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This paper describes the file format of the climate data container used by the DELPHIN, THERAKLES and NANDRAD simulation programs. The climate data container format holds a binary representation of annual and continuous climatic data needed for hygrothermal transport and building energy simulation models. The content of the C6B-Format is roughly equivalent to the epw-climate data format.

Made available in DSpace on 2017-07-21T14:19:33Z (GMT). No. of bitstreams: 1 Luiz Rafael Schmitke.pdf: 1854453 bytes, checksum: c7e3cc9cb3865213cd2b9f59a4cf211c (MD5) Previous issue date: 2012-06-30 / Agriculture is an activity where the weather has more impact, influencing techniques and crops employed. Much of the agricultural productivity is affected by climatic conditions that are created by natural factors and are not likely to control. Although you can’t control the weather, we can predict it, or even simulate their conditions to try minimize its impact on agriculture. To be able to make these predictions and simulations are necessary data collected from weather stations that can be conventional or automatic and must be without gaps or abnormal data. Most of these errors are caused by signal interference, disconnection, oxidation of cables and spatio-temporal variation of climate which consequently end up generating those problems at the climates bases. Thus, this research work has as main objective to create a model capable of correcting gaps in climate databases, observing that not to correct abnormal observations or replace statistical methods for the same purpose. Therefore a model was created to correct the gaps in weather data between stations using the P2P architecture. With this model, an application was created to test its performance to correct the gaps. Also to perform the tests were used bases in the cities of Ponta Grossa, Fernandes Pinheiro and Telêmaco Borba provided by Instituto Tecnológico SIMEPAR, and bases of the cities of Castro, Carambeí, Pirai do Sul and Tibagi provided by Fundação ABC, which are collected daily on automatic stations. As a result it was observed that the performance of P2P correction model was satisfactory when compared to the simulator used in the tests, with lower results only in February, which corresponds to the period of summer, to the autumn, winter and spring the model P2P was better than simulated. Although it was found that the number of stations participating in the network at the time of correcting influences the results, and the higher it is, the better the results obtained with the correcting. / A agricultura é uma das atividades onde o clima tem mais impacto, influenciando as técnicas e os cultivos empregados. Grande parte da produtividade agrícola se deve as condições climáticas que são criadas por fatores naturais e não são passíveis de controle. Embora não seja possível controlar o clima, pode-se prevê-lo ou até simular suas condições para tentar minimizar seu impacto na agricultura. Para que seja possível realizar estas previsões e simulações são necessários dados coletados em estações climáticas que podem ser convencionais ou automáticas e que precisam estar sem dados anormais ou lacunas. Grande parte desses erros se deve a interferência no sinal, desconexão, oxidação de cabos e a variação espaço-temporal do clima que por consequência acabam gerando aqueles problemas nas bases climáticas. Desta forma, este trabalho de pesquisa tem como objetivo principal criar um modelo capaz de corrigir as lacunas existentes nas bases de dados climáticas, salientando-se que não visa à correção de observações anormais e nem a substituição dos métodos estatísticos para o mesmo fim. Para tanto foi criado um modelo de correção das lacunas em dados climáticos entre as estações utilizando a arquitetura P2P. Com este modelo, foi criada uma aplicação para testar seu desempenho em corrigir as lacunas encontradas. Também para a realização dos testes foram utilizadas bases das cidades de Ponta Grossa, Fernades Pinheiro e Telêmaco Borba, fornecidas pelo Instituto Tecnológico SIMEPAR, e bases das cidades de Castro, Carambeí, Tibagi e Pirai do Sul fornecidas pela Fundação ABC, sendo estes dados, diários e coletados em estações automáticas. Como resultados foi possível observar que o desempenho do modelo de correção P2P foi satisfatório quando comparado ao simulador utilizado nos testes, apresentando resultados inferiores somente no mês de fevereiro, que corresponde ao período de verão, para as estações de outono, inverno e primavera o modelo P2P foi melhor que o simulado. Ainda foi verificado que a quantidade de estações que participa da rede na hora da correção influencia os resultados, sendo que quanto maior ela for, melhores são os resultados obtidos com a correção. Palavras-chave: Redes P2P, Correção, Dados Climáticos.

