Policy makers, water managers, farmers and many other sectors of the society in southern Africa are confronting increasingly complex decisions as a result of the marked day-to-day, intra-seasonal and inter-annual variability of climate. Hence, forecasts of hydro-climatic variables with lead times of days to seasons ahead are becoming increasingly important to them in making more informed risk-based management decisions. With improved representations of atmospheric processes and advances in computer technology, a major improvement has been made by institutions such as the South African Weather Service, the University of Pretoria and the University of Cape Town in forecasting southern Africa’s weather at short lead times and its various climatic statistics for longer time ranges. In spite of these improvements, the operational utility of weather and climate forecasts, especially in agricultural and water management decision making, is still limited. This is so mainly because of a lack of reliability in their accuracy and the fact that they are not suited directly to the requirements of agrohydrological models with respect to their spatial and temporal scales and formats. As a result, the need has arisen to develop a GIS based framework in which the “translation” of weather and climate forecasts into more tangible agrohydrological forecasts such as streamflows, reservoir levels or crop yields is facilitated for enhanced economic, environmental and societal decision making over southern Africa in general, and in selected catchments in particular. This study focuses on the development of such a framework. As a precursor to describing and evaluating this framework, however, one important objective was to review the potential impacts of climate variability on water resources and agriculture, as well as assessing current approaches to managing climate variability and minimising risks from a hydrological perspective. With the aim of understanding the broad range of forecasting systems, the review was extended to the current state of hydro-climatic forecasting techniques and their potential applications in order to reduce vulnerability in the management of water resources and agricultural systems. This was followed by a brief review of some challenges and approaches to maximising benefits from these hydro-climatic forecasts. A GIS based framework has been developed to serve as an aid to process all the computations required to translate near real time rainfall fields estimated by remotely sensed tools, as well as daily rainfall forecasts with a range of lead times provided by Numerical Weather Prediction (NWP) models into daily quantitative values which are suitable for application with hydrological or crop models. Another major component of the framework was the development of two methodologies, viz. the Historical Sequence Method and the Ensemble Re-ordering Based Method for the translation of a triplet of categorical monthly and seasonal rainfall forecasts (i.e. Above, Near and Below Normal) into daily quantitative values, as such a triplet of probabilities cannot be applied in its original published form into hydrological/crop models which operate on a daily time step. The outputs of various near real time observations, of weather and climate models, as well as of downscaling methodologies were evaluated against observations in the Mgeni catchment in KwaZulu-Natal, South Africa, both in terms of rainfall characteristics as well as of streamflows simulated with the daily time step ACRU model. A comparative study of rainfall derived from daily reporting raingauges, ground based radars, satellites and merged fields indicated that the raingauge and merged rainfall fields displayed relatively realistic results and they may be used to simulate the “now state” of a catchment at the beginning of a forecast period. The performance of three NWP models, viz. the C-CAM, UM and NCEP-MRF, were found to vary from one event to another. However, the C-CAM model showed a general tendency of under-estimation whereas the UM and NCEP-MRF models suffered from significant over-estimation of the summer rainfall over the Mgeni catchment. Ensembles of simulated streamflows with the ACRU model using ensembles of rainfalls derived from both the Historical Sequence Method and the Ensemble Re-ordering Based Method showed reasonably good results for most of the selected months and seasons for which they were tested, which indicates that the two methods of transforming categorical seasonal forecasts into ensembles of daily quantitative rainfall values are useful for various agrohydrological applications in South Africa and possibly elsewhere. The use of the Ensemble Re-ordering Based Method was also found to be quite effective in generating the transitional probabilities of rain days and dry days as well as the persistence of dry and wet spells within forecast cycles, all of which are important in the evaluation and forecasting of streamflows and crop yields, as well as droughts and floods. Finally, future areas of research which could facilitate the practical implementation of the framework were identified. / Thesis (Ph.D.)-University of KwaZulu-Natal, Pietermaritzburg, 2007.

Weather-related delays in the aviation sector will always occur, however, through effective delay management and improved weather forecasting, the impact and duration of delays can be reduced. The research examined the type of weather that caused departure delays, due to adverse weather at the departure station, namely O. R. Tambo International Airport (ORTIA), over the period 2010 to 2013. It was found that the most significant weather that causes such delays are thunderstorms, followed by fog. Other noteworthy elements are rainfall, without the influence of other weather elements, and icing. It was also found that the accuracy of a weather forecast does not impact on the number of departure delays, and thus departure delays due to weather at the departure station are largely unavoidable. However, the length and impact of such delays can be reduced through improved planning. The study highlights that all weather-related delays can be reduced by improved weather forecasts, effective assessment of the weather forecast, and collaborative and timely decision making. A weather impact index system was designed for ORTIA and recommendations for delay reductions are made. / Geography / M. Sc. (Geography)

