Climate change is an important issue for public policy. Unfortunately, although there are volumes of data about climate change, many members of the public are informed about the issue by politicized interpretations of the data. This is an impediment to planning policies and strategies to counter the impact of climate change, and identifies a need for climate awareness in the public. This thesis explores using gamification to motivate people to learn about long term trends in climate data. As a model for this edutainment activity, we choose a medium that engages millions of players to learn about large sets of data - Fantasy Sports. Fantasy sports have been shown to increase the player’s knowledge and understanding about the domain of the sport being played. With the huge amount of weather data available, we have designed and developed a fantasy weather game. People manage a team of cities with the goal of predicting weather better than other players in their league, and in the process gain an understanding of the weather patterns and climate change trends for those cities. We do a user-study to evaluate our application and prove its feasibility. An evaluation of the fantasy weather game indicates that the game had the desired effect of causing players to explore weather data in more detail. The evaluation also pointed out a number of potential improvements to the current prototype. Overall, the evaluation supports using the model of fantasy sports to motivate people to learn more about weather and climate data.

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A lot of research has been conducted in the past decades on urban heat island (UHI) all over the world. Nevertheless, the UHI effect has not been included in weather data used by building services engineers to design buildings and size their heating and cooling plants. This research was carried out to investigate the UHI effect in Greater Manchester by setting up fixed point monitoring stations over the city. Woodford Met Office ground observation station was selected to be the rural reference point. A multiple regression model was developed to incorporate the heat island effect into the Manchester weather data for engineering usage.It was found that the urban heat island intensity (the difference between the rural and urban area temperatures) can be as high as 8°C in summer and 10°C in winter in Manchester. Clear and calm nocturnal temperature data was used (when maximum heat island occurs ) to find the relationship between the UHI intensity and sky view factor (SVF), distance away from the city centre, evapotranspiration fraction (EF), wind speed, cloud cover and rural reference temperature. Results indicate that all factors have a negative linear relationship with UHI intensity. All measured data were fed into a statistical software package to create general linear regression models. Validation showed that these models were capable of predicting average UHI effect to a good accuracy. The maximum heat island effect peaks are not so accurate. However, an analytical model was developed based on energy balance equations to predict the maximum heat island effect. Validation shows a good prediction for summer but not so good for winter. This is probably due to the lower average UHI intensity in winter than in summer.

Many scientists have accepted that human activities are the major cause of climate change and global warming. Knowledge on the effect this will have on office buildings and energy consumption in the future is essential. Thus the assessment of future building energy consumption is becoming more important especially in countries such as Malaysia where the majority of the office buildings depend on air-conditioning to maintain the occupants level of comfort. This research explores the effect of future climate change weather on the energy consumption of office buildings in Malaysia, by using simulation software. Simulated weather data sets HadCM3 were supplied by the Hadley Centre in the United Kingdom for the recent past and for the future up to 2099. Test Reference Years (TRYs) were selected from this data using the Finkelstein-Schafer Statistic (FS) method for four time slices, namely TRYs 1990-2007, 2010-2039, 2040-2069 and 2070-2099. The HadCM3 data was validated by comparing the 1990-2007 TRY with a TRY selected by the same method and period from the measured weather. The Hadley data was supplied as daily values, but the building simulation software required hourly values. Algorithms were therefore used to generate hourly values from the daily data for the relevant variables (dry bulb temperature, relative humidity, wind speed and global solar radiation) and to decompose global solar radiation into direct and diffuse radiation. Two different office building were modelled in the simulation software, one imaginary simplified typical building and one real building. The sensible and latent annual cooling loads were found for each building for each different TRY. A sensitivity analysis was also performed to investigate the effect on cooling load of changes in building design as possible ways of mitigating the effects of climate change. It was found that climate change will increases the building energy consumption by 13.6 percent in future and better understanding on building design will reduce this effect.

Agricultural production is highly sensitive to climate and weather perturbations. Maize is the main crop cultivated in South Africa and production is predominantly rain-fed. South Africa’s climate, especially rainfall, is extremely variable which influences the water available for agriculture and makes rain-fed cropping very risky. In the aim to reduce the uncertainty in the climate of the forthcoming season, this study investigates whether seasonal climate forecasts can be used to predict maize yields for South Africa with a usable level of skill. Maize yield, under rain-fed conditions, is simulated for each of the magisterial districts in the primary maize producing region of South Africa for the period from 1979 to 1999. The ability of the CERES-Maize model to simulate South African maize yields is established by forcing the CERES-Maize model with observed weather data. The simulated maize yields obtained by forcing the CERES-Maize model with observed weather data set the target skill level for the simulation systems that incorporate Global Circulation Models (GCMs). Two GCMs produced the simulated fields for this study, they are the Conformal Cubic Atmospheric Model (CCAM) and the ECHAM4.5 model. CCAM ran a 5 and ECHAM4.5 a 6- member ensemble of simulations on horizontal grids of 2.1° x 2.1° and 2.8° x 2.8° respectively. Both models were forced with observed sea-surface temperatures for the period 1979 to 2003. The CERES-Maize model is forced with each ensemble member of the CCAM-simulated fields and with each ensemble member of the ECHAM4.5-simulated fields. The CERES-CCAM simulated maize yields and CERES-ECHAM4.5 simulated maize yields are combined to form a Multi-Model maize yield ensemble system. The simulated yields are verified against actual maize yields. The CERES-Maize model shows significant skill in simulating South Africa maize yields. CERES-Maize model simulations using the CCAM-simulated fields produced skill levels comparable to the target skill, while the CERES-ECHAM4.5 simulation system illustrated poor skill. The Multi-Model system presented here could therefore not outscore the skill of the best single-model simulation system (CERES-CCAM). Notwithstanding, the CERES-Maize model has the potential to be used in an operational environment to predict South African maize yields, provided that the GCM forecast fields used to force the model are adequately skilful. Such a yield prediction system does not currently exist in South Africa. / Dissertation (MSc)--University of Pretoria, 2009. / Geography, Geoinformatics and Meteorology / Unrestricted

