Submitted by Élida Maeli Fernandes Quirino (maely_sax@hotmail.com) on 2018-05-15T14:29:50Z No. of bitstreams: 1 IURY ARAUJO MACÊDO DANTAS - DISSERTAÇÃO PPGSA PROFISSIONAL 2015..pdf: 775043 bytes, checksum: 495dac8a12002b24056de4c579cf4cad (MD5) / Made available in DSpace on 2018-05-15T14:29:50Z (GMT). No. of bitstreams: 1 IURY ARAUJO MACÊDO DANTAS - DISSERTAÇÃO PPGSA PROFISSIONAL 2015..pdf: 775043 bytes, checksum: 495dac8a12002b24056de4c579cf4cad (MD5) Previous issue date: 2015-07-22 / A falta d’água no semiárido brasileiro muitas vezes tornou-se fator limitante para desenvolvimento urbano, agrícola e industrial, interferindo diretamente na vida e renda das pessoas. Este trabalho teve como objetivo determinar o índice de reconhecimento de secas em região semiárida nordestina, usando como referência a evapotranspiração potencial diária obtida através do método Penman-Monteith (FAO) e de dados meteorológicos observados na bacia do Piancó-Piranhas-Açu para um período de 23 anos, esses dados foram adquiridos na estação meteorológica do INMET localizada no Perímetro Irrigado de São Gonçalo-Sousa- PB, com isso foi comparado com os métodos de Thornthwaite, Hargreaves e Blaney- Criddle, para períodos anuais, semestrais, trimestrais e mensais. Onde seus impactos foram calculados usando os métodos estatísticos da Raiz do Erro Quadrado Médio e a Média de Erro de Bias. Onde nove dos 23 anos foram detectados com seca. Semestralmente a seca mais forte aconteceu no segundo semestre, sendo classificada como seca de classes extremas, separado por trimestre os dois últimos apresentaram secas mais fortes, enquanto que mensalmente seus índices são bem próximos, em sua maior parte os resultados equivalente a anos de seca mais severa, iniciando no mês de maio. O método Thornthwaite foi o que mais se aproximou do método de referencia, sendo o mais indicado para ser usado na região, onde o mesmo é de fácil estimação necessitando somente de dados meteorológicos (temperatura) / The water shortage in the Brazilian semiarid often became a limiting factor for urban, agricultural and industrial development, directly interfering with life and income of people. This study aimed to determine the dry recognition index in northeastern semiarid region, with reference to the daily potential evapotranspiration obtained by the Penman-Monteith method (FAO) and meteorological data observed in the basin of Piancó-Piranhas-Açu for a period 23, these data were acquired in the meteorological station of INMET located in the Irrigated Perimeter of São Gonçalo, Sousa-PB, with this was compared with the methods of Thornthwaite, Hargreaves and Blaney-Criddle, for annual, semi-annual, quarterly and monthly periods . Where its impacts were calculated using statistical methods of Mean Square Error Root and Bias Error Ages. Where nine of the 23 years were detected with dry, semi-annually the most severe drought occurred in the second half, being classified as dry extreme classes, separated by a quarter the last two were more severe droughts, while monthly its contents are very close in their Most of the results equivalent to more years of severe drought, starting in May. The Thornthwaite method was the one closest to the reference method, the most suitable for use in the region, where it is easy pet needing only meteorological data (temperature).

This work investigates the thermal performance of buildings in Cyprus and application of a particular passive technology; Phase Change Materials (PCMs) for the ultimate aim of reducing indoor air temperatures and energy supplied for the cooling season.PCMs for passive building applications are emerging technology and have not been tested for the buildings of Cyprus neither by computer simulations nor by practical applications. In this work, particular PCM end product; wallboard, having phase change temperature of 26 oC is employed together with various construction materials and simulated for buildings of Cyprus. Description of the current state in Cyprus has been carried out in terms of low energy building studies, widely used building fabric and building statistics. There is a huge gap in Cyprus in the field of energy performance and thermal comfort of buildings, which creates big room for research. Climatic design of buildings has been abandoned resulting in poor thermal comfort and increased energy consumption. There is still no regulation in place regarding the thermal performance of buildings in North Cyprus.Recent weather data of different Cyprus locations has been investigated and compared with the simulation weather data files that are employed in this work. The author has demonstrated that Finkelstein-Schafer statistics between recent weather data of Cyprus and simulation weather data files are close enough to obtain accurate results.Dynamic thermal simulations has been carried out by using Energy Plus, which is a strong and validated thermal simulation program that can model PCMs. Simulations are done for two different building geometry; “simple building” and “typical building” by employing different construction materials. Simple building is a small size box shaped building and typical building is a real existing building and selected by investigation of the building statistics.Simulation results showed that with this particular PCM product, indoor air temperatures and cooling energies supplied to simple building is reduced up to 1.2 oC and 18.64 % when heavier construction materials are used and up to 1.6 oC and 44.12 % when lighter construction materials are used. These values for typical building are found to be 0.7 oC, 3.24 % when heavier construction materials are used and 1.2 oC, 3.64 % when lighter construction materials are used. It is also found that, if thinner walls and slabs are used in the buildings the effectiveness of the PCM lining increases in significant amount.

