Dissertations / Theses on the topic 'Synoptic climatology Weather forecasting'

This thesis describes the development of real-time flood forecasting models at selected catchments in the three countries, using rain gauge and radar derived rainfall estimates and time-series analysis. An extended inter-comparison of real-time flood forecasting models has been carried out and an attempt has been made to rank the flood forecasting models. It was found that an increase in model complexity does not necessarily lead to an increase in forecast accuracy. An extensive analysis of group calibrated transfer function (TF) models on the basis of antecedent conditions of the catchment and storm characteristics has revealed that the use of group model resulted in a significant improvement in the quality of the forecast. A simple model to calculate the average pulse response has also been developed. The development of a hybrid genetic algorithm (HGA), applied to a physically realisable transfer function model is described. The techniques of interview selection and fitness scaling as well as random bit mutation and multiple crossover have been included, and both binary and real number encoding technique have been assessed. The HGA has been successfully applied for the identification and simulation of the dynamic TF model. Four software packages have been developed and extensive development and testing has proved the viability of the approach. Extensive research has been conducted to find the most important adjustment factor of the dynamic TF model. The impact of volume, shape and time adjustment factors on forecast quality has been evaluated. It has been concluded that the volume adjustment factor is the most important factor of the three. Furthermore, several attempts have been made to relate the adjustment factors to different elements. The interaction of adjustment factors has also been investigated. An autoregressive model has been used to develop a new updating technique for the dynamic TF model by the updating of the B parameters through the prediction of future volume adjustment factors over the forecast lead-time. An autoregressive error prediction model has also been combined with a static TF model. Testing has shown that the performance of both new TF models is superior to conventional procedures.

Reliability and accuracy of the forcing data plays a vital role in the Hydrological Streamflow Prediction. Reliability of the forcing data leads to accurate predictions and ultimately reduction of uncertainty. Currently, Numerical Weather Prediction (NWP) models are developing ensemble forecasts for various temporal and spatial scales. However, it is proven that the raw products of the NWP models may be biased at the basin scale; unlike model grid scale, depending on the size of the catchment. Due to the large space-time variability of precipitation, bias-correcting the ensemble forecasts has proven to be a challenging task. In recent years, Ensemble Pre-Processing (EPP), a statistical approach, has proven to be helpful in reduction of bias and generation of reliable forecast. The procedure is based on the bivariate probability distribution between observation and single-value precipitation forecasts. In the current work, we have applied and evaluated a Bayesian approach, based on the Copula density functions, to develop an ensemble precipitation forecasts from the conditional distribution of the single-value precipitation. Copula functions are the multivariate joint distribution of univariate marginal distributions and are capable of modeling the joint distribution of two variables with any level of correlation and dependency. The advantage of using Copulas, amongst others, includes its capability of modeling the joint distribution independent of the type of marginal distribution. In the present study, we have evaluated the capability of copula-based functions in EPP and comparison is made against an existing and commonly used procedure for same i.e. meta-Gaussian distribution. Monthly precipitation forecast from Climate Forecast System (CFS) and gridded observation from Parameter-elevation Relationships on Independent Slopes Model (PRISM) have been utilized to create ensemble pre-processed precipitation over three sub-basins in the western USA at 0.5-degree spatial resolution. The comparison has been made using both deterministic and probabilistic frameworks of evaluation. Across all the sub-basins and evaluation techniques, copula-based technique shows more reliability and robustness as compared to the meta-Gaussian approach.

Thesis (M.S. in Meterology and Physical Oceanography)--Naval Postgraduate School, September 2005.<br>Thesis Advisor(s): Tom Murphree, Robin T. Tokmakian. Includes bibliographical references (p. 137-143). Also available online.

The reliance on freshwater released by mountain glaciers and ice caps demands that the effects of climate change on these thermally-sensitive systems are evaluated thoroughly. Coupling climate variability to processes of mass and energy exchange at the glacier scale is challenged, however, by a lack of climate data at an appropriately fine spatial resolution. The thesis addresses this challenge through attempting to reconcile this scale mismatch: glacier boundary-layer observations of meteorology and ablation at Vestari Hagafellsjökull, Iceland, and Storglaciären, Sweden, are related to synoptic-scale meteorological variability recorded in gridded, reanalysis data. Specific attention is directed toward synoptic controls on: i) near-surface air temperature lapse rates; ii) stationarity of temperature-index melt model parameters; and iii) glacier-surface ablation. A synoptic weather-typing procedure, which groups days of similar reanalysis meteorology into weather categories , forms the basis of the analytical approach adopted to achieve these aims. Lapse rates at Vestari Hagafellsjökull were found to be shallowest during weather categories characterised by warm, cloud-free weather that encouraged katabatic drainage; steep lapse rates were encountered in weather categories associated with strong synoptic winds. Quantitatively, 26% to 38% of the daily lapse-rate variability could be explained by weather-category and regression-based models utilizing the reanalysis data: a level of skill sufficient to effect appreciable improvements in the accuracy of air temperatures extrapolated vertically over Vestari Hagafellsjökull. Weather categories also highlighted the dynamic nature of the temperature-ablation relationship. Notably, the sensitivity of ablation to changes in air temperature was observed to be non-stationary between weather categories, highlighting vulnerabilities of temperature-index models. An innovative solution to this limitation is suggested: the relationship between temperature and ablation can be varied as a function of weather-category membership. This flexibility leads to an overall improvement in the simulation of daily ablation compared to traditional temperature-index formulations (up to a 14% improvement in the amount of variance explained), without the need for additional meteorological data recorded in-situ. It is concluded that weather categories are highly appropriate for evaluating synoptic controls on glacier meteorology and surface energetics; significant improvements in the parameterization of boundary-layer meteorology and ablation rates are realised through their application.