The worldwide challenges to achieve cost effective protection against global warming impacts and to acquire reliable decision making tools continually force new developments in the area of climate change research. Climate change impacts projections involve several steps: emission scenarios generation, Global Circulation Models and Regional Climate Models (GCM/RCM) runs, downscaling, impact model running, analysis of results and decision making. Unfortunately, GCM/RCMs outputs are often biased and need to be processed before being fed into impact models. This thesis describes the effort carried out to alleviate the burden of downscaling coarse hydro-climatology data outputs from GCM/RCM and making results readily available for climate change impact analysis for specific regions, particularly in the African continent. GCM/RCM outputs are highly unreliable at the sub-grid scale to be used for region specific impact analysis (Wilby, Hay, & Leavesly, 1999). Furthermore, raw GCM/RCM outputs are often downscaled under the premises that the latter offer very coarse spatial resolution. The Internet is a common resource for users of climate change data to access relevant information. Web-based interfaces offer users the capability to retrieve such data. This thesis involves the development of a new web-portal, which addresses the demand for climate change data at the daily scale. It is a user-friendly interactive web-based interface with multiple functionalities including: capacity to process information, capacity to search, sort, retrieve and filter data and download features. Six climate variables are considered in this project: precipitation, maximum temperature, minimum temperature, wind speed, relative humidity and solar radiation. The aforementioned climate variables have been downscaled to specific geographical locations and results have been made available at a fine temporal resolution – the daily scale. The data portal currently hosts climate change data for nine stations in western Africa: Agadez, Brini N’Konni, Gaya, Maine Soroa, Maradi Airport, Niamey Airport, Tahoua, Tillabery and Zinder Airport. The above mentioned climate stations are all located in Niger. Nonetheless, the project aims to expand and cover further ground in Africa. Quantile - Quantile downscaling, also known as Quantile-Quantile mapping, matching or transformation is a statistical procedure used in this project to downscale raw GCM/RCM outputs. GCM/RCM outputs from the AMMA-Ensemble sets under the SRES A1B scenario were used as raw data.

Cyclones are complex weather phenomena, affected by multiple variables such as pressure, wind, temperature and more. Therefore, how cyclones are formed, what affects them and how they can be tracked is still actively researched today. Cyclones can have multiple centers (eyes), which can split and merge during its lifetime, which make them even more complex to define mathematically. In this thesis, how multi-center cyclones can be meaningfully visualized for domain scientists using multivariate visualization is investigated. An important aspect of the visualization is how a cyclone’s spread and boundary can be defined. The result is a visualization where the cyclonic region is defined by segmenting a pressure volume, and then a surface is extracted to get the cyclone’s boundary. Temperature is visualized using color mapping onto surfaces, while the wind velocity is shown using particles. The framework allows domain scientists to affect the visualization by picking criteria for segmenting the volume, color maps, and more. In conclusion, an improved cyclonic region could be defined by using multiple fields instead of only pressure, and the visualization would be improved with a greater detail put into the wind part. / <p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska högskolan, Linköpings universitet</p>

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Submitted by Sandro Camargo (sandro.camargo@unipampa.edu.br) on 2015-05-09T22:40:42Z No. of bitstreams: 1 117110027.pdf: 2705925 bytes, checksum: 22617f402005b98190c3067b1402ee2f (MD5) / Made available in DSpace on 2015-05-09T22:40:42Z (GMT). No. of bitstreams: 1 117110027.pdf: 2705925 bytes, checksum: 22617f402005b98190c3067b1402ee2f (MD5) Previous issue date: 2014-04-01 / A variação do comportamento de consumo elétrico ao longo do dia vem sendo um constante desafio para planejamentos e operação de sistemas de distribuição de energia elétrica. A diversidade de ocorrência dos picos de demanda, considerando diferentes classes de consumo, para um transformador de distribuição, são determinados de uma forma estatística, assim possibilitando uma aproximação do real comportamento dos consumidores de energia elétrica. Mas não basta apenas considerar dados estatísticos, e sim adicionar outros fatores que são determinísticos para definição real desse comportamento destes consumidores ao longo do dia. Neste contexto, é fundamental considerar dados climáticos. Durante um período de 12 meses foi realizada uma campanha de medições e paralelamente um arquivamento de informações utilizando sites da internet sobre dados climáticos da região. Como as medições (amostragens) foram, geograficamente, muito próximas, foi possível acompanhar e perceber a modificação de comportamento dos consumidores, como a utilização de condicionadores de ar e refrigeração em geral. Portanto, como objetivo de aperfeiçoar a caracterização de curvas típicas de demanda de energia elétrica, neste trabalho, utilizando metodologias de redes neurais, serão agrupadas as curvas de demanda considerando: classes, subclasses, consumo médio (últimos 12 meses) de energia elétrica, e adicionalmente dados climáticos. / The variation of the behavior of electrical consumption throughout the day has been a constant challenge for planning and operation of electric power distribution systems. The diversity of occurrence of peak demand, considering different classes of consumption to a distribution transformer are determined in a statistical manner, allowing an approximation of the actual behavior of consumers of electricity. But not enough to consider only statistical data, but add other factors that are deterministic for real definition of the behavior of these consumers throughout the day. In this context, it is crucial to consider climate data. During a period of 12 months, a measurement campaign was carried out in parallel and an archive of information using the internet sites on climatic data of the region. Because measurements (samples) were geographically very close, it was possible to follow and realize the change in consumer behavior, such as the use of air conditioners and cooling in general. Therefore, the objective of improving the characterization of typical curves of electricity demand, this paper, using methodologies neural networks, are grouped considering the demand curves: classes, subclasses, middle (last 12 months) of electricity, and additionally climatic data.

