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

This thesis explores the post-war history of the British Meteorological Office (MO), which saw the development of public weather services and a more prominent profile for the organisation in British public life. Situated within a post-war growth in the scientific civil service and the government’s use of science in policy making, the emergence of MO extreme weather warnings and forecasts afforded the organisation an authoritative expert position. Part of meteorology’s twentieth century professionalisation, the services developed through the application of advances in meteorological practice and technologies, significantly altered the organisation’s public profile and status as a scientific expert body.By considering these developments the thesis illuminates how, as the MO increasingly presented forecasts and warnings to all sectors of British society, they became managers of the risks posed by extreme weather. Through exploring these historical developments at the MO, we see a broader narrative emerge on how the communication of risk by scientific experts interacts with public expectations and manifestations of blame. Central to the narrative presented is the role of extreme weather events themselves in affecting response, policy developments, new MO warning services, and the manifestation of blame.

Geographies of Responsibility: The Cultural Logic of 21st Century Weather Emergencies analyzes the role of narrative in contemporary severe weather events. The speed and diversity of media through which we now communicate "the weather" significantly impact how U.S. communities experience these events and their possible social, cultural, and political meanings. This project explores four weather emergencies, covering physical geographies of the far northwest, Great Plains, mid-Atlantic, and Caribbean, that were circulated and reframed via a range of media--from newspapers to television, social, and new media--who discussed these events, and to what ends. Chapter 1 examines reporting on the 2004 Alaska wildfires directed at U.S. national and Alaska state communities to explore the importance of the "nation" as a continuing relevant relative spatial scale. Chapter 2 investigates the 2007 Greensburg tornado and subsequent "green" (re)development of the town. Chapter 3 analyzes the 2010 "Snowmageddon" blizzards in Washington, D.C., which initiated "playful" acts that highlighted how urban economic realities and historical social geographies of race are embedded in particular urban sites. Chapter 4 explores the 2010 Haiti earthquake, which evoked economies of responsibility across multiple scales of mobilization that reiterated the cultural and historical "weather map" laid down by Hurricane Katrina. These mass mediated weather events each mobilized attention and response through narratives that evoked an emergency to communities across multiple geographic scales put into relationships with one another through storylines far more complex than an analysis of how "global" and local weather systems co-create each other.

Energetic electrons undergo significant flux variations in the Earth’s outer radiation belt, where magnetospheric waves play an important role in changing the energetic electron dynamics. In particular, electromagnetic ion cyclotron (EMIC) waves are suggested to drive efficient pitch angle scattering of relativistic electrons, which results in relativistic electron precipitation into the upper atmosphere. Such precipitation provides an important source of energy input into the upper atmosphere, where precipitating electrons can affect atmospheric chemistry and ionization. However, the quantitative role of EMIC waves in energetic electron precipitation in various regions of the magnetosphere is not fully understood. This dissertation aims to answer outstanding open questions on the characteristics and quantification of EMIC-driven precipitation, such as the spatial extent and the energy range of electron precipitation. The relationship between EMIC waves and electron precipitation is evaluated by analyzing magnetic conjunction events when EMIC waves are detected in the magnetosphere by near-equatorial satellites (Van Allen Probes, GOES) and precipitating electrons are measured by Low-Earth-Orbiting satellites (POES, FIREBIRD). Quasi-linear theory is used to quantify the role of various observed magnetospheric waves (e.g., EMIC waves, plasmaspheric hiss, magnetosonic waves) in the electron precipitation. Several in-depth case analyses show that EMIC waves are the main driver of the observed relativistic electron precipitation, while other waves play a minor role. The precipitation events were clearly identified within L shell of ~7.5, favorably near the dusk and night sectors. The analysis shows that each precipitation event was localized on average spatial scales of ~0.3 L, suggesting that the resonance conditions are satisfied in a very localized region of the magnetosphere. The electron precipitation was observed at the expected relativistic (> ~MeV) energies; however, the minimum energy of efficient electron precipitation was newly found to extend down to at least ~200–300 keV. The quantitative analysis using multi-point measurements combined with theoretical calculations in this dissertation provides a more comprehensive understanding of EMIC-driven precipitation, which is a critical electron loss process in the magnetosphere. Moreover, the results are helpful to improve currently existing models of radiation belt, ring current and atmosphere dynamics, as well as theories of wave-particle interactions.

