The land–atmosphere interactions and the coupling between climate and land surface hydrological processes are gaining interest in the recent past. The increased knowledge in hydro climatology and the global hydrological cycle, with terrestrial and atmospheric feedbacks, led to the utilization of the climate variables and atmospheric tele-connections in modeling the hydrological processes like rainfall and runoff. Numerous statistical and dynamical models employing different combinations of predictor variables and mathematical equations have been developed on this aspect. The relevance of predictor variables is usually measured through the observed linear correlation between the predictor and the predictand. However, many predictor climatic variables are found to have been switching the relationships over time, which demands a replacement of these variables. The unsatisfactory performance of both the statistical and dynamical models demands a more authentic method for assessing the dependency between the climatic variables and hydrologic processes by taking into account the nonlinear causal relationships and the instability due to these nonlinear interactions. The most obvious cause for limited predictability in even a perfect model with high resolution observations is the nonlinearity of the hydrological systems [Bloschl and Zehe, 2005]. This is mainly due to the chaotic nature of the weather and its sensitiveness to initial conditions [Lorenz, 1963], which restricts the predictability of day-to-day weather to only a few days or weeks. The present thesis deals with developing association rules to extract the causal relationships between the climatic variables and rainfall and to unearth the frequent predictor patterns that precede the extreme episodes of rainfall using a time series data mining algorithm. The inherent nonlinearity and uncertainty due to the chaotic nature of hydrologic processes (rainfall and runoff) is modeled through a nonlinear prediction method. Methodologies are developed to increase the predictability and reduce the predictive uncertainty of chaotic hydrologic series. A data mining algorithm making use of the concepts of minimal occurrences with constraints and time lags is used to discover association rules between extreme rainfall events and climatic indices. The algorithm considers only the extreme events as the target episodes (consequents) by separating these from the normal episodes, which are quite frequent and finds the time-lagged relationships with the climatic indices, which are treated as the antecedents. Association rules are generated for all the five homogenous regions of India (as defined by Indian Institute of Tropical Meteorology) and also for All India by making use of the data from 1960-1982. The analysis of the rules shows that strong relationships exist between the extreme rainfall events and the climatic indices chosen, i.e., Darwin Sea Level Pressure (DSLP), North Atlantic Oscillation (NAO), Nino 3.4 and Sea Surface Temperature (SST) values. Validation of the rules using data for the period 1983-2005, clearly shows that most of the rules are repeating and for some rules, even if they are not exactly the same, the combinations of the indices mentioned in these rules are the same during validation period with slight variations in the representative classes taken by the indices. The significance of treating rainfall as a chaotic system instead of a stochastic system for a better understanding of the underlying dynamics has been taken up by various studies recently. However, an important limitation of all these approaches is the dependence on a single method for identifying the chaotic nature and the parameters involved. In the present study, an attempt is made to identify chaos using various techniques and the behaviour of daily rainfall series in different regions. Daily rainfall data of three regions with contrasting characteristics (mainly in the spatial area covered), Malaprabha river basin, Mahanadi river basin and All India for the period 1955 to 2000 are used for the study. Auto-correlation and mutual information methods are used to determine the delay time for the phase space reconstruction. Optimum embedding dimension is determined using correlation dimension, false nearest neighbour algorithm and also nonlinear prediction methods. The low embedding dimensions obtained from these methods indicate the existence of low dimensional chaos in the three rainfall series considered. Correlation dimension method is repeated on the phase randomized and first derivative of the data series to check the existence of any pseudo low-dimensional chaos [Osborne and Provenzale, 1989]. Positive Lyapunov exponents obtained prove the exponential divergence of the trajectories and hence the unpredictability. Surrogate data test is also done to further confirm the nonlinear structure of the rainfall series. A limit in predictability in chaotic system arises mainly due to its sensitivity to the infinitesimal changes in its initial conditions and also due to the ineffectiveness of the model to reveal the underlying dynamics of the system. In the present study, an attempt is made to quantify these uncertainties involved and thereby improve the predictability by adopting a nonlinear ensemble prediction. A range of plausible parameters is used for generating an ensemble of predictions of rainfall for each year separately for the period 1996 to 2000 using the data till the preceding year. For analyzing the sensitiveness to initial conditions, predictions are made from two different months in a year viz., from the beginning of January and June. The reasonably good predictions obtained indicate the efficiency of the nonlinear prediction method for predicting the rainfall series. Also, the rank probability