This study analyzes rainfall weather index insurance used for forage crops, in the Province of Ontario, Canada. The first objective of the study was to examine factors affecting the willingness of farmers to pay for forage rainfall index insurance, and a survey was undertaken. Some factors found to influence farmers' willingness to pay were knowledge and attitude regarding insurance, their risk profile, and socio-economic factors. A second objective of the study was to examine basis risk reduction approaches. Basis risk is the difference between the actual loss on a farm and the index measured loss payments that are determined by weather station data. The focus was to capture changing yield and weather relationships over crop growth stages. Using farm level forage yield and daily weather station data from Ontario, a multi-trigger index was designed using weighted crop cycle optimization, and results show that basis risk was substantially reduced. / May 2016

A Research Report submitted to the Faculty of Science in partial fulfilment of the requirements for the degree of Master of Science in the School of Statistics and Actuarial Science. 26 May 2016 / Exponential smoothing is a recursive time series technique whereby forecasts are updated for each new incoming data values. The technique has been widely used in forecasting, particularly in business and inventory modelling. Up until the early 2000s, exponential smoothing methods were often criticized by statisticians for lacking an objective statistical basis for model selection and modelling errors. Despite this, exponential smoothing methods appealed to forecasters due to their forecasting performance and relative ease of use. In this research report, we apply three commonly used exponential smoothing methods to two datasets which exhibit both trend and seasonality. We apply the method directly on the data without de-seasonalizing the data first. We also apply a seasonal naive method for benchmarking the performance of exponential smoothing methods. We compare both in-sample and out-of-sample forecasting performance of the methods. The performance of the methods is assessed using forecast accuracy measures. Results show that the Holt-Winters exponential smoothing method with additive seasonality performed best for forecasting monthly rainfall data. The simple exponential smoothing method outperformed the Holt’s and Holt-Winters methods for forecasting daily temperature data.

During the 1979 Atlantic hurricane season a tropical wave left the west coast of Africa and continued westward where satellite and ship observation indicated strengthening to a tropical depression from which Hurricane Frederic developed. This particular tropical disturbance has an interesting history. In its westward progression it intensified to hurricane strength lasting less than 24 hours followed by a weakening east of the Lesser Antilles. Continuing westward, this disturbance became extratropical over Southeast Cuba, but quickly reintensified to hurricane strength over Northwest Cuba, tracking through the warm Gulf waters and eventually making landfall near Mobile, Alabama. A number of experiments were carried out on a multi-level primitive equation (PE) model, a one-level PE model, and a higher resolution multi-level PE model (T63) in order to simulate the progression and intensification from a wave to a hurricane over a specified limited domain. Although previous experiments using this model with its comprehensive physical processes exhibit a reasonable predictive rate of success, the early experiments during this case study produced poor results. The most successful forecasts will be examined carefully and discussed entirely. There is strong indication that for mesoscale features a higher resolution model would achieve better results. / Typescript. / "Submitted to the Department of Meteorology in partial fulfillment of the requirements for the degree of Master of Science." / Advisor: T. N. Krishnamurti, Professor Directing Thesis. / Includes bibliographical references (leaves 165-167).

Thesis (M.Phil.)--Chinese University of Hong Kong. / Bibliography: leaves 122-126.

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This dissertation investigates, through weather metaphors in nature writing, how outdoor learning can be transformative. Although we have a robust history of books, essays, and poetry about experiences in weather-rich environments, education as a theoretical and applied field still lacks a philosophical foundation upon which to improve and expand outdoor pedagogy. Rather than proposing that the hermeneutical study of weather metaphors will lead to prescriptive lessons outdoors, this research aims to reveal the philosophy of transformative learning immanent in our experiences. With an increased philosophical understanding of the aesthetically transformative dimensions of outdoor experience, when our senses are most exposed, educators may take the next step of exploring what these experiences might do for the holistic education of students. This dissertation’s recognition of the aesthetic experiences students have in weather-saturated spaces promises to generate a richer definition of an effective learning environment.