<p> Since their development in the 1990s, gridded reanalysis data sets have proven quite useful for a broad range of synoptic climatological analyses, especially those utilizing a map pattern classification approach. However, their use in broad-scale, surface weather typing classifications and applications have not yet been explored. This research details the development of such a gridded weather typing classification (GWTC) scheme using North American Regional Reanalysis data for 1979-2010 for the continental United States. </p><p> Utilizing eight-times daily observations of temperature, dew point, pressure, cloud cover, u-wind and v-wind components, the GWTC categorizes the daily surface weather of 2,070 locations into one of 11 discrete weather types, nine core types and two transitional types, that remain consistent throughout the domain. Due to the use of an automated deseasonalized z-score initial typing procedure, the character of each type is both geographically and seasonally relative, allowing each core weather type to occur at every location, at any time of the year. Diagnostic statistics reveal a high degree of spatial cohesion among the weather types classified at neighboring locations, along with an effective partitioning of the climate variability of individual locations (via a Variability Skill Score metric) into these 11 weather types. Daily maps of the spatial distribution of GWTC weather types across the United States correspond well to traditional surface weather maps, and comparisons of the GWTC with the Spatial Synoptic Classification are also favorable. </p><p> While the potential future utility of the classification is expected to be primarily for the resultant calendars of daily weather types at specific locations, the automation of the methodology allows the classification to be easily repeatable, and therefore, easily transportable to other locations, atmospheric levels, and data sets (including output from gridded general circulation models). Further, the enhanced spatial resolution of the GWTC may also allow for new applications of surface weather typing classifications in mountainous and rural areas not well represented by airport weather stations.</p><p> </p>

A poorly consolidated concept base creates considerable difficulty amongst adolescents when it comes to the higher order task of analyzing complex, abstract and scientific weather phenomena as they appear on synoptic charts. The nature of the difficulties which they experience tends to encourage them to resort to the rote memorisation of concepts rather than understanding them. Kuhn (1962) avers that concepts, not enquiry methods, are at the core of rational thought. A well taught concept becomes a well developed mental construct which encourages understanding. It is the author's firm belief that in order for pupils to come to a thorough understanding of senior secondary meteorology - climatology and the synoptic chart, teaching of basic concepts should be graded and sequential. This thesis attempts to show the difficulties inherent in synoptic climatology. It relates these difficulties to adolescent cognitive development. Within this framework, the syllabus and past examinations of the Cape Education Department, and textbooks are examined. Teachers' views and pupils' understanding are gauged through the analyses of questionnaires and worksheets respectively. Conclusions are drawn and the problems which teachers and pupils encounter are addressed.

Thesis (Ph. D.)--Earth and Atmospheric Sciences, Georgia Institute of Technology, 2006.<br>Dr. Peter J. Webster, Committee Chair ; Dr. Judith A. Curry, Committee Member ; Dr. Robert Dickinson, Committee Member ; Dr. Robert X. Black, Committee Member ; Dr. Predrag Cvitanovic, Committee Member.

A large epizootic of the vector-borne disease bluetongue occurred in northern Europe from 2006-2009, costing the economies of the infected countries several hundreds of millions of euros. During this time, the United Kingdom (UK) was exposed to the risk of bluetongue by windborne incursions of infected Culicoides biting midges from the northern coast of mainland Europe. The first outbreaks which occurred in the UK in 2007 were attributed to this cause. Although bluetongue virus (BTV) no longer appears to be circulating in northern Europe, it is widely suggested that it and other midge-borne diseases may emerge again in the future, particularly under a changing climate. Spread of BTV is strongly influenced by the weather and climate however limited use has been made of meteorologically based models to generate predictions of its spread to the UK. The extent to which windborne BTV spread can be modelled at timescales from days to decades ahead, to inform tactical and strategic decisions taken to limit its transmission, is therefore examined here. An early warning system has been developed to predict possible incursion events on a daily timescale, based on an atmospheric dispersion model adapted to incorporate flight characteristics of the Culicoides vectors. The system’s warning of the first UK outbreak in September 2007 was found to be greatly beneficial to the UK livestock industry. The dispersion model is also shown to be a useful post-outbreak epidemiological analysis tool. A novel approach has been developed to predict BTV spread into the UK on climate-change timescales as dispersion modelling is not practical over extended periods of time. Using a combination of principal component and cluster analyses the synoptic scale atmospheric circulations which control when local weather conditions are suitable for midge incursions were determined. Changes in the frequency and timing of these large scale circulations over the period 2000 to 2050 were then examined using an ensemble of regional climate model simulations. The results suggest areas of UK under the influence of easterly winds may face a slight increase in risk and the length of the season where temperatures are suitable for BTV replication is likely to increase by around 20 days by 2050. However a high level of uncertainty is associated with these predictions so a flexible decision making approach should be adopted to accommodate better information as it becomes available in the future.

