Zugl.: München, Techn. Univ., Diss., 2009

München, Techn. University, Diss., 2009.

Os centros operacionais e de pesquisa em previsão numérica do tempo geralmente trabalham com uma grande quantidade de dados complexos multivariados, tendo que interpretá-los num curto espaço de tempo. Técnicas de visualização científica podem ser utilizadas para ajudar a entender o comportamento atmosférico. Este trabalho descreve a arquitetura e as facilidades apresentadas pelo sistema VisualMet, que foi implementado com base em um estudo das tarefas desenvolvidas pelos meteorologistas responsáveis pelo 8º Distrito de Meteorologia, em Porto Alegre. Este centro coleta dados meteorológicos três vezes ao dia, de 32 estações locais, e recebe dados similares do Instituto Nacional de Meteorologia, localizado em Brasília, e do National Meteorological Center, localizado nos Estados Unidos. Tais dados são resultados de observações de variáveis tais como temperatura, pressão, velocidade do vento e tipos de nuvens. As tarefas dos meteorologistas e as classes de dados foram observadas e analisadas para definir as características do sistema. A arquitetura e a implementação do VisualMet seguem, respectivamente, uma abordagem orientada a ferramentas e o paradigma de programação orientada a objetos. Dados obtidos das estações meteorológicas são instancias de uma classe chamada "Entidade". Três outras classes de objetos representando ferramentas que suportam as tarefas dos meteorologistas foram modeladas. Os objetos no sistema são apresentados ao usuário através de duas janelas, "Base de Entidades" e " Base de Ferramentas". A implementação da "Base de Ferramentas" inclui ferramentas de mapeamento (para produzir mapas de contorno, mapas de ícones e gráficos), ferramentas de armazenamento (para guardar e recuperar imagens geradas pelo sistema) e uma ferramenta de consulta (para ler valores de variáveis de estações selecionadas). E dada especial atenção a ferramenta de mapa de contorno, onde foi utilizado o método Multiquádrico para interpolação de dados. O trabalho apresenta ainda um estudo sobre métodos de interpolação de dados esparsos, antes de descrever detalhadamente os resultados obtidos com a ferramenta de mapa de contorno. Estes resultados (imagens) são discutidos e comparados com mapas gerados manualmente por meteorologistas do 8º Distrito de Meteorologia. Possíveis extensões do presente trabalho são também abordadas. / The weather forecast centers deal with a great volume of complex multivariate data, which usually have to be understood within short time. Scientific visualization techniques can be used to support both daily forecasting and meteorological research. This work reports the architecture and facilities of a system, named VisualMet, that was implemented based on a case study of the tasks accomplished by the meteorologists responsible for the 8th Meteorological District, in the South of Brazil. This center collects meteorological data three times a day from 32 local stations and receives similar data from both the National Institute of Meteorology, located in Brasilia, and National Meteorological Center, located in the United States of America. Such data result from observation of variables like temperature, pressure, wind velocity, and type of clouds. The tasks of meteorologists and the classes of application data were observed to define system requirements. The architecture and implementation of Visual- Met follow the tool-oriented approach and object-oriented paradigm, respectively. Data taken from meteorological stations are instances of a class named Entity. Three other classes of tools which support the meteorologists' tasks are modeled. Objects in the system are presented to the user through two windows, "Entities Base" and "Tools Base". Current implementation of the "Tools Base" contains mapping tools (to produce contour maps, icons maps and graphs), recording tools (to save and load images generated by the system) and a query tool (to read variables values of selected stations). The results of applying the multiquadric method to interpolate data for the construction of contour maps are also discussed. Before describing the results obtained with the multiquadric method, this work also presents a study on interpolation methods for scattered data. The results (images) obtained with the contour map tool are discussed and compared with the maps drawn by the meteorologists of the 8th Meteorological District. Possible extensions to this work are also presented.

Modeling and forecasting of the unemployment rate of a country is very important to be able to take precautions on the governmental policies. The available unemployment rate data of Turkey provided by the Turkish Statistical Institute (TURKSTAT) are not in suitable format to have a time series model. The unemployment rate data between 1988 and 2009 create a problem of building a reliable time series model due to the insufficient number and irregular form of observations. The application of disaggregation methods to some parts of the unemployment rate data enables us to fit an appropriate time series model and to have forecasts as a result of the suggested model.

