Aviation suffers many delays due to the lack of timely air traffic flow management. These delays are also caused by the uncertainty weather information; and the lack of efficient dissemination of weather products to pilots. It is clear that better models are needed to quantify air traffic flow in three flight regions - en-route, in the terminal, and on the ground, to determine aviation weather information requirements at each region, and to quantify their bandwidth requirements. Furthermore, the results from those models can be used to select alternative future aviation communication systems. In this research, the 'ITHINK' and 'MATLAB' software packages have been used to develop a lumped Air Traffic Flow Model (ATFM) and an Aviation Weather Information and Bandwidth Requirements Model (AWINBRM). The ATFM model is used to quantify the volume of air traffic in each phase of flight in three flight regions. This model can be used to study navigation, surveillance, and communication requirements. The AWINBRM model is used to study aviation weather information requirements in different flight phases of flight. Existing and potential communication systems used for transmitting aviation weather information are explored in this research. Finally, a usable and practical computer model - Aircraft Impacted and Detour Model (AIDM) around an aviation weather system is developed. This model is used to compare the costs between detoured flights around a weather system and delayed flights at the airports. The purpose of this research is to study air traffic flow and aviation weather information and bandwidth requirements through modeling. The ultimate goal of the models described here is to serve as a living laboratory where policies can be tried before implementing them into the real system. Moreover, these computer models can evolve dynamically through time allowing decision makers to exercise policies at various points in time to quantify results with ease. This research would be a first integrated model for combing air traffic flow and aviation weather requirements and determining the quantity of aviation weather information between pilot and ground service centers. This research would be a guideline for aviation industry to build an efficient and timely aviation weather information transmission system with minimum budget. Consequently, this research will reduce aviation delays and improve aviation safety. / Ph. D.

One of the factors contributing to motor vehicle crashes is lack of sufficient friction at the tire-pavement interface. Although the relationship between surface friction and roadway safety has long been recognized, attempts to quantify the effect of pavement skid resistance on wet accident rates have produced inconsistent results. This thesis analyzes the relationships between skid resistance, accident, and traffic data for the state of Virginia. The correlation between wet skid resistance measured with a locked-wheel trailer using a smooth tire and wet accident rates is examined. Additionally, the influence of traffic volumes on accident rates is considered. The research used accident and skid data from the Virginia wet accident reduction program as well as from sections without pre-identified accident or skid problems. The wet accident data was aggregated in 1.6 km (1 mi) sections and divided by the annual traffic to obtain wet accident rates. The minimum skid number measured on each of these sections was then obtained and added to the database. Regression analyses indicated that there is statistically significant effect of skid resistance on wet accident rate; the wet accident rate increases with decreasing skid numbers. However, as expected, skid resistance alone does a poor job of modeling the variability in the wet accident rates. In addition, the wet accident rate also decreases with increasing traffic volume. Based on the data studied, a target skid number (SN(64)S) of 25 to 30 appears to be justified. / Master of Science

The cryosphere has been shown to be particularly adept as a proxy for climate change by various studies. Accordingly, historical records from the field of ice phenology have been harvested by climate scientists for the express purpose of studying the temporal variation of ice phenomena, namely freeze-up and ice-out. Ice-out records from 20 lakes in Maine, U.S.A. were collected and clustered by z-score for this thesis. Rather than attempt to relate ice-out to spring air temperature or global teleconnections/oscillations, the Spatial Synoptic Classification (SSC) method was used to encapsulate several meteorological variables that could have a bearing on ice-out variation. The balance between occurrence of relatively cool Moist Polar (MP) and relatively warm Dry Moderate (DM) weather-types during the winter-spring "superseason" was found to be a synoptic barometer of whether ice-out would occur seasonably early or late. The significance of this is predicated upon the finding that quantity of DM days has steadily risen at the expense of MP days during the latter-half of the twentieth-century, in accordance with observed climatic warming during the same period. The remaining SSC weather-types, most notably omnipresent Dry Polar (DP), remained generally stable during the historical record in Maine, further undergirding the significance of the DM-MP relationship. / Master of Science / The seasonal phenomenon of “ice-out,” the date on which the ice cover of a lake, pond, or river breaks up, has been well documented for many lakes in Maine. Numerous studies from around the world have linked progressively earlier ice-out dates to climate change and sought to use ice-out records, which often pre-date accurate temperature records, to better understand the effects of climate change. Synoptic weather-typing, or the characterization of daily surface weather conditions into archetypical classes, was the chief method of analysis in an effort to derive the link between shifting weather conditions (a manifestation of climate change) and the ice-out of 20 Maine lakes. In particular, the Spatial Synoptic Classification (SSC) method was selected due to its strong record in research and local availability. So-called “polar” weather-types, specifically Dry Polar (DP) and Moist Polar (MP), make up the majority of days in winter and early spring, but the latter-half of the twentieth century has seen MP days on the decline. The loss of MP days was found to be to the gain of the comparatively warmer Dry Moderate (DM) weather-type. MP and DM days each account for about 20% of the composition of the winter-spring “superseason,” on average; thus the balance between the two weather-types represents a synoptic barometer that provides an indication of a relatively early or late ice-out.

