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Charles, Stephen Philip. "Statistical downscaling from numerical climate models". Thesis, Charles, Stephen Philip (2002) Statistical downscaling from numerical climate models. PhD thesis, Murdoch University, 2002. https://researchrepository.murdoch.edu.au/id/eprint/51653/.
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Statistical downscaling techniques address the disparity between the coarse spatial scales of numerical climate models (NCMs), typically 100-500 km, and point meteorological observations. However, there has been limited success in developing statistical downscaling techniques that can reproduce important properties of daily precipitation such as long runs of wet- or dry-spells. There has also been a lack of techniques applicable to multi-site networks, where there are strong inter-site correlations in daily precipitation. When used in climate change investigations, there has been limited success in producing downscaled precipitation projections in accordance with the general trends indicated by the host NCM.
In this thesis, an extended nonhomogeneous hidden Markov model (extended-NHMM) for multi-site, daily precipitation occurrence and amounts is developed. Its performance is assessed according to:
•its physical realism;
•its ability to reproduce the multi-site, daily precipitation statistics of a moderately dense site network;
•its ability to successfully downscale NCM simulations of present day climate; and,
•its ability, when used for climate change projection, to produce daily precipitation projections for the site network in accordance with the trends indicated by the host NCM.
The model is applied to a moderately dense network of 30 rain gauge stations in southwest Western Australia (SWA) using 15 years (1978 to 1992) of historical ‘winter’ (May-October) daily precipitation and atmospheric data. The extended-NHMM assumes that multi-site, daily precipitation occurrence patterns are driven by a finite number of unobserved weather states that evolve temporally according to a first order Markov chain. The weather state transition probabilities are a function of observed or modelled synoptic-scale atmospheric predictors such as mean sea level pressure. Within each weather state, the site daily precipitation amounts are modelled as regressions of transformed amounts at a given site on precipitation occurrence at neighbouring sites.
Results indicate that the extended-NHMM successfully reproduces the at-site and inter-site statistics of daily precipitation (frequency of wet-days, dry- and wet-spell length distributions, amount distributions, and inter-site correlations in occurrence and amounts). The weather states provide a regional hydroclimatology of the study region. They represent the dominant spatial patterns of daily precipitation occurrence that are related to synoptic conditions, and thus climate variability, via the optimum selection of a small set of atmospheric predictors.
The extended-NHMM fitted to observed SWA data has been driven with atmospheric predictor sets extracted from General Circulation Model (GCM) and Limited Area Model (LAM) present day climate runs, an atmospheric GCM hindcast run forced by observed SSTs, and a climate change (2xC02) LAM run. Downscaling from the GCM and LAM present day climate predictors reproduces the observed statistics of daily precipitation. Downscaling from the SST-forced GCM hindcast only reproduces the statistics of the recent period, with poor performance for earlier periods attributed to inadequacies in the forcing SST data. Climate change (2xC02) precipitation occurrence projections in accord with the trend indicated by the LAM were only obtained from an NHMM that included a predictor representing relative moisture. Thus assessing predictor set selection for climate change downscaling is critically important.
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Li, Qinglan 1971. "Statistical downscaling and simulation of daily temperature extremes". Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=99521.
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There is now a broad scientific consensus that the global climate is changing in ways that could have a profound impact on human society and the natural environment over the coming decades. In particular, changes in the frequency and magnitude of extreme temperatures are likely to have more substantial impacts on the environment and human activities than changes in the mean temperature. The present study is therefore addressing three main objectives: (a) to propose a systematic data analysis method for characterizing the variability of daily extreme temperatures at different sites; (b) to develop new statistical downscaling models that could accurately describe the linkage between large-scale climate variables and the characteristics of temperature extremes at a local site; and (c) to develop a stochastic method for simulating accurately the extreme temperature processes.Firstly, a systematic data analysis procedure was proposed for analyzing the variability of daily maximum (Tmax) and minimum (Tmin) temperature characteristics. The suggested procedure consists of performing a detailed statistical analysis of twelve relevant temperature indices that are important for various practical application purposes: mean of diurnal temperature range, frost season length, growing season length, freeze and thaw cycle, 90th percentile of Tmax, 10th percentile of Tmin, means and standard deviations of Tmax, Tmin, and the daily mean temperature. The suggested method was applied to the analysis of daily Tmax and Tmin data for 20 stations in Quebec. The available records used are different from station to station, varying from 44 years to 107 years. In general, it was found that, depending on the temperature index considered as well as on the particular season of the year, there are some significant increasing or decreasing trends at some locations in Quebec. Results of this analysis would provide valuable information on the temporal and spatial variations of daily extreme temperature processes in the region. Furthermore, it can be observed that no systematic spatial variability of the increasing or decreasing trends of any of the twelve temperature indices considered could be identified for a given area in Quebec.
Secondly, two new statistical downscaling models were proposed using the stepwise and robust regression methods in order to describe the linkage between largescale climate variables and the characteristics of Tmax and Tmin at a local site. The performance of these two models was tested using daily extreme temperature data available at Dorval Airport station in Quebec and the NCEP data for 25 different climate variables for the 1961-1990 period. It was found that the proposed stepwise and robust regression downscaling models can provide accurate estimates of fundamental statistical and physical properties of Tmax and Tmin. In addition, it has been observed that three climate variables, the mean sea level pressure, the 850hPa-geopotential height, and the near surface specific humidity, had the most significant effect on Tmax and Tmin at Dorval Airport. Furthermore, as compared with the popular SDSM model, the stepwise and robust regression models can provide more accurate estimates of the local Tmax and Tmin characteristics. In particular, the robust regression model was found to be the most accurate.
Finally, a new stochastic simulation procedure was developed in this study for simulating the Tmax and Tmin temperature time series at a local site using the combination of the first-order autoregressive AR(1) model and the SVD technique. Results of the evaluation of the proposed AR(1)-SVD simulation method using daily extreme temperature data at Dorval Airport for the 1961-1990 period have indicated the feasibility of this method in describing accurately the observed basic statistical properties (mean, standard deviation, and first order autocorrelation) of the daily Tmax and Tmin time series at a local site.
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Ferreira, Juan Gabriel de Almeida. "Reconstrução climática para Portugal através de downscaling dinâmico". Doctoral thesis, Universidade de Aveiro, 2012. http://hdl.handle.net/10773/8991.
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Doutoramento em FisicaApresenta-se uma avaliação de vários métodos de downscaling dinâmico. Os métodos utilizados vão desde o método clássico de aninhar um modelo regional nos resultados de um modelo global, neste caso as reanálises do ECMWF, a métodos propostos mais recentemente, que consistem em utilizar métodos de relaxamento Newtoniano de forma a fazer tender os resultados do modelo regional aos pontos das reanálises que se encontram dentro do domínio deste. O método que apresenta melhores resultados envolve a utilização de um sistema variacional de assimilação de dados de forma a incorporar dados de observações com resultados do modelo regional. A climatologia de uma simulação de 5 anos usando esse método é testada contra observações existentes sobre Portugal Continental e sobre o oceano na área da Plataforma Continental Portuguesa, o que permite concluir que o método desenvolvido é apropriado para reconstrução climática de alta resolução para Portugal Continental.
An evaluation of various methods of dynamic downscaling is presented. The methods used range from the classic method of nesting a regional model results in a global model, in this case the ECMWF reanalysis, to more recently proposed methods, which consist in using Newtonian relaxation methods in order to nudge the results of the regional model to the reanalysis. The method with better results involves using a system of variational data assimilation to incorporate observational data with results from the regional model. The climatology of a simulation of 5 years using this method is tested against observations on mainland Portugal and the ocean in the area of the Portuguese Continental Shelf, which shows that the method developed is suitable for the reconstruction of high resolution climate over continental Portugal.
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Babaei, Masoud. "Multiscale wavelet and upscaling-downscaling for reservoir simulation". Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/10684.
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The unfortunate case of hydrocarbon reservoirs being often too large and filled with uncertain details in a large range of scales has been the main reason for developments of upscaling methods to overcome computational expenses. In this field lots of approaches have been suggested, amongst which the wavelets application has come to our attention. The wavelets have a mathematically multiscalar nature which is a desirable property for the reservoir upscaling purposes. While such a property has been previously used in permeability upscaling, a more recent approach uses the wavelets in an operator-coarsening- based upscaling approach. We are interested in enhancing the efficiency in implementation of the second approach. the performance of an wavelet-based operator coarsening is compared with several other upscaling methods such as the group renormalization, the pressure solver and local-global upscaling methods. An issue with upscaling, indifferent to the choice of the method, is encountered while the saturation is obtained at coarse scale. Due to the scale discrepancy the saturation profiles are too much averaged out, leading to unreliable production curves. An idea is to downscale the results of upscaling (that is to keep the computational benefit of the pressure equation upscaling) and solve the saturation at the original un-upscaled scale. For the saturation efficient solution on this scale, streamline method can then be used. Our contribution here is to develop a computationally advantageous downscaling procedure that saves considerable time compared to the original proposed scheme in the literature. This is achieved by designing basis functions similar to multiscale methods used to obtain a velocity distribution. Application of our upscaling-downscaling method on EOR processes and also comparing it with non-uniform quadtree gridding will be further subjects of this study.