Este estudo tem como objetivo a verificação das previsões diárias, das temperaturas máxima e mínima e precipitação acumulada, realizadas pelo modelo operacional de previsão numérica do tempo WRF (Weather Research Forecasting) para o estado de São Paulo. As condições iniciais e de fronteira fornecidas pela análise e previsão das 00UTC do modelo Global Forecast System (GFS), são usados no processamento do WRF, para previsões de 72 horas, em duas grades aninhadas (espaçamentos horizontais de grade de 50 km, D1, e 16,6 km, D2). O período avaliado foi de abril de 2010 a março de 2011. As comparações diárias das temperaturas máxima e mínima foram realizadas entre os valores preditos e observados nas estações de superfície de Registro, São Paulo, Paranapanema, Campinas, Presidente Prudente e Votuporanga (dados da CIIAGRO); através do erro médio (EM) e raiz do erro médio quadrático (REQM), para os prognósticos das 36, 60 e 72 horas. A precipitação acumulada diária é avaliada com relação ao produto MERGE, pela aplicação da ferramenta MODE, na previsão das 36 horas, para um limiar de 0,3 mm, no domínio espacial abrangendo o Estado de São Paulo e vizinhanças. Primeiramente, fez-se uma análise, comparando os pares de grade dos campos previsto e observado, utilizando os índices estatísticos de verificação tradicional de probabilidade de acerto (PA); índice crítico de sucesso (ICS); viés (VIÉS); probabilidade de detecção (PD) e razão de falso alarme (RFA). Posteriormente, foram analisados os campos de precipitação com relação à razão de área (RA); distância dos centroides (DC); razões de percentil 50 (RP50) e 90 (RP90). Os resultados evidenciaram que as saídas numéricas do modelo WRF com D2 tiveram desempenho melhor comparado à grade de menor resolução (maior espaçamento de grade horizontal, D1), tanto no prognóstico diário das temperaturas (máxima e mínima) quanto da precipitação acumulada. A temperatura apresentou um padrão de amortecimento, com temperaturas diárias máxima subestimada e mínima superestimada. Com relação à precipitação, as saídas numéricas do modelo GFS e WRF com D2 mostraram desempenho semelhante, com o D2 apresentando índices ligeiramente melhores, enquanto que as saídas numéricas do modelo WRF com D1 exibiram pior desempenho. Verificou-se um padrão de superestimativa, tanto em termos de abrangência espacial quanto em intensidade, para o modelo GFS e WRF em ambos os domínios simulados, ao longo de todo o período analisado. O percentil 50 é, geralmente, maior que o observado; entretanto, o percentil 90 é mais próximo ao observado. Os resultados também indicam que o viés dos modelos varia ao longo do ano analisado. Os melhores índices tanto com relação à precipitação quanto à temperatura foram obtidos para a estação de verão, com o modelo WRF com D2 apresentando melhores prognósticos. Entretanto, os modelos apresentam os maiores erros no inverno e no outono. Estes erros foram decorrentes de subestimativas das temperaturas máximas e superestimativas de área e intensidade de precipitação. / Forecasts of daily maximum and minimum temperatures and rainfall performed by the operational numerical weather prediction WRF (Weather Research Forecasting) model in the São Paulo are evaluated. Initial and boundary conditions provided by the 00UTC Global Forecast System (GFS) Model and WRF run for 72 hours, with two nested grids (with horizontal grid spacing of 50 km, D1, and 16.6 km, D2). The study was made for April 2010 to March 2011 period. Daily maximum and minimum temperatures comparisons were made, between predicted and observed data of the surface weather stations of Registro, São Paulo, Paranapanema, Campinas, Presidente Prudente and Votuporanga (CIIAGRO Data), through the mean error (ME) and root mean square error(RMSE), for the 36, 60 and 72 hours forecasts. The daily accumulated rainfall is evaluated using MODE with respect to the MERGE product, for the 36 hours forecast, with threshold of 0.3 mm over the spatial domain covering the State of São Paulo and neighborhoods. First, an analysis was made comparing grid pairs of predicted and observed fields, through the traditional statistical verification indexes: accuracy (PA), critical success index (ICS), bias (VIES), probability of detection (PD) and false alarm ratio (RFA). Subsequently, we analyzed the precipitation field with respect to area ratio (AR), distance from the centroids (DC), ratio of the 50th percentile (RP50) and ratio of the 90th percentile (RP90). The WRF, with D2 nested grid, had better performance compared to the grid of lower space resolution (higher horizontal grid spacing, D1) for both, daily temperatures (maximum and minimum) and the accumulated rainfall forecasts. The temperature forecast presented a damped pattern, with underestimated maximum and overestimated minimum values. Rainfall was overall overestimated spatially and in intensity for the three models throughout the analized period. The forecasted 50th percentile is generally higher than that observed, however, the 90th percentile is closer to observations. The results also indicate that the bias of the models varies annually. The best performances for both rainfall and temperature were obtained for the summer season, with the D2 showing slightly better results. However, the models had the biggest errors during the winter and autumn seasons. These errors were due to underestimation of maximum temperatures and overestimation in area and intensity of precipitation.