Accurate forecasts of weather and climate will become increasingly important as the world adapts to anthropogenic climatic change. Forecasts' accuracy is limited by the computer power available to forecast centres, which determines the maximum resolution, ensemble size and complexity of atmospheric models. Furthermore, faster supercomputers are increasingly energy-hungry and unaffordable to run. In this thesis, a new means of making computer simulations more efficient is presented that could lead to more accurate forecasts without increasing computational costs. This 'scale-selective reduced precision' technique builds on previous work that shows that weather models can be run with almost all real numbers represented in 32 bit precision or lower without any impact on forecast accuracy, challenging the paradigm that 64 bits of numerical precision are necessary for sufficiently accurate computations. The observational and model errors inherent in weather and climate simulations, combined with the sensitive dependence on initial conditions of the atmosphere and atmospheric models, renders such high precision unnecessary, especially at small scales. The 'scale-selective' technique introduced here therefore represents smaller, less influential scales of motion with less precision. Experiments are described in which reduced precision is emulated on conventional hardware and applied to three models of increasing complexity. In a three-scale extension of the Lorenz '96 toy model, it is demonstrated that high resolution scale-dependent precision forecasts are more accurate than low resolution high-precision forecasts of a similar computational cost. A spectral model based on the Surface Quasi-Geostrophic Equations is used to determine a power law describing how low precision can be safely reduced as a function of spatial scale; and experiments using four historical test-cases in an open-source version of the real-world Integrated Forecasting System demonstrate that a similar power law holds for the spectral part of this model. It is concluded that the scale-selective approach could be beneficially employed to optimally balance forecast cost and accuracy if utilised on real reduced precision hardware.