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Barcons, Roca Jordi. "A downscaling methodology for microscale wind modelling and forecasting". Doctoral thesis, Universitat Politècnica de Catalunya, 2017. http://hdl.handle.net/10803/461606.
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Near-surface wind fields are typically obtained from mesoscale Numerical Weather Prediction (NWP) models. These models describe the physics and dynamics of atmospheric phenomena with characteristic dimensions spanning from several hundreds down to few kilometres. Operational configurations use horizontal grid resolutions insufficient to capture flow effects over complex terrains. These effects are relevant for applications that include wind resource evaluation, wind power forecast, or simulation of wind-driven hazardous phenomena such as wildfire spreading or atmospheric dispersion of pollutants and toxic substances. In these applications, some mesoscale-to-microscale downscaling strategy turns necessary.
Traditionally, high-resolution near-surface winds have been obtained by diagnostic models. However, these models fail in representing flow phenomena such as recirculation behind obstacles, vortex shedding or surface boundary layer profiles. The increase in computational power is extending rapidly the use of Computational Fluid Dynamics (CFD) models the dynamical NWP-CFD model coupling methodologies allow capturing physical phenomena that are not implicit in the simpler mass-consistent models. However, the computational cost of CFD models still precludes the use of dynamical downscaling strategies in operational weather forecast. Therefore, although the ABL flow is intrinsically dynamic, operational high-resolution wind modelling below the mesoscale range should be headed towards less computationally intensive physical-statistical methodologies.
This Ph.D. thesis proposes a novel downscaling methodology for wind field characterisation and forecast. The downscaling is based on a model chain, which considers a NWP, a CFD model, and the methodologies to couple both models physically-statistically. The Ph.D. focuses on three main objectives:
1) This first study evaluates the ability of WRF-3DVar and LAPS to assimilate surface automatic weather stations for the mesoscale model initialisation. Results show different assimilation patterns; 3DVar shows unrealistic large-scale features missing in representing the inhomogeneous nature of the near-surface fields; LAPS reproduces small-scale features and provides an initial condition much consistent with observations. The validation shows that high-resolution WRF forecasts initialized with LAPS analyses improve substantially the forecasted wind fields.
2) The second objective faces the Alya-CFDWind (CFD-RANS) model simulation of diurnal cycles to circumvent part of the limitations of the neutral atmosphere assumption. These transient simulations provide a suitable framework to incorporate atmospheric stability considerations in the downscaling. As a test case, a wind resource assessment incorporating this capability shows promising results and substantially improves the annual energy production with respect to the neutral stratified assumption.
3) The third objective focuses on the development of the downscaling strategy. The methodology combines a domain segmentation technique with the use of transfer functions. This strategy preserves the mesoscale pattern and incorporates the unresolved mesoscale model sub-grid terrain forcing effects from pre-computed microscale simulations. Finally, the downscaling is successfully applied to simulate atmospheric CO2 dispersal from a limnic eruption occurred at Lake Nyos (Cameroon) in 1986.
The fulfilment of these objectives has resulted in an efficient and operationally affordable downscaling methodology designed as a NWP model post-process tool for wind field characterisation and forecast. At present, the methodology is ready to be implemented at the Meteorological Service of Catalonia (SMC) operational setup as a prototype for its validation and evaluation.Els camps de vent pròxims a la superfície es solen obtenir a partir de models numèrics de predicció meteorològica mesoescalar (Numerical Weather Prediction: NWP). Aquests models descriuen la física i la dinàmica de fenòmens atmosfèrics amb extensions que van des de diversos centenars fins a pocs quilòmetres. En configuracions operacionals, aquests models treballen a resolucions insuficients per capturar els efectes que exerceixen orografies complexes sobre el flux. Aquests efectes poden ser rellevants per aplicacions com l'avaluació i previsió del recurs eòlic o la simulació de fenòmens perillosos deguts al vent, com la propagació d'incendis forestals o la dispersió atmosfèrica de substàncies tòxiques. Per aquestes aplicacions, és necessària una estratègia de downscaling mesoescala-microescala. Tradicionalment, els vents en alta resolució s'obtenen mitjançant models de diagnòstic. Aquests models, però, no són capaços de representar fenòmens com els de la recirculació darrere d'obstacles o els perfils de vent en la capa límit atmosfèrica. Gràcies a l'increment del poder computacional, l'ús de models Computational Fluid Dynamics (CFD) s'està estenent ràpidament. Les metodologies per acoblar dinàmicament models mesoescalars i CFD permeten capturar fenòmens físics que no són resolts per models més simples. Tanmateix, el cost computacional dels CFD n'impedeix l'ús en predicció operacional. Per tant, tot i que la capa límit atmosfèrica és intrínsecament dinàmica, la modelització eòlica operativa en alta resolució ha d'enfocar-se en mètodes computacionalment menys exigents, com per exemple, mètodes estadístics o físic-estadístics. Aquesta tesi doctoral proposa una nova metodologia per a la caracterització i pronòstic del vent en alta resolució. El downscaling es basa en una cadena de models; un model mesoescalar, un model microescalar CFD, i les metodologies per l'acoblament físic-estadístic. El doctorat es centra en tres objectius principals: 1) S'avalua la capacitat d'assimilar estacions meteorològiques automàtiques en superfície de WRF-3DVar i LAPS, per a la inicialització del model mesoescalar WRF. Els resultats mostren patrons d'assimilació diferents; el 3DVar mostra característiques de gran escala sense representar la naturalesa no homogènia dels camps superficials; el LAPS reprodueix característiques de petita escala i proporciona una condició inicial coherent amb les observacions. La validació mostra que les prediccions del model WRF inicialitzades amb els anàlisis de LAPS milloren substancialment els camps de vent pronosticats. 2) S'afronta la simulació de cicles diaris amb Alya-CFDWind (CFD-RANS) per tal de pal·liar part de les limitacions provinents de l'assumpció d'atmosfera neutra. Aquestes simulacions transitòries proporcionen un marc adequat per incorporar consideracions tèrmiques degudes a l'estratificació atmosfèrica. Els resultats de l'avaluació del recurs eòlic en un enclau a l'estat Puebla (Mèxic) són prometedors i substancialment millors que els obtinguts amb l'assumpció d'estratificació neutra. 3) Es desenvolupa l'estratègia de downscaling. La metodologia combina una tècnica de segmentació de dominis amb l'ús de funcions de transferència. Aquesta estratègia demostra la capacitat de preservar el patró mesoescalar i d'incorporar els efectes microescalars no resolts pel model mesoescalar gràcies a CFD pre-correguts. Finalment, el downscaling s'aplica amb èxit en la simulació de dispersió atmosfèrica de CO2 procedent d'una erupció límnica al Llac Nyos (Camerun, 1986). El compliment d'aquests objectius ha donat com a resultat una metodologia de downscaling eficient i operacionalment assumible, dissenyada com a post-procés del model mesoescalar i que permet la caracterització i el pronòstic del camp de vents. Actualment, la metodologia està preparada per ser implementada al Servei Meteorològic de Catalunya com a prototip per a la seva validació i avaluació.
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Schipper, Janus Willem. "Downscaling of Precipitation in the Upper Danube Catchment Area". Diss., lmu, 2005. http://nbn-resolving.de/urn:nbn:de:bvb:19-41638.
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Mehrotra, Rajeshwar Civil & Environmental Engineering Faculty of Engineering UNSW. "Multisite rainfall stochastic downscaling for climate change impact assessment". Awarded by:University of New South Wales. Civil and Environmental Engineering, 2005. http://handle.unsw.edu.au/1959.4/23327.
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This thesis presents the development and application of a downscaling framework for multi site simulation of daily rainfall. The rainfall simulation is achieved in two stages. First, rainfall occurrences at multiple sites are downscaled, which is followed by the generation of daily rainfall amounts at each site identified as wet. A continuous weather state based nonparametric downscaling model conditional on atmospheric predictors and a previous day average rainfall state is developed for simulation of multi site rainfall occurrences. A nonparametric kernel density approach is used for simulation of rainfall amounts at individual sites conditional on atmospheric variables and the previous day rainfall amount. The proposed model maintains spatial correlation of rainfall occurrences by simulating concurrently at all stations and of amounts by using random innovations that are spatially correlated yet serially independent. Temporal dependence is reproduced in the occurrence series by conditioning on previous day average wetness fraction and assuming the weather states to be Markovian, and in the amount series by conditioning on the previous day rainfall amount. The seasonal transition is maintained by simulating rainfall on a day-to-day basis using a moving window formulation. The developed downscaling framework is calibrated using the relevant atmospheric variables and rainfall records of 30 stations around Sydney, Australia. Results indicate a better representation of the spatio-temporal structure of the observed rainfall as compared to existing alternatives. Subsequently, the framework is applied to predict plausible changes in rainfall in warmer conditions using the same set of atmospheric variables for future climate obtained as a General Circulation Model simulation. While the case studies presented are restricted to a specific region, the downscaling model is designed to be useful in any generic catchment modelling and management activity and/or for investigating possible changes that might be experienced by hydrological, agricultural and ecological systems in future climates.
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Zerenner, Tanja [Verfasser]. "Atmospheric downscaling using multi-objective genetic programming / Tanja Zerenner". Bonn : Universitäts- und Landesbibliothek Bonn, 2017. http://d-nb.info/1149154055/34.
