setting involves a wind-evaporation-SST (WES) feedback expressed in enhanced trade winds and consequently, moisture transport divergence over all of the Caribbean, except in portions of the Northwestern Caribbean in May. A contribution from the preceding winter cold ENSO event is also discernible during dry ERS years. Dry LRS years are due to the summertime onset of an El Niño event, developing an inter-basin SLP pattern that moves moisture out of the Caribbean, except in portions of the Northwestern Caribbean in November. Both large-scale climate drivers would have the opposite effect during their opposite phases leading to wet years in both seasons. Existing methodologies that calculate S2S rainfall characteristics were not found to be suitable for a region like the Caribbean, given its complex rainfall pattern; therefore, a novel and comprehensive method is devised and utilized to calculate onset, demise, and MSD characteristics in the Caribbean. When applying the method to calculate S2S characteristics in the Caribbean, meteorological onsets and demises, which are calculated via each year’s ERS and LRS mean thresholds, effectively characterize the seasonal evolution of mean onsets and demises in the Caribbean. The year-to-year variability of MSD characteristics, and onsets and demises that are calculated by climatological ERS and LRS mean thresholds resemble the variability of seasonal rainfall totals in the Caribbean and are statistically significantly correlated with the identified dynamical processes that impact each seasonal component of the rainfall cycle. Finally, the seasonal prediction of the Caribbean rainfall cycle is assessed using the identified variables that could provide predictive skill of S2S rainfall characteristics in the region. Canonical correlation analysis is used to predict seasonal rainfall characteristics of station-averaged sub-regional frequency and intensity of the ERS and LRS wet days, and magnitude of the MSD. Predictor fields are based on observations from the ERA-Interim reanalysis and GCM output from the North America Multi-Model Ensemble (NMME). Spearman Correlation and Relative Operating Characteristics are applied to assess the forecast skill. The use of SLP, 850-hPa zonal winds (u850), vertically integrated zonal (UQ), and meridional (VQ) moisture fluxes show comparable, if not better, forecast skill than SSTs, which is the most common predictor field for regional statistical prediction. Generally, the highest ERS predictive skill is found for the frequency of wet days, and the highest LRS predictive skill is found for the intensity of wet days. Rainfall characteristics in the Central and Eastern Caribbean have statistically significant predictive skill. Forecast skill of rainfall characteristics in the Northwestern and Western Caribbean are lower and less consistent. The sub-regional differences and consistently significant skill across lead times up to at least two months can be attributed to persistent SST/SLP anomalies during the ERS that resemble the North Atlantic Oscillation pattern, and the summer-time onset of the El Niño-Southern Oscillation during the LRS. The spatial pattern of anomalies during the MSD bears resemblance to both the ERS and LRS spatial patterns. The findings from this thesis provide a more comprehensive and complete understanding of the climate dynamics, variability, and annual mean state of the Caribbean rainfall cycle. These results have important implications for prediction, decision-making, modeling capabilities, understanding the genesis of hydro-meteorological disasters, investigating rainfall under other modes of variability, and Caribbean impact studies regarding weather risks and future climate.

The rise in machine learning has made the subject interesting for new types of uses. This Master thesis implements and evaluates an LSTM-based algorithm on the conflict forecasting problem. Data is structured in country-month pairs, with information about conflict, economy, demography, democracy and unrest. The goal is to forecast the probability of at least one conflict event in a country based on a window of historic information. Results show that the model is not as good as a Random Forest. There are also indications of a lack of data with the network having difficulty performing consistently and with learning curves not flattening. Naive models perform surprisingly well. The conclusion is that the problem needs some restructuring in order to improve performance compared to naive approaches. To help this endeavourpossible paths for future work has been identified.

