A developed seamless extreme heat validation approach (Ford et al. 2018) is applied to three Subseasonl Experiment’s (SubX’s) medium-range forecast models, which arethe U.S. National Oceanic and Atmospheric Administration’s Earth System Research Laboratory FIM-iHYCOM (ESRL), the U.S. National Aeronautics and Space Administration’s Earth System Research Laboratory’s Goddard Earth Observing System Atmosphere-Ocean General Circulation Model, Version 5 (GMAO), and the U.S. National Centers for Environmental Prediction’s Global Ensemble Forecast System, version 11 (GEFS), for evaluating their heat wave predictability. Moreover, two land surface initializations, green vegetation fraction (GVF) and heat fluxes (LE/H), of each model are evaluated for understanding the interaction between heat wave predictability and the inconsistencies in the terrestrial segment of land-atmosphere feedbacks. The validation approach shows the overestimated autocorrelation of maximum temperature heat waves causing (1) the lowest reliability and overestimation of heat waves hindcasts, (2) lower heat wave hindcast skill of ensemble mean, and (3) higher discrimination between heat wave hindcast and observations of each ensemble member over lead times for all three models. Both ESRL and GEFS present the relationship between GVF and heat wave hindcast is positive, but negative relationship is shown on the GMAO. In addition, both ESRL and GEFS modelsunderestimate latent heat flux, but overestimate sensible heat flux in the Midwest. Therefore, for both ESRL and GEFS models, the relationship between heat wave and sensible heat fluxes (or GVF) is positive, and negative for the relationship between heat wave and latent heat flux (or evapotranspiration). In contrast, the GMAO model overestimates both latent and sensible heat fluxes in the Midwest. Therefore, for the GMAO model, the relationship between heat wave and latent/sensible heat fluxes (or GVF) is positive, and negative for the relationship between heat wave and evapotranspiration.

This two part analysis looks at forecasting models in the United States' 2016 presidential election and breaks down the elections fundamental and contingency factors. This paper argues that political science forecasting models could be improved through a more localized approach and by utilizing additional contingency factors. The psychology study of this analysis explores the already established relationship between political conservatism and favorability ratings, as well as the relationship between perceived similarity between voter personality and candidate personality, referred to as personality mirroring, and favorability ratings. The study uses past research to suggest that these relationships for the 2016 presidential candidates, Hillary Clinton and Donald Trump, can be explained through mediating variables: leader effectiveness and trust. The study used participants recruited through Amazon’s Mechanical Turk for data, all adults who voted in the 2016 US presidential election. The study found that there was a full mediation of leadership effectiveness for Donald Trump and significant partial mediation for the other three explored relationships.

O trabalho aqui apresentado é uma análise retrospectiva de modelos desenvolvidos, no Brasil, sobre o estudo da previsão de insolvência das empresas, objetivando-se avaliar a aplicação de métodos quantitativos para fins de análise de demonstrações contábeis. Considera-se que é relevante a avaliação da continuidade da empresa, e que, se for possível identificar fato em contrário, o uso de modelos de previsão é de importância no que tange à decisão de concessão de crédito, tanto no âmbito da intermediação financeira, realizada pelos bancos, quanto no âmbito de transações comerciais entre fornecedores e clientes. Desta última, pode-se inferir sobre a avaliação da concessão ou não da Concordata para uma empresa, servindo aqueles modelos como ferramental de análise da capacidade da empresa em cumprir o acordo da concordata, ponto esse explorado nesta pesquisa. Através da aplicação dos modelos sobre uma amostra de empresas que haviam solicitado a concordata, pôde-se avaliar se mantinham uma capacidade de discriminar as empresas que lograriam êxito na concordata. Como ferramental estatístico, é utilizada a Análise Discriminante, técnica de análise multivariada, que busca classificar os dados em dois grupos específicos. Neste trabalho, foram definidos como grupo de empresas solventes e grupo de empresas insolventes. Verificou-se que as premissas para utilização da técnica estatística de Análise Discriminante podem limitar, não invalidar, esses modelos. Há necessidade de se avaliarem os dados das amostras para se verificar se é possível ou não o uso da técnica de Análise Discriminante, além do que necessitam recorrentemente, de ser recalculados. Essa limitação