O crescimento populacional e a degradação dos corpos d’água vêm exercendo pressão à agricultura moderna, a proporcionar respostas mais eficientes quanto ao uso racional da água. Para uma melhor utilização dos recursos hídricos, faz-se necessário compreender o movimento da água na natureza, onde o conhecimento prévio dos fenômenos atmosféricos constitui uma importante ferramenta no planejamento de atividades que utilizam os recursos hídricos como fonte primária de abastecimento. Nesse trabalho foram realizadas previsões de longo prazo com antecedência de sete meses e intervalo de tempo mensal de níveis no Sistema Hidrológico do Taim, utilizando previsões de precipitação geradas por um modelo de circulação global. Para realizar as previsões foi elaborado um modelo hidrológico empírico de regressão, onde foram utilizadas técnicas estatísticas de análise e manipulação de séries históricas para correlacionar os dados disponíveis aos níveis (volumes) de água no banhado. Partindo do pressuposto que as previsões meteorológicas são a maior fonte de incerteza na previsão hidrológica, foi utilizada a técnica de previsão por conjunto (ensemble) e dados do modelo COLA, com 30 membros, para quantificar as incertezas envolvidas. Foi elaborado um algoritmo para gerar todas as possibilidades de regressão linear múltipla com os dados disponíveis, onde oito equações candidatas foram selecionadas para realizar as previsões. Numa análise preliminar dos dados de entrada de precipitações previstas foi observado que o modelo de circulação global não representou os extremos observados de forma satisfatória, sendo executado um processo de remoção do viés. O modelo de empírico de simulação foi posteriormente executado em modo continuo, gerando previsões de longo prazo de níveis para os próximos sete meses, para cada mês no período de junho/2004 a dezembro/2011. Os resultados obtidos mostraram que a metodologia utilizada obteve bons resultados, com desempenho satisfatórios até o terceiro mês, decaindo seu desempenho nos meses posteriores, mas configurando-se em uma ferramenta para auxílio à gestão dos recursos hídricos do local de estudo. / Population growth and degradation of water bodies have been pressuring modern agriculture, to provide more efficient responses about the rational use of water. For a better use of water resources, it is necessary to understand the movement of water in nature, where prior knowledge of atmospheric phenomena is an important tool in planning activities that use water as the primary source of supply. In this study were performed long-term forecasts of water levels (seven months of horizon, monthly time-step) in the Hydrological System Taim, using rainfall forecasts generated by a global circulation model as input. To perform predictions was developed an empirical hydrological regression model. This model was developed based on statistical techniques of analysis and manipulation of historical data to correlate the input data available to the levels (volume) of water in a wetland. Assuming that weather forecasts are a major source of uncertainty in hydrological forecasting, we used an ensemble forecast from COLA 2.2 with 30 members to quantify the uncertainties involved. An algorithm was developed to generate all the multiple linear regression models with the available data, where eight candidates equations were selected for hydrological forecasting. In a preliminary analysis of the precipitation forecast was observed that the global circulation model did not achieve a good representation of extremes values, thus a process of bias removal was carried out. Then the empirical model was used to generate water levels forecast for the next seven months, in each month of the period june/2004 to december/2011. The results showed that the methodology used has a satisfactory performance until the lead time three (third month in the future) where the performance starts to show lower values. Beside the sharply lost of performance in the last lead times, the model is a support tool that can help the decision making in the management of water resources for the study case.

This thesis analyses comparison and verification of three global numeric weather models, GFS, ECMWF, NEMS. The research subjects are make comparison of their 48-hour forecast with, for this thesis created, index correspondence of models and evaluate predictability of weather. Next, introduce basic verification methods and their application to forecast verification, from previously mentioned models, against surface observations with resolution 2 ř x 2 ř lat/lon between 1. 6. 2017-28. 2. 2018. Results show, that the worst predictability is at areas with continental glaciers, extensive world mountain ranges and at ITCZ area. The best predictability is observed in subtropical anticyclones over the oceans. Verification of temperature we find out significant smoothing of diurnal cycle in all three models. Biases of relative humidity are strongly negative corelated with temperature bias, skill score for relative humidity is worse than for temperature. Performance of mean sea level pressure is the best for all verification metrics from all analysed quantities. Wind speed is for most world overestimated. Results of 3-hour precipitation depends on treshold. Models overestimate frequency of low intensity precipitation, opposite results are observed for high intensity precipitation, break occur at interval...