Indiana University-Purdue University Indianapolis (IUPUI) / With the increasing need for accurate weather predictions, we need large samples of data from different data sources for an accurate estimate. There are a number of data sources that keep publishing data periodically. These data sources have their own server protocols that a user needs to follow while writing client for retrieving data. This project aims at creating a generic semi-automatic client mechanism for retrieving scientific data from such sources. Also, with the increasing number of data sources there is also a need for a data model to accommodate data that is published in different formats. We have come up with a data model that can be used across various applications in the domain of scientific data retrieval.

No description available.

Extreme variability of rainfall during the growing season in the Prairies underlies the need to improve means of quantifying the amount of soil moisture available for plant growth in real time. This study was conducted to modify and validate the Versatile Soil Moisture Budget (VSMB) for estimating volumetric soil water content. A network of soil moisture hydra probes and weather stations were installed for continuous soil moisture monitoring and real-time weather data collection at 13 sites across Central and Western Manitoba during the 2009 and 2010 growing seasons. The data from the probes were validated and calibrated. Both the laboratory and field validations showed that the root mean square error of the default factory calibration increased with increasing clay content of the soil. Outputs from these probes were used to test the modified VSMB model. The model was most effective at simulating soil water content at the surface layers.

Extreme variability of rainfall during the growing season in the Prairies underlies the need to improve means of quantifying the amount of soil moisture available for plant growth in real time. This study was conducted to modify and validate the Versatile Soil Moisture Budget (VSMB) for estimating volumetric soil water content. A network of soil moisture hydra probes and weather stations were installed for continuous soil moisture monitoring and real-time weather data collection at 13 sites across Central and Western Manitoba during the 2009 and 2010 growing seasons. The data from the probes were validated and calibrated. Both the laboratory and field validations showed that the root mean square error of the default factory calibration increased with increasing clay content of the soil. Outputs from these probes were used to test the modified VSMB model. The model was most effective at simulating soil water content at the surface layers.

Ce travail de thèse s'inscrit dans le cadre du projet ANR FIABILITE qui porte sur la fiabilité des logiciels de simulation thermique dynamique et plus particulièrement sur les sources potentielles de biais et d'incertitude dans le domaine de la modélisation thermique et énergétique des bâtiments basse consommation. Les sollicitations telles que les occupants, la météo ou encore les scénarios de consommation des usages font partie des entrées les plus incertaines et potentiellement les plus influentes sur les performances d'un bâtiment basse consommation. Il est nécessaire pour pouvoir garantir des performances de déterminer les dispersions de sortie associées à la variabilité des entrées temporelles et d'en déterminer les variables responsables pour mieux réduire leur variabilité ou encore concevoir le bâtiment de manière robuste. Pour répondre à cette problématique, on se base sur les indices de sensibilité de Sobol adaptés aux modèles complexes à grandes dimensions tels que les modèles de bâtiment pour la simulation thermique dynamique. La gestion des entrées fonctionnelles étant un verrou scientifique pour les méthodes d'analyse de sensibilité standard, une méthodologie originale a été développée dans le cadre de cette thèse afin de générer des échantillons compatibles avec l'estimation de la sensibilité. Bien que la méthode soit générique aux entrées fonctionnelles, elle a été validée dans ce travail de thèse pour le cas des données météorologiques et tout particulièrement à partir des fichiers météo moyens (TMY) utilisés en simulation thermique dynamique. Les deux aspects principaux de ce travail de développement résident dans la caractérisation de la variabilité des données météorologiques et dans la génération des échantillons permettant l'estimation de la sensibilité de chaque variable météorologique sur la dispersion des performances d'un bâtiment. A travers différents cas d'application dérivés du modèle thermique d'une maison basse consommation, la dispersion et les paramètres influents relatifs à la variabilité météorologique sont estimés. Les résultats révèlent un intervalle d'incertitude sur les besoins énergétiques de l'ordre de 20% à 95% de niveau de confiance, dominé par la température extérieure et le rayonnement direct. / This thesis is part of the ANR project FIABILITE dealing with the reliability of dynamic thermal simulation softwares and particularly with the potential sources of bias and uncertainties in the field of thermal and energy modeling of low consumption buildings. The solicitations such as the occupancy schedules, the weather data or the usage scenarios are among the most uncertain and potentially most influential inputs on the performance of a low energy building. To ensure the efficiency of such buildings, we need to determine the outputs dispersion associated with the uncertainty of the temporal inputs as well as to emphasize the variables responsible for the dispersion of the output in order to design the building in a robust manner. To address this problem, we have used the sensitivity indices of Sobol adapted to complex models with high dimensions, such as building models for dynamic thermal simulations. The management of the functional inputs being a lock for the scientific methods of standard sensitivity analysis, an innovative methodology was developed in the framework of this thesis in order to generate consistent samples with the estimate of the sensitivity. Although the method can incorporate generic functional inputs, it has been validated in this thesis using meteorological data and especially the typical meteorological year (TMY files) used in dynamic thermal simulations. The two main aspects of this development work lie in the characterization of the variability of meteorological data and the generation of samples to estimate the sensitivity of each weather variable dispersion on the thermal and energy performances of a building. Through various case studies derived from the thermal model of a low-energy house, the dispersion and influential parameters for meteorological variability are estimated. Results show a large range of uncertainties in the energy requirements from about 20 % at a confidence level of 95%.