One of the key indicators of port performance lies in port’s efficiency in minimising port congestion. However, the port of Durban like many other ports in Africa and the world is faced with a congestion challenge. This study aimed to identify the causes of congestion and proffer a solution to alleviate congestion. By understanding the causes of congestion, adopting incremental solutions can achieve the desired outcome. A qualitative, exploratory research study was conducted with 14 participants from the maritime sector that have experienced port congestion. Data analysis was done through thematic analysis where all data collected was transcribed and the researcher observed and articulated emerging themes to attach meaning to the respondents’ interpretations and perceptions of their own lived reality on what causes port congestion in Durban and possible solutions thereof. The key findings confirmed that Wind, Labour issues and Equipment are the main causes of congestion in the Port of Durban. Further research to determine the impact of climate change on congestion is needed. The incremental and radical solutions proffered by the participants was compared to the causes of congestion. This study contributes to the field of maritime studies, by understanding the causes of congestion in the Port and the field of innovation studies by contributing to innovative theory. / Mini Dissertation (MBA)--University of Pretoria, 2021. / Gordon Institute of Business Science (GIBS) / MBA / Unrestricted

The aim of my master's thesis is to design and then implement suitable weather classification algorithms mainly focused on the detection of precipitation and fog, including parameterization. The first half of my thesis is devoted to a theoretical description of weather and it's impact on transport. Furthermore, the theory of image processing and neural networks and already existing solutions is approached. Subsequently, the used datasets are described. The practical part of thesis is devoted to the design of possible algorithms based on the theoretical part of the thesis. After the design, the individual algorithms are implemented, tested and evaluated. Finally, a comparison of classical methods and neural networks is described.

The diploma thesis is focused on the design and implementation of a prototype wireless home weather station. The goal is to measure the air temperature, relative humidity and atmospheric pressure. Measured data are stored in external memory and it is posibility to send to the computer for subsequent processing and evaluation.

Research has shown that creating uniformity in video games has a larger potential to be objectively good. Realistic video games can create uniformity by implementing a dynamic weather system. The purpose of this thesis is to compare dynamic and static weather and their contribution to the overall player satisfaction. Three weather components were created: wind, rain and clouds. Which weather component that creates the most enjoyment have been examined. Two virtual environments, one dynamic and one static, were created and a test group answered a questionnaire regarding the simulations. Results indicate that dynamic weather is preferred over static weather regarding overall satisfaction.