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Trigo, Ricardo M. "Improving meteorological downscaling methods with artificial neural network models". Thesis, University of East Anglia, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.327283.
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Bergin, Emma Jean. "Statistical downscaling for hydrological applications in the tropical Andes". Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/23980.
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The analysis of statistical downscaling methods has become an active area of hydrological research in recent years because of the potential to investigate climate change impacts at the hydrological scale. In particular the applicability of downscaling methods to remote and often data sparse regions provides a significant challenge to hydrology, not least because such remote regions are often perceived to be vulnerable to the impacts of climate change. The research has considered the potential of using remote sensing, reanalysis and other rainfall and climate data products to overcome some of the issues of data scarcity and quality before evaluating the climate teleconnections within the tropical Andes of South America. The main conclusions of the research are that remote sensing products may provide a useful addition to rainfall runoff modelling studies, but are not applicable to downscaling studies because of their short duration. The TRMM 3B42 product was found to provide a better representation of river runoff than the PERSIANN product when routed through a calibrated hydrological model, suggesting that this product in particular may be useful in sparsely gauge regions. The main conclusions of the statistical downscaling were that the GlimClim downscaling model may be applied to a remote region, but that some of the model assumptions mean that it is often difficult to achieve a good model fit. Additional conclusions relate to the propogation of uncertainty through the modelling chain with respect to the simulation of the future A1B climate scenario. 10 GCMs were used to evaluate the climate uncertainty, with the envelope of simulations showing an increase for future time slices (2020's, 2050's and 2080's) compared with the current 20C3M emissions scenario. However, all GCMs showed that there is a projected decrease in rainfall and runoff.
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Champion, Adrian J. "Extreme Precipitation and Extra-Tropical Cyclones:A Dynamical Downscaling Study". Thesis, University of Reading, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.654485.
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Chun, Kwok Pan. "Statistical downscaling of climate model outputs for hydrological extremes". Thesis, Imperial College London, 2010. http://hdl.handle.net/10044/1/6972.
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Changing climate poses an unprecedented challenge for hydrology. The quantification of knowledge on occurrence, circulation and distribution of the waters of the Earth becomes increasingly complex under climate projections because of uncertain effects due to anthropogenic emissions. Traditional understanding of the hydrological cycle needs to be re-examined, and new tools and frameworks for modelling hydrological series with non-stationary characteristics are required for assessing climate change impacts. The aims of this thesis are to (i) understand the relationship between climate change and hydrology at a catchment scale and (ii) develop tools to support climate change adaptation and mitigation. To achieve the aims, this thesis employs a stochastic rainfall model based on generalised linear models (GLMs) to downscale information from regional and global climate models for projecting drought conditions and annual rainfall extremes. Using a state space approach, important global circulation variables for catchment drought characteristics in the Midlands and South East of England are investigated. For annual rainfall extremes, a new approach for studying rainfall simulation series ensemble is proposed based on extreme value theory. Using a statistical modelling methodology related to GLMs, a novel potential evaporation model has been put forward and evaluated. In UK catchment scale application, the results provide insight into possible changes and implications in the shift of rainfall and drought patterns under scenarios of climate in the 2080s. The quality of potential evaporation estimation is shown to be sensitive to the interrelationship of global climate variables. For monthly maxima of potential evaporation, the projected change is high in the southern UK (~25%) but is low in the northern UK (~0%). Furthermore, 2080s streamflows have also been projected. The results show that uncertainty in streamflow projections depend on which GCMs and RCMs are used. Overall, this dissertation provides improved methods for further development in understanding our non-stationary water cycle.
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Cannon, Alex Jason. "Multivariate statistical models for seasonal climate prediction and climate downscaling". Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/2892.
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This dissertation develops multivariate statistical models for seasonal forecasting and downscaling of climate variables. In the case of seasonal climate forecasting, where record lengths are typically short and signal-to-noise ratios are low, particularly at long lead-times, forecast models must be robust against noise. To this end, two models are developed. Robust nonlinear canonical correlation analysis, which introduces robust cost functions to an existing model architecture, is outlined in Chapter 2. Nonlinear principal predictor analysis, the nonlinear extension of principal predictor analysis, a linear model of intermediate complexity between multivariate regression and canonical correlation analysis, is developed in Chapter 3. In the case of climate downscaling, the goal is to predict values of weather elements observed at local or regional scales from the synoptic-scale atmospheric circulation, usually for the purpose of generating climate scenarios from Global Climate Models. In this context, models must not only be accurate in terms of traditional model verification statistics, but they must also be able to replicate statistical properties of the historical observations. When downscaling series observed at multiple sites, correctly specifying relationships between sites is of key concern. Three models are developed for multi-site downscaling. Chapter 4 introduces nonlinear analog predictor analysis, a hybrid model that couples a neural network to an analog model. The neural network maps the original predictors to a lower-dimensional space such that predictions from the analog model are improved. Multivariate ridge regression with negative values of the ridge parameters is introduced in Chapter 5 as a means of performing expanded downscaling, which is a linear model that constrains the covariance matrix of model predictions to match that of observations. The expanded Bernoulli-gamma density network, a nonlinear probabilistic extension of expanded downscaling, is introduced in Chapter 6 for multi-site precipitation downscaling. The single-site model is extended by allowing multiple predictands and by adopting the expanded downscaling covariance constraint.
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Wang, Li-Pen. "Improved rainfall downscaling for real-time urban pluvial flood forecasting". Thesis, Imperial College London, 2012. http://hdl.handle.net/10044/1/10127.
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Traditionally, hydrologists had a relatively minor role in rainfall data processing; they usually simply took data from meteorologists. However, meteorological organisations usually provide weather service over a larger area and scale (i.e. country level); the applicability of this large-scale information to urban hydrological applications is therefore questionable. This work tries to provide a local view on rainfall processing, aiming to improve the suitability (in terms of accuracy and resolution) of operational rainfall data for urban hydrological uses. This work explores advanced downscaling and adjustment techniques to address the identified issues in urban hydrology: accuracy and resolution. On the basis of a a review and the testing of state of the art techniques, the Bayesian-based adjustment technique and the newly-developed cascade-based downscaling techniques are found to be suitable tools to improve respectively the accuracy, and the resolution of operational radar (and raingauge) rainfall estimates. In addition, a combined application of these two techniques is tested; the results suggested that, although extra uncertainty may appear, this combination demonstrates a clear potential for providing accurate and high-resolution (street-scale and 5-min) rainfall estimates.
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Yang, Wei. "Discrete-continuous downscaling model for generating daily precipitation time series". [S.l. : s.n.], 2008. http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-35156.
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Athari, Hazhir. "FRICTION MATERIAL DOWNSCALING AND ITS EFFECT ON BRAKE SYSTEM PERFORMANCE". OpenSIUC, 2017. https://opensiuc.lib.siu.edu/theses/2183.
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Coefficient of friction is considered to be a system property. Unlike physical properties of a material, coefficient of friction is dependent on an entire system. Surface roughness, wear rate, temperature and velocity are some of the factors that influence the COF in a brake system. Due to these factors, current testing strategies fail to make an accurate prediction about the performance of a brake system in smaller scale. This paper explores how a small-scale tester correlates to a full scale dynamometer test when proper scaling strategies are utilized. Series of tests are carried out on non-asbestos organic, semi metallic and low steel using the Bruker’s Universal Mechanical Tester (UMT) TriboLab. Results are then compared to the Greening full-scale dual ended brake dynamometer (Horiba) using AK master standard procedure. Area based Scaling laws are applied as the approach to scale down the conditions of the full- scale dyno test for UMT test. As friction is a system property, dynamometer and UMT tests showed different results. However, performance (COF) for these tests between UMT and Dyno has the same general trend. Therefore, with more repetition on different friction material, it is possible to make more relevant and accurate predictions of performance.
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Cheung, Chi-shing Calvin, i 張志成. "Using statistical downscaling to project the future climate of Hong Kong". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/208623.
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Climate in Hong Kong is very likely to be modified due to global climate change. In this study the output of General Circulation Models (GCMs) was statistically downscaled to produce future climate projections for the time periods 2046 –2065 and 2081 –2100 for Hong Kong. The future climate projections are based on two emission scenarios provided by the Intergovernmental Panel on Climate Change (IPCC). The emission scenarios, A1B (rapid economic growth with balanced energy technology) and B1 (global environmental sustainability), make assumptions on future human development, and the resulting emissions of greenhouse gases.
This study established a method to evaluate GCMs for use in statistical downscaling and utilised six GCMs, selected from the 3rd phase of the Coupled Model Intercomparison Project (CMIP3). They were evaluated based upon their performance in simulating past climate in the southeast China region on three aspects: 1) monthly mean temperature; 2) sensitivity to greenhouse gases and 3) climate variability. Three GCMs were selected for statistical downscaling and climate projection in this study.
Downscaling was undertaken by relating large scale climate variables, from NCEP/NCAR reanalysis, a gridded data set incorporating observations and climate models, to local scale observations. Temperature, specific humidity and wind speed were downscaled using multiple linear regressions methods. Rain occurrence was determined using logistic regression and rainfall volume from a generalised linear model. The resultant statistical models were subsequently applied to future climate projections.