skill score and the rank histograms show that the ensembles generated are reliable with a good spread and skill. A comparison of results of the three regions indicates that although they are chaotic in nature, the spatial averaging over a large area can increase the dimension and improve the predictability, thus destroying the chaotic nature. The predictability of the chaotic daily rainfall series is improved by utilizing information from various climatic indices and adopting a multivariate nonlinear ensemble prediction. Daily rainfall data of Malaprabha river basin, India for the period 1955 to 2000 is used for the study. A multivariate phase space is generated, considering a climate data set of 16 variables. The redundancy, if any, of this atmospheric data set is further removed by employing principal component analysis (PCA) method and thereby reducing it to 8 principal components (PCs). This multivariate series (rainfall along with 8 PCs) are found to exhibit a low dimensional chaotic nature with dimension 10. Nonlinear prediction is done using univariate series (rainfall alone) and multivariate series for different combinations of embedding dimensions and delay times. The uncertainty in initial conditions is thus addressed by reconstructing the phase space using different combinations of parameters. The ensembles generated from multivariate predictions are found to be better than those from univariate predictions. The uncertainty in predictions is reduced or in other words, the predictability is improved by adopting multivariate nonlinear ensemble prediction. The restriction on predictability of a chaotic series can thus be reduced by quantifying the uncertainty in the initial conditions and also by including other possible variables, which may influence the system. Even though, the sensitivity to initial conditions limit the predictability in chaotic systems, a prediction algorithm capable of resolving the fine structure of the chaotic attractor can reduce the prediction uncertainty to some extent. All the traditional chaotic prediction methods are based on local models since these methods model the sudden divergence of the trajectories with different local functions. Conceptually, global models are ineffective in modeling the highly unstable structure of the chaotic attractor [Sivakumar et al., 2002a]. This study focuses on combining a local learning wavelet analysis (decomposition) model with a global feedforward neural network model and its implementation on phase space prediction of chaotic streamflow series. The daily streamflow series at Basantpur station in Mahanadi basin, India is found to exhibit a chaotic nature with dimension varying from 5-7. Quantification of uncertainties in future predictions are done by creating an ensemble of predictions with wavelet network using a range of plausible embedding dimension and delay time. Compared with traditional local approximation approach, the total predictive uncertainty in the streamflow is reduced when modeled with wavelet networks for different lead times. Localization property of wavelets, utilizing different dilation and translation parameters, helps in capturing most of the statistical properties of the observed data. The need for bringing together the characteristics of both local and global approaches to model the unstable yet ordered chaotic attractor is clearly demonstrated.

Climate science or climatology is the scientific study of the earth’s climate, where climate is the term representing weather conditions averaged over a period of time. Climate models are mathematical models used to quantitatively describe, simulate and study the interactions among the components of the climate system -atmosphere, ocean, land and sea-ice. CCSM (Community Climate System Model) is a state-of-the-art climate model, and a long-running coupled multicomponent parallel application involving component models for simulating the components of the climate system. Each of the component models is a large-scale parallel application, and the parallel components exchange climate data through a specialized component called coupler. Typical multi-century climate simulations using CCSM take several weeks or months to execute on most parallel systems. In this thesis, we study the applicability of a computational grid for effective execution of long-running coupled multi-component climate applications like CCSM. Initial studies of the application characteristics led us to develop a dynamic component extension strategy for temporal inter-component load-balancing. By means of experiments on different parallel platforms with different number of processors, we showed that using our strategy can lead to about 15% reduction and savings of several days in execution times of CCSM for 1000-year simulation runs. Our initial studies also indicated that unlike typical grid applications, CCSM has limits on scalability to very large number of processors and hence cannot directly benefit from the large number of processors on a computational grid. However, its long-running nature and the limits of execution imposed on jobs on most multi-user batch queueing systems, led us to investigate the benefits of its execution on a grid of batch systems. The idea is that multiple batch queues can improve the processor availability rate with respect to the application thereby possibly improving its effective throughput. We explored this idea in detail with simulation studies involving various system and application characteristics, and execution models. By conducting large number of simulations with different workload characteristics and queuing policies of the systems, processor allocations to components of the application, distributions of the components to the batch systems and inter-cluster bandwidths, we