Changes in weather patterns worldwide and global warming increased the demand for high-performance buildings resilient to climate change. Building Performance Simulation (BPS) is a robust technique to test, assess, and enhance energy efficiency measures and comply with stringent energy codes of buildings. Climate has a considerable impact on the buildings' thermal environment and energy performance; therefore, choosing reliable and accurate weather data is crucial for building performance evaluation and reducing the performance gap. Typical Weather Years (TWYs) have been traditionally used for energy simulation of buildings. Even if detailed energy assessments can be performed using available multi-year weather data, most simulations are carried out using a typical single year. As a result, this fictitious year must accurately estimate the typical multi-year conditions. TWYs are widely used because they accelerate the modeling process and cut down on computation time while generating relatively accurate long-term predictions of building energy performance. However, there is no certainty that a single year can describe the changing climate and year-by-year variations in weather patterns. Nowadays, with increased computational power and higher speeds in calculation processes, it is possible to adopt multi-year weather datasets to fully assess long-term building energy performance and avoid errors and inaccuracies during the preliminary selection procedures. This study aims to investigate the impact of Typical Weather Years and Actual Weather Years (AWYs) on a single-family house and a university building under two opposite climates, Winnipeg (cold) and Catania (hot). First, a single-family house in Winnipeg, Canada, was selected to evaluate how typical weather years affect the energy performance of the building and compare it with AWYs simulation. Two widely used typical weather data, CWEC and TMY, were selected for the simulation. The results were compared with the outcomes of simulation using AWYs derived from the same weather station from 2015 to 2019, which covered the latest climate changes. The results showed that typical weather years could not sufficiently capture the year-by-year variation in weather patterns. The typical weather years overestimated the cooling load while underestimating the heating demands compared to the last five actual weather years. A more extensive study was conducted for more confidence in the findings and understanding of the weather files. The research was expanded by comparing the results of building performance simulation of the single-family house and an institutional building with more complex envelope characteristics belonging to the University of Manitoba under cold (Winnipeg, Canada) and hot (Catania, Italy) climates. Overall, 48 simulations were performed using ten actual weather years from 2010 to 2019 and two TWYs from each climate for both buildings. The results showed that while the TWYs either overestimate or underestimate the cooling and heating demands of both buildings, cooling load predictions were highly overestimated in the heating-dominant climate of Winnipeg, ranging from 10.5% to 82.4% for both buildings by CWEC and TMY weather data. In the cooling-dominant climate of Catania, energy simulations using IWEC and TMY typical weather data highly overestimated the heating loads between 2.8% and 82.4%.

Weather is a major contributing factor in aviation accidents, incidents and delays. Doppler weather radar has emerged as a potent tool to observe weather. Aircraft carry an onboard radar but its range and angular resolution are limited. Networks of ground-based weather radars provide extensive coverage of weather over large geographic regions. It would be helpful if these data can be transmitted to the pilot. However, these data are highly voluminous and the bandwidth of the ground-air communication links is limited and expensive. Hence, these data have to be compressed to an extent where they are suitable for transmission over low-bandwidth links. Several methods have been developed to compress pictorial data. General-purpose schemes do not take into account the nature of data and hence do not yield high compression ratios. A scheme for extreme compression of weather radar data is developed in this thesis that does not significantly degrade the meteorological information contained in these data. The method is based on contour encoding. It approximates a contour by a set of systematically chosen ‘control’ points that preserve its fine structure upto a certain level. The contours may be obtained using a thresholding process based on NWS or custom reflectivity levels. This process may result in region and hole contours, enclosing ‘high’ or ‘low’ areas, which may be nested. A tag bit is used to label region and hole contours. The control point extraction method first obtains a smoothed reference contour by averaging the original contour. Then the points on the original contour with maximum deviation from the smoothed contour between the crossings of these contours are identified and are designated as control points. Additional control points are added midway between the control point and the crossing points on either side of it, if the length of the segment between the crossing points exceeds a certain length. The control points, referenced with respect to the top-left corner of each contour for compact quantification, are transmitted to the receiving end. The contour is retrieved from the control points at the receiving end using spline interpolation. The region and hole contours are identified using the tag bit. The pixels between the region and hole contours at a given threshold level are filled using the color corresponding to it. This method is repeated till all the contours for a given threshold level are exhausted, and the process is carried out for all other thresholds, thereby resulting in a composite picture of the reconstructed field. Extensive studies have been conducted by using metrics such as compression ratio, fidelity of reconstruction and visual perception. In particular the effect of the smoothing factor, the choice of the degree of spline interpolation and the choice of thresholds are studied. It has been shown that a smoothing percentage of about 10% is optimal for most data. A degree 2 of spline interpolation is found to be best suited for smooth contour reconstruction. Augmenting NWS thresholds has resulted in improved visual perception, but at the expense of a decrease in the compression ratio. Two enhancements to the basic method that include adjustments to the control points to achieve better reconstruction and bit manipulations on the control points to obtain higher compression are proposed. The spline interpolation inherently tends to move the reconstructed contour away from the control points. This has been somewhat compensated by stretching the control points away from the smoothed reference contour. The amount and direction of stretch are optimized with respect to actual data fields to yield better reconstruction. In the bit manipulation study, the effects of discarding the least significant bits of the control point addresses are analyzed in detail. Simple bit truncation introduces a bias in the contour description and reconstruction, which is removed to a great extent by employing a bias compensation mechanism. The results obtained are compared with other methods devised for encoding weather radar contours.