Overall, all three GCMs, via statistical downscaling, show that daily average, minimum and maximum temperatures, along with specific humidity, will increase under future climate scenarios. Comparing the model ensemble mean projections with current climate (1981 –2010), the annual average temperature in Hong Kong is projected to increase by 1.0 °C (B1) to 1.6 °C (A1B) in 2046 –2065, and by 1.4 °C (B1) to 2.2 °C (A1B) in 2081 –2100. Furthermore, the projections in this study show an increase of high temperature extremes (daily average temperature ≥ 29.6 °C), by three to four times in 2046 –2065 and four to five times in 2081 –2100.
The projections of rainfall indicate that annual rainfall will increase in the future. Total annual rainfall is projected to increase by 4.9% (A1B) to 8% (B1) in 2046 –2065, and by 8.7% (B1) to 21.5% (A1B) in 2081 –2100. However, this change in rainfall is seasonally dependent; summer and autumn exhibit an increase in rainfall whilst spring and winter exhibit decreases.
In order to test one possible impact of this change in climate, the downscaled climate variables were used to estimate how outdoor thermal comfort (using the Universal Thermal Comfort Index) might change under future climate scenarios in Hong Kong. Results showed that there will be a shift from 'No Thermal Stress' towards 'Moderate Heat Stress' and 'Strong Heat Stress' during the period 2046 –2065, becoming more severe for the later period (2081 –2100). The projections of future climate presented in this study will be important when assessing potential climate change impacts, along with adaptation and mitigation options, in Hong Kong.published_or_final_version
Geography
Doctoral
Doctor of Philosophy
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Maillard, Elodie. "Transport and degradation of pesticides in wetland systems : a downscaling approach". Phd thesis, Université de Strasbourg, 2014. http://tel.archives-ouvertes.fr/tel-01019664.
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A mechanistic understanding of transport and degradation processes of modern agricultural pesticides, including chiral pesticides, is critical for predicting their fate in the environment. In agricultural landscapes, wetlands can intercept pesticide-contaminated runoff or groundwater and improve water quality through various retention and degradation processes, which remain unknown. In a downscaling approach, three different wetlands receiving agricultural runoff were used as 'natural laboratories' to investigate the fate of widely used pesticides. Overall, our results showed that dynamics of hydrological and redox conditions largely influenced pesticide sorption mechanisms and their distribution over time within wetland compartments, thereby controlling degradation processes. While large-scale studies provide integrative information on pesticide dissipation and distribution patterns with respect to wetland functioning, small-scale investigations using novel methods such as isotope and enantiomer analyses characterize underlying molecular processes governing pesticide degradation.
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Bedia, Jiménez Joaquín. "Downscaling of climate scenarios for wildfire danger assessment: Development and Applications". Doctoral thesis, Universidad de Cantabria, 2015. http://hdl.handle.net/10803/382486.
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El peligro de incendios, desde una perspectiva climática, es el descriptor resultante de la integración de las principales variables atmosféricas que afectan de forma directa al inicio, propagación y dificultad de control de un incendio forestal en un momento determinado. Uno de los más utilizados a nivel mundial es el sistema canadiense, conocido como FWI, acrónimo del inglés Fire Weather Index. En esta tesis se desarrollan escenarios futuros de FWI (e indicadores derivados de éste) a varias escalas espaciales, a partir de diferentes proyecciones de cambio climático y aplicando diversas técnicas de regionalización. Se analizan las relaciones entre peligro de incendios y áreas quemadas a nivel global para la identificación de las zonas del planeta más sensibles al cambio climático, y se analizan algunos aspectos metodológicos clave insuficientemente tratados hasta el momento, como la resolución temporal de las variables de entrada, la aplicación de técnicas de regionalización estadística apropiadas y las ventajas y limitaciones del uso de modelos numéricos para la generación de escenarios de FWI.From a climatic standpoint, fire danger can be defined as the descriptor resultant after the integration of the main atmospheric variables most directly involved in the ignition, propagation and difficulty of suppression of a forest fire. One of the most popular fire danger indicators worldwide is the Canadian Fire Weather Index (FWI). This PhD Thesis is focused on the generation of future FWI (and other FWI-derived indicators) scenarios at different spatial scales, building upon different future climate projections and downscaling techniques. The relationship between fire danger and burned area is analyzed at a global scale in order to identify the most sensitive areas to climate change. Several key methodological aspects, insufficiently analyzed in previous studies, are addressed such as the time resolution of input variables, the use of adequate statistical downscaling techniques and the advantages and limitations of using numerical model simulations for the generation of FWI scenarios.
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Carvalho, Daniel Matos de. "Downscaling estoc?stico para extremos clim?ticos via interpola??o espacial". Universidade Federal do Rio Grande do Norte, 2010. http://repositorio.ufrn.br:8080/jspui/handle/123456789/17008.
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Previous issue date: 2010-05-31Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico
Present day weather forecast models usually cannot provide realistic descriptions of local and particulary extreme weather conditions. However, for lead times of about a small number of days, they provide reliable forecast of the atmospheric circulation that encompasses the subscale processes leading to extremes. Hence, forecasts of extreme events can only be achieved through a combination of dynamical and statistical analysis methods, where a stable and significant statistical model based on prior physical reasoning establishes posterior statistical-dynamical model between the local extremes and the large scale circulation. Here we present the development and application of such a statistical model calibration on the besis of extreme value theory, in order to derive probabilistic forecast for extreme local temperature. The dowscaling applies to NCEP/NCAR re-analysis, in order to derive estimates of daily temperature at Brazilian northeastern region weather stations
Os dados de rean?lise de temperatura do ar e precipita??o do NCEP National Centers for Environmental Predictions ser?o refinados para a produ??o dos n?veis de retorno para eventos extremos nas 8 capitais do Nordeste Brasileiro - NB: S?o Luis, Teresina, Fortaleza, Natal, Jo?o Pessoa, Recife, Macei?, Aracaju e Salvador. A grade do Ncep possui resolu??o espacial de 2.5? x 2.5? disponibilizando s?ries hist?ricas de 1948 a atualidade. Com esta resolu??o a grade envolve o NB utilizando 72 localiza??es (s?ries). A primeira etapa consiste em ajustar os modelos da Distribui??o Generalizada de Valores Extremos (GEV) e da Distribui??o Generalizada de Pareto (GPD) para cada ponto da grade. Utilizando o m?todo Geoestat?stico denominado Krigagem, os par?metros da GEV e GPD ser?o interpolados espacialmente. Considerando a interpola??o espacial dos par?metros, os n?veis de retorno para extremos de temperatura do ar e precipita??o poder?o ser obtidos aonde o NCEP n?o fornece informa??o relevante. Visando validar os resultados desta proposta, ser?o ajustados os modelos GEV e GPD as s?ries observacionais di?rias de temperatura e precipita??o de cada capital nordestina, e assim comparar com os resultados obtidos a partir da interpola??o espacial. Por fim o m?todo de Regress?o Quant?lica ser? utilizado como m?todo mais tradicional com a finalidade de compara??o de m?todos.
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Boucher, Alexandre. "Downscaling of satellite remote sensing data : application to land cover mapping /". May be available electronically:, 2007. http://proquest.umi.com/login?COPT=REJTPTU1MTUmSU5UPTAmVkVSPTI=&clientId=12498.
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Herath, Mudiyanselage Sujeewa Malwila Herath. "Downscaling approach to evaluate future climate change impacts on urban hydrology". Thesis, Curtin University, 2017. http://hdl.handle.net/20.500.11937/54089.
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This research introduces an approach to develop IDF relations under the context of climate change. SDSM tool is used for spatial downscaling and GEV distribution is used for temporal downscaling of GCM data. Also an empirical relationship between the extreme rainfalls and daily maximum temperature is evaluated. Developed IDF relations are used for hydrological modelling to evaluate the combined impacts of climate change and land use change on the urban catchments and urban stormwater management.
23
Tiwari, Pushp Raj. "Dynamical downscaling for wintertime seasonal prediction of precipitation over northwest India". Thesis, IIT Delhi, 2016. http://localhost:8080/xmlui/handle/12345678/7091.
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Beyer, Ulrike. "Regionale Niederschlagsänderungen in Namibia bei anthropogen verstärktem Treibhauseffekt Abschätzungen mit statistischem Downscaling /". [S.l.] : [s.n.], 2001. http://deposit.ddb.de/cgi-bin/dokserv?idn=96428880X.
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Huang, Bo [Verfasser]. "East Asian summer monsoon simulations: dynamical downscaling and seasonal prediction / Bo Huang". Berlin : Freie Universität Berlin, 2017. http://d-nb.info/1138630616/34.
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Munday, Paul. "Downscaling scenarios to local landscapes : a case study of the Norfolk Broads". Thesis, University of East Anglia, 2010. https://ueaeprints.uea.ac.uk/32238/.
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Gao, Lu. "Validation and statistical downscaling of ERA-Interim reanalysis data for integrated applications". Diss., Ludwig-Maximilians-Universität München, 2013. http://nbn-resolving.de/urn:nbn:de:bvb:19-173179.
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Duan, Juan. "Uncertainty in statistical downscaling of rainfall : case study of south-east UK". Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/24555.