reduziu-se quando se utilizaram os modelos em conjunto ou integrados, como verificado nos testes realizados. Outra técnica utilizada nesse estudo foi a de se gerar um modelo que congregue os melhores indicadores dos modelos analisados, obtendo-se um modelo de previsão, que pode ser considerado híbrido ou misto. Esse modelo foi testado quanto à sua capacidade de avaliar se as empresas concluiriam suas concordatas e, também, em sua capacidade de discriminar as empresas nos dois grupos anteriormente descritos (Solventes e Insolventes), ambos formados por empresas situadas em Belo Horizonte, Betim e Contagem. Como ressaltado, existem limitações ao uso desses modelos, que se iniciam pela própria ferramenta da Análise Discriminante. Porém, a sua utilização pode tornar mais objetiva a decisão de se conceder ou não a Concordata a uma empresa, ou, até mesmo, uma linha de crédito especial para cliente de um fornecedor ou de uma instituição bancária que se encontre nessa situação. Portanto, verificou-se ser possível, através das demonstrações contábeis das empresas objeto do estudo, a previsão da tendência de solvência ou insolvência daquelas, avaliando-se se lograriam êxito com a concordata. / This study is a retrospective analysis of models developed in Brazil with respect to the study of forecasting company insolvency, aimed at evaluating the application of quantitative methods to the financial analysis of financial statements. Evaluating the going-concern of companies is considered relevant. If facts can be identified indicating the opposite, the use of forecasting models is important what the decision on the extension of credit is concerned, not only in the field of financial intermediation, realized by banks, but also in the field of commercial transactions between suppliers and clients. From this decision, inferences can be made about the evaluation of whether a composition of debt will be conceded to a company, in which the models mentioned above will serve as tools for analyzing the company’s capacity to fulfill the composition agreement, an issue that is dealt with in this research. By means of the application of those models to a sample of companies that had applied for composition of debt, it could be evaluated whether the models maintained their capacity to distinguish the companies that were successful in the composition of debt. As a statistical tool, the Discriminant Analysis is used. This is a multivariate analysis technique that seeks to classify the data in two specific groups. In this study, they were defined as solvent companies group and insolvent companies group. It was verified that the premises for using the statistical technique of Discriminant Analysis can limit, but not invalidate these models. The data of the samples need to be assessed in order to verify whether it is possible or not to use the Discriminant Analysis technique. In addition, they recurrently need to be recalculated. This limitation was reduced when the models were used together or in an integrated way, as verified in the accomplished tests. Another technique used in this study was the creation of a model that unites the best indicators of the models that were analyzed, obtaining a forecasting model, which can be considered a hybrid or mixed. This model was tested for its capacity to evaluate whether the companies would conclude the composition of debt as well as its capacity to discriminate the companies in the two groups previously described (Solvent and Insolvent), both of which consist of companies located in Belo Horizonte, Betim and Contagem. As highlighted, the use of these models is limited, starting with the Discriminant Analysis tool itself. Nevertheless, their utilization can make the decision on the concession of debt composition to a company more objective, or even the decision on extending a special credit line to the customer of a supplier or to the client of a bank who finds himself in this situation. Therefore, it was confirmed that the analysis of the financial statements of the firms included in this study permits to forecast the possibility to determine the solvency or insolvency trend of the firms, as well as to assess their eventual success with the concordat.