Práce se zabývá webovými knihovnami pro práci s mapou a jejich možným využitím pro implementaci grafického uživatelského rozhraní nativní aplikace Weather Information Service (WIS) společnosti Honeywell. V práci jsou představeny prvky WIS, které je třeba implementovat pro úspěšné převedení aplikace do webového rozhraní. Dále jsou vysvětleny základy mapových knihoven a podrobněji popsány knihovny Leaflet a Altus Map Engine, které byly vybrány pro vytvoření demonstrační aplikace. Jedna kapitola je věnována metodám použitelným pro vykreslování ve webovém prostředí. Práce dále popisuje implementaci demonstrační aplikace, vytvořené za účelem prezentace možností mapových knihoven implementovat prvky WIS. Na závěr jsou diskutovány výhody a nevýhody obou zkoumaných knihoven a jejich použitelnost pro případné využití v aplikaci WIS.

In many developing economies, a high percentage of domestic energy demand is for cooking based on fossil and biomass fuels. Their use has serious health consequences affecting almost 3 billion people. Cleaner cooking systems have been promoted in these countries such as solar cooking and smokeless stoves with varying degrees of success. In parallel, solar electrolytic hydrogen systems have been developed and increasingly used during the last 25 years for electricity, heat and automobile fueling applications. This study has developed and tested experimentally in the laboratory a solar hydrogen plant numerical model suitable for small communities, to generate and store cooking fuel. The numerical model was developed in TRNSYS and consists of PV panels supplying a PEM electrolyser of 63.6% measured stack efficiency and hydrogen storage in metal hydride cylinders for household distribution. The model includes novel components for the operation of the PEM electrolyser, its controls and the metal hydride storage, developed based on data of hydrogen generation, stack temperature and energy use from a purpose constructed small-scale experimental rig. The model was validated by a second set of experiments that confirmed the accurate prediction of hydrogen generation and storage rates under direct power supply from PV panels. Based on the validated model, large-scale case studies for communities of 20 houses were developed. The system was sized to generate enough hydrogen to provide for typical domestic cooking demand for three case-studies; Jamaica, Ghana and Indonesia. The daily cooking demands were calculated to be 2.5kWh/day for Ghana, 1.98kWh/day for Jamaica and 2kWh/day for Indonesia using data mining and a specific quantitative survey for Ghana. The suitability of weather data used in the model was evaluated through Finkelstein Schafer statistics based on composite and recent weather data and by comparing simulation results. A difference of 0.9% indicated that the composite data can be confidently used. Simulations results indicate that a direct connection system to the PV plant rather than using a battery is the optimal design option based on increased efficiency and associated costs. They also show that on average 10tonnes of CO2/year/household can be saved by replacing biomass fuel with hydrogen. The potential of total savings in the three case-study countries is shown in the form of novel solar hydrogen potential maps. The results of this study are a contribution towards better understanding the use of hydrogen systems and enhancing their role in renewable energy policy.