Thesis (M.S.)--Humboldt State University, 2009. / Includes bibliographical references (leaves 46-48). Also available via Humboldt Digital Scholar.

Os centros operacionais e de pesquisa em previsão numérica do tempo geralmente trabalham com uma grande quantidade de dados complexos multivariados, tendo que interpretá-los num curto espaço de tempo. Técnicas de visualização científica podem ser utilizadas para ajudar a entender o comportamento atmosférico. Este trabalho descreve a arquitetura e as facilidades apresentadas pelo sistema VisualMet, que foi implementado com base em um estudo das tarefas desenvolvidas pelos meteorologistas responsáveis pelo 8º Distrito de Meteorologia, em Porto Alegre. Este centro coleta dados meteorológicos três vezes ao dia, de 32 estações locais, e recebe dados similares do Instituto Nacional de Meteorologia, localizado em Brasília, e do National Meteorological Center, localizado nos Estados Unidos. Tais dados são resultados de observações de variáveis tais como temperatura, pressão, velocidade do vento e tipos de nuvens. As tarefas dos meteorologistas e as classes de dados foram observadas e analisadas para definir as características do sistema. A arquitetura e a implementação do VisualMet seguem, respectivamente, uma abordagem orientada a ferramentas e o paradigma de programação orientada a objetos. Dados obtidos das estações meteorológicas são instancias de uma classe chamada "Entidade". Três outras classes de objetos representando ferramentas que suportam as tarefas dos meteorologistas foram modeladas. Os objetos no sistema são apresentados ao usuário através de duas janelas, "Base de Entidades" e " Base de Ferramentas". A implementação da "Base de Ferramentas" inclui ferramentas de mapeamento (para produzir mapas de contorno, mapas de ícones e gráficos), ferramentas de armazenamento (para guardar e recuperar imagens geradas pelo sistema) e uma ferramenta de consulta (para ler valores de variáveis de estações selecionadas). E dada especial atenção a ferramenta de mapa de contorno, onde foi utilizado o método Multiquádrico para interpolação de dados. O trabalho apresenta ainda um estudo sobre métodos de interpolação de dados esparsos, antes de descrever detalhadamente os resultados obtidos com a ferramenta de mapa de contorno. Estes resultados (imagens) são discutidos e comparados com mapas gerados manualmente por meteorologistas do 8º Distrito de Meteorologia. Possíveis extensões do presente trabalho são também abordadas. / The weather forecast centers deal with a great volume of complex multivariate data, which usually have to be understood within short time. Scientific visualization techniques can be used to support both daily forecasting and meteorological research. This work reports the architecture and facilities of a system, named VisualMet, that was implemented based on a case study of the tasks accomplished by the meteorologists responsible for the 8th Meteorological District, in the South of Brazil. This center collects meteorological data three times a day from 32 local stations and receives similar data from both the National Institute of Meteorology, located in Brasilia, and National Meteorological Center, located in the United States of America. Such data result from observation of variables like temperature, pressure, wind velocity, and type of clouds. The tasks of meteorologists and the classes of application data were observed to define system requirements. The architecture and implementation of Visual- Met follow the tool-oriented approach and object-oriented paradigm, respectively. Data taken from meteorological stations are instances of a class named Entity. Three other classes of tools which support the meteorologists' tasks are modeled. Objects in the system are presented to the user through two windows, "Entities Base" and "Tools Base". Current implementation of the "Tools Base" contains mapping tools (to produce contour maps, icons maps and graphs), recording tools (to save and load images generated by the system) and a query tool (to read variables values of selected stations). The results of applying the multiquadric method to interpolate data for the construction of contour maps are also discussed. Before describing the results obtained with the multiquadric method, this work also presents a study on interpolation methods for scattered data. The results (images) obtained with the contour map tool are discussed and compared with the maps drawn by the meteorologists of the 8th Meteorological District. Possible extensions to this work are also presented.