Thesis: S.M., Joint Program in Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Department of Mechanical Engineering; and the Woods Hole Oceanographic Institution), 2014. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 79-81). / The ocean is a complex, constantly changing, highly dynamical system. Prediction capabilities are constantly being improved in order to better understand and forecast ocean properties for applications in science, industry, and maritime interests. Our overarching goal is to better predict the ocean environment in regions of complex topography with a continental shelf, shelfbreak, canyons and steep slopes using the MIT Multidisciplinary Simulation, Estimation and Assimilation Systems (MSEAS) primitive-equation ocean model. We did this by focusing on the complex region surrounding Taiwan, and the period of time immediately following the passage of Typhoon Morakot. This area and period were studied extensively as part of the intense observation period during August - September 2009 of the joint U.S. - Taiwan program Quantifying, Predicting, and Exploiting Uncertainty Department Research Initiative (QPE DRI). Typhoon Morakot brought an unprecedented amount of rainfall within a very short time period and in this research, we model and study the effects of this rainfall on Taiwan's coastal oceans as a result of river discharge. We do this through the use of a river discharge model and a bulk river-ocean mixing model. We complete a sensitivity study of the primitive-equation ocean model simulations to the different parameters of these models. By varying the shape, size, and depth of the bulk mixing model footprint, and examining the resulting impacts on ocean salinity forecasts, we are able to determine an optimal combination of salinity relaxation factors for highest accuracy. / by Jennifer Jacobs Landry. / S.M.

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Meteorology and Physical Oceanography, 1982. / Microfiche copy available in Archives and Science. / Vita. / Bibliography: leaves 218-225. / by Randall M. Dole. / Ph.D.

Internet of Things(IoT) has in recent years become a topic of broad and current interest. The purpose of this thesis is to anticipate weather conditions by constructing a system for collecting information about atmospheric pressure. The development of the system will solve the following problem: it should be possible to implement a system that allows for the collection of information from sensors through a mobile phone. The problem was solved through an iOS application together with a Micro Controller Unit (MCU) and a sensor. To collect weather data, the BME280, with its atmospheric pressure, temperature and humidity sensor, was used. Bluetooth was chosen for the interaction between the Automat and the iOS application. This proved to be a possible solution to a problem in a growing area of application. An advantage to this hardware solution is the mobility and flexibility of the Automat, making it ideal for mobile IoT solutions. Arduino is, however, the better choice for developers, as it has a larger community and clear documentation. / Internet of Things (IoT) har på senare år blivit ett alltmer omtalat område. Syftet med tesen är att förutspå väderförhållanden genom att konstruera ett IoT system som samlar in information om lufttryck, detta för att besvara frågeställningen: Det bör gå att samla in sensordata med hjälp av mobiltelefon. För att besvara detta följdes Ekholms modell för teknisk forskning och arbetsmetoden Scrum. Frågestallningen löstes genom en iOS applikation med tillhörande Microcontroller Unit(MCU) och sensor. För att samla in väderdata användes sensorn BME280, som har lufttrycks-, temperaturoch luftfuktighetssensorer, tillsammans med MCU:n Automat. För interaktionen mellan Automat och iOS applikationen tillämpades bluetooth-kommunikation. Detta var en möjlig lösning på ett problem i ett växande tillämpningsområde. Fördelar med denna lösning av hårdvara är att den är välanpassad till mobila IoT lösningar tack vare Automats minimala storlek i förhållande till funktionalitet. I många fall är däremot Arduino ett bättre val för utvecklaren, då den har större samfund och tydligare dokumentation.