期望波動度在投資組合的選擇、避險策略、資產管理，以及金融資產的評價上是關鍵性因素，因此，在波動度變化甚巨的金融市場中，找出具有良好預測波動度能力的模型，是絕對必要的。過去從事資產價格行為的相關研究都假設資產的價格過程是隨機的，且呈對數常態分配、變異數固定。然而實證結果一再顯示：變異數是隨時間而變動的（如 Mandelbrot(1963)、 Fama(1965)）。為預測波動度（或變異數），Eagle(1982)首先提出了 ARCH 模型，允許預期條件變異數作為過去殘差的函數，因此變異數能隨時間而改變。此後 Bollerslve(1986)提出 GARCH 模型，修正ARCH 模型線性遞減遞延結構，將過去的殘差及變異數同時納入條件變異數方程式中。 Nelson(1991)則提出 EGARCH 模型以改進 GARCH 模型的三大缺點，此模型對具有高度波動性的金融資產提供更成功的另一估計模式。除上列之 ARCH-type 模型外，Hull and White(1987)提出連續型隨機波動模型(continuous time stochastic volatility model)，用以評價股價選擇權，此模型不僅將過去的變異數納入條件變異數的方程式中，同時該條件變異數也會因隨機噪音(random noise)而變動。近年來，上述模型均被廣泛運用在模擬金融資產的波動性，均是相當實用的模型。 本文以隨機漫步(random walk)、GARCH(1,1)、EGARCH(1,1)及隨機波動模型(stochastic volatility)進行不同期間下，股價指數與外匯波動度之預測，並以實證結果判斷上述四種模型在預測外匯及股價指數波動度的能力表現。實證結果顯示：隨機波動模型不論在股價指數或外匯、長期或短期的波動度預測上，都是最佳的波動度預測模型，因此建議各大金融機構可採隨機波動模型預測金融資產未來的波動度。 / Volatility forecast is extremely important factor in portfolio chice, hedging strategies, asset management, asset pricing and option pricing. Identifying a good forecast model of volatility is absolutely necessary, especially for the highly volatile Taiwan stock marek. Due to increasing attention to the impact of marke risk on asset returns, academic researchers and practicians have developed ways to control risk and methodologies to forecast return volatility. Past researches on asset price behavior usually assumed that asset price behavior follows random walk, and its probability distribution is a log-normal distribution with a constant variance (or constant volatility). This assumption is in fact in violation of empirical evidence showing that volatility tends to vary over time (e.g., Mandelbrot﹝1963﹞ and Fama﹝1965﹞). To forecast volatility (or variance), Engle(1982) is the first scholar to propose a forecast model, now well-known as ARCH, whose conditional variance is a funtion of past squared returns residuals. Accordingly, the forecast variance(or volatility) varies over time. Bollerslev(1986) proposed a generalized model, called GARCH, which allows the current conditional variance depends not only on past squared residuals, but also on past conditional variances. However, Nelson(1991) has recently proposed a new model, called EGARCH, which attempts to remove the weakness of the GARCH model. The EGARCH model has been shown to be successful to forecast volatility and to describe successful stock price behavior. In addition, Hull and white(1987) employed a continuous-time stochastic volatility model to develop in option pricing model. Their stochastic volatility model not only admits the past variance, but also depends on random noise of volatility. The above-mentioned models have been widely implemented in practice to simulate and to forecast asset return volatility. This thesis investigates whether random walk, GARCH(1,1), EGARCH(1,1) and stochastic volatility model differ in their ability to predict the volatility of stock index and currency returns over short-term and long-term horizons. The results strongly support that the best volatility predictions are generated by the stochasic volatility model. Therefore, it is recommended that financial institutions may adopt stochastic volatility model to predict asset return volatility.

Trata da apresentação e discussão de um modelo de previsão de demanda de médicos para atendimentos de pacientes internados pelo SUS, com estudo de caso para o Estado do Rio de Janeiro. O modelo é baseado nos dados do Sistema de Informações Hospitalares do SUS (SIH/SUS) e nas alterações esperadas de tamanho e composição da população, segundo o IBGE. Descreve a trajetória e a motivação que levaram à construção do modelo, a partir da ideia de maior utilização do enorme potencial das bases de dados brasileiras para o planeamento e gestão dos RHS. Faz também comentários sobre conceitos da Tecnologia da Informação, que são de interesse para uma melhor compreensão das bases de dados, incluindo a utilização de padrões. Apresenta e comenta os resultados da aplicação do modelo, para o período de 2002 a 2022, para o Estado do Rio de Janeiro. Propõe sugestões de pesquisas com objetivo de melhorar a integração entre as bases de dados estudadas, a discussão da construção e utilização de indicadores, assim como uma proposta de evolução para o apoio à decisão na área de RHS. / The text presents and discuss forecast model of physicians demand attendance of patients admitted on the SUS, with a study of case for the State of Rio de Janeiro. The model is based on the data of Hospital Information System of SUS (SIH/SUS) and on the foresight alterations of size and composition of the population, according to the Brazilian Institute of Geography and Statistics (IBGE). It describes the trajectory and the motivation that had taken to the model set up, starting from the idea of a better utilization of the enormous potential of the Brazilian databases for the planning and management of the Health Human Resources (HHR). The text also brings some commentaries on concepts of the Information Technology, that are of interest for better understanding of databases, including the use of standards. The results of the models application for the period of 2002 the 2022, for the State of Rio de Janeiro, are presented and discussed. Several data sources were studied previously the models set up. Suggestions of research with objective to improve the integration among the studied databases are presented, as well as the quarrel of the construction and use of indicators and a proposal of further research on the support to the decision in the HHR field.