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Pressures on water resources in much of England are set to rise owing to factors such as climate change and population growth. This is particularly the case in south-east UK, with its high population density, relatively low number of reservoirs and heavy reliance on groundwater supplies. Investigating the sustainability of water supply in this region requires a better understanding the rainfall distribution both temporally and spatially. Statistical downscaling methods were commonly adopted to simulate rainfall including under climate change scenarios. When projecting the downscaling model into future, it is often assumed that the statistical relationships observed in the historical fitting period remain stationary under the climate scenarios. However, this assumption is questionable as many downscaling models are trained on only around 30 years of data. The objectives of the thesis are therefore to 1) develop long-term statistical downscaling rainfall models to characterise the spatial and temporal distribution of rainfall for south-east UK; 2) identify the non-stationarity in statistical downscaling models; and 3) project the rainfall under climate change scenarios and assess the uncertainties in rainfall projections. To address these challenges, regression models of monthly rainfall for south-east UK were developed. Conditioned on 50 gauged sites, the model infilled and simulated the historic record from 1855-2011 in both space and time. The long record length allows more insight into the variability of rainfall and potentially a stronger basis for risk assessment than is generally possible. It is shown that, although localised biases exist in both space and time, the model results are generally consistent with the observed record including for a range of inter-annual droughts and spatial statistics. The non-stationarity was then assessed using two approaches, including visualising the non-stationarity by plotting the time series of regression coefficient estimates derived by using a moving window of 30 years, and testing whether the decadal scale change in the coefficient values is statistically significant. The results illustrate the existence of significant non-stationarity in the model, which could not be removed by adding additional available input variables to the regression. The models fitted from five 30-year control periods and climate data from five GCMs were used to generate rainfall projections in 2021-2050. Both the uncertainty introduced by non-stationarity and the GCMs was visualised. The results show that in some months the source of uncertainty introduced by non-stationarity can lead to significant uncertainty in the projections. The uncertainties in projections were then estimated using ensembles.
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Brynjarsdóttir, Jenný. "Dimension Reduced Modeling of Spatio-Temporal Processes with Applications to Statistical Downscaling". The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1312935520.
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Luo, Wen. "A Dynamic Downscaling Method to Estimate Climate Change for Vulnerable Infrastructure Identification". University of Akron / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=akron1589465412871131.
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Bellone, Enrica. "Nonhomogeneous hidden Markov models for downscaling synoptic atmospheric patterns to precipitation amounts /". Thesis, Connect to this title online; UW restricted, 2000. http://hdl.handle.net/1773/8979.
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NGUYEN, Thi Nhat Thanm. "Downscaling Aerosol Optical Thickness from Satellite Observations: Physics and Machine Learning Approaches". Doctoral thesis, Università degli studi di Ferrara, 2012. http://hdl.handle.net/11392/2389451.
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In recent years, the satellite observation of aerosol properties has been
greatly improved. As a result, the derivation of Aerosol Optical Thickness
(AOT), one of the most popular atmospheric parameters used in
air pollution monitoring, over ocean and continents from satellite observations
shows comparable quality to ground-based measurements.
Satellite AOT products is often applied for monitoring at global scale
because of its coarse spatial resolution. However, monitoring at local
scale such as over cities requires more detailed AOT information.
The increase spatial resolution to suitable level has potential for applications
of air pollution monitoring at global-to-local scale, detecting
emission sources, deciding pollution management strategies, localizing
aerosol estimation, etc. In this thesis, we investigated, proposed, implemented
and validated algorithms to derive AOT maps with spatial
resolution increased up to 1×1 km2 from MODerate resolution Imaging
Spectrometer (MODIS) observations provided by National Aeronautics
and Space Administration (NASA), while MODIS standard
aerosol products provide maps at 10×10 km2 of spatial resolution.
The solutions are considered on two perspectives: dynamical downscaling
by improving the algorithm for remote sensing of tropospheric
aerosol from MODIS and statistical downscaling using Support Vector
Regression.
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Balhane, Saloua. "Improving the dynamical downscaling over Morocco in the context of climate change". Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAX105.
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Le Maroc est l'une des régions les plus vulnérables au changement climatique. Il se caractérise par des interactions complexes entre diverses caractéristiques géographiques, notamment l'océan Atlantique, la mer Méditerranée, les montagnes du Haut Atlas et le Sahara. Il est essentiel de comprendre la variabilité spatio-temporelle du climat dans cette région pour une meilleure gestion des ressources. Les modèles climatiques globaux jouent un rôle important dans ce contexte, car ce sont les seuls modèles à prendre en compte tous les réservoirs d'eau et d'énergie, y compris les réservoirs à mouvement lent comme les océans, qui modulent le climat et son évolution. Cependant, les modèles climatiques globaux sont encore sujets à des biais systématiques qui limitent leurs performances et ont généralement des résolutions grossières, limitant leur utilisation pour l’évaluation d’aspects locaux. Les modèles climatiques régionaux peuvent améliorer la représentation de certains processus (processus orographiques, brises, etc.). Cependant, ils présentent des défauts qui peuvent altérer de manière significative la crédibilité des trajectoires du changement climatique, car il est impossible de distinguer l'impact des biais systématiques dans les modèles globaux de forçage du rôle d'une meilleure description de petite échelle.Ce travail explore différentes manières de surmonter ces limitations.Dans la première partie, nous évaluons une gamme de différents ensembles à haute résolution, issus de la réduction d'échelle statistique (NEXGDDP) et dynamique (Euro-CORDEX et Euro-CORDEX ajusté au biais) tout en analysant la valeur ajoutée potentielle que la correction du biais "a posteriori" peut avoir sur la simulation des précipitations et des températures moyennes et extrêmes sur le Maroc.Dans la deuxième partie, nous utilisons le modèle LMDZ, la composante atmosphérique de la dernière version du modèle de l’IPSL, dans une configuration couplée avec le modèle de surface terrestre ORCHIDEE. Nous avons conçu une configuration du modèle à grille raffinée adaptée aux études régionales sur le Maroc, qui est suffisamment stable numériquement pour exécuter des simulations de changement climatique et qui permet i) une haute résolution sur la région et ii) une résolution suffisante à l'extérieur du zoom pour reproduire la circulation de grande échelle. Nous utilisons une méthode de correction de biais en ligne, qui consiste à corriger les erreurs systématiques des variables atmosphériques de grande échelle à l'aide des statistiques d'une simulation guidée par des réanalyses climatologiques. Cette approche permet de travailler avec une résolution fine à un coût de calcul modéré sans compromettre la cohérence entre les climats global et régional, cruciale pour le Maroc.L’évaluation du climat présent (1979-2014) a mis en évidence des améliorations notables après raffinement de la grille, notamment dans la circulation générale moyenne. La simulation libre à grille zoomée se compare favorablement aux observations de précipitations et de températures à l'échelle locale. Le climat moyen est considérablement amélioré après la correction de biais par rapport aux simulations non corrigées, et des améliorations dans le transport d’humidité, des précipitations et de la température de l’air sont observés.Pour le climat futur, la température de surface de la mer (SST) et la concentration de glace de mer (SIC) déduites de quatre modèles CMIP6 couplés, forcés par les gaz à effet de serre et les aérosols correspondant au scénario Shared Socioeconomic Pathway-8.5 (SSP-8.5), sont utilisées pour forcer la configuration régionale corrigée du LMDZ6-OR. Des simulations de 20 ans sont produites pour un niveau de réchauffement global de 3 Kelvin afin d'évaluer la réponse du climat régional moyen, des précipitations et de la température au changement des SST et SICMorocco is one of the most vulnerable regions to climate change. Its climate is characterized by complex interactions between various geographical features, including the Atlantic Ocean, the Mediterranean Sea, the High Atlas Mountains, and the Sahara Desert. Understanding the spatiotemporal variability of climatic patterns in this region is crucial for effective climate change adaptation strategies, natural resource management, and sustainable development planning. Global climate models (GCMs) play a significant role within this context, as they are the only models to take into account all the water and energy reservoirs, including slow-moving reservoirs such as the oceans, which modulate the climate and its evolution. Yet, global climate models are still subject to systematic biases that constrain their performance and have generally coarse resolutions, limiting the assessment of local climate patterns. Regional climate models can improve the representation of certain processes (orographic processes, breezes, etc.). They do, however, have flaws that can significantly alter the credibility of climate change trajectories, as it is impossible to distinguish the impact of systematic biases in the forcing GCMs from the role of better small-scale description.This work explores different ways of overcoming these limitations.In the first part, we evaluate a range of different widely used high-resolution ensembles issued from statistical (NEXGDDP) and dynamical (Euro-CORDEX and bias-adjusted Euro-CORDEX) downscaling while investigating the potential added value that “a posteriori'' bias adjustment may have on the simulation of mean and extreme precipitation and temperature over Morocco.In the second part, we use the LMDZ model, the atmospheric component of the latest version of the IPSL model (IPSLCM6), in a coupled configuration with the ORCHIDEE land-surface model. We designed a refined-grid configuration of the model adapted for regional studies over Morocco that is numerically stable enough for running climate change simulations and allows i) a high resolution over the region and ii) a sufficient resolution on the outside of the zoom area to reproduce large-scale patterns. To deal with the systematic large-scale dynamical biases, a run-time bias correction approach, which consists of bias-correcting the systematic errors in large-scale atmospheric variables using the statistics of a nudged simulation towards climate reanalysis, is used. This method allows for high resolution at a moderate computational cost without compromising the coherence between the global and regional climates. Indeed, preserving this coherence is crucial for Morocco since large-scale circulation patterns play a vital role in shaping regional climate patterns in the region.The evaluation of the present climate (1979–2014) has shown significant improvements after grid refinement, particularly in the mean general circulation. The free refined-grid run compares favorably to precipitation and temperature observations at the local scale. The mean climate is considerably improved after bias correction compared to the uncorrected simulations, and improvements in moisture transport, precipitation and air temperature are observed.For future climate, sea surface temperature (SST) and sea ice concentration (SIC) deduced from four coupled CMIP6 models, forced by greenhouse gases and aerosols corresponding to the Shared Socioeconomic Pathway-8.5 (SSP-8.5) scenario, are used to force the corrected regional configuration of LMDZ6-OR. Twenty-year simulations are produced for a global warming level of 3 Kelvin to assess the response of mean regional climate, precipitation and temperature to changes in SST and SIC
34
Canon, Barriga Julio Eduardo. "Downscaling Climate and Vegetation Variability Associated with Global Climate Signals: a new Statistical Approach Applied to the Colorado River Basin". Diss., The University of Arizona, 2009. http://hdl.handle.net/10150/195379.