O crescimento populacional e a degradação dos corpos d’água vêm exercendo pressão à agricultura moderna, a proporcionar respostas mais eficientes quanto ao uso racional da água. Para uma melhor utilização dos recursos hídricos, faz-se necessário compreender o movimento da água na natureza, onde o conhecimento prévio dos fenômenos atmosféricos constitui uma importante ferramenta no planejamento de atividades que utilizam os recursos hídricos como fonte primária de abastecimento. Nesse trabalho foram realizadas previsões de longo prazo com antecedência de sete meses e intervalo de tempo mensal de níveis no Sistema Hidrológico do Taim, utilizando previsões de precipitação geradas por um modelo de circulação global. Para realizar as previsões foi elaborado um modelo hidrológico empírico de regressão, onde foram utilizadas técnicas estatísticas de análise e manipulação de séries históricas para correlacionar os dados disponíveis aos níveis (volumes) de água no banhado. Partindo do pressuposto que as previsões meteorológicas são a maior fonte de incerteza na previsão hidrológica, foi utilizada a técnica de previsão por conjunto (ensemble) e dados do modelo COLA, com 30 membros, para quantificar as incertezas envolvidas. Foi elaborado um algoritmo para gerar todas as possibilidades de regressão linear múltipla com os dados disponíveis, onde oito equações candidatas foram selecionadas para realizar as previsões. Numa análise preliminar dos dados de entrada de precipitações previstas foi observado que o modelo de circulação global não representou os extremos observados de forma satisfatória, sendo executado um processo de remoção do viés. O modelo de empírico de simulação foi posteriormente executado em modo continuo, gerando previsões de longo prazo de níveis para os próximos sete meses, para cada mês no período de junho/2004 a dezembro/2011. Os resultados obtidos mostraram que a metodologia utilizada obteve bons resultados, com desempenho satisfatórios até o terceiro mês, decaindo seu desempenho nos meses posteriores, mas configurando-se em uma ferramenta para auxílio à gestão dos recursos hídricos do local de estudo. / Population growth and degradation of water bodies have been pressuring modern agriculture, to provide more efficient responses about the rational use of water. For a better use of water resources, it is necessary to understand the movement of water in nature, where prior knowledge of atmospheric phenomena is an important tool in planning activities that use water as the primary source of supply. In this study were performed long-term forecasts of water levels (seven months of horizon, monthly time-step) in the Hydrological System Taim, using rainfall forecasts generated by a global circulation model as input. To perform predictions was developed an empirical hydrological regression model. This model was developed based on statistical techniques of analysis and manipulation of historical data to correlate the input data available to the levels (volume) of water in a wetland. Assuming that weather forecasts are a major source of uncertainty in hydrological forecasting, we used an ensemble forecast from COLA 2.2 with 30 members to quantify the uncertainties involved. An algorithm was developed to generate all the multiple linear regression models with the available data, where eight candidates equations were selected for hydrological forecasting. In a preliminary analysis of the precipitation forecast was observed that the global circulation model did not achieve a good representation of extremes values, thus a process of bias removal was carried out. Then the empirical model was used to generate water levels forecast for the next seven months, in each month of the period june/2004 to december/2011. The results showed that the methodology used has a satisfactory performance until the lead time three (third month in the future) where the performance starts to show lower values. Beside the sharply lost of performance in the last lead times, the model is a support tool that can help the decision making in the management of water resources for the study case.