Trata da apresentação e discussão de um modelo de previsão de demanda de médicos para atendimentos de pacientes internados pelo SUS, com estudo de caso para o Estado do Rio de Janeiro. O modelo é baseado nos dados do Sistema de Informações Hospitalares do SUS (SIH/SUS) e nas alterações esperadas de tamanho e composição da população, segundo o IBGE. Descreve a trajetória e a motivação que levaram à construção do modelo, a partir da ideia de maior utilização do enorme potencial das bases de dados brasileiras para o planeamento e gestão dos RHS. Faz também comentários sobre conceitos da Tecnologia da Informação, que são de interesse para uma melhor compreensão das bases de dados, incluindo a utilização de padrões. Apresenta e comenta os resultados da aplicação do modelo, para o período de 2002 a 2022, para o Estado do Rio de Janeiro. Propõe sugestões de pesquisas com objetivo de melhorar a integração entre as bases de dados estudadas, a discussão da construção e utilização de indicadores, assim como uma proposta de evolução para o apoio à decisão na área de RHS. / The text presents and discuss forecast model of physicians demand attendance of patients admitted on the SUS, with a study of case for the State of Rio de Janeiro. The model is based on the data of Hospital Information System of SUS (SIH/SUS) and on the foresight alterations of size and composition of the population, according to the Brazilian Institute of Geography and Statistics (IBGE). It describes the trajectory and the motivation that had taken to the model set up, starting from the idea of a better utilization of the enormous potential of the Brazilian databases for the planning and management of the Health Human Resources (HHR). The text also brings some commentaries on concepts of the Information Technology, that are of interest for better understanding of databases, including the use of standards. The results of the models application for the period of 2002 the 2022, for the State of Rio de Janeiro, are presented and discussed. Several data sources were studied previously the models set up. Suggestions of research with objective to improve the integration among the studied databases are presented, as well as the quarrel of the construction and use of indicators and a proposal of further research on the support to the decision in the HHR field.

O trabalho aqui apresentado é uma análise retrospectiva de modelos desenvolvidos, no Brasil, sobre o estudo da previsão de insolvência das empresas, objetivando-se avaliar a aplicação de métodos quantitativos para fins de análise de demonstrações contábeis. Considera-se que é relevante a avaliação da continuidade da empresa, e que, se for possível identificar fato em contrário, o uso de modelos de previsão é de importância no que tange à decisão de concessão de crédito, tanto no âmbito da intermediação financeira, realizada pelos bancos, quanto no âmbito de transações comerciais entre fornecedores e clientes. Desta última, pode-se inferir sobre a avaliação da concessão ou não da Concordata para uma empresa, servindo aqueles modelos como ferramental de análise da capacidade da empresa em cumprir o acordo da concordata, ponto esse explorado nesta pesquisa. Através da aplicação dos modelos sobre uma amostra de empresas que haviam solicitado a concordata, pôde-se avaliar se mantinham uma capacidade de discriminar as empresas que lograriam êxito na concordata. Como ferramental estatístico, é utilizada a Análise Discriminante, técnica de análise multivariada, que busca classificar os dados em dois grupos específicos. Neste trabalho, foram definidos como grupo de empresas solventes e grupo de empresas insolventes. Verificou-se que as premissas para utilização da técnica estatística de Análise Discriminante podem limitar, não invalidar, esses modelos. Há necessidade de se avaliarem os dados das amostras para se verificar se é possível ou não o uso da técnica de Análise Discriminante, além do que necessitam recorrentemente, de ser recalculados. Essa limitação reduziu-se quando se utilizaram os modelos em conjunto ou integrados, como verificado nos testes realizados. Outra técnica utilizada nesse estudo foi a de se gerar um modelo que congregue os melhores indicadores dos modelos analisados, obtendo-se um modelo de previsão, que pode ser considerado híbrido ou misto. Esse modelo foi testado quanto à sua capacidade de avaliar se as empresas concluiriam suas concordatas e, também, em sua capacidade de discriminar as empresas nos dois grupos anteriormente descritos (Solventes e Insolventes), ambos formados por empresas situadas em Belo Horizonte, Betim e Contagem. Como ressaltado, existem limitações ao uso desses modelos, que se iniciam pela própria ferramenta da Análise Discriminante. Porém, a sua utilização pode tornar mais objetiva a decisão de se conceder ou não a Concordata a uma empresa, ou, até mesmo, uma linha de crédito especial para cliente de um fornecedor ou de uma instituição bancária que se encontre nessa situação. Portanto, verificou-se ser possível, através das demonstrações contábeis das empresas objeto do estudo, a previsão da tendência de solvência ou insolvência daquelas, avaliando-se se lograriam êxito com a concordata. / This study is a retrospective analysis of models developed in Brazil with respect to the study of forecasting company insolvency, aimed at evaluating the application of quantitative methods to the financial analysis of financial statements. Evaluating the going-concern of companies is considered relevant. If facts can be identified indicating the opposite, the use of forecasting models is important what the