El Niño Southern Oscillation arises in the tropical Pacific due to coupled atmosphere-ocean interactions. The nature of the dynamical system of the atmosphere is chaotic and its predictability is sensitive to initial conditions, which constraints our ability to foresee the evolution of ENSO for very long time in advance. This dissertation is dedicated to extending the state-of-the-art prediction of the phenomenon. It focuses on the identification of precursory signals in the ocean and atmosphere that improve the understanding and long-lead forecasts of ENSO. A new statistical modelling technique based on dynamic components and state-space methods is developed. Very early premonitory signals that are a result of an in-depth analysis of the processes accompanying the origin and evolution of El Niño, especially in the subsurface ocean, which is less impacted by initial conditions, are established. These tracers are defined in the far western and central tropical Pacific and are shown to anticipate El Niño two and a half years before its peak. Initial intensification of the easterly winds at this time is associated with convergence of mass, downwelling and warming of the subsurface ocean layers in the far west. Thus the South Equatorial Current and the Equatorial Undercurrent are strengthened, which leads to the propagation of warm subsurface anomalies eastward. These anomalous patterns later lead to changes in the circulation and warming of the surface of the ocean in the central tropical Pacific, which leads to the suppression of the easterly winds. The area of tropical convection shifts to the east, which weakens the Walker circulation and triggers the Bjerknes feedback. This allows the further propagation of the subsurface warm anomalies, which reach the eastern Pacific and are upwelled to the surface, which marks the onset of an El Niño . Warm anomalies in the subsurface equatorial ocean have been previously used as precursors in statistical ENSO models via the integration of the upper ocean heat content or the incorporation of anomalies of the 20°C isotherm. In this way the propagation feature of the anomalies is not considered, and no direct connection is made between the first anomalous patterns and the occurrence of a warm event. Hence, the predictive potential of the incipient warming in the western Pacific is not harnessed. Thus, the work presented in the dissertation provides implications for the possibility to improve the long-lead capabilities of other models. The definition of ENSO predictors at specific depths and regions in the ocean and atmosphere requires the reliable surface and subsurface measurements of various climate variables. Regular measurements have only begun with the satellite era in the 1980s, and with the placement of an observation system after the Tropical Ocean Global Atmosphere Program (1985-1994). Forecasts with the model developed here substantially improve after 1994, and the change is distinct for the long-lead forecasts that rely on good-quality subsurface information about the ocean thermal structure. Therefore, the higher temporal and spatial resolution data sets of key variables are now long enough for statistical forecasting models to make better use of. The practical utility of multi-year forecasts is also explored. A well established link exists between ENSO and local climate in the coastal areas of Ecuador. Following El Niño is a warmer surface temperature and enhanced precipitation. These two variables control the dynamics of mosquito population, and in this way affect the incidence of dengue. An experiment is performed where long-lead forecasts of El Niño are used within a dengue model and the prospects for developing an early warning system is investigated.<br>El Niño Oscilación Sur (ENSO) es un fenómeno climático interanual que surge en el Pacífico tropical como resultado de las interacciones acopladas entre la atmósfera y el océano. Es el modulador más prominente de la variabilidad climática en esta escala del tiempo. La naturaleza del sistema dinámico de la atmósfera es caótica y su predictibilidad es sensible a las condiciones iniciales, lo que limita nuestra capacidad de prever la evolución del ENSO por un período de tiempo ilimitado de antemano. Esta tesis se dedica a explorar la posibilidad de extender y mejorar la predicción de última generación. En particular, se centra en la identificación de señales precursoras en el océano y en la atmósfera que mejoran la comprensión y los pronósticos a largos plazos de los eventos. Además, se desarrolla una nueva técnica de modelación estadística basada en componentes dinámicos y métodos de modelos de espacio de estado, y las covariables precursoras seleccionadas se incorporan en su diseño. Esta metodología extremadamente flexible ha sido probada y verificada en otras áreas de la ciencia como la ingeniería y la econometría, pero está comenzando a entrar en el campo de la ciencia del clima. En esta tesis se demuestra que en cierta medida esta estrategia metodológica puede unir los conceptos puramente dinámicos y puramente estadísticos de un ejercicio de pronóstico. También se explora la utilidad práctica de los pronósticos multi-anuales de El Niño. Un vínculo bien establecido es el existente entre ENSO y el climá local en las zonas costeras de Ecuador. Después de un evento de El Niño hay una temperatura superficial más cálida y una precipitación intensificada. Estas dos variables controlan la dinámica de la población de mosquitos y de esta manera afectan la incidencia de dengue. Se realiza un experimento en el que las predicciones de largo plazo de El Niño se utilizan en un modelo de predicción del dengue en el sur de la costa de Ecuador. El análisis sirve como una demostración del potencial para un servicio climático operacional en apoyo de la comunidad de salud pública en Ecuador.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior<br>An investigation of the atmospheric environments prone to severe convective weather is conducted for the subtropics of South America, east of the Andes Mountain Range. Upper air soundings valid at 00Z and 12Z for six sounding sites in subtropical South America (namely, Porto Alegre/BRA, Florianópolis/BRA, Curitiba/BRA, Foz do Iguaçu/BRA, Resistência/ARG e Buenos Aires/ARG) are employed to generate a short 12-yr climatology (from January 1998 to December 2009) of some of the main necessary ingredients for severe thunderstorm development: moisture availability, conditional instability, and vertical wind shear (VWS). The goal of the climatology is to document the typical magnitudes and seasonal variability of those ingredients with the aid of convective parameters. Threshold magnitudes for such parameters were objectively determined (via quantile analysis) and tested as cut-off criteria utilized to discriminate the severe weather environments. To that end, classic North-American threshold values extracted from the literature were also tested and the results compared vis-à-vis the South-American counterparts. Distinct combinations of such thresholds were employed to select atmospheric profiles theoretically conducive to severe thunderstorms and tornadoes. Atmospheric profiles obtained from the NCEP-NCAR Reanalysis data valid at 18Z were also used. The time and space distribution of the selected severe weather profiles were examined, emphasizing the seasonal variation and geographic distribution. From these sampled profiles, composite analysis were built for and Principal Component Analysis were applied to relevant meteorological variables at distinct vertical levels in order to search for the associated synoptic-scale patterns. The climatology succesfully reproduced the expected seasonal behavior of parameters that are indicative of conditional instability and VWS. Compared to the North-American climatology, the magnitudes found for the 700-500hPa lapse rates, 0-6km bulk shear and height of the LCL were lower in South America. The seasonal variability and space distribution of the severe weather profiles suggest that there exists an equatorward [poleward] displacement of the severe weather activity as winter [summer] approaches. The synoptic pattterns associated with the severe weather environments is different from those traditionally recognized for North America, particularly at the surface.<br>Neste trabalho é realizada uma investigação dos ambientes favoráveis ao desenvolvimento de convecção severa nos subtrópicos da América do Sul (AS) à leste dos Andes. Sondagens de ar superior das 00Z e 12Z de seis estações distribuídas na região subtropical da AS (quais sejam: Porto Alegre/BRA, Florianópolis/BRA, Curitiba/BRA, Foz do Iguaçu/BRA, Resistência/ARG e Buenos Aires/ARG), foram utilizadas para gerar uma pequena climatologia (12 anos; de janeiro de 1998 a dezembro de 2009) de alguns dos principais ingredientes necessários para o desenvolvimento de convecção severa: oferta de umidade, instabilidade condicional termodinâmica e cisalhamento vertical do vento (CVV). A climatologia visou documentar as magnitudes típicas e a variação sazonal destes parâmetros para a região de interesse. Valores significativos dos parâmetros foram objetivamente identificados via análise de quantis e testados como limiares combinados para salientar ambientes favoráveis ao desenvolvimento de tempestades severas. Limiares para a América do Norte documentados na literatura também foram testados para este fim e confrontados com os respectivos valores da amostragem sul-americana. Diferentes combinações destes limiares foram empregadas para se extrair os perfis atmosféricos considerados teoricamente favoráveis a tempestades severas e tornados. Perfis de tempo severo também foram extraídos dos dados da Reanálise do NCEP-NCAR válidos às 18Z. Foi examinada a distribuição temporal e espacial destes ambientes de tempo severo explorando sua sazonalidade e distribuição geográfica. Partindo destes perfis, foram analisados os padrões sinóticos predominantes na região através da composição média e Análise de Componentes Principais para as variáveis meteorológicas mais relevantes em diferentes níveis verticais. A climatologia reproduziu bem o comportamento sazonal esperado dos parâmetros de instabilidade termodinâmica e CVV. Em comparação com a climatologia da América do Norte encontrou-se para a América do Sul valores mais baixos de lapse rates na média troposfera, CVV na camada entre 0-6km, e altura do NCL. A distribuição sazonal e espacial dos perfis de tempo severo sugere uma migração geográfica das condições de tempo severo das latitudes mais baixas [altas] para as latitudes mais altas [baixas] à medida que nos aproximamos dos meses de verão [inverno]. O padrão sinótico de tempo severo na América do Sul é diferente daquele tradicionalmente documentado para a América do Norte, especialmente em superfície.