A Previsão de Safras tem se constituído em importante ferramenta para o estabelecimento de políticas agrícolas públicas e privadas. Em geral, a Previsão de Safras consiste na previsão do clima e na estimativa do rendimento das partes de interesse econômico de uma cultura. A previsão do clima pode ser realizada pela análise de séries históricas dos parâmetros climáticos e dos efeitos de fenômenos conhecidos, a exemplo do El Niño Oscilação Sul (ENSO), o qual pode ser medido pelo Índice de Oscilação Sul (IOS). Também pode ser realizada pela integração numérica das equações diferenciais que regem os movimentos da atmosfera no planeta Terra, também conhecida como previsão numérica. A estimativa do rendimento das culturas também pode ser realizada pela análise estatística de séries históricas ou pela integração numérica de equações diferenciais que regem a fisiologia e o desenvolvimento das plantas, ambos conhecidos como modelo de culturas. O principal objetivo deste trabalho foi propor uma metodologia para a Previsão de Safras no Brasil, tendo como ponto de partida e protótipo o estudo do rendimento de grãos de milho na região Centro-Sul do país. Para tanto, séries históricas com 60 anos de precipitação pluvial em 24 locais da região Centro-Sul do Brasil foram comparadas aos Índices de Oscilação Sul medidos no mesmo período, inferindo-se que o fenômeno ENSO apresenta efeito marcante, e distinto, apenas em locais mais ao Sul e a Nordeste da região Centro-Sul. Concluiu-se pela impossibilidade de utilização do IOS para a previsão de parâmetros climáticos diários, o que também é prejudicado pela carência de séries históricas dos parâmetros climáticos com 60 ou mais anos no Brasil. Ainda quanto à previsão do clima, as previsões de radiação solar, precipitação pluvial, temperaturas máxima e mínima e umidade relativa do ar, geradas pelo modelo Eta a cada seis horas entre os dias 16/7/1997 e 15/6/2002, foram comparadas às respectivas medidas diárias desses parâmetros climáticos, concluindo-se pela possibilidade da aplicação das previsões geradas pelo modelo Eta na Previsão de Safras, à exceção dos locais mais ao Sul e mais a Nordeste da região Centro-Sul do Brasil. Acerca da estimativa do rendimento de grãos de milho, foi proposto um modelo de cultura baseado na integração das equações que regem a fisiologia e o desenvolvimento das plantas. Comparando-se os rendimentos de grãos de milho estimados nos 24 locais durante as safras 1997/98 a 2001/02, conclui-se pela possibilidade da estimativa do rendimento de grãos de milho na região Centro-Sul pelo modelo proposto. Porém, as discrepâncias entre os rendimentos estimados e os respectivos rendimentos medidos nos locais mais ao Sul e nos locais com textura de solo arenosa apontam a necessidade de correção da estimativa da dinâmica de água realizada pelo modelo de cultura proposto. Como conclusão geral, verificou-se que a metodologia proposta para a Previsão de Safras tem virtudes que devem ser exploradas no sentido de sua implementação no Brasil. Porém, essa implementação depende substancialmente da gestão dos trabalhos, de modo a propiciar as condições necessárias. Cabe destacar que o país tem realizado notáveis avanços nesse setor, caso da implementação da rede meteorológica nacional e do conhecimento gerado pelo Centro de Estudos e Previsões do Clima e pela Empresa Brasileira de Pesquisa Agropecuária, entre outras instituições. Ainda assim, essa área do conhecimento, fundamental para um país agrícola como o Brasil, carece de estudos. / Crop forecast has become an important tool for the private and public agricultural policies to be established. Generally, crop forecast is composed by climatic forecast and the yield estimative of growth of economically interesting parts of crops. The climatic forecast can be performed through the analyses of historical series of the climatic features and of the known phenomena, such as the El Niño Southern Oscillation (ENSO), which can be measured by the Southern Oscillation Index (IOS). It can also be done through a numerical integration of differential equations that rule the atmospheric movements of the Earth, a.k.a. numerical forecast. The estimate of crop yields can also be done through the statistical analysis of historical series or through the integration of differential equations that rule the plant physiology and development, both known as crop models. The main objective of this study was to indicate a methodology for Crop Forecast in Brazil, having as a starting point and prototype the study of corn grain yield in the Center-South region of Brazil. Thus, historical series of 60 years of precipitation in 24 sites of the studied region were compared to the IOS measured in the same period, inferring that the phenomenon ENSO has a remarkable effect, distinctly in the most southern and northeast portions of the studied region. One concluded due to the impossibility of using the IOS for daily climatic forecast, which is threatened by the lack of historical series of climatic features with 60 or more years in Brazil. Regarding the climatic forecast, the forecasts of solar radiation maximum and minimum temperatures and air moisture generated by the model Eta on every 6 hours between July 16, 1997 and June 15, 2002 were compared to the respective daily measurements of these climatic parameters. This provided subsidies for the conclusion that the data generated by the model Eta could be used in the Crop Forecast, except for the most southern and northeast regions in the Center-South region of Brazil. For the estimate of corn grain yield, a model based in the integration of equations that rule the plant physiology and development was proposed. Comparing corn grain yields estimated in 24 sites from the agricultural year 1997/98 to 2001/02, one concluded the possibility of estimating the corn grain yield for the studied region by the proposed model. Although the differences between the estimated and the measured yields in the most southern sites and in those with sandy soils indicate the demand for correction of the estimative of water dynamics performed by the proposed model. As a general conclusion, the methodology proposed for crop forecasting brings positive points which should be explored in the sense of its implementation in Brazil. On the other hand, this implementation depends substantially on the work management, propitiating the necessary conditions. One should highlight that the country has developed notably in this sector, such as the cases of the implementation of the national meteorological net and of the knowledge broadcasted by the Center of Climatic Studies and Forecasting and by the The Brazilian Agricultural Research Corporation (EMBRAPA), among other institutions. Even though, this area of knowledge - vital to an agricultural country as Brazil - demands more research.