Space weather has become an international issue due to the catastrophic impact it can have on modern societies. Solar flares are one of the major solar activities that drive space weather and yet their occurrence is not fully understood. Research is required to yield a better understanding of flare occurrence and enable the development of an accurate flare prediction system, which can warn industries most at risk to take preventative measures to mitigate or avoid the effects of space weather. This thesis introduces novel technologies developed by combining advances in statistical physics, image processing, machine learning, and feature selection algorithms, with advances in solar physics in order to extract valuable knowledge from historical solar data, related to active regions and flares. The aim of this thesis is to achieve the followings: i) The design of a new measurement, inspired by the physical Ising model, to estimate the magnetic complexity in active regions using solar images and an investigation of this measurement in relation to flare occurrence. The proposed name of the measurement is the Ising Magnetic Complexity (IMC). ii) Determination of the flare prediction capability of active region properties generated by the new active region detection system SMART (Solar Monitor Active Region Tracking) to enable the design of a new flare prediction system. iii) Determination of the active region properties that are most related to flare occurrence in order to enhance understanding of the underlying physics behind flare occurrence. The achieved results can be summarised as follows: i) The new active region measurement (IMC) appears to be related to flare occurrence and it has a potential use in predicting flare occurrence and location. ii) Combining machine learning with SMART¿s active region properties has the potential to provide more accurate flare predictions than the current flare prediction systems i.e. ASAP (Automated Solar Activity Prediction). iii) Reduced set of 6 active region properties seems to be the most significant properties related to flare occurrence and they can achieve similar degree of flare prediction accuracy as the full 21 SMART active region properties. The developed technologies and the findings achieved in this thesis will work as a corner stone to enhance the accuracy of flare prediction; develop efficient flare prediction systems; and enhance our understanding of flare occurrence. The algorithms, implementation, results, and future work are explained in this thesis.

No description available.

Space weather has become an international issue due to the catastrophic impact it can have on modern societies. Solar flares are one of the major solar activities that drive space weather and yet their occurrence is not fully understood. Research is required to yield a better understanding of flare occurrence and enable the development of an accurate flare prediction system, which can warn industries most at risk to take preventative measures to mitigate or avoid the effects of space weather. This thesis introduces novel technologies developed by combining advances in statistical physics, image processing, machine learning, and feature selection algorithms, with advances in solar physics in order to extract valuable knowledge from historical solar data, related to active regions and flares. The aim of this thesis is to achieve the followings: i) The design of a new measurement, inspired by the physical Ising model, to estimate the magnetic complexity in active regions using solar images and an investigation of this measurement in relation to flare occurrence. The proposed name of the measurement is the Ising Magnetic Complexity (IMC). ii) Determination of the flare prediction capability of active region properties generated by the new active region detection system SMART (Solar Monitor Active Region Tracking) to enable the design of a new flare prediction system. iii) Determination of the active region properties that are most related to flare occurrence in order to enhance understanding of the underlying physics behind flare occurrence. The achieved results can be summarised as follows: i) The new active region measurement (IMC) appears to be related to flare occurrence and it has a potential use in predicting flare occurrence and location. ii) Combining machine learning with SMART's active region properties has the potential to provide more accurate flare predictions than the current flare prediction systems i.e. ASAP (Automated Solar Activity Prediction). iii) Reduced set of 6 active region properties seems to be the most significant properties related to flare occurrence and they can achieve similar degree of flare prediction accuracy as the full 21 SMART active region properties. The developed technologies and the findings achieved in this thesis will work as a corner stone to enhance the accuracy of flare prediction; develop efficient flare prediction systems; and enhance our understanding of flare occurrence. The algorithms, implementation, results, and future work are explained in this thesis.

點預測為目前使用最多之預測陳述,其效率評估亦多以最小平方和誤差(minimum of sum of square errors)為主。每日或月的經濟或財金指標預測是點預測最常見的例子。但是隨著區間時間數列真正需求與軟計算(soft computing)科技的發展,區間計算與預測愈來愈受重視。本文提出幾種區間時間數列預測的方法,並研究其效率評估。在第三章,我們定義區間誤差和,並將其對應到實數值,以便用傳統的方法計算。最後我們以影響經濟作物的天氣預測,作實證研究分析。考慮在無參數條件下,幾種預測方法作效率評估與準確性探討。天氣預測是區間預測的例子,建立合適的的區間預測方法與效率評估,對各研究領域將會有莫大的幫助。 / Currently, the most use of forecasts is the point forecasting, whose efficiency evaluations are major in the least squares and error (minimum of sum of square errors). The common examples of the point forecasting are daily or monthly economy index or financial estimation. But along with the real demand of interval time series and the development of soft computation (soft computing), the interval computation and the forecasting are more and more important. This article provides some interval time series forecasting methods, and studies the efficiency evaluation. In chapter 3, we define sum errors of interval and correspond them to the real numbers, so as to compute with traditional way. Finally, we decide to use the weather forecasting which can affect the cash crop to be the empirical study analysis. Consider some forecasting methods under the non-parameter condition to be the efficiency evaluations and the accurate discussion. The weather forecasting is an example of interval forecasting. It will be more helpful of each research area if we establish the appropriate interval forecasting method and the efficiency evaluation.