This thesis addresses the extratropical transition (ET) of tropical cyclones. ET is the process by which a tropical cyclone, upon encountering a baroclinic environment at higher latitudes, loses its tropical characteristics and transforms into an extratropical cyclone. The three main goals of the thesis are to develop a historical climatology of global ET occurrence, to examine future projections of ET using a global climate model, and to advance the predictive understanding of ET. A global climatology of ET from 1979-2017 is presented, which explores frequency of occurrence, geographical and seasonal patterns, climate variability, and environmental settings associated with different types of ET in global ocean basins. ET is defined objectively by means of tropical cyclones' trajectories through the Cyclone Phase Space (CPS), which is calculated using storm tracks from best track data and geopotential height fields from reanalysis datasets. Two reanalysis datasets are used and compared for this purpose, the Japanese 55-year Reanalysis (JRA-55) and the ECMWF Interim Reanalysis (ERA-Interim). Results show that ET is most common in the western North Pacific and the North Atlantic, where about half of the tropical cyclones transition into extratropical cyclones. Coastal regions in these basins also face the highest rates of landfalling ET storms. In the Southern Hemisphere basins, ET percentages range from about 20% to 40%. Different "ET pathways" through the CPS are linked to different geographical trajectories and environmental settings: A majority of ETs start with the tropical cyclone becoming thermally asymmetric and end with the formation of a cold core. This pathway typically occurs over warmer sea surface temperatures and takes longer than the reverse pathway, in which a tropical cyclone undergoes ET by developing a cold core before becoming asymmetric. The classifications of tropical cyclones into "ET storms" (tropical cyclones that undergo at some point in their lifetimes) and "non-ET storms" (tropical cyclones that do not undergo ET) obtained from JRA-55 and ERA-Interim are evaluated against the classification obtained from the best track records. In contrast to the CPS definition of ET, which is automated and objective, the best track definition of ET is given by the subjective judgment of human forecasters who take into account a wider range of data. According to the F1 score and the Matthews correlation coefficient, two performance metrics that balance classification sensitivity and specificity, the CPS classification agrees most with the best track classification in the western North Pacific and the North Atlantic, and least in the eastern North Pacific. The JRA-55 classification achieves higher performance scores than does the ERA-Interim classification, mostly because ERA-Interim has a bias toward cold-core structures in the representation of tropical cyclones. Future projections of ET are examined using a five-member ensemble of a coupled global climate model, the Flux-Adjusted Forecast-oriented Low Ocean Resolution (FLOR-FA) version of CM2.5 developed at the Geophysical Fluid Dynamics Laboratory. First, CPS is applied to 1979-2005 FLOR-FA output to develop a historical ET climatology, which is compared to the 1979-2005 ET climatology obtained from JRA-55. This comparison shows that FLOR-FA simulates many unrealistic low-latitude ET events, due to strong local maxima in the geopotential height fields used as input to calculate the CPS parameters. These local maxima, which arguably result from strong grid-scale convective updrafts, mislead the CPS to detect an upper-level cold core where one is not present. Three solutions to this problem are examined: changing the algorithm to compute the CPS parameters such that it uses 95th percentile values of geopotential instead of the maxima, a temporal smoothing of the CPS parameters, and a combination of the previous two. All three modifications largely correct the misdiagnosed cases. Future (2071-2100) projections of ET activity under the Representative Concentration Pathway 4.5 are then explored. A number of changes between the future and historical simulations are robust with respect to the different modifications to the CPS described above, though few are statistically significant. A statistical model that predicts ET in the western North Pacific and the North Atlantic is introduced. The model, a logistic regression with elastic net regularization, was developed with a focus on predictive performance as well as physical interpretability and thus resides at the interface between machine learning and traditional statistics. It uses eight predictors that characterize the storm and its environment, the most important ones being latitude and sea surface temperature. The model is shown to have skill in forecasting ET at lead times up to two days, and it can predict the phase evolution of storms that undergo ET as well as of storms that remain tropical throughout their lifetimes. When used as an instantaneous diagnostic of a storm's tropical/extratropical status, the model performs about as well as the CPS in the western North Pacific and better than the CPS in the North Atlantic, and it predicts the timings of the transitions better than the CPS in both basins. The model can be integrated into statistical tropical cyclone risk models, or may be applied to provide baseline guidance for operational forecasts.