The Madden-Julian oscillation (MJO) and the quasi-biennial oscillation (QBO) are two key modes of variability in the tropical atmosphere. The MJO, characterized by propagating, planetary-scale signals in convection and winds, is the main source of subseasonal variability and predictability in the tropics. The QBO is a ~28-month cycle in which the tropical stratospheric zonal winds alternate between easterly and westerly regimes. Via thermal wind balance these winds induce temperature anomalies, and both wind and temperature signals reach the tropopause. Recent observational results show a remarkably strong link between the MJO and the QBO during boreal winter: the MJO is stronger and more predictable when QBO winds in the lower stratosphere are easterly than when winds are westerly. Despite its important implications for MJO theory and prediction, the physical processes driving the MJO-QBO interaction are not well-understood. In this thesis, we use a hierarchy of models – including a cloud-resolving model, a forecast model, and a global climate model – to examine whether models can reproduce the MJO-QBO link, and better understand the possible mechanisms driving the connection. Based in part on our modeling findings, we further explore observed QBO temperature signals thought to be important for the MJO-QBO link. After providing necessary background and context in the first two chapters, the third chapter looks at the MJO-QBO link in a small-domain, cloud-resolving model. The model successfully simulates convection associated with two MJO events that occurred during the DYNAMO field campaign. To examine the effect of QBO, we add various QBO temperature and wind anomalies into the model. We find that QBO temperature anomalies alone, without wind anomalies, qualitatively affect the model MJO similarly to the observed MJO-QBO connection. QBO wind anomalies have no clear effect on the modeled MJO. We note however that the MJO response is quite sensitive to the vertical structure of the QBO temperature anomalies, and for realistic temperature signals the model response is very small. In the fourth chapter, we look at the MJO-QBO link in a state-of-the-art global forecast model with a good representation of the MJO. We conduct 84 hind-cast experiments initialized on dates across winters from 1989-2017. For each of these dates, we artificially impose an easterly and a westerly QBO in the stratospheric initial conditions, and examine the resulting changes to the simulated MJO under different stratospheric states. We find that the effect of the QBO on the model MJO is of the same sign as observations, but is much smaller. A large sample size is required to capture any QBO signal, and tropospheric initial conditions seem more important than the stratosphere in determining the behavior of the simulated MJO. Despite the weak signal, we find that simulations with stronger QBO temperature anomalies have a stronger MJO response. In the fifth chapter, we conduct experiments in recent versions of a NASA general circulation model. We find that a version with a high vertical resolution generates a reasonable QBO and MJO, but has no MJO-QBO link. However, this model has weaker-than-observed QBO temperature anomalies, which may explain the lack of an MJO impact. To explore this potential bias, we impose the QBO by nudging the model stratospheric winds towards reanalysis, leading to more realistic simulation of QBO temperature anomalies. Despite this, the model still fails to show a strong MJO-QBO link across several ensemble experiments and sensitivity tests. We conclude with discussion of possible reasons why the model fails to capture the MJO-QBO connection. The sixth chapter examines QBO temperature signals in a range of observational and reanalysis datasets. In particular, we are motivated by two elements of the MJO-QBO relationship which are especially puzzling: the seasonality (i.e. that the MJO-QBO link is only significant in boreal winter) and long-term trend (i.e. that the MJO-QBO link seems to have only emerged since the 1980s). By examining QBO temperature signals around the tropopause, we highlight changes to the strength and structure of QBO temperature anomalies both in boreal winter and in recent decades. Whether these changes are linked to the MJO-QBO relationship, and what more generally might explain them, is not presently clear. Overall, we demonstrate that capturing the MJO-QBO relationship in a variety of models is a difficult task. The majority of evidence indicates that QBO-induced temperature anomalies are a plausible pathway through which the QBO might modulate the MJO, but the theoretical description of precisely how these temperature anomalies may impact convection is lacking and likely more nuanced than the literature to date suggests. Most models show only a weak modulation of the MJO associated with changes in upper-tropospheric temperatures, and even when those temperature signals are artificially enhanced, comprehensive GCMs still fail to show a significant MJO-QBO connection. Our observational study indicates that temperature anomalies associated with the QBO show striking modulations on various timescales of relevance to the MJO-QBO link, but do not conclusively demonstrate a clear connection to the MJO. This difficulty simulating a strong MJO-QBO connection suggests that models may lack a key process in driving the MJO and coupling the tropical stratosphere and troposphere. It is further possible that the observed link may be in some regards different than is currently theorized -- for example statistically not robust, due to non-stratospheric processes, or driven by some mechanism that has not been suitably explored.

Identifying factors that affect the flow of mutual fund capital and betweenmutualfund types hasthe potential, among others,to relief fund management and investors from unnecessary administrative costs. This study investigated the role media reported weather shocks have on socially responsible and conventional mutual trust funds’capital flow.The study also has compared the magnitude of influence media reported weather shocks has on capital flow between socially responsible-and convectional mutualtrustfunds.It gives conclusionafter empirically studying all accessible socially responsible mutual trust fundswith relevant accessible financial data, originated, and actively traded in the Swedish financial market with the Swedish currency (Kronor) as well as taking conventional mutual trustfundswith similar maturity. And, the study result shows that media reported weather shocks has statistically significant role in the flow of capital, on bothsocially responsible-and conventional mutual funds in Sweden. It also shows that there is no significant difference in the role media reported weather shocks play between the two fundtypes. The result is concurrent with Hirshleifer & Shumway (2003)’s study which indicate that weather affecting investors mood and behavior. The result is interesting as it implicates to the psychological and emotional factorsplaying a significant role in affecting the flow of investment capital in general, in contrast to the rational economic behavior characterized by fund return and risk performance.