Os centros operacionais e de pesquisa em previsão numérica do tempo geralmente trabalham com uma grande quantidade de dados complexos multivariados, tendo que interpretá-los num curto espaço de tempo. Técnicas de visualização científica podem ser utilizadas para ajudar a entender o comportamento atmosférico. Este trabalho descreve a arquitetura e as facilidades apresentadas pelo sistema VisualMet, que foi implementado com base em um estudo das tarefas desenvolvidas pelos meteorologistas responsáveis pelo 8º Distrito de Meteorologia, em Porto Alegre. Este centro coleta dados meteorológicos três vezes ao dia, de 32 estações locais, e recebe dados similares do Instituto Nacional de Meteorologia, localizado em Brasília, e do National Meteorological Center, localizado nos Estados Unidos. Tais dados são resultados de observações de variáveis tais como temperatura, pressão, velocidade do vento e tipos de nuvens. As tarefas dos meteorologistas e as classes de dados foram observadas e analisadas para definir as características do sistema. A arquitetura e a implementação do VisualMet seguem, respectivamente, uma abordagem orientada a ferramentas e o paradigma de programação orientada a objetos. Dados obtidos das estações meteorológicas são instancias de uma classe chamada "Entidade". Três outras classes de objetos representando ferramentas que suportam as tarefas dos meteorologistas foram modeladas. Os objetos no sistema são apresentados ao usuário através de duas janelas, "Base de Entidades" e " Base de Ferramentas". A implementação da "Base de Ferramentas" inclui ferramentas de mapeamento (para produzir mapas de contorno, mapas de ícones e gráficos), ferramentas de armazenamento (para guardar e recuperar imagens geradas pelo sistema) e uma ferramenta de consulta (para ler valores de variáveis de estações selecionadas). E dada especial atenção a ferramenta de mapa de contorno, onde foi utilizado o método Multiquádrico para interpolação de dados. O trabalho apresenta ainda um estudo sobre métodos de interpolação de dados esparsos, antes de descrever detalhadamente os resultados obtidos com a ferramenta de mapa de contorno. Estes resultados (imagens) são discutidos e comparados com mapas gerados manualmente por meteorologistas do 8º Distrito de Meteorologia. Possíveis extensões do presente trabalho são também abordadas. / The weather forecast centers deal with a great volume of complex multivariate data, which usually have to be understood within short time. Scientific visualization techniques can be used to support both daily forecasting and meteorological research. This work reports the architecture and facilities of a system, named VisualMet, that was implemented based on a case study of the tasks accomplished by the meteorologists responsible for the 8th Meteorological District, in the South of Brazil. This center collects meteorological data three times a day from 32 local stations and receives similar data from both the National Institute of Meteorology, located in Brasilia, and National Meteorological Center, located in the United States of America. Such data result from observation of variables like temperature, pressure, wind velocity, and type of clouds. The tasks of meteorologists and the classes of application data were observed to define system requirements. The architecture and implementation of Visual- Met follow the tool-oriented approach and object-oriented paradigm, respectively. Data taken from meteorological stations are instances of a class named Entity. Three other classes of tools which support the meteorologists' tasks are modeled. Objects in the system are presented to the user through two windows, "Entities Base" and "Tools Base". Current implementation of the "Tools Base" contains mapping tools (to produce contour maps, icons maps and graphs), recording tools (to save and load images generated by the system) and a query tool (to read variables values of selected stations). The results of applying the multiquadric method to interpolate data for the construction of contour maps are also discussed. Before describing the results obtained with the multiquadric method, this work also presents a study on interpolation methods for scattered data. The results (images) obtained with the contour map tool are discussed and compared with the maps drawn by the meteorologists of the 8th Meteorological District. Possible extensions to this work are also presented.