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.<br>Dissertation (MSc)--University of Pretoria, 2000.<br>Geography, Geoinformatics and Meteorology<br>unrestricted

Thesis (M. S.)--Florida State University, 2005.<br>Advisor: Dr. Feifei Jin, Florida State University, College of Arts and Sciences, Dept. of Meteorology. Title and description from dissertation home page (viewed Sept. 19, 2005). Document formatted into pages; contains vii, 42 pages. Includes bibliographical references.

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.<br>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.

Data are presented from an automatic weather station on the Brintnell-Bologna Icefield that operated from August 2014 to August 2016 in Nahanni National Park Reserve. This location is notable for being the northernmost mass balance alpine study location of the federal government’s glaciology program (NRCan/GSC). The link between atmospheric forcing at the synoptic scale and response at the glacier surface has been shown to be strongly dependent on continentality and latitude. In this region, however, many aspects of the physical processes controlling the interaction between atmospheric forcing and snowpack response are virtually unknown, especially at the daily to hourly timescale. The character of snowfalls during the accumulation seasons for this icefield are investigated using high resolution time series from two acoustic snow depth sensors and other relevant meteorological parameters. It is found that the most drastic changes in snow depth occur from infrequent large snowfalls. Using an adaption of an Environment Canada snow depth algorithm, snowfall events are identified and their timing is quantified based on a system of thresholds, running averages and ratios between the snow depth sensors. Synoptic conditions are examined using meteorological reanalysis data and trajectory analysis to determine the moisture origin and pathway.<br>Graduate

Submitted to the Information Technology (IT) Department in conformity with the requirements for the degree of Doctor of Philosophy in Information Technology, Durban University of Technology. Durban, South Africa, 2016.<br>This doctoral research introduces an integration of satellite systems and new stratospheric platforms for weather observation, imaging and transfer of meteorological data to the ground infrastructures. Terrestrial configuration and satellite communication subsystems represent well-established technologies that have been involved in global satellite sensing and weather observation area for years. However, in recent times, a new alternative has emerged based on quasi-stationary aerial platforms located in the Stratosphere called High Altitude Platform (HAP) or Stratospheric Communication Platforms (SCP). The SCP systems seem to represent a dream come true for communication engineers since they preserve most of the advantages of both terrestrial and satellite communication systems. Today, SCP systems are able to help, in a more cost effective way, developments of space Earth sensing and weather observation and weather sensing and observation. This new system can provide a number of forms ranging from a low altitude tethered balloon to a high altitude (18 – 25 km) fuel-powered piloted aircraft, solar-powered unmanned airplanes and solar-powered airship.

Weather and climate events are costly to society both financially and in terms of human health and well being. The costs associated with extreme climate events have motivated governments, NGOs, private investors, and insurance companies to use the data and tools at their disposal to estimate the past, present, and future hazards associated with a wide range of natural phenomena in an effort to develop mitigation and/or adaptation strategies. The nonstationary nature of climate risks requires the use of numerical climate models, often general circulation models (GCMs), to project future risk. The climate risk field, however, currently finds itself in a predicament because GCMs can be biased and do not provide a clear way to credibly estimate their uncertainty with respect to simulations of future surface climate conditions. In response to this predicament, I lay the groundwork for a set of GCM credibility assessments by identifying the large-scale drivers of surface climate events that evolve over a range of timescales ranging from daily to multi-decadal. I specifically focus on three types of climate events relevant to the water and energy sectors: 1) seasonal precipitation, which impacts drinking water supplies and agricultural productivity; 2) extreme precipitation and the costly associated riverine flooding; and 3) temperature, wind, and solar radiation fields that modulate both electricity demand and potential renewable electricity supply. In chapter I, I derive a set of atmospheric indices and investigate their efficacy to predict distributed seasonal precipitation throughout the conterminous United States. These indices can also be used to diagnose the impact of tropical sea surface temperature heating patterns on conterminous United States precipitation. This is particularly of interest in the aftermath of the unexpected precipitation patterns in the conterminous United States during the 2015-2016 El Niño event. I show that the set of atmospheric indices, which I derive from zonal winds over the conterminous United States and portions of the North Atlantic and Pacific oceans, can skillfully predict precipitation over most regions of the conterminous United States better than previously recognized mid-latitude atmospheric and tropical oceanic indices. This work contributes a set of intermediate atmospheric indices that can be used to assess the efficacy of forecasting and simulation climate models to capture signal that exists between tropical heating, mid-latitude circulation, and mid-latitude precipitation. In chapter II, I first show that the frequency of regional extreme precipitation events, which are predictive of riverine flooding, in the Ohio River Basin are poorly simulated by a GCM relative to historical precipitation observations. I then illustrate that the same GCM is much better able to simulate the statistical characteristics of a set of atmospheric field-derived indices that I show to be strongly related to the precipitation events of interest. Thus, I develop a statistical model that allows for the simulation of the precipitation events based on the GCM's atmospheric fields, which allows me to estimate future hazard based on credibly simulated GCM fields. Lastly, I validate the fully Bayesian statistical model against historical observations and use the statistical model to project the future frequency of the regional extreme precipitation events. I conclude that there is evidence of increasing regional riverine flood hazard in the Central US river basin out to the year 2100, but that there is high uncertainty regarding the magnitude of the trend. This work suggests that the identification of atmospheric circulation patterns that modulate the probability of extreme precipitation and riverine flood risk may improve flood hazard projections by allowing risk analysts to assess GCMs with respect to their ability to simulate relevant atmospheric patterns. In chapter III, I present the first comprehensive assessment of quasi-periodic decadal variations in wind and solar electricity potential and of covariability between heating and cooling electricity demand and potential wind and solar electricity production. I focus on six locations/regions in the conterminous United States that represent different climate zones and contain major load centers. The decadal variations are linked to quasi-oscillatory variations of the global climate system and lead to time-varying risks of meeting heating + cooling demand using wind/solar power. The quasi-cyclical patterns in renewable energy availability have significant ramifications for energy systems planning as we continue to increase our reliance on renewable, weather- and climate-dependent energy generation. This work suggests that certain modes of low frequency climate variability influence potential wind and solar energy supplies and are thus especially important for GCMs to credibly simulate. All of the investigations are designed to be broadly applicable throughout the mid-latitudes and are demonstrated with specific case studies in the conterminous United States. The dissertation sections represent three cases where statistical techniques can be used to understand surface climate and climate hazards. This understanding can ultimately help to mitigate and adapt to climate variabilities and secular changes, which impact society, by assisting in the development, improvement, and credibility assessment of GCMs capable of reliably projecting future climate hazards.