Adverse weather is considered as one of the important factors contributing to injuries and severe crashes. During rainy conditions, it can reduce travel visibility, increase stopping distance, and create the opportunity hydroplaning. This study quantified the relative crash risk on Oregon 217 southbound direction under rainy conditions by using a match-paired approach, applied one-year traffic data, crash data and NEXRAD Level II radar weather data. There are 26 crashes occurred in match-paired weather conditions for Oregon 217 in year 2007. The results of this study indicate that a higher crash risk and a higher property-damage-only crash risk occurred during rainy days. The crash risk level varies by the location of the highway, at milepost 2.55 station SW Allen Blvd has the highest driving risks under rainy conditions.

Accurate and reliable traffic state estimation is essential for the identification of congested areas and bottleneck locations. It enables the quantification of congestion characteristics, such as intensity, duration, reliability, and spreading which are indispensable for the deployment of appropriate traffic management plans that can efficiently ameliorate congestion problems. Similarly, it is important to categorize known congestion patterns throughout a long period of time, so that corresponding traffic simulation models can be built for the investigation of the performance of different traffic management plans. This study conducts cluster analysis to identify days with similar travel conditions and congestion patterns. To this end, travel, traffic and weather data from the Smart Mobility Living Lab of Thessaloniki, Greece is used. Representative days per cluster are determined to facilitate the development of traffic simulation models that typify average traffic conditions within each cluster. Moreover, spatio-temporal matrices are developed to illustrate time-varying traffic conditions along different routes for the representative days. Results indicate that the proposed clustering technique can produce valid classification of days in groups with common characteristics, and that spatio-temporal matrices enable the development of traffic management plans which encompass routing information for competing routes in the city of Thessaloniki.

<p>Information about the conditions of specific fields and assets is critical for farm managers to make operational decisions. Location, rainfall, windspeed, soil moisture, and temperature are examples of metrics that influence the ability to perform certain tasks. Monitoring these events in real time and being able to store historical data can be done using Internet of Things (IoT) devices such as sensors. The abilities of this technology have previously been communicated, yet few farmers have adopted these connected devices into their work. A lack of reliable internet connection, the high annual cost of current on-market systems, and a lack of technical awareness have all contributed to this disconnect. One technology that can better meet the demand of farmers is LoRaWAN because of its long range, low power, and low cost. To assist farmers in implementing this technology on their farms the goal was to build a LoRaWAN network with several sensors to measure metrics such as weather data, distribute these systems locally, and provide context to the operation of IoT networks. By leveraging readily available commercial hardware and opens source software two examples of standalone networks were created with sensor data stored locally and without a dependence on internet connectivity. The first use case was a kit consisting of a gateway and small PC mounted to a tripod with 6 individual sensors and cost close to $2200 in total. An additional design was prepared for a micro-computer-based version using a Raspberry Pi, which made improvements to the original design. These adjustments included a lower cost and complication of hardware, software with more open-source community support, and cataloged steps to increase approachability. Given outside factors, the PC architecture was chosen for mass distribution. Over one year, several identical units were produced and given to farms, extension educators, and vocational agricultural programs. From this series of deployments, all units survived the growing season without damage from the elements, general considerations about the chosen type of sensors and their potential drawbacks were made, the practical observed average range for packet acceptance was 3 miles, and battery life among sensors remained usable after one year. The Pi-based architecture was implemented in an individual use case with instructions to assist participation from any experience level. Ultimately, this work has introduced individuals to the possibilities of creating and managing their own network and what can be learned from a reasonably simple, self-managed data pipeline.</p>

Climate analysts use Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations to make sense of models performance in predicting extreme events such as heavy precipitation. Similarly, weather analysts use numerical weather prediction models (NWP) to simulate weather conditions either by perturbing initial conditions or by changing multiple input parameterization schemes, e.g., cumulus and microphysics schemes. These simulations are used in operational weather forecasting and for studying the role of parameterization schemes in synoptic weather events like storms. This work addresses the need for visualizing the differences in both CMIP5 and NWP model output. This work proposes three glyph designs used for communicating CMIP5 model error. It also describes Ensemble Visual eXplorer tool that provides multiple ways of visualizing NWP model output and the related input parameter space. The proposed interactive dendrogram provides an effective way to relate multiple input parameterization schemes with spatial characteristics of model uncertainty features. The glyphs that were designed to communicate CMIP5 model error are extended to encode both parameterization schemes and graduated uncertainty, to provide related insights at specific locations such as storm center and the areas surrounding it. The work analyzes different ways of using glyphs to represent parametric uncertainty using visual variables such as color and size, in conjunction with Gestalt visual properties. It demonstrates the use of visual analytics in resolving some of the issues such as visual scalability. As part of this dissertation, we evaluated three glyph designs using average precipitation rate predicted by CMIP5 simulations, and Ensemble Visual eXplorer tool using WRF 1999 March 4th, North American storm track dataset.