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This research presents a new multivariate statistical approach to downscale hydroclimatic variables associated with global climate signals, from low-resolution Global Climate Models (GCMs) to high-resolution grids that are appropriate for regional and local hydrologic analysis. The approach uses Principal Component Analysis (PCA) and Multichannel Singular Spectrum Analysis (MSSA) to: 1) evaluate significant variation modes among global climate signals and spatially distributed hydroclimatic variables within certain spatial domain; 2) downscale the GCMs' projections of the hydroclimatic variables using these significant modes of variation and 3) extend the results to other correlated variables in the space domain. The approach is applied to the Colorado River Basin to determine common oscillations among observed precipitation and temperature patterns in the basin and the global climate signals El Nino Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO). These common oscillations serve as a basis to perform the downscaling of ENSO-related precipitation and temperature projections from GCMs, using a new gap-filling algorithm based on MSSA. The analysis of spatial and temporal correlations between observed precipitation, temperature and vegetation activity (represented by the Normalized Difference Vegetation Index, NDVI) is used to extend the downscaling of precipitation to vegetation responses in ten ecoregions within the basin. Results show significant common oscillations of five and 15-year between ENSO, PDO and annual precipitation in the basin, with wetter years during common ENSO and PDO positive phases and dryer years during common negative phases. Precipitation also shows an increase in variability in the last 20 years of record. Highly correlated responses between seasonally detrended NDVI and precipitation were also identified in each ecoregion, with distinctive delays in vegetation response ranging from one month in the southern deserts (in the fringe of the monsoon precipitation regime), to two months in the mid latitudes and three months to the north, affected by seasonal precipitation. These results were used to downscale precipitation and temperature from two GCMs that perform well in the basin and have a distinctive ENSO-like signal (MPI-ECHAM5 and UKMO-HADCM3) and to extend the downscaling to estimate vegetation responses based on their significant correlations with precipitation.
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Ayres, Ana Carolina. "Variabilidade e desastres naturais da região do Vale do Paraíba/SP: passado e futuro". Universidade de Taubaté, 2010. http://www.bdtd.unitau.br/tedesimplificado/tde_busca/arquivo.php?codArquivo=139.
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A região do Vale do Paraíba, situada em uma planície cortada pelo Rio Paraíba do Sul, entre as Serras da Mantiqueira e do Mar, possui alternância entre períodos secos e chuvosos, alcançando cerca de 1300 mm por ano. Todas estas características físicas somadas à disposição de moradias em várzeas e áreas com alta declividade contribuem para a ocorrência de desastres naturais. Deste modo, foi realizado o levantamento da vulnerabilidade climática aos desastres naturais da região do Vale do Paraíba. A pesquisa foi dividida em duas partes, passado e futuro. No passado (1990-2008) a região apresentou municípios vulneráveis aos desastres naturais como São José dos Campos, Jacareí, Campos dos Jordão, Taubaté e Aparecida. Os desastres naturais de maior ocorrência foram às inundações (54%) e as tempestades severas (25%) com maior frequência nos meses de janeiro, fevereiro e março. Para o futuro foram analisados dados de precipitação (2070-2100) para os cenários A2 e B2, a partir dos dados de simulação climática futura, modelo ETA/CCS, pela técnica de downscaling dinâmico, o modelo apontou para redução da precipitação na região, sendo de 44% para o cenário A2 e 35% para o cenário B2. Além da redução no total de precipitação, os dados futuros apontam para o aumento do período de dias de permanência de chuva, com predomínio de chuvas leves (0,1 a 5 mm), ou seja, haverá redução nos eventos extremos de precipitação, o que contribuiria para a diminuição de processos geradores de desastres naturais na região do Vale do Paraíba.The region of Paraíba Valley, situated on a plain crossed by Paraiba do Sul River, between Mantiqueira and Mar mountain rigdes, alternates dry and wet periods, getting about 1300 mm of rain per year. The physical characteristics combined with the location of homes in low and flat lands alongside a watercourse and in areas with steep slopes contribute to the occurrence of natural disasters. This study of climate vulnerability and natural disasters in the region of Paraíba Valley. This research is divided into two parts: past and future. In the past (1990-2008) the region has vulnerable cities to natural disasters. Such as São José dos Campos, Jacareí, Campos do Jordão, Taubaté, and Aparecida. In these cities, the predominant natural disasters were floods (54%) and severe storms (25%) that occur frequently in the months of January, February and March. For the future precipitation data modeled (2070-2100) were analyzed for the scenarios A2 and B2 of IPCC, from the data of future climate simulation, (ETA / CCS model) by applying the dynamic downscaling technique. The model indicates reduced precipitation in the region (44% for A2 scenario and 35% for scenario B2). Besides the reduction in total precipitation, the future data point to the increase in the number of rainy days with the predominancy of ligth rains (0.1 to 5 mm), so, it will have a reduction in extreme precipitation events that could contribute to a decrease of natural disaster generating processes in the region of Paraíba Valley.
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Yang, Wei [Verfasser]. "Discrete-continuous downscaling model for generating daily precipitation time series / von Wei Yang". Stuttgart : Inst. für Wasserbau, 2008. http://d-nb.info/997170654/34.
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Choux, Mathieu. "Development of new predictor climate variables for statistical downscaling of daily precipitation process". Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=98951.
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Statistical downscaling (SD) procedures have been frequently used for assessing the potential impacts of climate change and variability on hydrological regime. These procedures are based on the empirical relationships between large-scale atmospheric variables (predictors) and surface environment parameters (e.g., precipitation and temperature). The present research work is hence concerned with the development of new predictor climate variables that could be used for improving the accuracy of downscaling of daily precipitation process at a local site. The new predictors should be able to provide a more accurate simulation of the local variable since they could describe more accurately the physical characteristics of the precipitation process. In particular, a better reproduction of summer rainfall event is expected through an improved inclusion of main thermodynamic forcings from humidity and stability parameters.The first part of this study focuses on the re-computation of the geostrophic circulation predictor variables developed by Wilby and Wigley (2000), reconstructed from mean sea level pressure or geopotential heights. The same circulation variables are re-computed from prognostic winds of the National Centre for Environmental Prediction (NCEP) re-analysis data set (Kalnay et al., 1996). Assessment of the performance of the re-computed predictors is carried out using the Statistical DownScaling Model (SDSM), developed by Wilby et al. (2002), and based on a number of climate indices characterizing the frequency, intensity and extremes of daily precipitation process. Two different predictor sets are considered, the first consisting of circulation-only variables, the second including a raw specific humidity predictor. For each predictor set, results obtained from the two computation techniques are compared. Daily precipitation data available at Montreal-Dorval Airport station for the 1961-1990 period were used in this assessment. Results indicated that the re-computation of geostrophic variables for both sets could yield significant improvements in the reproduction of local precipitation characteristics for the validation 1976-1990 period. The most striking improvement can be achieved for winter, as expected from the greater influence of large-scale circulation forcings on precipitation in this season. In the second part, new advection variables are developed based on a generalized omega equation. It is found that the Laplacian of temperature advection and the differential vorticity advection appear as direct forcings of the vertical velocity, strongly correlated with the precipitation process. Precipitable water and atmospheric instability indices are also included in the predictor range, mainly to reach a better simulation of convective precipitation. Next, a new statistical downscaling scheme is developed, combining a Principal Component Analysis (PCA) of the new predictors and the SDSM model. Analysis of the different computed principal components confirms the major role of the two identified advection terms and the humidity/instability predictors. Assessment of the new PCA+SDSM scheme shows significant improvements of the simulation of precipitation intensity, although results are less conclusive regarding the precipitation occurrence.
Finally, the influence of the calibration period length on the new downscaling scheme performance was carried out by comparing the simulation results obtained from two calibration runs of 15 and 30 years of length: for the 1961-1975 period and for the 1961-1990 one. It was found that doubling the calibration period length could lead to significant improvements in the reproduction of the local precipitation characteristics.