showed that multiple batch executions lead to upto 55% average increase in throughput over single batch executions for long-running CCSM. Having convinced ourselves of possible advantages in performance, we then ventured to construct an application-level middleware framework. Our framework supports long duration execution of multi-component applications spanning multiple submissions to queues on multiple batch systems. It coordinates the distribution, execution, rescheduling, migration and restart of the application components across resources on different sites. It also addresses challenges including execution time limits for jobs, and differences in job-startup times corresponding to different components. Further, within the framework, we developed robust rescheduling policies that decide when and where to reschedule the components to the available resources based on the application execution characteristics and queue dynamics. Our grid middleware framework resulted in multi-site executions that provided larger application throughput than single-site executions, typically performed by climate scientists, and also removed the bottlenecks associated with a single system execution. We used this framework for long-running executions of CCSM to study the effect of increased black carbon aerosols and dust aerosols on the Indian monsoons. Black Carbon aerosols are essentially of anthropogenic origin and occur due to improper burning of fossil fuels, and dust is a naturally occurring aerosol. The concentrations of both these aerosols is high over the Indian region. We study the impact of these aerosols on precipitation and sea surface temperature (SST) through multi-decadal simulations conducted with our grid-enabled climate system model. Our observations indicated that increasing the concentrations of aerosols leads to an increase in precipitation in the central and eastern parts of India, and a decrease in SST over most of Indian ocean.

No description available.

Master of Arts / Department of Geography / Douglas G. Goodin / My thesis characterizes the temporal and spatial behavior of ozone and fine particulate matter in the Kansas City metropolitan area. I also investigate the capability of a synoptic weather typing scheme, the Spatial Synoptic Classification, to characterize and explain the behavior of ozone and fine particulate matter in the Kansas City area. Daily maximum ozone concentrations from nine active ozone monitoring stations and daily average particulate concentrations six active PM2.5 monitoring stations were compared to daily SSC weather type records from 2004-2010. Analysis of Variance (ANOVA) tests were conducted on the ozone and PM2.5 data to analyze temporal and spatial behavior. A non-parametric recursive partitioning technique was used to create a conditional inference tree-based regression model to analyze the association between the different SSC weather types and the selected pollutants. The ANOVA results showed significant seasonal trends with both pollutants. In general, ozone concentrations are typically lower in the spring and autumn months and higher during the summer months. PM2.5 concentrations were not as dependent on the season, however, they did tend to be higher in the late summer months and lower in the autumn months. The results also showed significant differences for both pollutants in average concentration depending on location. The ozone concentrations generally tended to be higher in the areas that are located downwind of Kansas City and lowest at the station located in the middle of the urban area. Fine particulates also seemed to be highest in the downwind portion of the urban area and lowest in the region upwind of the city. The conditional inference tree showed that higher concentrations of both pollutants are associated with tropical air masses and lower concentrations are associated with polar air masses.

Master of Arts / Department of Geography / John A. Harrington Jr / The Rainwater Basin Wetland Complex of south-central Nebraska is a region of great climatic variability, as well as tremendous ecological importance. The Rainwater Basin Wetland Complex is located at the focal point of the Central North American migratory bird flyway, and supports in excess of twelve million birds during the spring migration period. The physical landscape has been significantly altered from its pre-settlement state by agricultural conversion via the draining of over ninety percent of the native wetlands. Due to the region’s highly variable continental climate, interannual wetland water levels are also highly variable and currently unpredictable. I have used multi-year analysis, including the construction of a regional water budget assessment, to study which climatic variables play the most crucial role in the late-winter filling of wetlands. Research objectives were met by analyzing ten cold season (Oct – Feb) climatic variables and an annual measure of wetland area for five years, in order to better understand possible climatic drivers of wetland hydrologic functioning levels in March. Longer time series of winter season climatic information were also assessed to help place the recent and more detailed analysis into a longer climatic context. Research results will aid local management agencies in the future through enhanced knowledge of how climatic variation impacts wetland function. Seasonal precipitation and temperature was favored by the linear regression analysis, while the multiple regression analysis placed higher emphasis on February evapotranspiration rates, February snow depth, and February snowfall. Lastly, the hydrologic water budget that was created for the study area had several highly correlated output variables with basin-wide flooded hectares, particularly annual snow storage.