The beginning of the 21st century was plagued with unprecedented instances of corporate fraud. In an attempt to address apparent non-existent or “broken” corporate governance policies, sweeping measures of financial reporting reform ensued, having specific requirements relating to the composition of audit committees, the interaction between audit committees and external auditors, and procedures concerning auditors’ assessment of client risk. The purpose of my dissertation is to advance knowledge about “good” corporate governance by examining the association between meeting-or-beating analyst forecasts and audit fees, audit committee compensation, and audit committee tenure and “busyness”. Using regression analysis, I found the following: 1) the frequency of meeting-or-just beating (just missing) analyst forecasts is negatively (positively) associated with audit fees, 2) the extent by which a firm exceeds analysts’ forecasts is positively (negatively) associated with audit committee compensation that is predominately equity-based (cash-based), and 3) the likelihood of repeatedly meeting-or-just beating analyst forecasts is positively associated with audit committee tenure and “busyness”. These results suggest that auditors consider clients who frequently meet-or-just beat forecasts as being less “risky”, and clients that frequently just miss as being more “risky”. The results also imply that cash-based director compensation is more successful in preserving the effectiveness of the audit committee’s financial reporting oversight role, that equity-based compensation motivates independent audit committee directors to focus on short-term performance thereby aligning their interests with management, and that audit committee director tenure and the degree of director “busyness” can affect an audit committee member’s effectiveness in providing financial reporting oversight. Collectively, my dissertation provides additional insights regarding corporate governance practices and informs policy-makers for future relevant decisions.

Prior to the year 2007, Eskom Distribution followed a method of load forecasting (now referred to as legacy method in this report) that was based on collecting customer applications, historical load trending, and relied on the planner’s knowledge of the area to a large extent. It was based in a conventional Microsoft Excel spreadsheet. On seeking to improve its load forecasting approach, the utility adopted a technique that was based on spatial forecasting. This new technique was called a geographical based load forecasting (GLF) technique which was performed by using a custom based tool, called PowerGLF. The aim of this research was to assess any improvements (or lack thereof) that were brought about by adopting the GLF method as compared to the legacy method that was used previously. The hypothesis to be tested was declared as: “The use of the GLF method that was introduced to Eskom Distribution Planning brings about the improvement on the planning process of infrastructure that is adequate, reliable and economic, when compared to the legacy method that was used before it.” To carry out this assessment, a case study method was followed. Real network studies that were compiled in 2006 and 2007 were used. These network studies were based on GLF method and the legacy method. The load forecasts from the case studies were evaluated on forecast accuracy, how they influenced the planning of adequate, reliable and economic (ARE) network infrastructure and their impact on the procurement and construction of the network infrastructure (which represent the actual utility expenditure on infrastructure). The statistical comparative analysis was done. The research results revealed that the legacy method was more accurate than the GLF method in both the case studies that were evaluated. However, regarding the ability of a load forecast method to support the planning process, the GLF method showed to be supporting the planning of adequate, reliable and economic infrastructure better than the legacy method. It was found that the forecast error for the GLF and legacy method do not affect the utility infrastructure procurement and construction. Based on the test results, the study reached a conclusion that the use of the GLF method that was introduced to Eskom Distribution Planning brings about the improvement in the planning process of infrastructure that is adequate, economic and reliable when compared to the legacy method that was used before it. The author wishes to express that the results of this study must not be taken as a generic conclusive finding regarding the evaluated load forecasting methods; they are applicable to the tested case studies. To get to a general conclusive result, more case studies would need to be carried out where clear and consistent evidence on performance of these load forecasting methods will be seen. The findings of this study can be used as part of a larger sample if such a larger population of case studies was to be evaluated. The methodology followed in this research can be repeated and followed when similar assessments are done in future.