The physical mechanisms and predictability associated with extreme daily rainfall in South East South America (SESA) are investigated for the December-February season. Through a k-mean analysis, a robust set of daily circulation regimes is identified and then it is used to link the frequency of rainfall extreme events with large-scale potential predictors at subseasonal-to-seasonal scales. This basic set of daily circulation regimes is related to the continental and oceanic phases of the South Atlantic Convergence Zone (SACZ) and wave train patterns superimposed on the Southern Hemisphere Polar Jet. Some of these recurrent synoptic circulation types are conducive to extreme rainfall events in the region through synoptic control of different meso-scale physical features and, at the same time, are influenced by climate phenomena that could be used as sources of potential predictability. Extremely high rainfall (as measured by the 95th- and 99th-percentiles) is preferentially associated with two of these weather types, which are characterized by moisture advection intrusions from lower latitudes and the Pacific; another three weather types, characterized by above-normal moisture advection toward lower latitudes or the Andes, are preferentially associated with dry days (days with no rain). The analysis permits the identification of several subseasonal-to-seasonal scale potential predictors that modulate the occurrence of circulation regimes conducive to extreme rainfall events in SESA. It is conjectured that a cross-timescale interference between the different climate drivers improves the predictive skill of extreme precipitation in the region. The potential and real predictive skill of the frequency of extreme rainfall is then evaluated, finding evidence indicating that mechanisms of climate variability at one timescale contribute to the predictability at another scale, i.e., taking into account the interference of different potential sources of predictability at different timescales increases the predictive skill. This fact is in agreement with the Cross-timescale Interference Conjecture proposed in the first part of the thesis. At seasonal scale, a combination of those weather types tends to outperform all the other potential predictors explored, i.e., sea surface temperature patterns, phases of the Madden-Julian Oscillation, and combinations of both. Spatially averaged Kendall’s τ improvements of 43% for the potential predictability and 23% for realtime predictions are attained with respect to standard models considering sea-surface temperature fields alone. A new subseasonal-to-seasonal predictive methodology for extreme rainfall events is proposed, based on probability forecasts of seasonal sequences of these weather types. The cross-validated realtime skill of the new probabilistic approach, as measured by the Hit Score and the Heidke Skill Score, is on the order of twice that associated with climatological values. The approach is designed to offer useful subseasonal-to-seasonal climate information to decision-makers interested not only in how many extreme events will happen in the season, but also in how, when and where those events will probably occur. In order to gain further understanding about how the cross-timescale interference occurs, an externally-forced Lorenz model is used to explore the impact of different kind of forcings, at inter-annual and decadal scales, in the establishment of constructive interactions associated with the simulated “extreme events”. Using a wavelet analysis, it is shown that this simple model is capable of reproducing the same kind of cross-timescale structures observed in the wavelet power spectrum of the Niño3.4 index only when it is externally forced by both inter-annual and decadal signals: the annual cycle and a decadal forcing associated with the natural solar variability. The nature of this interaction is non-linear, and it impacts both mean and extreme values in the time series. No predictive power was found when using metrics like standard deviation and auto-correlation. Nonetheless, it was proposed that an early warning signal for occurrence of extreme rainfall in SESA may be possible via a continuous monitoring of relative phases between the cross-timescale leading components.