Water supply forecasting in the western United States is the prediction of the volume of water passing a given point on a stream during the primary snowmelt runoff season. Most water supply forecasts are produced from multiple linear regression models using snowpack, precipitation, and streamflow measurements as independent variables. In recent years, conceptual watershed simulation models, typically using a time step of one day, have also been used to produce these forecasts. This study examines model usage for: water supply forecasting in the West and has three specific purposes. The first is to examine the traditional usage of multiple linear regression and develop improved regression techniques to overcome several recognized weaknesses in traditional practice. Four techniques have been used in this study to improve water supply forecasts based on regression. They are: (1) basing the regression model only on data: known at forecast time (no future data); (2) principal components regression; (3) cross-validation; and (4) systematic searching for optimal or near-optimal combinations of variables. The second purpose of the study is to develop a monthly streamflow simulation model suitable for use in water supply forecasting. Such a model has not previously been used in this application, and it provides a forecasting tool midway in complexity between regression procedures and conceptual watershed simulation models. The third purpose of the study is to compare the accuracy of forecasts from regression, the monthly model developed here, and two conceptual watershed simulation models. It has generally been assumed, but not tested, that complex simulation models will give more accurate forecasts than simpler models. This study attempts to begin determining if this is true. Conceptual modeling results from previous studies on three basins in Idaho and Montana were obtained to represent current practice in the usage of this type of model. The results of the study led to the following conclusions: (1) significant improvements in forecast accuracy over past practice with regression can be obtained by the use of the four techniques developed here; (2) the monthly model performed better than the conceptual watershed models most of the time, for both seasonal volumes and monthly flows; (3) for the three test watersheds, regression provided the best forecast accuracy among the three modeling techniques most of the time, for both seasonal volumes and monthly flows; (4) optimal use of conceptual watershed models requires automated calibration schemes; and (5) in basins of complex orography, denser data networks will be required to calculate meaningful values of mean areal precipitation. This study has contributed to the practice of water supply forecasting by providing improvements to regression techniques, providing a new monthly model, developing a mean areal precipitation and temperature procedure based on kriging, and giving some initial direction for further investigations in the use of conceptual watershed models. The inability of the two simulation approaches to surpass regression in forecast accuracy brings up several issues with respect to modeling. These issues are in the areas of model calibration, model conceptualization, spatial and temporal aggregation, and areal averaging of input data. Further investigation is required to elucidate these issues before clear conclusions can be made about the relative forecasting abilities of simple and complex models. Further investigation is also required to study water management decision making and the kinds and accuracies of forecast information required to optimize these decisions.

Leveraging the knowledge of an organization is an ongoing challenge that has given rise to the field of knowledge management. Yet, despite spending enormous sums of organizational resources on Information Technology (IT) systems, executives recognize there is much more knowledge to harvest. Prediction markets are emerging as one tool to help extract this tacit knowledge and make it operational. Yet, prediction markets, like other markets, are subject to pathologies (e.g., bubbles and crashes) which compromise their accuracy and may discourage organizational use. The techniques of experimental economics were used to study the characteristics of prediction markets. Empirical data was gathered from an on-line asynchronous prediction market. Participants allocated tickets based on private information and, depending on the market type, public information indicative of how prior participants had allocated their tickets. The experimental design featured three levels of feedback (no-feedback, percentages of total allocated tickets and frequency of total allocated tickets) presented to the participants. The research supported the hypothesis that information assimilation in feedback markets is composed of two mechanisms - information collection and aggregation. These are defined as: Collection - The compilation of dispersed information - individuals using their own private information make judgments and act accordingly in the market. Aggregation - The market's judgment on the implications of this gathered information - an inductive process. This effect comes from participants integrating public information with their private information in their decision process. Information collection was studied in isolation in no feedback markets and the hypothesis that markets outperform the average of their participants was supported. The hypothesis that with the addition of feedback, the process of aggregation would be present was also supported. Aggregation was shown to create agreement in markets (as measured by entropy) and drive market results closer to correct values (the known probabilities). However, the research also supported the hypothesis that aggregation can lead to information mirages, creating a market bubble. The research showed that the presence and type of feedback can be used to modulate market performance. Adding feedback, or more informative feedback, increased the market's precision at the expense of accuracy. The research supported the hypotheses that these changes were due to the inductive aggregation process which creates agreement (increasing precision), but also occasionally generates information mirages (which reduces accuracy). The way individual participants use information to make allocations was characterized. In feedback markets the fit of participants' responses to various decision models demonstrated great variety. The decision models ranged from little use of information (e.g., MaxiMin), use of only private information (e.g., allocation in proportion to probabilities), use of only public information (e.g., allocating in proportion to public distributions) and integration of public and private information. Analysis of all feedback market responses using multivariate regression also supported the hypothesis that public and private information were being integrated by some participants. The subtle information integration results are in contrast to the distinct differences seen in markets with varying levels of feedback. This illustrates that the differences in market performance with feedback are an emergent phenomenon (i.e., one that could not be predicted by analyzing the behavior of individuals in different market situations). The results of this study have increased our collective knowledge of market operation and have revealed methods that organizations can use in the construction and analysis of prediction markets. In some situations markets without feedback may be a preferred option. The research supports the hypothesis that information aggregation in feedback markets can be simultaneously responsible for beneficial information processing as well as harmful information mirage induced bubbles. In fact, a market subject to mirage prone data resembles a Prisoner's Dilemma where individual rationality results in collective irrationality.