Complex network theory provides an elegant and powerful framework to statistically investigate the topology of local and long range dynamical interrelationships, i.e., teleconnections, in the climate system. Employing a refined methodology relying on linear and nonlinear measures of time series analysis, the intricate correlation structure within a multivariate climatological data set is cast into network form. Within this graph theoretical framework, vertices are identified with grid points taken from the data set representing a region on the the Earth's surface, and edges correspond to strong statistical interrelationships between the dynamics on pairs of grid points. The resulting climate networks are neither perfectly regular nor completely random, but display the intriguing and nontrivial characteristics of complexity commonly found in real world networks such as the internet, citation and acquaintance networks, food webs and cortical networks in the mammalian brain. Among other interesting properties, climate networks exhibit the "small-world" effect and possess a broad degree distribution with dominating super-nodes as well as a pronounced community structure. We have performed an extensive and detailed graph theoretical analysis of climate networks on the global topological scale focussing on the flow and centrality measure betweenness which is locally defined at each vertex, but includes global topological information by relying on the distribution of shortest paths between all pairs of vertices in the network. The betweenness centrality field reveals a rich internal structure in complex climate networks constructed from reanalysis and atmosphere-ocean coupled general circulation model (AOGCM) surface air temperature data. Our novel approach uncovers an elaborately woven meta-network of highly localized channels of strong dynamical information flow, that we relate to global surface ocean currents and dub the backbone of the climate network in analogy to the homonymous data highways of the internet. This finding points to a major role of the oceanic surface circulation in coupling and stabilizing the global temperature field in the long term mean (140 years for the model run and 60 years for reanalysis data). Carefully comparing the backbone structures detected in climate networks constructed using linear Pearson correlation and nonlinear mutual information, we argue that the high sensitivity of betweenness with respect to small changes in network structure may allow to detect the footprints of strongly nonlinear physical interactions in the climate system. The results presented in this thesis are thoroughly founded and substantiated using a hierarchy of statistical significance tests on the level of time series and networks, i.e., by tests based on time series surrogates as well as network surrogates. This is particularly relevant when working with real world data. Specifically, we developed new types of network surrogates to include the additional constraints imposed by the spatial embedding of vertices in a climate network. Our methodology is of potential interest for a broad audience within the physics community and various applied fields, because it is universal in the sense of being valid for any spatially extended dynamical system. It can help to understand the localized flow of dynamical information in any such system by combining multivariate time series analysis, a complex network approach and the information flow measure betweenness centrality. Possible fields of application include fluid dynamics (turbulence), plasma physics and biological physics (population models, neural networks, cell models). Furthermore, the climate network approach is equally relevant for experimental data as well as model simulations and hence introduces a novel perspective on model evaluation and data driven model building. Our work is timely in the context of the current debate on climate change within the scientific community, since it allows to assess from a new perspective the regional vulnerability and stability of the climate system while relying on global and not only on regional knowledge. The methodology developed in this thesis hence has the potential to substantially contribute to the understanding of the local effect of extreme events and tipping points in the earth system within a holistic global framework. / Die Theorie komplexer Netzwerke bietet einen eleganten Rahmen zur statistischen Untersuchung der Topologie lokaler und langreichweitiger dynamischer Zusammenhänge (Telekonnektionen) im Klimasystem. Unter Verwendung einer verfeinerten, auf linearen und nichtlinearen Korrelationsmaßen der Zeitreihenanalyse beruhenden Netzwerkkonstruktionsmethode, bilden wir die komplexe