The cloud and haze properties in the troposphere and stratosphere of Saturn are investigated in this thesis by analysing Cassini/VIMS spectra at 0.8-3.5 μm and 4.5-5.1 μm. The aerosol properties are derived from VIMS data by using the retrieval tool NEMESIS developed at the University of Oxford. Near-infared VIMS data between 4.5 and 5.1 μm are mainly sensitive to deeper tropospheric levels down to approximately 5 bar. At such pressure levels, thermochemical models predict the formation of condensed clouds made of ammonia and ammonium hydrosulphide ices, although none of these species has been spectrally detected so far. In addition, phosphine and ammonia are responsible for most of the gaseous absorption at these wavelengths. Therefore, the cloud properties and gas distributions can be retrieved from VIMS near-infared spectra. In this thesis, the analysis of limb-darkening data at 4.5-5.1 μm is performed in order to constrain the aerosol properties in Saturn’s atmosphere. The best-fitting model consists of a scattering cloud between 2 and 3 bar, and a scattering haze layer which can be placed anywhere between 10 and 500 mbar. The composition is still poorly constrained for both the deep cloud and haze layer. The haze physical and optical properties can however be independently retrieved from VIMS near-infrared spectra at shorter wavelengths in the 0.8-3.5 μm spectral range. Saturn’s hazes in the troposphere and stratosphere reflect the sunlight at 0.8-3.5 μm. The properties and vertical structure of tropospheric and stratospheric hazes are then investigated from VIMS reflection spectra in the near-infared. The latitudinal variation of haze properties is compared to a thermal feature known as "the temperature knee", corresponding to a local increase of temperature right below the tropopause. The north-south temperature variations in the troposphere are obtained from the analysis of thermal infrared data measured with the Composite InfraRed Spectromete (CIRS) instrument on board Cassini. Finally, VIMS near-infrared data at 0.8-3.5 and 4.5-5.1 μm are combined in order to obtain a cloud and haze model coherent with both wavelength ranges.

Thesis (PhD (Civil Engineering))--University of Stellenbosch, 2011. / ENGLISH ABSTRACT: In South Africa, wind constitutes the most critical environmental loading affecting the design of the built environment. The wind climatic information, which is currently incorporated in structural design standards, is based on the analysis of records from a limited number of wind recording stations, mainly located in large cities, and was done several decades ago. In view of the size and the climatological diversity of South Africa, this information cannot be deemed to be adequate. Therefore, the incorporation of welldistributed and updated information on wind climate is essential. The present study endeavoured to address this issue. A strong wind climatology was developed with the use of observed climate data, with the most significant result that a mixed strong wind climate is prevalent in the greater part of South Africa. Statistical approaches to estimate extreme wind speeds were investigated with applicable wind data, with the optimum approach guided by the unique climatological environment and the statistical properties of the utilised data set: For the wind gust analysis the Peak-Over-Threshold method with the exponential distribution is recommended, while in a mixed strong wind climate the “mixed climate” approach is preferred. For the analysis of the hourly mean wind speeds the choice is between the Gumbel distribution and the mixed climate approach, depending on the strong wind climate. The estimation and incorporation of environmental correction factors to the measured wind speeds were necessary as the surroundings of most weather stations did not correspond to the reference Terrain Category. For some of the weather stations it was impossible to compensate for the inadequate exposure and surrounding complex topography, so that a reduced number of weather stations were available for the strong wind analyses. The values estimated for the design wind speeds, adjusted for the short lengths of data records, as well as techniques developed to guide the spatial interpolation of the quantiles, were utilised to develop updated maps of the regional design wind speeds. A comparative study between the results of this study, and that of the previous study on which the current loading code in South Africa is based, indicates that the present study should produce more reliable quantile estimations. / AFRIKAANSE OPSOMMING: Wind vorm die mees kritieke omgewingslading wat die ontwerp van die beboude omgewing in Suid-Afrika beïnvloed. Die windklimaat-inligting wat tans gebruik word in die ontwerp spesifikasies is gebaseer op die statistiese analiese van veskeie dekades gelede op ‘n beperkte aantal windmeting-stasies, hoofsaaklik gesentreer in groot stede. Indien die grootte sowel as die klimatologiese diversiteit van Suid-Afrika in ag geneem word kan hierdie inligting nie as voldoende gereken word nie. Die gebruik van heelwat beter