Past research has primarily focused on tornado outbreak intensity; however, this study presents an updated ranking index scheme that provides intensity ranks for both hail and tornado-dominant outbreaks. All outbreaks spanning 1960 - 2022 were obtained using a 24-hour kernel-density-based approach to map the severe weather report density. Notably, secular trends in the annual means of many of these variables (such as the number of hail and wind reports) showed a significant upward trend until 2010, after which that trend flattened. Thus, these fields were detrended using support vector regression that better fit these parameters' underlying annual time series. The resulting indices delineate between tornado and hail-dominant outbreaks, allowing further investigation into mixed-mode outbreaks and synoptic-scale precursors of these unique outbreak modes. It also provides an objective measure of outbreak intensity which can be useful when assessing potential future impacts from events with similar meteorological characteristics.

The weather derivative concept was created in the United States of America as a result of the deregulation of its energy industry. When other countries learnt of this concept they decided to enter the market as well. Thus a body called the Weather Risk Management Association was established. This body’s main function is to collate information pertaining to weather risk and to help the process of advancement and growth within the market. The weather risk market has grown tremendously and various participants across the world are using weather derivative products to protect the revenue of their respective companies against adverse weather condition. South Africa entered the weather risk market and it’s contracted its first weather contract in February 2000. The objective of this study is to evaluate the South African financial market perceptions on weather derivatives and to establish the feasibility of use. The study also places emphasis on the importance of evaluating the South African economic conditions in order to achieve the goal of the study. Hence the study evaluates the different aspects in terms of the legal, accounting, taxation, weather data, and structuring and pricing implications of a weather derivative transaction. Thus a survey was designed, forwarded, and received back from professionals in the legal, accounting, taxation, weather data, and structuring and pricing fields. This analysis was conducted to evaluate the South African financial market’s perceptions on weather derivative applications. / Prof. C.H. van Schalkwyk

