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This thesis aims to provide public science dissemination of space weather data by integrating a space weather analysis system used by experts in the field into an interactive visualization software called OpenSpace; designed to visualize the entire known Universe. Data and images from complex space weather models were processed and used as textures on different surface geometries, which are then positioned, oriented and scaled correctly relative other planets in the solar system. The obtained results were within the goals of the thesis and has successfully incorporated several features that will help understanding of space weather phenomena.

This research has been conducted in order to develop a short-range ensemble numerical weather prediction system over southern Africa using the Conformal-Cubic Atmospheric Model (CCAM). An ensemble prediction system (EPS) combines several individual weather model setups into an average forecast system where each member contributes to the final weather forecast. Four different EPSs were configured and rainfall forecasts simulated for seven days ahead for the summer months of January and February, 2009 and 2010, for high (15 km) and low (50 km) resolution over the southern African domain. Statistical analysis was performed on the forecasts so as to determine which EPS was the most skilful at simulating rainfall. Measurements that were used to determine the skill of the EPSs were: reliability diagrams, relative operating characteristics, the Brier skill score and the root mean square error. The results show that the largest ensemble is consistently the most skilful for all forecasts for both the high and the low resolution cases. The higher resolution forecasts were also seen to be more skilful than the forecasts made at the low resolution. These findings conclude that the largest ensemble at high resolution is the best system to predict rainfall over southern Africa using the CCAM. / Dissertation (MSc)--University of Pretoria, 2014. / gm2014 / Geography, Geoinformatics and Meteorology / unrestricted

臺灣被國際視為「自然災害很多，極度高風險的地區」，其中的天然災害，包括颱風。由於臺灣位於颱風路徑要衝，每年飽受颱風的威脅，根據氣象局的統計，從西元1911年至2015年總計有360個颱風侵襲臺灣，平均每年有3到4個，颱風引進的西南氣流或是伴隨而來的強降雨，更經常造成許多脆危地區嚴重災損。 現今科技的進步，預報技術日新月異，讓民眾可提早得知颱風警報，尤其電視新聞中有氣象報告之後，氣象主播提供天氣、颱風動態和影響的解說，更成了民眾日常生活中獲得天氣資訊的主要管道，根據國外文獻調查，觀眾對電視新聞最感興趣就是收看氣象。 螢光幕前負責氣象報告的氣象主播可以說是氣象科學、電視傳播媒體和社會大眾之間的重要橋樑，面對全球極端氣候，災變性氣候頻率增加，有許多天氣的最新動態、科學方面的知識和氣象素養更需要詳盡的解說，氣象主播的重要職責就是做好氣象傳播，提供民眾預警訊息。 西元1978年開始，臺灣出現專業的氣象主播，電視產業從早年的三臺壟斷到現今有線電視蓬勃發展，競爭白熱化的電視新聞生態，氣象報告時間也成了新聞戰場，各臺氣象主播更是搶收視率的重，要利器。氣象新聞相互較勁，每一家電視臺氣象主播使出渾身解數並強調自己的解說最權威，最精準，但民眾怎麼看？氣象主播所播報的訊息對於民眾做好防颱準備真的有幫助嗎？為了收視率，播報颱風動態，氣象主播創造新用語或是誇大形容，強化預警效果，民眾認同度又是如何？面對災變性天氣，氣象主播在風險溝通過程中，怎麼看待自己的定位？ 當網路科技興起之後，電腦或行動裝置日益改變民眾接受訊息的習慣，收看氣象主播播報天氣資訊的觀眾，收視行為是否也會有所轉變？本文將以蘇迪勒颱風災害防救的個案分析角度，一來了解氣象主播在災害防救上發揮怎麼樣的關鍵影響力，同時也希望在氣象主播的主題上做先探性的研究。 / Taiwan has been regarded by international as a high-risk area where suffers a lot of natural disasters such as typhoons. According to the Bureau of Meteorology statistics, from 1911 to 2015, a total amount of 360 typhoons attacked Taiwan, with an average of three to four per year. The southwest air flow introduced by typhoons as well as accompanied heavy rains have contributed serious damage to many areas. Thanks to today’s technology and the rapid advance of forecasting technology, people can access the typhoon information in advance, especially from the TV weather forecast. The weather anchor provides information on weather, typhoon dynamics, and impact with the audience. Watching weather report has become part of many people’s daily routine. According to foreign literature survey, the audience is most interested in weather report when they watch TV news. The weather anchor can be viewed as a person who builds the bridge between meteorological science, broadcast media and the public. Due to extreme weather and climate change and the increased rates of catastrophic natural disasters, the audience gets an urge to learn more about the weather and knowledge related to meteorology. Thus, the major duty of a weather anchor is to provide accurate weather information to help prepare the audience for natural disasters. Since 1978, we have had professional weather anchor in the local broadcast industry. Taiwan’s TV industry has gone from the three television stations (TTV, CTV, and CTS), representing the central government’s monopoly on television broadcasting, to booming cable TV today. TV Weather forecasting has become a battlefield in today’s broadcast industry— every weather anchor is competing for the ratings. Every weather anchor claims that they deliver the most authoritative and accurate weather information. However, what does the audience think of it? Is the information provided by the weather anchor helpful for them when it comes to disaster prevention? For the ratings, most weather anchors create fancy words or rather prone to exaggeration, how does the public respond to it? Facing the extreme weather and climate change, how does a weather anchor identify themselves in the process of weather communication? Today the internet has changed the way people access information. Does it also affect people’s behavior when it comes to watching weather news on TV? In this paper, we aim to focus on what the role a weather anchor plays in disaster prevention using a case study approach. Also, we hope our explorations and insights will contribute to your understanding of the role of a weather anchor.