decision on the extension of credit is concerned, not only in the field of financial intermediation, realized by banks, but also in the field of commercial transactions between suppliers and clients. From this decision, inferences can be made about the evaluation of whether a composition of debt will be conceded to a company, in which the models mentioned above will serve as tools for analyzing the company’s capacity to fulfill the composition agreement, an issue that is dealt with in this research. By means of the application of those models to a sample of companies that had applied for composition of debt, it could be evaluated whether the models maintained their capacity to distinguish the companies that were successful in the composition of debt. As a statistical tool, the Discriminant Analysis is used. This is a multivariate analysis technique that seeks to classify the data in two specific groups. In this study, they were defined as solvent companies group and insolvent companies group. It was verified that the premises for using the statistical technique of Discriminant Analysis can limit, but not invalidate these models. The data of the samples need to be assessed in order to verify whether it is possible or not to use the Discriminant Analysis technique. In addition, they recurrently need to be recalculated. This limitation was reduced when the models were used together or in an integrated way, as verified in the accomplished tests. Another technique used in this study was the creation of a model that unites the best indicators of the models that were analyzed, obtaining a forecasting model, which can be considered a hybrid or mixed. This model was tested for its capacity to evaluate whether the companies would conclude the composition of debt as well as its capacity to discriminate the companies in the two groups previously described (Solvent and Insolvent), both of which consist of companies located in Belo Horizonte, Betim and Contagem. As highlighted, the use of these models is limited, starting with the Discriminant Analysis tool itself. Nevertheless, their utilization can make the decision on the concession of debt composition to a company more objective, or even the decision on extending a special credit line to the customer of a supplier or to the client of a bank who finds himself in this situation. Therefore, it was confirmed that the analysis of the financial statements of the firms included in this study permits to forecast the possibility to determine the solvency or insolvency trend of the firms, as well as to assess their eventual success with the concordat.

Låga moln definieras av att ha molnbasen från 0 till 2 km ovanför markytan. Molnbildande bygger på att den relativa fuktigheten stiger med höjden tills vattenångan i luften kondenseras. Prognosmodeller för moln bygger på grundläggande termodynamiska och fluiddynamiska ekvationer. Områden delas in i ett rutnät och ekvationerna löses med numeriska metoder. För jämförelse kan mätinstrument samt observationer användas, såsom ceilometrar, radar eller observatörer.  Resultat från fyra olika experiment med prognosmodeller för moln används i detta arbete, som är en litteraturstudie för att undersöka modellers förmåga att simulera låga moln. Olika platser, på global och lokal skala, undersöks. Makroskopiska parametrar såsom molnandel och molnfrekvens är i fokus.  WRF-modellen fungerar bäst med 12 km horisontell upplösning, med en viss överskattning av molnfrekvensen. Modellen CAM5 simulerar molnandel väl men vatteninnehåll och isinnehåll underskattas respektive överskattas. Säsongscykler av låga moln fångas väl av modellerna ECMWF, ARPEGE, RACMO och Met Office, med viss överskattning från samtliga modeller. GFS-modellen överskattar molnandelen långt från ekvatorn med upp mot 80% men underskattar nära ekvatorn med 10–20%. Överskattningar och underskattningar kan bero på faktorer såsom otillräcklig representation av mikrofysik eller möjligtvis felaktiga mätdata. Det denna studie visar är däremot att prognosmodeller på lokal skala kan ge bra simuleringar av makroskopiska parametrar av låga moln. / Cloud types are defined by the height of their bases. Low-level clouds have cloud base heights between 0 and 2 km. They are formed when the relative humidity in the air reaches 100 %, leading to the formation of cloud droplets. Forecast models simulate clouds by integrating thermodynamic and fluid dynamic equations using numerical methods. Instruments and observations, such as ceilometers or observers, are used to assess the accuracy of these simulations.  This study uses four previous works, where forecast models have been used to forecast clouds, to study the accuracy of low-level cloud forecasts. This is done on both local and global scales, focusing on macroscopic characteristics such as cloud fractions and frequencies. The results show that the WRF model works best with a horizontal resolution of 12 km, with slight overestimation of cloud frequencies. The climate model CAM5 simulates cloud fractions well, but liquid- and ice content deviate significantly from measurements. Seasonal cycles are generated well by ECMWF, ARPEGE, RACMO and Met Office Unified Model, with reoccurring overestimations by all models. The GFS model overestimates cloud fractions in higher latitudes by up to 80%, but underestimates near the equator by 10-20%.  Lacking representation of microphysics in the models, or faulty data, can be the causes for deviations in the models. However, this study has shown that forecast models can simulate macroscopic parameters of low-level clouds on a local scale well.