A thesis submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Doctor of Philosophy. July 2014. / South Africa’s climate is highly variable, implying that the national agricultural sector should make provision to have early warning services in place in order to reduce the risks of disasters. More than 70% of natural disasters worldwide are caused by weather and climate or weather and climate related hazards. Reliable Seasonal Climate Forecasting (SCF) for South Africa would have the potential to be of great benefit to users in addressing disaster risk reduction. A disaster is a serious disruption of the functioning of a community or a society, causing widespread human, material, economic or environmental losses, which exceed the ability of the affected community or society to cope when using their own resources. The negative impacts on agricultural production in South Africa due to natural disasters including disasters due to increasing climate variability and climate change are critical to the sector. The hypothesis assumed in the study is the improved early warning service and better SCF dissemination lead to more effective and better decision making for subsequent disaster risk reduction in the agricultural sector. The most important aspect of knowledge management in early warning operations is that of distributing the most useful service to the target group that needs it at the right time. This will not only ensure maximum performance of the entity responsible for issuing the early warnings, but will also ensure the maximum benefit to the target group. South Africa is becoming increasingly vulnerable to natural disasters that are afflicted by localised incidents of seasonal droughts, floods and flash floods that have devastating impacts on agriculture and food security. Such disasters might affect agricultural production decisions, as well as agricultural productivity. Planting dates and plant selection are decisions that depend on reliable and accurate meteorological and climatological knowledge and services for agriculture. Early warning services that could be used to facilitate informed decision making includes advisories on iv future soil moisture conditions in order to determine estimated planting times, on future grazing capacity, on future water availability and on forecasts of the following season’s weather and climate, whenever that is possible. The involvement of government structures, obviously, is also critical in immediate responses and long term interventions. The importance of creating awareness, of offering training workshops on climate knowledge and SCF, and of creating effective early warning services dissemination channels is realized by government. This is essential in order to put effective early warning services in place as a disaster-risk coping tool. Early warning services, however, can only be successful if the end-users are aware of what early warning systems, structures and technologies are in place, and if they are willing that those issuing the early warning services become involved in the decision-making process. Integrated disaster-risk reduction initiatives in government programmes, effective dissemination structures, natural resource-management projects and communityparticipation programmes are only a few examples of actions that will contribute to the development of effective early warning services, and the subsequent response to and adoption of the advices/services strategies by the people most affected. The effective distribution of the most useful early warning services to the end-user, who needs it at the right time through the best governing structures, may significantly improve decision making in the agricultural, food security and other water-sensitive sectors. Developed disaster-risk policies for extension and farmers as well as other disaster prone sectors should encourage self-reliance and the sustainable use of natural resources, and will reduce the need for government intervention. The SCF producers (e.g. the South African Weather Service (SAWS)) have issued new knowledge to intermediaries for some years now, and it is important to determine whether this knowledge has been used in services, and if so whether these services were applied effectively in coping with disaster-risks and in disaster v reduction initiatives and programmes. This study for that reason also intends to do an evaluation of the knowledge communication processes between forecasters, and intermediaries at national and provincial government levels. It therefore, aims to assess and evaluate the current knowledge communication structures within the national agricultural sector, seeking to improve disaster-risk reduction through effective early warning services. A boundary organisation is an organization which crosses the boundary between science, politics and end-users as they draw on the interests and knowledge of agencies on both sides to facilitate evidence base and socially beneficial policies and programmes. Reducing uncertainty in SCF is potentially of enormous economic value especially to the rural communities. The potential for climate science to deliver reduction in total SCF uncertainty is associated entirely with the contributions from internal variability and model uncertainty. The understanding of the limitations of the SCFs as a result of uncertainties is very important for decision making and to end-users during planning. Disappointing, however, is that several studies have shown a fairly narrow group of potential users actually receive SCFs, with an even a smaller number that makes use of these forecasts In meeting the objectives of the study the methodology to be followed is based on knowledge communication. For that reason two types of questionnaires were drafted. Open and closed questionnaires comprehensively review the knowledge, understanding, interpretation of SCFs and in early warning services distribution channels. These questionnaires were administered among the SCF producers and intermediaries and results analysed. Lastly the availability of useful SCFs knowledge has important implications for agricultural production and food security. Reliable and accurate climate service, as one of the elements of early warning services, will be discussed since they may be used to improve agricultural practices such as crop diversification, time of planting vi and changes in cultivation practices. It was clear from the conclusions of the study that critical elements of early warning services need to receive focused attention such as the SCF knowledge feedback programme should be improved by both seasonal climate producers and intermediaries, together with established structures through which reliable, accurate and timely early warning services can be disseminated. Also the relevant dissemination channels of SCFs are critical to the success of effective implementation of early warning services including the educating and training of farming communities. The boundary organisation and early warning structures are important in effective implementation of risk reduction measures within the agricultural sector and thus need to be prioritised. Enhancing the understandability and interpretability of SCF knowledge by intermediaries will assist in improving action needed to respond to SCFs. Multiple media used by both SCF producers and intermediaries in disseminating of SCFs should be accessible by all users and end-users. The Government should ensure that farming communities are educated, trained and well equipped to respond to risks from natural hazards.

Traditional methods such as distance weighing, correlation and data driven methods have been used in the estimation of missing precipitation data. Also common is the use of radar (NEXRAD) data to provide better spatial distribution of precipitation as well as infilling missing rain gage data. Conventional regression models are often used to capture highly variant nonlinear spatial and temporal relationships between NEXRAD and rain gage data. This study aims to understand and model the relationships between radar (NEXRAD) estimated rainfall data and the data measured by conventional rain gages. The study is also an investigation into the use of emerging computational data modeling (inductive) techniques and mathematical programming formulations to develop new optimal functional approximations. Radar based rainfall data and rain gage data are analyzed to understand the spatio-temporal associations, as well as the effect of changes in the length or availability of data on the models. The upper and lower Kissimmee basins of south Florida form the test-bed to evaluate the proposed and developed approaches and also to check the validity and operational applicability of these functional relationships among NEXRAD and rain gage data for infilling of missing data. / by Delroy Peters. / Thesis (M.S.)--Florida Atlantic University, 2008. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2008. Mode of access: World Wide Web.

