Thesis (D.Phil. (Economics)) -- University of Pretoria, 2007. / Abstract in English. Includes bibliographical references.

The purpose of this study was to undertake a sensitivity analysis of selected input parameters of the pattern search-exponential smoothing forecasting system. The inputs subjected to the analysis were: 1) maximum number of pattern moves, 2) minimum step size, 3) pattern search step size, 4) step size reduction factor, 5) exponential smoothing constants (A, B and C). As the values of these input parameters were changed during the course of the analysis the resultant changes in certain criterion variables of the system were noted. These variables were: 1) forecast error standard deviation, 2) number of iterations (or pattern moves), 3) exponential smoothing constants (A, B and C). The three separate time series that were used in this study were furnished by the Frazer Valley Milk Producer's Association. The data series are composed of unit sales of fluid milk segregated according to container size, butterfat content and channel of distribution. Each of the time series analysed represents a different type of trend factor. One each for rising, falling and stable trend factors. The three time series were subjected to identical analytical procedures. The results were then compared across the three time series in order to determine if the response patterns of the pattern search system were sensitive to changes in the series trend. As measured by the response patterns of the criterion variables, the accuracy of the system is not influenced significantly by changes in the input parameters. Throughout the sensitivity analysis there developed a consistent pattern of minimal change in the forecast error standard deviation and the exponential smoothing constants. The search process was able to consistently reach very similar forecast error standard deviation values and exponential smoothing constant values, given the range of input values tested. The only dependent variable that experienced any marked change was the number of iterations. There does appear to be certain input values that minimize the number of iterations that the pattern search system needs, to arrive at solution values. Neither the maximum number of pattern moves nor the minimum step size exerted much of an effect on the size of the forecast error standard deviation or the "optimum values" for the exponential smoothing constants. However, changes in the minimum step size do affect the number of iterations the pattern search system makes before reaching a minimum forecast error standard deviation. If the minimum step size is decreased the number of iterations is increased. The opposite is also true, and if the minimum step size is increased, the number of iterations is decreased. Changes in the maximum number of pattern moves have no effect on the number of iterations. The pattern search system also appears to be unresponsive to changes in the pattern search step size. Neither the forecast error standard deviation nor the expotential smoothing, constant values can be improved through the use of different pattern search step sizes. The number of iterations is somewhat more responsive. Both large and small pattern search step size yield larger numbers of iterations than do middle values i.e. .10 - .20. Like the other inputs, the step size reduction factor, also does not elicit change in the results of the search process. Movements in the forecast error standard deviation and the exponential smoothing constants are small enough to be considered insignificant. Step size reduction factor values from .100 to .500 minimize the number of iterations, although within this interval there is little change. Larger values of the step size reduction factor tend to increase the number of iterations. There is little responsiveness in the pattern search system to changes in the initial values for the exponential smoothing constants. Between the three time series used, there is little consistency with regards to the effects of changes in the initial constant values on the number of iterations. The rising series benefits most from small values i.e. .250. The falling series benefited most with a middle value i.e. .500. The stable series reacted opposite to the falling one and benefited most with values at the extremes i.e. .250 and .750. One important finding is that most of the responsiveness of the pattern search system takes place before the first step size reduction. The bulk of all improvement in the forecast error standard deviation and the majority of all change in the exponential smoothing constants occurs in this first set of pattern moves. This is an important result as it explains the insensitivity of the search system to changes in the maximum number of pattern moves, the minimum step size and the step size reduction factor. / Business, Sauder School of / Graduate