Heavy rainfall and flooding often occur over South Africa. A high percentage of the heavy rainfall events occur over the eastern interior of South Africa and generally during the late summer (January to March) when the influence of tropical weather systems becomes dominant. Research into forecasting techniques best suited for tropical weather systems over southern Africa has been neglected since the early 1970's. The aim of this research was to develop a Model for the Identification of Tropical Weather Systems (MITS) as well as a Tropical Heavy Rainfall Identification System (THERIS) for operational use in the weather forecasting offices of Southern Africa. This study explains the dynamical properties of tropical weather systems and identifies those variables, which favour the development of heavy rainfall. Three case studies are presented to illustrate the dynamical properties of tropical weather systems. THERIS is tested and verified for historical heavy rainfall events over South Africa. The heavy rainfall events of February 2000 over the northern Provinces of South Africa are discussed and both THERIS and MITS are tested for operational functionality. Results indicate that MITS can be used to identify tropical weather systems and that THERIS determines areas of heavy rainfall. It is recommended that the two products be tested and used operationally. / Dissertation (MSc)--University of Pretoria, 2000. / Geography, Geoinformatics and Meteorology / unrestricted

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.

It has been shown in ionospheric research that modelling total electron content (TEC) during storm conditions is a big challenge. In this study, mathematical equations were developed to estimate TEC over Sutherland (32.38⁰S, 20.81⁰E), during storm conditions, using the Empirical Orthogonal Function (EOF) analysis, combined with regression analysis. TEC was derived from GPS observations and a geomagnetic storm was defined for Dst ≤ -50 nT. The inputs for the model were chosen based on the factors that influence TEC variation, such as diurnal, seasonal, solar and geomagnetic activity variation, and these were represented by hour of the day, day number of the year, F10.7 and A index respectively. The EOF model was developed using GPS TEC data from 1999 to 2013 and tested on different storms. For the model validation (interpolation), three storms were chosen in 2000 (solar maximum period) and three others in 2006 (solar minimum period), while for extrapolation six storms including three in 2014 and three in 2015 were chosen. Before building the model, TEC values for the selected 2000 and 2006 storms were removed from the dataset used to construct the model in order to make the model validation independent on data. A comparison of the observed and modelled TEC showed that the EOF model works well for storms with non-significant ionospheric TEC response and storms that occurred during periods of low solar activity. High correlation coefficients between the observed and modelled TEC were obtained showing that the model covers most of the information contained in the observed TEC. Furthermore, it has been shown that the EOF model developed for a specific station may be used to estimate TEC over other locations within a latitudinal and longitudinal coverage of 8.7⁰ and 10.6⁰ respectively. This is an important result as it reduces the data dimensionality problem for computational purposes. It may therefore not be necessary for regional storm-time TEC modelling to compute TEC data for all the closest GPS receiver stations since most of the needed information can be extracted from measurements at one location.