Scientific visualization computer applications generate visual representations of large and complex sets of science data. These types of applications allow scientists to gain greater knowledge and insight into their data. For example, the visualization of environmental data is of particular interest to biologists when trying to understand how complex variables interact. Modern robotics and sensors have expanded the ability to collect environmental data, thus, the size and variety of these data-sets have likewise grown. Oftentimes, the collected data are deposited into files and databases where they sit in their separate and unique formats. Without easy to use visualization tools, it is difficult to understand and interpret the information within these data-sets. NORUS, the North America-Norway educational program, has a scientific focus on how climate-induced changes impact the living resources and ecosystems in the Arctic. In order to obtain the necessary science data, the NORUS program utilizes the Slocum Glider, a form of Autonomous Underwater Vehicle (AUV). This thesis aims to create a compelling, efficient, and easy to use interactive system for visualizing large sets of science data collected by the Slocum Glider. This goal is obtained through the implementation of various methods taken from scientific visualization, real time rendering, and scattered data interpolation. Methods include visualizations of the surrounding terrain, the ability to map various science data to glyphs, control over color mapping, scattered data interpolation and interactive camera control.

In scientific computing, it is very common to visualize the approximate solution obtained by a numerical PDE solver by drawing surface or contour plots of all or some components of the associated approximate solutions. These plots are used to investigate the behavior of the solution and to display important properties or characteristics of the approximate solutions. In this thesis, we consider techniques for drawing such contour plots for the solution of two and three dimensional PDEs. We first present three fast contouring algorithms in two dimensions over an underlying unstructured mesh. Unlike standard contouring algorithms, our algorithms do not require a fine structured approximation. We assume that the underlying PDE solver generates approximations at some scattered data points in the domain of interest. We then generate a piecewise cubic polynomial interpolant (PCI) which approximates the solution of a PDE at off-mesh points based on the DEI (Differential Equation Interpolant) approach. The DEI approach assumes that accurate approximations to the solution and first-order derivatives exist at a set of discrete mesh points. The extra information required to uniquely define the associated piecewise polynomial is determined based on almost satisfying the PDE at a set of collocation points. In the process of generating contour plots, the PCI is used whenever we need an accurate approximation at a point inside the domain. The direct extension of the both DEI-based interpolant and the contouring algorithm to three dimensions is also investigated. The use of the DEI-based interpolant we introduce for visualization can also be used to develop effective Adaptive Mesh Refinement (AMR) techniques and global error estimates. In particular, we introduce and investigate four AMR techniques along with a hybrid mesh refinement technique. Our interest is in investigating how well such a `generic' mesh selection strategy, based on properties of the problem alone, can perform compared with a special-purpose strategy that is designed for a specific PDE method. We also introduce an \`{a} posteriori global error estimator by introducing the solution of a companion PDE defined in terms of the associated PCI.

In scientific computing, it is very common to visualize the approximate solution obtained by a numerical PDE solver by drawing surface or contour plots of all or some components of the associated approximate solutions. These plots are used to investigate the behavior of the solution and to display important properties or characteristics of the approximate solutions. In this thesis, we consider techniques for drawing such contour plots for the solution of two and three dimensional PDEs. We first present three fast contouring algorithms in two dimensions over an underlying unstructured mesh. Unlike standard contouring algorithms, our algorithms do not require a fine structured approximation. We assume that the underlying PDE solver generates approximations at some scattered data points in the domain of interest. We then generate a piecewise cubic polynomial interpolant (PCI) which approximates the solution of a PDE at off-mesh points based on the DEI (Differential Equation Interpolant) approach. The DEI approach assumes that accurate approximations to the solution and first-order derivatives exist at a set of discrete mesh points. The extra information required to uniquely define the associated piecewise polynomial is determined based on almost satisfying the PDE at a set of collocation points. In the process of generating contour plots, the PCI is used whenever we need an accurate approximation at a point inside the domain. The direct extension of the both DEI-based interpolant and the contouring algorithm to three dimensions is also investigated. The use of the DEI-based interpolant we introduce for visualization can also be used to develop effective Adaptive Mesh Refinement (AMR) techniques and global error estimates. In particular, we introduce and investigate four AMR techniques along with a hybrid mesh refinement technique. Our interest is in investigating how well such a `generic' mesh selection strategy, based on properties of the problem alone, can perform compared with a special-purpose strategy that is designed for a specific PDE method. We also introduce an \`{a} posteriori global error estimator by introducing the solution of a companion PDE defined in terms of the associated PCI.