Tornadoes cause numerous deaths and significant property damage each year, yet how tornado activity varies across climate states, particularly under global warming, remains poorly understood. Importantly, severe weather events arise during transient periods of extreme thermodynamic environments whose variability may differ from that of the environmental mean state. This study analyzes the climatological relationships between commonly-used severe weather environmental proxies (the product of convective available potential energy and bulk vertical wind shear, energy-helicity index, and the significant tornado parameter) and tornado density on three dominant timescales of climate forcing: diurnal, seasonal, and interannual. We utilize reanalysis data to calculate the spatial distributions of the mean, median, and a range of extreme percentiles of these proxies across each timescale as well as for the full climatology. We then test the extent to which each measure captures the spatiotemporal variability of tornado density over the continental United States. Results indicate that the mean is a suitable statistic when used with the full climatology of the energy-helicity index and the significant tornado parameter without using convective inhibition in calculations, the diurnal cycle for convective available potential energy and the product of convective available potential energy and bulk vertical wind shear, and the interannual variations for all proxies except convective available potential energy. The mean is outperformed by extreme percentiles otherwise. This understanding of climatological relationships between tornadoes and the large scale environments can improve prediction of tornado frequency and provides a foundation for understanding how changes in the statistics of large-scale environments may affect tornado activity in a future warmer climate state.

This thesis develops an innovative process-based analysis of contemporary model performance of precipitation over southern Africa. This region is typically understudied and not fully understood due to the complexity of various influences and drivers of precipitation. Historical simulations of precipitation are assessed including principal drivers, sources of biases and dominant modes of interannual variability. The South Indian Ocean Convergence Zone (SIOCZ), a large-scale, austral summer rainfall feature extending across southern Africa into the south-west Indian Ocean, is evaluated as the feature of interest in historical simulations. Most CMIP5 models simulate an SIOCZ feature, but are typically too zonally oriented and discontinued between land and the adjacent Indian Ocean. Excessive precipitation over the continent is likely associated with excessively high low-level moisture flux around the Angola Low, which is almost entirely due to model circulation biases. Drivers of precipitation over southern Africa include three dominant moisture flux transport pathways which originate from flow around the SIOHP and SAOHP and monsoon winds. Interannual variability in the SIOCZ is shown by a clear dipole pattern, indicative of a northeast-southwest movement of the SIOCZ. Drivers of this shift are significantly related to the El Niño Southern Oscillation and the subtropical Indian Ocean dipole in observations. However models do not capture these teleconnections well, limiting confidence in model representation of variability. A large majority of the population rely heavily on precipitation over southern Africa for agricultural purposes. Therefore spatial and temporal changes in precipitation are crucial to identify and understand with intentions to ultimately provide useful climate information regarding water security over the region. Key climate change signals over southern Africa are established in this thesis (OND and DJF), in which the dominant regional mechanisms of precipitation change over southern Africa are quantified. Robustness and credibility of these changes are additionally quantified. The most notable projected change in precipitation over southern Africa is the distinct drying signal evident in the pre-summer season (OND). This has the implication of delaying the onset of the rainy season affecting planting and harvesting times. Future projections of the SIOCZ are determined, which indicate a northward shift of approximately 200km. A dipole pattern of precipitation wetting/drying is evident, where wetting occurs to the north of the climatological axis of maximum rainfall, hence implying a northward shift of the ITCZ, consistent with the SIOCZ shift. Using a decomposition method it is established that ΔP's dipole pattern emerges largely from the dynamic component, which holds most uncertainty, particularly over the south-west Indian Ocean. Changes in precipitation over land are not solely driven by dynamical changes but additionally driven by thermodynamic contributions, implying projected changes over land and ocean regions require different approaches. SST patterns of warming over the Indian Ocean corroborate the warmest-get-wetter mechanism driving wetting over the south-west Indian Ocean, which is robust in both key seasons. Coherent model behaviour is understood via across model correlation plots of principal components, whereby patterns of coherent warming patterns are identified. Composite analyses of diagnostic variables across models illustrate patterns driving projected precipitation changes. Drying is more robust over land than over the south-west Indian Ocean. Clear robust drying signal in OND, however magnitude is uncertain. Drivers of uncertainty include SST pattern changes, which modulate atmospheric circulation patterns. Therefore reductions in uncertainty rely on the accurate representation of these processes within climate models to become more robust. There is a desire from both climate scientists and policy-makers to reduce uncertainty in future projections. No one particular methodology is unanimously agreed upon, however one approach is analysed in this thesis. Uncertainties of future precipitation projections are addressed using a process-based model ranking framework. Several metrics most applicable to southern African climate are selected and ranked, which include aspects of both mean state and variability. A sensitivity test via a Monte Carlo approach is performed for various sub-samples of “top” performing models within the CMIP5 model dataset. Uncertainty is significantly reduced when particular sub-sets of “top” performing models are selected, however only for austral summer over the continent. The result has the implication that potential value is established in performing a process-based model ranking over southern Africa. However additional investigation is required before such an approach may become viable and sufficiently credible and robust. Reductions in model spread are additionally established in SIOCZ projections, whereby model processes of change exhibit agreement, despite differing initial SIOCZ conditions. Therefore model process convergence and coherence is established with respect to projected changes in the SIOCZ, irrespective of initial climatology biases.