verspreide en opgedateerde inligting oor die windklimaat is daarom noodsaaklik en die studie poog om hierdie leemte aan te spreek. ‘n Sterk-wind klimatologie van Suid-Afrika is ontwikkel deur die gebruik van waargenome klimaatdata, met die mees betekenisvolle bevinding dat ‘n gemengde sterk-wind klimaat in die grootste gedeelte van Suid-Afrika heers. Statistiese benaderings om ekstreme winde te beraam is ondersoek met die beskikbare winddata, met die optimale benadering wat sal afhang van die klimatologiese omgewing van die weerstasie en die statistiese eienskappe van die betrokke windrekord: Vir die wind-stoot analieses word die “Piek-Oor-Drumpel” metode met die eksponensïele verdeling aanbeveel, behalwe in ‘n gemengde sterkwind klimaat waar die “gemengde klimaat” benadering gebruik word. Vir die analiese van die uurlikse gemiddelde winde is die keuse tussen die Gumbel verdeling en die gemengde klimaat benadering, afhangende van die sterk-wind klimaat. Die skatting en toepassing van omgewingskorreksiefaktore vir die windspoed was nodig, aangesien die omgewings waarin die meeste weerstasies is nie ooreenkom met die verwysings Terrein Kategorie nie. Vir sommige weerstasies was dit onmoontlik om vir die onvoldoende blootstelling te vergoed, met die gevolg dat minder stasies beskikbaar was vir die sterk wind analiese. Die geskatte waardes vir die ontwerp-windsnelhede, asook tegnieke ontwikkel vir ruimtelike interpolasie, is gebruik vir die ontwikkeling van kaarte van die omgewings-ontwerpsnelhede, na verstellings van die waardes om te vergoed vir die kort data rekords wat gebruik is. ‘n Kritiese vergelykingstudie wat gedoen is tussen die resultate van die huidige studie, en die vorige waarop die huidige laskodes vir Suid-Afrika gebaseer is, dui aan dat die huidige studie betroubaarder skattings van die kwantiele behoort op te lewer.

L'énergie utilisable est la partie de l'énergie d'un système qui est virtuellement convertible afin de créer un travail. Elle est aussi connue sous le nom d'exergie (littéralement «travail extractible») en thermodynamique. Une notion d'énergie utilisable a été développée indépendamment dans le domaine de la météorologie, tout d'abord par Margules (1903) et Lorenz (1955), puis par Dutton (1973) et Pearce (1978). Le but de cette thèse est l'étude des propriétés locales de la fonction enthalpie utilisable spécifique qui est une forme particulière de l'exergie. Il apparaît que l'enthalpie utilisable synthétise et précise les travaux de Dutton et de Pearce pour le cas d'une atmosphère en équilibre hydrostatique, c'est aussi un cas particulier de la pseudo-énergie introduite par Shepherd (1993), un autre cas particulier étant l'approche de Lorenz. Un cycle énergétique analogue à ceux de Lorenz et de Pearce est défini pour l'étude d'une couche isobare d'un domaine limité. Une application numérique au cas d'une onde barocline d'échelle sous-synoptique est ensuite présentée, elle démontre l'importance des termes dus aux flux aux frontières du domaine, alors que dans le même temps les chemins barocline et barotrope des conversions d'énergie restent analogues à ceux classiquement envisagés en météorologie. Enfin, on décrit une généralisation au cas d'une atmosphère humide où l'influence énergétique des différentes phases de l'eau est explicitement prise en compte

Le climat des années 2000 a été marqué en Europe par une vague d'épisodes chauds battant tous les précédents records saisonniers de température et s'accompagnant d'impacts sociétaux et environnementaux particulièrement sévères. Cette thèse se propose de contribuer à la compréhension des mécanismes physiques responsables de tels extrêmes, dans le but d'anticiper leurs réponses au changement climatique futur. Ce travail mêle ainsi analyses statistiques de données d'observations et de projections climatiques, et expériences de modélisation régionale. Nous montrons dans un premier temps que si la variabilité inter-annuelle du climat européen est pilotée par les fluctuations de la dynamique atmosphérique nord-atlantique, le récent réchauffement apparaît dissocié de potentiels changements dans la circulation. Cette divergence s'illustre particulièrement lors de l'automne exceptionnellement doux de 2006. Les récentes conditions chaudes en surface de l'océan Atlantique nord semblent contribuer au réchauffement européen en automne--hiver, tandis que des processus amplificateurs plus locaux apparaissent au printemps--été (e.g., influence de l'humidité des sols). La seconde partie de notre travail, basée sur les projections climatiques de l'International Panel on Climate Change, montre que la divergence constatée sur la période présente semble se poursuivre dans les projections futures. En particulier la variabilité interne de la dynamique nord-atlantique n'apparaît que peu affectée par le changement climatique. Les futurs extrêmes de température seraient alors associés à des circulations similaires aux circulations observées lors des extrêmes récents. Dans ce cadre, l'hiver européen de 2009/10 fournit une illustration d'un événement extrême froid dans un climat déjà plus chaud.