Korrelationsstruktur eines multivariaten klimatologischen Datensatzes auf ein Netzwerk ab. Dabei identifizieren wir die Knoten des Netzwerkes mit den Gitterpunkten des zugrundeliegenden Datensatzes, während wir Paare von besonders stark korrelierten Knoten als Kanten auffassen. Die resultierenden Klimanetzwerke zeigen weder die perfekte Regularität eines Kristallgitters, noch eine vollkommen zufällige Topologie. Vielmehr weisen sie faszinierende und nichttriviale Eigenschaften auf, die charakteristisch für natürlich gewachsene Netzwerke wie z.B. das Internet, Zitations- und Bekanntschaftsnetzwerke, Nahrungsnetze und kortikale Netzwerke im Säugetiergehirn sind. Besonders erwähnenswert ist, dass in Klimanetzwerken das Kleine-Welt-Phänomen auftritt. Desweiteren besitzen sie eine breite Gradverteilung, werden von Superknoten mit sehr vielen Nachbarn dominiert, und bilden schließlich regional wohldefinierte Untergruppen von intern dicht vernetzten Knoten aus. Im Rahmen dieser Arbeit wurde eine detaillierte, graphentheoretische Analyse von Klimanetzwerken auf der globalen topologischen Skala durchgeführt, wobei wir uns auf das Netzwerkfluss- und Zentralitätsmaß Betweenness konzentrierten. Betweenness ist zwar lokal an jedem Knoten definiert, enthält aber trotzdem Informationen über die globale Netzwerktopologie. Dies beruht darauf, dass die Verteilung kürzester Pfade zwischen allen möglichen Paaren von Knoten in die Berechnung des Maßes eingeht. Das Betweennessfeld zeigt reichhaltige und zuvor verborgene Strukturen in aus Reanalyse- und Modelldaten der erdoberflächennahen Lufttemperatur gewonnenen Klimanetzen. Das durch unseren neuartigen Ansatz enthüllte Metanetzwerk, bestehend aus hochlokalisierten Kanälen stark gebündelten Informationsflusses, bringen wir mit der Oberflächenzirkulation des Weltozeans in Verbindung. In Analogie mit den gleichnamigen Datenautobahnen des Internets nennen wir dieses Metanetzwerk den Backbone des Klimanetzwerks. Unsere Ergebnisse deuten insgesamt darauf hin, dass Meeresoberflächenströmungen einen wichtigen Beitrag zur Kopplung und Stabilisierung des globalen Oberflächenlufttemperaturfeldes leisten. Wir zeigen weiterhin, dass die hohe Sensitivität des Betweennessmaßes hinsichtlich kleiner Änderungen der Netzwerktopologie die Detektion stark nichtlinearer physikalischer Wechselwirkungen im Klimasystem ermöglichen könnte. Die in dieser Arbeit vorgestellten Ergebnisse wurden mithilfe statistischer Signifikanztests auf der Zeitreihen- und Netzwerkebene gründlich auf ihre Robustheit geprüft. In Anbetracht fehlerbehafteter Daten und komplexer statistischer Zusammenhänge zwischen verschiedenen Netzwerkmaßen ist diese Vorgehensweise besonders wichtig. Weiterhin ist die Entwicklung neuer, allgemein anwendbarer Surrogate für räumlich eingebettete Netzwerke hervorzuheben, die die Berücksichtigung spezieller Klimanetzwerkeigenschaften wie z.B. der Wahrscheinlichkeitsverteilung der Kantenlängen erlauben. Unsere Methode ist universell, weil sie zum Verständnis des lokalisierten Informationsflusses in allen räumlich ausgedehnten, dynamischen Systemen beitragen kann. Deshalb ist sie innerhalb der Physik und anderer angewandter Wissenschaften von potentiell breitem Interesse. Mögliche Anwendungen könnten sich z.B. in der Fluiddynamik (Turbulenz), der Plasmaphysik und der Biophysik (Populationsmodelle, neuronale Netzwerke und Zellmodelle) finden. Darüber hinaus ist der Netzwerkansatz für experimentelle Daten sowie Modellsimulationen gültig, und eröffnet folglich neue Perspektiven für Modellevaluation und datengetriebene Modellierung. Im Rahmen der aktuellen Klimawandeldebatte stellen Klimanetzwerke einen neuartigen Satz von Analysemethoden zur Verfügung, der die Evaluation der lokalen Vulnerabilität und Stabilität des Klimasystems unter Berücksichtigung globaler Randbedingungen ermöglicht. Die in dieser Arbeit entwickelten und untersuchten Methoden könnten folglich in der Zukunft, innerhalb eines holistisch-globalen Ansatzes, zum Verständnis der lokalen Auswirkungen von Extremereignissen und Kipppunkten im Erdsystem beitragen.

This paper describes the file format of the climate data container used by the DELPHIN, THERAKLES and NANDRAD simulation programs. The climate data container format holds a binary representation of annual and continuous climatic data needed for hygrothermal transport and building energy simulation models. The content of the C6B-Format is roughly equivalent to the epw-climate data format.:1 Introduction 1.1 General File Layout 1.2 Principle Data Types 2 Magic Header and File Version 2.1 Version Number Encoding 3 Meta Data Section 4 Data Section 4.1 Cyclic annual data 4.2 Non-cyclic/continuous data

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