Research has shown that self-affirmation often leads to more adaptive responses to messages that focus on behaviour-specific, individual threats. However, little is known about the effects of self-affirmation in the context of a multifaceted collective threat, such as climate change. In the current thesis I apply self-affirmation theory to the psychology of climate change. More specifically, I propose that differentially polarized environmental orientations can have an impact on self-affirmation effects. In Chapter 1, I provide a general integration of the self-affirmation literature, the literature on sceptical responses to climate change, and the findings reported in the current thesis. The results from six empirical studies are presented in the following four chapters. In Chapter 2, I present findings that indicated that sceptical responses to climate change information are not always reduced through self-affirmation, but are instead strongly dependent on people's initial levels of rejection of environmental problems. In Chapter 3, I suggest that in the absence of a persuasive threatening message, self-affirmation can serve to validate a person's initial worldviews about environmental issues. In line with this suggestion, results demonstrated that self-affirmation led to more pro-environmental motives among participants with positive ecological worldviews but led to less pro-environmental motives among participants with negative ecological worldviews. In Chapter 4, I examine self-affirmation effects on the acceptance of climate change information. Results showed that self-affirmation promoted perceptions of greater climate change consequences and more self-efficacy among initially sceptical participants. Additionally, self-affirmation reduced pessimism among less sceptical participants. In Chapter 5, I present evidence that showed that self-affirmation resulted in more acceptance of information portraying the UK's contribution to climate change problems among participants with high national identification, while group-affirmation resulted in more information acceptance among participants with low national identification. These effects were only apparent among participants with negative ecological worldviews.

This study uses a very high resolution numerical weather prediction (NWP) model to investigate the complex structure and behaviour of cold fronts along the New South Wales coast during the warmer months of the year, the complex interaction between the wind flow and coastlines and elevated areas as well as the lee-trough effect, particularly the way it affects waters off the east coast of Tasmania, The study also investigates the utility of the higher resolution NWP model to better predict wind fields compared to a lower resolution model. The University of New South Wales very high resolution model (HIRES), nested in the Australian Bureau of Meteorology's coarse NWP model (GASP), was run at various horizontal resolutions (from 15 to 25km) in order to investigate the above-mentioned features. It was found to bave very good skill in resolving the features and was also found to be very accurate in the prediction of surface wind fields for various yacht race events out to at least four days ahead. It can be concluded that there is considerable skill in the ability of high-resolution NWP models such as HIRES, to predict the major features of the wind fields over the ocean out to several days ahead. Moreover, it was also able to more accurately simulate the complex structure of the summer-time cool change as it progressed along the NSW coast than the lower resolution model runs. The influence of coastlines, particularly ones with complex topographical features, on the wind flow was demonstrated to a limited extent throughout the study. Finally the following concepts were also verified as a result of the study: - air flow takes the path of least resistance - the shape of topography can help generate local turbulence - the orientation of the wind flow to a mountain range is important in determining turbulent effects. - under certain airflow and stability situations, standing wave activity and a lee trough can be observed in the lee of mountains, hills or even high coastal cliffs.