The design, management and operation (as well as the associated costs) of major water resource projects are directly related to the assessment of the anticipated volumes of runoff to be handled by the project. In remote or sparsely gauged regions it is often very difficult to determine these volumes due to a lack of data. The meteorological satellites, and in particular, the Geostationary Operational Environmental Satellites (GOES) can provide good areal coverage of the Earth and its weather systems potentially every half hour day and night. Since the first meteorological satellite images were transmitted, many attempts have been made to estimate rainfall using the images to identify specific cloud characteristics and correlating these with expected rainfall. However, these methods have been limited to convective rainfall in the tropics or near tropics. A method is presented here for estimating half-hourly rainfall which relates the vertical updraft velocities and hence, the moisture flux into the cloud, to the rate of vertical and horizontal growth of the cloud top as revealed in the GOES infrared images. The method performs well in estimating rainfall from widespread frontal systems common over British Columbia. A number of computational difficulties which arose during the research were resolved. One was to ascertain cloud top temperature contours for those GOES infrared images which were not enhanced. This involved the use of a video camera-special effects generator-video monitor system. The second was one of bookkeeping to "keep track of" the individual cloud cells. This was taken care of through the use of computer routines which directed the input and output of data, accounted for the growth and movement of the storm cells over the region and interpolated for rainfall at those locations which fell between adjacent precipitation contours. The method was used to estimate rainfall for a number of test storms occurring over British Columbia. The results were remarkably successful although there were some local inadequacies. An updating -procedure was developed in which the satellite estimated values of rainfall were improved by taking into consideration the information provided by concurrent rainfall observations. Furthermore, the parameters of the updating model (determined for gauged -locations) can be used to update rainfall estimates for ungauged locations. In the light of present raingauge installation and operating costs and the limitations of radar in mountainous areas, the satellite rainfall estimation procedure provides an economical operational supplement to existing conventional precipitation data collection. / Applied Science, Faculty of / Civil Engineering, Department of / Graduate

No description available.

Dust forecasting has become important to military operations over the past three decades. Rules of thumb have been the primary resource for forecasting dust. In recent years, algorithms for weather models have been created to produce atmospheric dust concentration forecasts and are now coming into use operationally. The question becomes how good are the models and what causes errors in their forecasts? This study examines the accuracy of the U. S. Navy's Coupled Ocean Atmospheric Mesoscale Model dust module during the United Arab Emirates Unified Aerosol Experiment. The study also attempts to determine what causes any error if present. The primary method to verify the model's aerial coverage accuracy is through equitable threat score. Case studies are then conducted to verify the scores and identify sources of any errors identified. Results indicate the model performs well with respect to sourcing dust plumes. Errors in modeled aerial coverage as compared to real world observations appear to be the result of an inability for the model to properly advect suspended dust near the surface layer. Unconfirmed dust plumes in the model seemed to be the result of inaccurate surface characteristics.

Thesis (Ph. D.)--University of Washington, 2006. / Vita. Includes bibliographical references (p. 107-112).

Thesis (M.S.)--University of Utah, 1996. Thesis from the University of Utah's Department of Meteorology explores the sensitivity of the pecipitation-predicting model known as the Utah Limited Area Model (LAM) to the way that the lateral and upper boundary conditions are applied. The approach is different from most past studies of LAM boundary specification because it is founded upon a medium-range simulation using real data. Many other studies of boundary conditions have used idealized cases or short-term (a few days or less) predictions. / Title from web page (viewed Oct. 30, 2003). "96-084." "August 1996." Includes bibliographical references p. [110]-112. Also available in print version.

Thesis (M. Soc. Sc.)--University of Hong Kong, 1989.

published_or_final_version / Medical Sciences / Master / Master of Medical Sciences

The subject of this study is rainfall-runoff forecasting and flood warning. Denote by (X(t),Y(t)) a sequence of equally spaced bivariate random variables representing rainfall and runoff, respectively. A flood is said to occur at time period (n + 1) if Y(n + 1) > T where T is a fixed number. The main task of flood warning is that of deciding whether or not to issue a flood alarm for the time period n + 1 on the basis of the past observations of rainfall and runoff up to and including time n. With each decision, warning or no warning, there is a certain probability of an error (false alarm or no alarm). Using notions from classical decision theory, the optimal solution is the decision that minimizes Bayes risk. In Chapter 1 a more precise definition of flood warning will be given. A critical review (Chapter 2) of classical methods for forecasting used in hydrology reveals that these methods are not adequate for flood warning and similar types of decision problems unless certain Gaussian assumptions are satisfied. The purpose of this study is to investigate the application of a nonparametric technique referred to as the k-nearest neighbor (k-NN) methods to flood warning and least squares forecasting. The motivation of this method stems from recent results in statistics which extends nonparametric methods for inferring regression functions in a time series setting. Assuming that the rainfall-runoff process can be cast in the framework of Markov processes then, with some additional assumptions, the k-NN technique will provide estimates that converge with an optimal rate to the correct decision function. With this in mind, and assuming that our assumptions are valid, then we can claim that this method will, as the historical record grows, provide the best possible estimate in the sense that no other method can do better. A detailed description of the k-NN estmator is provided along with a scheme for calibration. In the final chapters, the forecasts of this new method are compared with the forecasts of several other methods commonly used in hydrology, on both real and simulated data.