XTREND consists of the following methodical Parts. Time interval extraction (Part 1) to analyse different parts of a time series; extreme events detection (Part 2) with robust smoothing; magnitude classification (Part 3) by hand; occurrence rate estimation (Part 4) with kernel functions; bootstrap simulations (Part 5) to estimate confidence bands around the occurrence rate. You work interactively with XTREND (parameter adjustment, calculation, graphics) to acquire more intuition for your data. Although, using “normal” data sizes (less than, say, 1000) and modern machines, the computing time seems to be acceptable (less than a few minutes), parameter adjustment should be done carefully to avoid spurious results or, on the other hand, too long computing times. This Report helps you to achieve that. Although it explains the statistical concepts used, this is generally done with less detail, and you should consult the given references (which include some textbooks) for a deeper understanding.

No description available.

Aus der Einleitung: The calculation and prediction of river flow is a very old problem. Especially extremely high values of the runoff can cause enormous economic damage. A system which precisely predicts the runoff and warns in case of a flood event can prevent a high amount of the damages. On the basis of a good flood forecast, one can take action by preventive methods and warnings. An efficient constructional flood retention can reduce the effects of a flood event enormously.With a precise runoff prediction with longer lead times (>48h), the dam administration is enabled to give order to their gatekeepers to empty dams and reservoirs very fast, following a smart strategy. With a good timing, that enables the dams later to store and retain the peak of the flood and to reduce all effects of damage in the downstream. A warning of people in possible flooded areas with greater lead time, enables them to evacuate not fixed things like cars, computers, important documents and so on. Additionally it is possible to use the underlying rainfall-runoff model to perform runoff simulations to find out which areas are threatened at which precipitation events and associated runoff in the river. Altogether these methods can avoid a huge amount of economic damage.

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.

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.

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.

With the insertion of the numerical models, it became possible to carry out a more reliable and short-term forecast of the time in search of more efficient results besides being necessary to improve the model and to acquire knowledge of its performance. In this work we apply simple statistical methods to compare the performance of the model by comparing the meteorological variables with observed data from an automated weather station (AWS) and data simulated by the WRF model. The analyzed data of these variables were from July 10 to 19, 2017, using statistical and computational tools: atmospheric mesoscale model (WRF), spreadsheets, as well as specific programming languages and scripts for data execution. The (AWS) from which the observed data was extracted is located in Maceió. The WRF simulations were validated through data series and statistical analyzes and it was verified that in the variables there was efficiency of the WRF in the predictions. The results expected with correlation coefficient between average and strong for 24h in most simulated and a mean correlation for 48h and 72h in most of the simulated, it was possible to conclude that the model is adjusted to predict average values and that in some moments that the minimum and maximum results could not be simulated, it is therefore possible to indicate the model as a tool to be used to carry out short-term forecasting provided there is updating of topography and land use for the city of Maceió. In this research it was possible to obtain expected results, but the equipment of measurement and data processing can be improved to obtain even more satisfactory results. / Neste trabalho aplicam-se métodos simples de estatística para comparar o desempenho do modelo realizando o comparativo das variáveis meteorológicas com dados observados de uma estação automática e dados simulados pelo modelo WRF. Com a inserção dos modelos numéricos, se tornou possível realizar previsão do tempo com melhor confiabilidade e um curto prazo em busca de resultados mais eficientes. Os dados analisados dessas variáveis foram do período de 10 a 19 de julho de 2017, utiliza-se estatística e ferramentas computacionais: modelo atmosférico de mesoescala (WRF), planilhas eletrônicas, além de linguagens de programação específicas e scripts para execução dos dados. A estação automática da qual foram extraídos os dados observados está localizada em Maceió. As simulações do WRF foram validadas através de séries de dados e análises estatísticas e ficou comprovado que nas variáveis houve eficiência do WRF nas previsões. Os resultados mostraram-se eficiente com coeficiente de correlação entre média e forte para 24h na maioria dos simulados e uma correlação média para 48h e 72h na maioria dos simulados, foi possível concluir que o modelo está ajustado para prever valores médios e que em alguns momentos que os resultados mínimos e máximos não conseguiram ser simulados, logo é possível indicar o modelo como uma ferramenta a ser utilizada para realizar previsão de curto prazo desde que haja atualização de topografia e uso do solo para o município de Maceió. Nessa pesquisa foi possível obter resultados satisfatórios, porém pode-se aperfeiçoar os equipamentos de medição e processamento de dados para se obter resultados ainda mais satisfatórios.