Combining forecasts have been proven as one of the most successful methods to improve predictive performance. However, while there often is a focus on theoretically optimal methods, this is an ill-posed issue in practice where the problem of robustness is of more empirical relevance. This thesis focuses on the latter issue, where the risk associated with different combination methods is examined. The problem is addressed using Monte Carlo experiments and an application to automated forecasting with data from the M4 competition. Overall, our results indicate that the choice of combining methodology could constitute an important source of risk. While equal weighting of forecasts generally works well in the application, there are also cases where estimating weights improve upon this benchmark. In these cases, many robust and simple alternatives perform the best. While estimating weights can be beneficial, it is important to acknowledge the role of estimation uncertainty as it could outweigh the benefits of combining. For this reason, it could be advantageous to consider methods that effectively acknowledge this source of risk. By doing so, a forecaster can effectively utilize the benefits of combining forecasts while avoiding the risk associated with uncertainty in weights.

Purpose – Forecasting is a firm's ability to anticipate or predict the future demand givenon a set of assumptions. For a company to implement an appropriate forecast model whichcan make accurate assumptions, the model needs to be aligned with the company's businesssituation and enhanced through supply chain relationships. Therefore, the purpose of thisstudy is: Investigate how small sized wholesalers benefit from demand forecasting. The purpose is divided into two research questions RQ1: How can a company influenced by a seasonal demand select an appropriateforecast model according to its business environment? RQ2: Why do information sharing issues between supply chain partners occur and howcan wholesalers overcome this resistance? Method – The researchers executed a singular case study at one of the local small-sizedfurniture wholesalers in Sweden. The data collection methods implemented in this studyare interviews, document analysis and a survey addressed towards downstream membersof the wholesalers’ chain, retailers (five participants). The combination of both qualitativeas well as quantitative methods was based on a triangulation principle which helped theresearchers provide a comprehensive understanding of the problem as well as increasevalidity and credibility of the study. Findings – The result of the study raises the importance of selecting a forecast model inaccordance with the company's business situation. Furthermore, by the help of a selfdesigned four-step forecast process the company could identify its influencing factors(seasonality, lead-times, lack of information sharing, etc.), available data, and finally selectthe appropriate model corresponding with the business situation. In this study the Holt-Winters model was selected due to the promotion of simplicity considering the casecompany. Also, the issue regarding information sharing among supply chain partners wasidentified where retailers promotes the performance of the whole supply chain anddemands a partnership as a requirement for sharing information. Implications – As every firm is unique and different in its nature it therefore requires itsown specific forecast process in which can select the appropriate model. However, thestudy revealed how selecting the appropriate forecast model can enhance the businessmeeting their seasonal demand. Additionally, the fact that small-sized companies need toestablish a partnership to receive demand information from their retailers. Based on theresult, the study reveals how companies can enhance their situation through demandforecasting. Limitations - As each model is based on each specific company the results regarding theselected forecast model can be questioned. Furthermore, due to the limited time-period ofthe research a specific forecast process had to be constructed which could only cover thescope of the research and not how the forecast model performed over time. Therefore, alonger time-period of the research could have included extra activities in the forecastprocess which would have validated the model.

The objective of this paper is to retrospectively evaluate forecast models for polling data, to be used prospectively for the Swedish general election in 2022. One of the simplest ways of forecasting an election result is through opinion polls, and using the latest observation as the forecast. This paper considers five different forecasting models on polling data which are evaluated based on different error measures and the results are compared to previous research done on the same topic. The data in this paper consists of time series data of party-preference polls from Statistics Sweden. When forecasting polling data, the naive forecasting model was the most accurate, but forecasting the election in 2018 resulted in the forecast combinations model being the most accurate. Finally, the models are used to make forecasts on the Swedish general election taking place in September of 2022.

<p> This dissertation consists of three essays that evaluate long-term agricultural projections. The first essay focus on evaluating Congressional Budget Office’s (CBO) baseline projection of United States Department of Agriculture (USDA) mandatory farm and nutrition programs. The second essay examine USDA soybean ending stock projections, and the third essay investigate impact of macroeconomic assumptions on USDA’s baseline farm income projections.  We use publicly available data from Congressional Budget Office (CBO) and United States Department of Agriculture (USDA)</p>