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Kim, Ju Eun. "Multisite statistical downscaling of daily temperature extremes for climate-related impact assessment studies". Thesis, McGill University, 2013. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=117151.
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Global climate change has been considered in many engineering studies due to its drastic impacts on the design and planning of various infrastructures. In order to reduce the risks of those impacts, the present study focuses on accurate prediction of daily temperature extremes for future periods under different climate change scenarios. The main objectives of this study are therefore: (a) to detect the evidences of climate change from the statistical analysis of existing observed daily extreme temperature data; (b) to assess the performance of single-site and multi-site statistical downscaling (SD) approaches in order to identify the best SD model that could describe accurately the linkage between global scale climate variables and the observed statistical properties of daily temperature extremes at a given local site; and (c) to provide a prediction of daily temperature extremes for future periods based on the best SD model identified under different climate change scenarios. Firstly, a detailed statistical analysis of daily extreme temperature data available during the 1973-2009 period from a network of 25 weather stations located in South Korea was carried out to identify the possible trends in 18 different temperature characteristics. Results of this data analysis have indicated significant changes in the characteristics of daily maximum temperature (Tmax) and daily minimum temperature (Tmin) during this period. In particular, the positive trends in annual means of Tmax and Tmin were found statistically significant. In addition, the number of cold events tends to decrease while the number of warm events tends to increase at most of the stations considered. Secondly, statistical downscaling methods were used to describe the linkage between the coarse resolution of General Circulation Model (GCM) climate variables and the daily extreme temperature characteristics at a local site for impact assessments. Most previous studies have been dealing with downscaling of daily temperature extremes at a single site. However, more recent studies have been conducted to develop improved downscaling methods for many sites concurrently. This study was carried out to assess the performance of the multi-site SD method based on the Singular Value Decomposition (SVD) method as compared with the performance of the popular SDSM for single-site downscaling. The application of the multi-site and single-site SD methods was performed using the observed daily Tmax and Tmin data from the 25 stations in South Korea and the corresponding NCEP re-analysis data for the 1973-2001 period. It was found that the multi-site SD method and the single-site SDSM could accurately reproduce basic properties of Tmax and Tmin at each local site. However, the multi-site SD method could describe more accurately the temporal and spatial correlations of daily temperature extremes than the SDSM. Overall, the multi-site SD method was found to be more accurate than the SDSM. Finally, future prediction of daily extreme temperatures was accomplished based on the multi-site SD method under the A1B and A2 climate scenarios provided by the third version of the Canadian Global Climate Model (CGCM3). The increasing trends were found in the monthly means of Tmax and Tmin, the monthly90th percentiles of Tmax, and the monthly10th percentiles of Tmin for the future 2010-2100 period over South Korea.Le changement climatique global a été introduit dans les études d'ingénierie en raison de ses impacts importants sur la conception et la planification des infrastructures. Afin de réduire ces impacts, la présente étude se concentre sur la possibilité d'obtenir des prévisions précises des températures extrêmes pour les périodes futures sous divers scénarios de changement climatique. Les objectifs principaux de cette étude sont alors (a) de détecter les éléments reliés au changement climatique à partir de l'analyse statistique des données disponible de températures extrêmes journalières; (b) d'évaluer la performance des techniques de mise en échelle statistique (SD : Statistical Downscaling) en un site et en plusieurs sites pour identifier le meilleur modèle SD qui est capable décrire correctement le lien entre les variables climatiques globales et les propriétés statistiques observées des températures extrêmes journalières en un site choisi; et (c) de fournir une prévision des températures extrêmes pour le futur en se basant sur le meilleur modèle SD identifié pour divers scénarios de changement climatique. Premièrement, une analyse statistique détaillée des données de températures extrêmes journalières disponibles pour la période de 1973-2009 d'un réseau de 25 stations météorologiques situé en la Corée du Sud a été effectuée pour identifier les tendances possibles dans les 18 propriétés de température. Les résultats de cette analyse des données ont indiqué des changements significatifs sur les caractéristiques de température maximale journalière (Tmax) et de température minimale journalière (Tmin) pendant cette période. En particulier, des tendances positives statistiquement significatives dans les moyennes annuelles de Tmax et Tmin ont été identifiées. De plus, le nombre d'événements froids a eu une tendance de diminution tandis que le nombre d'événements chauds a eu une tendance d'augmentation pour la plupart de stations considérées. Deuxièmement, les méthodes SD ont été utilisées pour établir le lien entre la résolution grossière des variables climatiques du modèle de circulation générale (MCG) et les propriétés de températures extrêmes journalières en un site pour les évaluations d'impact. La plupart des études antérieures ont été effectuées sur la mise en échelle des températures extrêmes journalières en un seul site. Cependant, des études plus récentes ont été réalisées pour élaborer des méthodes de mise en échelle en plusieurs sites simultanément. Cette étude a été effectuée pour évaluer la performance de la méthode SD en multi-sites basée sur la technique de décomposition en valeurs singulières (SVD) en comparant avec celle de la méthode de mise en échelle statistique populaire SDSM en un site. L'application de ces deux méthodes a été réalisée en utilisant les données observées de températures extrêmes journalières Tmax et Tmin de 25 stations météorologiques en Corée du Sud et les données de NCEP correspondantes pour la période de 1973 à 2001. On a trouvé que les méthodes de mise en échelle multi-sites et en un site SDSM sont capables de reproduire correctement les caractéristiques statistiques de base de Tmax et Tmin en chaque site. Toutefois, l'approche multi-site peut reproduire d'une façon plus précise les corrélations temporelle et spatiale que la méthode SDSM. En général, l'approche multi-site peut fournir des estimations plus précises des caractéristiques Tmax et Tmin que celles données par la méthode SDSM. Enfin, la prévision des températures extrêmes quotidiennes est faite en appliquant l'approche multi-sites SD pour les scénarios de changement climatique A1B et A2 fournis par le modèle climatique global canadien (MCCG3). Les résultats de cette prévision ont indiqué une tendance croissante dans les valeurs moyennes mensuelles de Tmax et Tmin, la 90e percentile de Tmax et la 10e percentile de Tmin pour la période de 2010 à 2100 à travers de la Corée du Sud.
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Wilkie, Craig John. "Nonparametric statistical downscaling for the fusion of in-lake and remote sensing data". Thesis, University of Glasgow, 2017. http://theses.gla.ac.uk/8626/.
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Lakes are vital components of the global biosphere, supporting complex ecosystems and playing important roles in the global biogeochemical cycle. However, they are vulnerable to the threat from climate change and their responses to climate forcing, eutrophication and other pressures, and their possibly confounding interactions, are not yet well understood. Monitoring lake health is therefore essential, in order to understand the changing patterns over space and time. Traditionally, in-situ data, which are collected directly from within lakes and analysed in laboratories, have been available for analysis. However, although these data are assumed to be accurate within measurement error, they are expensive to collect, so that few, if any, in-situ sampling locations are available for each lake, often with infrequent sampling at each location. On the other hand, remotely-sensed data, which are derived from reflectance measurements of the Earth's surface, obtained from satellites, have recently become widely available. These data have good spatial coverage of up to 300 metre resolution, covering entire lakes, often with a monthly-average time-scale, but they must firstly be calibrated with the in-situ data to ensure accuracy, before inferences are made. The data for this research were provided by the GloboLakes project (www.globolakes.ac.uk), which is a consortium research project that is investigating the state of lakes and their responses to environmental drivers on a global scale. The research primarily focusses on log(chlorophyll-a) data for Lake Balaton, in Hungary, and for the Great Lakes of North America. The key question of interest for this research is: ``How can data fusion be performed for in-situ and remotely-sensed lake water quality data, accounting for the spatiotemporal change of support between the point-location, point-time in-situ data and the grid-cell-scale, monthly-averaged remotely-sensed data, producing a fused dataset that takes accuracy from the in-situ data and spatial and temporal information from the remotely-sensed data?" In order to answer this question, this thesis presents the following work: An initial analysis of the data for Lake Balaton motivates the following work, by demonstrating the spatial and temporal patterns in the data, using mixed-effects models, generalised additive models, kriging and principal components analysis. Following the identification of statistical downscaling as an appropriate method for fusion of the data, statistical downscaling models are developed, specifically in the framework of Bayesian hierarchical models with spatially-varying coefficients, for the novel application to data for log(chlorophyll-a), producing fully calibrated maps of fused data across lake surfaces, with associated comprehensive uncertainty measures. Bivariate and multiple-lakes statistical downscaling models are developed and applied, motivated by the assumption that sharing information between variables and between lakes can improve the accuracy of model predictions. The statistically novel method of nonparametric statistical downscaling is developed, to account for both the spatial and temporal aspects of the change of support between the in-situ and remotely-sensed data. Using methodology from both functional data analysis and statistical downscaling, the model treats in-situ and remotely-sensed data at each location as observations of smooth functions over time, estimated using bases, with the basis coefficients related via a spatially-varying coefficient regression. This is computed within a Bayesian hierarchical model, enabling the calculation of comprehensive uncertainties. This thesis presents the background, motivation, model development and application of the novel method of nonparametric statistical downscaling, filling the gap in the literature of accounting for changing temporal support in statistical downscaling modelling. Results are presented throughout this thesis, to demonstrate the utility of the method for real lake water quality data.