This thesis consists of three chapters, each dealing with a different aspect of the impact of climate change on agriculture: the analysis of past evidence, the possible new solutions and the anticipation of future problems. The topics chosen are different but complementary and reflect the complex and multifaceted impact of this phenomenon on agriculture. This work uses global spatial data and information from the literature, combines weather forecast with a crop model, and uses an economic model coupled with robust econometric estimation approaches. The findings indicate that major crop yields in tropical and subtropical regions will likely suffer adverse effects, while temperate and continental areas, historically less favourable for agriculture, may experience mainly positive impacts. Under a medium development scenario, global crop production is projected to remain largely unaffected, masking a compensatory mechanism between tropical and temperate regions. Adaptation covers a significant positive role, and short- and medium-range weather forecasting can be an important and affordable tool for farmers to adapt their agricultural practices, if they know how to use it. The adoption of such meteorological information can enable rural households in developing countries to increase yields of staple crops, although the potential contribution of it may be hampered by social and economic barriers. However, adaptation in agriculture can have negative externalities, potentially creating a vicious circle, and the livestock sector is particularly vulnerable. Indeed, changing climate conditions may induce farmers to adjust the distribution of grazing livestock per unit of land in order to maximise profits. Temperate and continental countries may increase the number of grazing livestock per unit of land as climatic conditions improve for agricultural purposes, thereby increasing carbon dioxide emissions. On the other hand, tropical areas, mainly populated by developing countries, will see a deterioration of agricultural conditions and less livestock can be raised on rangelands and pasturelands. Once again, countries with pressing agricultural productivity needs bear a disproportionate burden of climate change effects, exacerbating already precarious living conditions. Conversely, northern countries, primarily developed, are likely to experience more beneficial effects.

Coronal Mass Ejections (CMEs) and solar flares are energetic events taking place at the Sun that can affect the space weather or the near-Earth environment by the release of vast quantities of electromagnetic radiation and charged particles. Solar active regions are the areas where most flares and CMEs originate. Studying the associations among sunspot groups, flares, filaments, and CMEs is helpful in understanding the possible cause and effect relationships between these events and features. Forecasting space weather in a timely manner is important for protecting technological systems and human life on earth and in space. The research presented in this thesis introduces novel, fully computerised, machine learning-based decision rules and models that can be used within a system design for automated space weather forecasting. The system design in this work consists of three stages: (1) designing computer tools to find the associations among sunspot groups, flares, filaments, and CMEs (2) applying machine learning algorithms to the associations' datasets and (3) studying the evolution patterns of sunspot groups using time-series methods. Machine learning algorithms are used to provide computerised learning rules and models that enable the system to provide automated prediction of CMEs, flares, and evolution patterns of sunspot groups. These numerical rules are extracted from the characteristics, associations, and time-series analysis of the available historical solar data. The training of machine learning algorithms is based on data sets created by investigating the associations among sunspots, filaments, flares, and CMEs. Evolution patterns of sunspot areas and McIntosh classifications are analysed using a statistical machine learning method, namely the Hidden Markov Model (HMM).

7. SUMMARY This diploma thesis The information value and acceptability of forecasting is divided into a theoretical and a practical part. In the theoretical part the forecasting is described as a product of television newscast. I deal with the fact of forecasting entering the media communication. I deal with the information value that forecasts bring and show the way a piece of information is transferred from a producer to an addressee relating to the process of coding and decoding. I describe the acceptability of forecasts and its elements influencing comprehension. In relation to acceptability, textual linguistics is discussed in a separated subchapter. The last theoretical part deals with semiotics and semantics. These disciplines deal mainly with meaning and sings and that is the reason why they are not important only in the theoretical part but also in an analyzing part of this thesis. The analyzing part is based on semiotic survey analysis dealing with individual components of forecasts (visual, audio and audio-visual). The aim of this thesis is to find out which of components of forecasting brings the highest information value to respondents and which is best comprehensible for addressees. The survey results demonstrate that it is the audio forecasting that has a high information value and brings...