Embora a oscilação na pressão barométrica possa afetar o comportamento dos insetos, o seu efeito é ainda pouco explorado para a maioria das espécies. Neste contexto investigou-se em diferentes condições de pressão barométrica os comportamentos de alimentação e oviposição de Diabrotica speciosa (Germ.) (Coleoptera: Chrysomelidae) e Euschistus heros (F.) (Hemiptera: Pentatomidae), oviposição de Spodoptera frugiperda (Walker) (Lepidoptera: Noctuidae) e o parasitismo por Trissolcus basalis (Woll.) (Hymenoptera: Scelionidae), Habrobracon hebetor (Say) (Hymenoptera: Braconidae) e Cotesia flavipes (Cameron) (Hymenoptera: Braconidae). Para isso, foram simuladas as atividades de comportamento mencionadas, dentro de uma câmara barométrica, em condições controladas de pressão baixa, estável e alta, mantendo as demais condições abióticas estáveis. Evidenciou-se que a pressão barométrica pode exercer uma influência em diversas atividades comportamentais nos insetos, mas que não necessariamente possa ser generalizada para todas as espécies e situações. O comportamento de alimentação foi afetado de modo diferenciado para insetos sugadores e mastigadores. Foi observada uma diminuição no número de bainhas alimentares de adultos do percevejo E. heros submetidos a condições de baixa pressão barométrica. Por outro lado, o consumo foliar de adultos de D. speciosa não foi afetado pela pressão. O comportamento de oviposição de E. heros, D. speciosa e S. frugiperda, não foi influenciado significativamente pela pressão barométrica. O parasitismo por T. basalis não foi influenciado pelas condições de pressão barométrica. Entretanto, em condição de baixa pressão barométrica, houve redução no parasitismo por H. hebetor e C. flavipes. / Although barometric pressure oscillation may affect insects\' behavior, that effect is not explored for most species. Weather changes can be associated to patterns of barometric pressure fluctuations. In this sense, we investigated feeding of Diabrotica speciosa (Germ.) (Coleoptera: Chrysomelidae) and Euschistus heros (F.) (Hemiptera: Pentatomidae); oviposition of Spodoptera frugiperda (Walker) (Lepidoptera: Noctuidae), Diabrotica speciosa and Euschistus heros; and parasitism by Trissolcus basalis (Woll.) (Hymenoptera: Scelionidae), Habrobracon hebetor (Say) (Hymenoptera: Braconidae) and Cotesia flavipes (Hymenoptera: Braconidae). Those behaviors were simulated inside a barometric chamber, under controlled conditions of high, stable and low pressure, while other abiotic factor was stable. Barometric pressure clearly influences several insect behavioral activities, but it cannot be generalized to all species. Feeding behavior was distinctively affected for chewing and sucking insects. We observed a decrease in the number of adult salivary sheaths of E. heros submitted to conditions of low barometric pressure. Leaf consumption by Diabrotica speciosa was not affected by barometric pressure. Oviposition behavior was not affected by barometric pressure in any of the species evaluated. The parasitism by Trissolcus basalis did not show influence due to barometric pressure conditions. However, less parasitism of Habrobracon hebetor and Cotesia flavipes was observed under low pressure.

Radar rainfall estimates have become a decision making tool for scientists, engineers and water managers in their tasks for developing hydrologic models, water supply planning, restoration of ecosystems, and flood control. In the present study, the utility of a power function for linking the rain gauge and radar estimates has been assessed. Mean daily rainfall data from 163 rain gauges installed within the South Florida Water Management District network have been used and their records from January 1st, 2002 to October 31st, 2007 analyzed. Results indicate that the power function coefficients and exponents obtained by using a non-linear optimization formulation, show spatial variability mostly affected by type of rainfall events occurring in the dry or wet seasons, and that the linear distance from the radar location to the rain gauge has a significant effect on the computed values of the coefficients and exponents. / by Mario Mayes-Fernandez. / Thesis (M.S.C.S.)--Florida Atlantic University, 2009. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2009. Mode of access: World Wide Web.