Empirical techniques are developed to adjust dynamic model forecasts on the seasonal time scale for southern African summer rainfall. The techniques, called perfect prognosis and model output statistics (MOS), are utilized to statistically "recalibrate" general circulation model (GCM) large-scale fields to three equi-probable rainfall categories for December to February. The recalibration is applied to a GCM experiment where simultaneously observed sea-surface temperature (SST) fields serve as the lower boundary forcing, referred to as the simulation mode experiment. Cross-validation sensitivity tests are first performed over a 28-year climate period to design an optimal canonical correlation analysis (CCA) model for each of the two recalibration methods. After considering several potential predictor fields, the 700 hPa geopotential height field is selected as the single predictor field in the two sets of statistical equations that are subsequently used to produce recalibrated rainfall simulations over a 1 a-year independent test period. Patterns analysis of the predictor and predictand fields suggests that anomalously low (high) 700 hPa geopotential heights over the subcontinent are associated with wet (dry) conditions over land, an association that is supported by observational evidence of rain (drought) producing systems. Additionally, the dominant mode of the recalibration equations is associated with the EI Nino/Southern Oscillation (ENSO) phenomenon. Somewhat higher retro-active skill levels are found using the MOS technique, but the computationally less intensive perfect prognosis technique should also be able to produce usable seasonal rainfall forecasts over southern Africa in an operational forecast environment hampered by the lack of computing resources.<br>Dissertation (MSc)--University of Pretoria, 2006.<br>Geography, Geoinformatics and Meteorology<br>Unrestricted

Η συνοπτική ταξινόμηση των συστημάτων καιρού αφορά πληθώρα περιβαλλοντικών εφαρμογών. Προσφάτως, η γνωστική περιοχή για την οποία η συνοπτική ταξινόμηση έχει βαρύνουσα σημασία είναι αυτή της ατμοσφαιρικής ρύπανσης. Η γνώση της συνοπτικής κλιματολογίας μιας περιοχής, επιτρέπει την πρόγνωση και ενδεχομένως την αποφυγή επεισοδίων ρύπανσης, τα οποία οφείλονται είτε σε τοπικές πηγές είτε στην μεταφορά ρύπων. Η γνώση αυτή ενισχύεται σημαντικά μέσω της κατηγοριοποίησης (ταξινόμησης) των συνοπτικών καταστάσεων που επικρατούν σε μία δεδομένη περιοχή. Τα τελευταία χρόνια έχουν γίνει προσπάθειες «αυτόματης», μη εμπειρικής, ταξινόμησης με την χρήση Η/Υ. Οι μέχρι τώρα προσπάθειες επικεντρώνονται σε κλασικές στατιστικές μεθόδους. Σκοπός αυτής της διδακτορικής διατριβής είναι η ανάπτυξη μεθόδων και η υλοποίηση αλγορίθμων για την εξαγωγή και ψηφιακή επεξεργασία περιβαλλοντικών σημάτων και εικόνων. Η εφαρμογή των ανωτέρω οδηγεί στη δημιουργία έμπειρων συστημάτων συνοπτικής ταξινόμησης των συστημάτων καιρού, η οποία βασίζεται σε μεθόδους επεξεργασίας εικόνας, ανάλυσης και ομαδοποίησης δεδομένων, αναγνώρισης προτύπων και θεωρίας γράφων. Η σκοπιμότητα της παρούσης έρευνας διαφαίνεται από τη πρωτοτυπία που παρουσιάζει, η οποία έγκειται στο γεγονός ότι οι τεχνικές που παρουσιάζονται και που έχουν αντιμετωπίσει επιτυχώς σειρά προβλημάτων ταξινόμησης σε διάφορους γνωστικούς τομείς, εφαρμόζονται για πρώτη φορά στην Ελλάδα σε θέματα Μετεωρολογίας-Κλιματολογίας-Φυσικής του Περιβάλλοντος και συγκεκριμένα για την συνοπτική ταξινόμηση των συστημάτων καιρού. Τα χαρακτηριστικά των σύγχρονων μεθόδων επεξεργασίας εικόνας, θεωρίας γράφων και ανάλυσης δεδομένων, καθιστούν τις προτεινόμενες προσεγγίσεις αυτής της διατριβής ανταγωνιστικές τόσο σε επίπεδο ποιότητας ταξινόμησης όσο και σε υπολογιστικό χρόνο.<br>The synoptic classification of weather systems involves a variety of environmental applications. Recently, the synoptic classification has been found to be relevant with the cognitive area of air pollution. Knowing the synoptic climatology of a region, allows the prediction and possibly the prevention of pollution incidents, resulting in either local sources or in transport of pollutants. This knowledge is greatly enhanced by the categorization (classification) of the synoptic conditions in a given area. In recent years ‘automatic’, non-empirical, classification methods have been developed using computers. So far these efforts have been based on classical statistical methods. The aim of this PhD thesis is the development of methods and the implementation of algorithms to extract and process digital signals and environmental images. Consequently, expert systems for the synoptic classification of weather systems are created based on methods relative to image processing, data analysis and clustering, pattern recognition and graph theory. The objective of this research is demonstrated by its own originality which lies in the fact that the presented techniques have successfully addressed a number of classification problems in different topics. It is the first time that such methods have been applied on Meteorology-Climatology-Physics of the Environment in Greece, namely the synoptic classification of weather systems. The characteristics of the modern methods proposed in this PhD thesis are competitive both in classification quality and in computational time.