The valuation of weather derivatives is complex since the underlying temperature process has no negotiable price. This thesis introduces a selection of models for the valuation of weather derivative contracts, governed by a stochastic underlying temperature process. We then present a new weather pricing model, which is used to determine the fair hedging price of a weather derivative under the assumptions of mean self-financing. This model is then extended to incorporate a compensation (or market price of risk) awarded to investors who hold undiversifiable risks. This results in the derivation of a non-linear two-dimensional PDE, for which the numerical evaluation cannot be performed using standard finite-difference techniques. The numerical techniques applied in this thesis are based on a broad range of lattice based schemes, including enhancements to finite-differences, quadrature methods and binomial trees. Furthermore simulations of temperature processes are undertaken that involves the development of Monte Carlo based methods.

Numerical Weather Predictions (NWPs) are subject to systematic errors and biases. Hence, the continuous verification of NWP model outputs in order to contribute to model improvement became very important over recent years. Verification results provide numerical measures of how well NWP models perform, in an objective way. It also allows for monitoring of how NWPs improve over time. In the day-to-day operation of weather forecasting one might find biases in either forecasts generated by the NWP model, or biases that result from the weather forecaster’s interpretation of NWP output, or both. The use of verification statistics might help to identify the source of these biases, which might lead to research targeted to improve the scientific understanding of the underlying processes required to improve NWP forecasts. This study investigates the potential of the 20km x 20km resolution Limited Area Model over Africa (Africa LAM) developed by the United Kingdom Meteorological Office (UK Met Office) to be used as supplementary tool to improve weather forecast output to end-users over the Western Sahel (WS) and Nigeria. In the study, Africa LAM T+24h forecasts dataset was verified against daily observed rainfall, maximum and minimum temperature data, of 36 selected meteorological point stations over the WS from January 2005 to December 2006. 12 meteorological point stations were selected across each of the three identified climate zones of the WS, namely (1) Wet Equatorial (WE) climate zone (from the southern coastline up to a latitude of 8.00ºN), (2) Wet and Dry Tropics (WDT) climate zone (between latitude 8.00ºN and 12.00ºN) and (3) Semi-Arid (SA) climate zone (between latitude 12.00ºN and 15.00ºN). The dataset was also stratified into four seasons, namely (1) January-February-March (JFM), (2) April-May-June (AMJ), (3) July-August-September (JAS) and (4) October-November-December (OND). The verification algorithms and measures used in this study are in accordance with the WMO NWP verification standards. The verification results indicate that the Africa LAM model temperature forecasts show skill, more so during the raining seasons (AMJ and JAS) than during the dry seasons (JFM and OND) over the WS. The model rainfall forecasts, however, show more skill during the dry seasons (JFM and OND) than during the raining seasons (AMJ and JAS). The results further indicate that, on a regional basis, the model temperature forecasts show more spatial skill over the WE climate zone than over the WDT and SA climate zones of the WS, while rainfall forecasts show more skill over the SA climate zone than over the WDT and WE climate zones of the WS. Additional results from simple bias corrections and Model Output Statistics (MOS) which are some of the suggested post-processing techniques in this study are presented. These results give a better understanding of the model forecast errors, and also provide the feedback necessary for a possible improvement of Africa LAM forecasts by scientists at the UK Met Office. Copyright / Dissertation (MSc)--University of Pretoria, 2009. / Geography, Geoinformatics and Meteorology / MSc / Unrestricted

Open file dissertation.pdf to read abstract / Dissertation (MSc)--University of Pretoria, 2007. / Geography, Geoinformatics and Meteorology / Unrestricted