Muitos negócios possuem exposição às variações climáticas e com poucas alternativas para mitigar este tipo de risco. Nos últimos 20 anos o mercado de derivativos climáticos se desenvolveu principalmente em locais como Canadá, EUA e Europa para transferir os riscos relacionados às variações climáticas para investidores com maior capacidade de absorção, tais como seguradoras, resseguradoras e fundos de investimentos. Este trabalho implementou uma metodologia de precificação destes contratos para a variável temperatura média diária no Brasil. Foram utilizados os dados de 265 estações meteorológicas cadastras no site do BDMEP/INMET, utilizando-se observações diárias durante o período 1970-2012. Enquanto a maior parte dos trabalhos de precificação fora desenvolvida para um local específico, neste estudo buscou-se uma solução mais generalizada e que permitisse aos participantes deste novo mercado balizar suas expectativas de preço para qualquer ponto com uma estação meteorológica no país. O principal desafio para esta abordagem foram as falhas nas séries temporais e para isto desenvolveu-se uma metodologia de preenchimento utilizando as informações do projeto NCEP/NCAR. Cada estação foi submetida ao algoritmo de análise e modelagem das séries de temperatura. Considerou-se \"Sucesso\" (36.2% dos casos) as estações cujo processo de modelagem culminou em um resíduo ruído branco, estacionário e homoscedástico. Por \"Fracasso\" (63.8% das estações) entendem-se os casos que violaram pelo menos uma destas condições. Para a incorporação da tendência nos dados utilizou-se a Regressão Polinomial Local (LOESS). Para a estimação da sazonalidade foi empregada análise espectral e utilizada a série de Fourier. Para o tratamento da autocorrelação serial nos resíduos utilizou-se modelos ARFIMA, que contempla um parâmetro para memória longa do processo. A análise espacial dos resultados sugere uma maior taxa de \"Sucesso\" para a precificação de contratos na região Centro-Sul do país e piores para Norte e Nordeste. O método de preenchimento das falhas não deve ser utilizado indiscriminadamente por todo o país, uma vez que a correlação entre as séries do BDMEP/INMET e NCEP/NCAR não é constante, além de apresentar um claro padrão na dispersão espacial. A precificação dos contratos foi feita pelos métodos de \"Burning cost\", \"Modelagem do Índice\" e \"Modelagem da temperatura média diária\". Para este último caso as temperaturas simuladas apresentaram um viés ligeiramente acima dos dados históricos, podendo causar grandes distorções na precificação dos contratos. Deve-se realizar uma correção dos valores simulados antes da precificação dos contratos. A qualidade e consistência dos dados climáticos representam a maior ameaça para a utilização de derivativos climáticos no país, principalmente na região Cento-Oeste, aonde existem poucas estações meteorológicas, e Nordeste, com baixíssima taxa de \"Sucesso\", mesmo com um razoável número de estações. / Many business are exposed to weather variations and managers did not use to have a tool to avoid it. In the last twenty years, weather derivative markets has developed mainly in Canada, USA and Europe, transferring these risks to investors who are willing and able to assume it and receive a financial compensation for that, such as investment funds, insurance and reinsurance companies. This study developed a methodology to price weather contracts with daily average temperature as underlying. It was used 265 public weather stations from BDMEP/ INMET and data was collected from 1970 up to 2012. While the most part of studies in this area have focused in one or few stations, the goal of this study was to develop a more general pricing tool which would allow assessing weather risk and quoting it at any place in Brazil with an available weather station. The main issue was the gaps that occur so frequently in weather time series data and a methodology using interpolated data from NCEP/NCAR was proposed to deal with it. At the bottom of modelling process, weather stations were classified as \"Success\" (36.2%) or \"Failure\" (63.8%) according to the analysis of residuals. To be considered \"Success\", residuals of a time series must be stationary, homoscedastic and white-noise, i.e., free of autocorrelation. If at least one of these was not reached, the modelling process of this weather station was considered \"Failure\". Detrend data was done using Local Polynomial Regression (LOESS). Seasonality was estimated using spectral analysis and Fourier analysis. Autocorrelation of residuals was incorporated into the model using ARFIMA models, which have a parameter to deal with long memory process. Spatial analysis of results suggests a higher \"Success\" rate for contracts priced in the Center south region and worst results were obtained in North and Northeast. Methodology to fill the gaps should not be used in all situations, once correlation is not constant through the country and has a strong spatial pattern (clustering). Pricing was done using \"Burning cost\", \"Index modelling\" and \"Daily modelling of average temperature\". In this former case, simulated temperature has shown a slightly positive bias, which could create huge differences in prices compared with other models. A correction should be done to these values, to use it for pricing purposes. The quality and consistency of weather data is the main issue to develop a weather market in Brazil, mainly in Center-West region, where there is a small number of weather stations and Northeast with the lowest \"Success\" rate, even with a not so small number of weather stations.