Les modèles climatiques ont beaucoup progressé pour représenter les nuages. Pourtant la réponse et la rétroaction nuageuse demeure très différente d'un modèle à l'autre, et reste la principale source d'incertitude pour la sensibilité climatique prédite par les modèles de climat, et limite la fiabilité sur les projections du changement climatique dû au forçage anthropique. Il est donc crucial d'améliorer la représentation des processus nuageux dans les modèles climatiques. L'évaluation des nuages nécessite des observations précises. Jusqu'à récemment, des observations de plusieurs aspects fondamentaux des nuages comme la distribution tridimensionnelle des nuages existaient uniquement très grossièrement et obtenus de manière très indirecte par les satellites de télédétection passive (i.e. CERES, ERBE, ScaRab, ISCCP) qui mesurent les flux radiatifs au sommet de l'atmosphère. Les observations A-Train constituent des outils exceptionnels pour caractériser les propriétés nuageuses. L'objet de cette thèse est de tirer profit des observations de l'A-train afin d'évaluer la description des nuages simulée par les modèles climatiques. Nous utilisons le radiomètre CERES pour estimer l'effet radiatif des nuages, les radiomètres PARASOL et MODIS qui mesurent la réflectance, analysée ici comme un proxy de l'épaisseur optique des nuages et le lidar CALIPSO qui fournit des informations précises sur la distribution verticale des nuages. Les données co-localisées et analysées statistiquement constituent une occasion exceptionnelle de contraindre simultanément les propriétés radiatives des nuages et leur distribution tridimensionnelle. Le modèle du climat évalué est le LMDZ ainsi qu'une nouvelle version du modèle en cours de développement, où des nouvelles paramétrisations du bloc couche-limite/convection/nuages est testée. La méthode de comparaison des sorties des modèles climatiques aux grandeurs observées utilise d'une part le simulateur COSP (CFMIP Observation Simulator Package) qui comprend SCOPS, le simulateur lidar et le simulateur PARASOL et d'autre part les jeux des données (CFMIP-OBS) construits pour être compatibles avec les simulateurs. Nous étudions les propriétés nuageuses dans les tropiques par régime de circulation, et en classant les nuages par régions. Une nouvelle méthode a été développée : les observations sont analysées à haute résolution (spatiale et temporelle) au lieu des moyennes mensuelles et saisonnières utilisées habituellement afin de se placer à une échelle aussi proche que possible de celle des processus nuageux. Cette analyse a permis de contraindre les paramétrisations développées pour représenter les nuages et révéler des biais dans les deux versions du LMDZ. Des compensations d'erreurs ont été identifiées (i) sur la distribution verticale des nuages : la couverture nuageuse des nuages hauts et surestimée alors que les nuages bas et moyens sont significativement sous-estimés, (ii) entre la couverture nuageuse et l'épaisseur optique : la couverture nuageuse totale est sous-estimée mais les nuages qui se forment ont une épaisseur optique très élevée ce qui aboutit à une simulation correcte des flux au sommet de l'atmosphère par le modèle.