Recent improvements in numerical weather model resolution open the possibility of producing forecasts for lightning using indirect lightning threat indicators well in advance of an event. This research examines the feasibility of a statistical machine-learning algorithm known as a support vector machine (SVM) to provide a probabilistic lightning forecast for Mississippi at 9 km resolution up to one day in advance of a thunderstorm event. Although the results indicate that SVM forecasts are not consistently accurate with single-day lightning forecasts, the SVM performs skillfully on a data set consisting of many forecast days. It is plausible that errors by the numerical forecast model are responsible for the poorer performance of the SVM with individual forecasts. More research needs to be conducted into the possibility of using SVM for lightning prediction with input data sets from a variety of numerical weather models.

The primary purpose of this thesis is a description of some of the major changes which the population of Texas has undergone, particularly in the first half of the twentieth century. Other approaches are possible. For example, it is both possible and important to develop the relationship of population change to social problems. However, it is not the purpose of this thesis to investigate these relationships. It is the purpose here to view the population problem in almost entirely a factual sense, basing observations and interpretations on strictly demographic data.

No description available.

This dissertation offers a conceptual framework capturing forecasting related activities in a formal organizational context, and it empirically assesses how and how well an organization utilizes forecasting tools and results. Specifically, a multilevel model is formulated that suggests that forecasting capabilities and forecasting processes predict forecasting effectiveness. The model is tested through a field study utilizing a qualitative and quantitative research design. The findings suggest that there are great differences in how forecasting is done among mangers within the same organization, and that in the absence of process congruency (i.e., similar procedures for similar forecasters), the use of a bottom-up approach to forecasting contributes to inconsistent forecasting results. Further, the findings suggest that when it is difficult to establish solid market information, managers often look to competitors in order to establish pseudo-estimates of supply and demand. With respect to content congruency (i.e., the imposition of higher level forecasts onto lower level entities), the dissertation examines the consequences of making decisions based on data from different levels of analyses (and with different geographic scopes).The results highlight the consequences of relying on higher level forecasts when a mismatch exists between organizational and national “footprints”. Using various economic variables to predict housing starts across levels, the analyses found disparate results for the lower level of analysis. The results also reveal great differences in the strength of the forecasting models between different levels of analysis and between different entities at the same level. Different combinations of variables contribute toward predicting the key dependent variable, housing starts, at different levels, and even between geographic markets at the same level of analysis. The findings suggest that traditional organizational forecasting performed at the national level presents decision makers with a “hit or miss” scenario when trying to predict housing demand in the local markets. The inability to generate strong forecasts utilizing the same variables in different markets appears to be problematic. Thus, a “bottoms-up” approach to the technical generation of forecasts is desirable Recommendations for both future research and practice are suggested. / Ph. D.

In this thesis we study measures of verification, accuracy determination, and implementation of limited-area research and operational weather prediction models. We give a complete description of the implementation of the multivariate autoregressive research model. This includes a FORTRAN program and three order selection routines. We conclude with a discussion on possible extensions of our results and other applications. / M.S.