A Dissertação apresenta as principais mudanças verificadas no Setor Elétrico Brasileiro e os seus reflexos nas atividades de planejamento e estudos de mercado. Examina os desafios do mercado de energia para as distribuidoras de energia elétrica, advindos com o dinamismo comercial e as obrigações institucionais provenientes do novo contexto setorial. Evidencia que os estudos de mercado podem apoiar significativamente as distribuidoras, necessitando para tanto, efetuar mudanças estruturais e metodológicas. A utilização de modelos baseados em usos finais de energia e técnicas de cenários é a mais adequada. / After the restructuring of the Brazilian power sector during the 1990’s, a new set of rules and players (regulators, traders, etc.) was introduced. This situation resulted in significantly impacting the distribution companies in terms of market risks and commercial opportunities. Electric power market assessments that provide fundamental information to the system and tariff planning groups can also be used to support the distribution companies in analyzing various questions within the new rules that have been created. These questions include such items as: How much energy should be contracted in the future in the wholesale market? How much opportunity is available in offering commercial services to the customer? This work describes the main changes that have occurred due to the restructuring, such as: the privatization process, a wholesale market implementation, rules for energy trading, and planning and regulatory process. The main challenges for distribution companies as a result of these changes are identified, especially focusing on energy trading in retail and wholesale markets. The process of electric power market assessments is presented and describing the accepted methodology used for demand forecasting for distribution companies. Information required by distribution companies in order to deal with the market challenges are specified – e.g. energy trading, market risks and customer relationship. It is concluded there is a need to obtain detailed information about consumers and to develop market forecast for specific time frame. It must take into consideration all the issues around the retail market – the study needs to analyze the basic factors that impact customer consumption. In order to improve the electric market assessment, it has been useful to apply specific models. After reviewing the existing tools for electric power market assessments (analysis and forecast), it has been found that the models that combined methods of end use analysis with scenario analysis are most effective. In this way, the distribution companies needs are met and the process also results in better market data for planning and regulatory agencies.

Weather forecasting models are computationally intensive applications and traditionally they are executed in parallel machines. However, some issues prevent these models from fully exploiting the available computing power. One of such issues is load imbalance, i.e., the uneven distribution of load across the processors of the parallel machine. Since weather models are typically synchronous applications, that is, all tasks synchronize at every time-step, the execution time is determined by the slowest task. The causes of such imbalance are either static (e.g. topography) or dynamic (e.g. shortwave radiation, moving thunderstorms). Various techniques, often embedded in the application’s source code, have been used to address both sources. However, these techniques are inflexible and hard to use in legacy codes. In this thesis, we explore the concept of processor virtualization for dynamically balancing the load in weather models. This means that the domain is over-decomposed in more tasks than the available processors. Assuming that many tasks can be safely executed in a single processor, each processor is put in charge of a set of tasks. In addition, the system can migrate some of them from overloaded processors to underloaded ones when it detects load imbalance. This approach has the advantage of decoupling the application from the load balancing strategy. Our objective is to show that processor virtualization can be applied to weather models as long as an appropriate strategy for migrations is used. Our proposal takes into account the communication pattern of the application in addition to the load of each processor. In this text, we present the techniques used to minimize the amount of change needed in order to apply processor virtualization to a real-world application. Furthermore, we analyze the effects caused by the frequency at which the load balancer is invoked and a threshold that activates rebalancing. We propose an automatic strategy to find an optimal threshold to trigger load balancing. These strategies are centralized and work well for moderately large machines. For larger machines, we present a fully distributed algorithm and analyze its performance. As a study case, we demonstrate the effectiveness of our approach for dynamically balancing the load in Brams, a mesoscale weather forecasting model based on MPI parallelization. We choose this model because it presents a considerable load imbalance caused by localized thunderstorms. In addition, we analyze how other effects of processor virtualization can improve performance.