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Tisseuil, Clément. "Modéliser l'impact du changement climatique sur les écosystèmes aquatiques par approche de downscaling". Toulouse 3, 2009. http://thesesups.ups-tlse.fr/763/.
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L'objectif était d'évaluer l'impact du changement global sur les écosystèmes aquatiques au cours du 21ème siècle, dans le bassin Adour Garonne (S-O France). Une approche de " downscaling " a été développée à l'interface entre les sciences du climat, de l'hydro-chimie et de l'écologie. Les résultats suggèrent une augmentation globale des débits hivernaux ainsi que des débits d'étiages plus faibles. Les concentrations en nitrate pourraient augmenter, de même que la distribution des espèces de poissons les plus thermophiles. Toutefois, une diminution des gaz à effet de serre ainsi qu'une modification des pratiques agricoles (ex. Diminution des fertilisants azotés) pourraient limiter l'intensité des perturbations écologiques. Cette thèse offre une contribution originale, notamment pour la gestion future des ressources hydriques et écologiquesThis thesis aimed at assessing the impact of global change on freshwater ecosystems during the 21st century in the Adour Garonne area (SW France). A downscaling approach was developed linking techniques from climate, hydro-chemical and ecological sciences. The main results suggest an increase of high flows in winter as well as more severe low flows in summer. Nitrogen concentrations and thermophile fish species distribution may also increase. Reducing green house gas emissions and modifying agricultural practices (e. G reducing nitrate fertilizers) could reduce the intensity of ecological disturbances. This study is an original contribution to the management of future hydrological and ecological resources
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Dehn, Martin Boesler Klaus-Achim. "Szenarien der klimatischen Auslösung alpiner Hangrutschungen Simulation durch Downscaling allgemeiner Zirkulationsmodelle der Atmosphäre /". Sankt Augustin : In Kommission bei Asgard-Verlag, 1999. http://catalog.hathitrust.org/api/volumes/oclc/43398514.html.
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Hjältén, Alexander. "HOW IMAGE DOWNSCALING AND JPEG COMPRESSION AFFECTS IMAGE CLASSIFICATION PERFORMANCE - An experimental study". Thesis, Umeå universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-163308.
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the quality of an image plays a role in how well it can be correctly classified by an image classifying neural network. Artifacts such as blur and noise reduces classifilability. At the same time it is often motivated to reduce file sizes of images which also tends to introduce artifacts and reduce their quality. This leads to a trade-off between having small file sizes and achieving high classification accuracy. The two main approaches of reducing file sizes of images is to either reduce the number of pixels in them via image scaling or to use less data to represent each pixel via compression.The effects of these two approaches on image classification accuracy have been studied independently.In this study the effects of combining image scaling and compression in regards to image classifiability is examined for the first time. Images are downscaled using five popular methods before being compressed with different magnitudes of JPEG compression. Results are evaluated based on the fraction of the treated images that are correctly classified by the classifier as well as on the image file sizes.The results shows that the scaling method used has significant but weak effect on image classifiability. Thus the choice of scaling method does not seem to be critical in this context. There are however trends suggesting that the Lanczos scaling method created the most classifiable images and that the Gaussian method created the images with highest classiability to file size ratio. Both scaling magnitude and compression magnitude were found to be better predictors of image classifiability than scaling method.
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Di, Fusco Greta. "A Reliable Downscaling of ECG Signals for the Detection of T wave Heterogeneity Features". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016.
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In cardiovascular disease the definition and the detection of the ECG parameters related to repolarization dynamics in post MI patients is still a crucial unmet need. In addition, the use of a 3D sensor in the implantable medical devices would be a crucial mean in the assessment or prediction of Heart Failure status, but the inclusion of such feature is limited by hardware and firmware constraints.
The aim of this thesis is the definition of a reliable surrogate of the 500 Hz ECG signal to reach the aforementioned objective. To evaluate the worsening of reliability due to sampling frequency reduction on delineation performance, the signals have been consecutively down sampled by a factor 2, 4, 8 thus obtaining the ECG signals sampled at 250, 125 and 62.5 Hz, respectively. The final goal is the feasibility assessment of the detection of the fiducial points in order to translate those parameters into meaningful clinical parameter for Heart Failure prediction, such as T waves intervals heterogeneity and variability of areas under T waves.
An experimental setting for data collection on healthy volunteers has been set up at the Bakken Research Center in Maastricht. A 16 – channel ambulatory system, provided by TMSI, has recorded the standard 12 – Leads ECG, two 3D accelerometers and a respiration sensor. The collection platform has been set up by the TMSI property software Polybench, the data analysis of such signals has been performed with Matlab.
The main results of this study show that the 125 Hz sampling rate has demonstrated to be a good candidate for a reliable detection of fiducial points. T wave intervals proved to be consistently stable, even at 62.5 Hz. Further studies would be needed to provide a better comparison between sampling at 250 Hz and 125 Hz for areas under the T waves.
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Dean, John R. "Improving summer drought prediction in the Apalachicola-Chattahoochee-Flint river basin with empirical downscaling". unrestricted, 2008. http://etd.gsu.edu/theses/available/etd-07152008-200815/.
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Thesis (M.A.)--Georgia State University, 2008.Title from file title page. Jeremy E. Diem, committee chair; Jeremy W. Crampton, John W. Matthews, committee members. Electronic text (84 p. : ill. (some col., maps (some col.)) : digital, PDF file. Description based on contents viewed Oct. 1, 2008. Includes bibliographical references (p. 77-84).
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Dean, John Robert. "Improving Summer Drought Prediction in the Apalachicola-Chattahoochee- Flint River Basin with Empirical Downscaling". Digital Archive @ GSU, 2008. http://digitalarchive.gsu.edu/geosciences_theses/12.
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The Georgia General Assembly, like many states, has enacted pre-defined, comprehensive, drought-mitigation apparatus, but they need rainfall outlooks. Global circulation models (GCMs) provide rainfall outlooks, but they are too spatially course for jurisdictional impact assessment. To wed these efforts, spatially averaged, time-smoothed, daily precipitation observations from the National Weather Service cooperative network are fitted to eight points of 700 mbar atmospheric data from the NCEP/NCAR Reanalysis Project for climate downscaling and drought prediction in the Apalachicola-Chattahoochee-Flint (ACF) river basin. The domain is regionalized with a factor analysis to create specialized models. All models complied well with mathematical assumptions, though the residuals were somewhat skewed and flattened. All models had an R-squared > 0.2. The models revealed map points to the south to be especially influential. A leave-one-out cross-validation showed the models to be unbiased with a percent error of < 20%. Atmospheric parameters are estimated for 2008–2011 with GCMs and empirical extrapolations. The transfer function was invoked on both these data sets for drought predictions. All models and data indicate drought especially for 2010 and especially in the south.
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Pharasi, Sid. "Development of statistical downscaling methods for the daily precipitation process at a local site". Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=99786.
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Over the past decade, statistical procedures have been employed to downscale the outputs from global climate models (GCM) to assess the potential impacts of climate change and variability on the hydrological regime. These procedures are based on the empirical relationships between large-scale atmospheric predictor variables and local surface parameters such as precipitation and temperature. This research is motivated by the recognized lack of a comprehensive yet physically and statistically significant downscaling methodology for daily precipitation at a local site. The primary objectives are to move beyond the 'black box' approaches currently employed within the downscaling community, and develop improved statistical downscaling models that could outperform both raw GCM output and the current standard: the SDSM method. In addition, the downscaling methods could provide a more robust physical interpretation of the relationships between large-scale predictor climate variables and the daily precipitation characteristics at a local site.The first component of this thesis consists of developing linear regression based downscaling models to predict both the occurrence and intensity of daily precipitation at a local site using stepwise, weighted least squares, and robust regression methods. The performance of these models was assessed using daily precipitation and NCEP re-analysis climate data available at Dorval Airport in Quebec for the 1961-1990 period. It was found that the proposed models could describe more accurately the statistical and physical properties of the local daily precipitation process as compared to the CGCM1 model. Further, the stepwise model outperforms the SDSM model for seven months of the year and produces markedly fewer outliers than the latter, particularly for the winter and spring months. These results highlight the necessity of downscaling precipitation for a local site because of the unreliability of the large-scale raw CGCM1 output, and demonstrate the comparative performance of the proposed stepwise model as compared with the SDSM model in reproducing both the statistical and physical properties of the observed daily rainfall series at Dorval.
In the second part of the thesis, a new downscaling methodology based on the principal component regression is developed to predict both the occurrence and amounts of the daily precipitation series at a local site. The principal component analysis created statistically and physically meaningful groupings of the NCEP predictor variables which explained 90% of the total variance. All models formulated outperformed the SDSM model in the description of the statistical properties of the precipitation series, as well as reproduced 4 out of 6 physical indices more accurately than the SDSM model, except for the summer season. Most importantly, this analysis yields a single, parismonious model; a non-redundant model, not stratified by month or season, with a single set of parameters that can predict both precipitation occurrence and intensity for any season of the year.
The third component of the research uses covariance structural modeling to ascertain the best predictors within the principal components that were developed previously. Best fit models with significant paths are generated for the winter and summer seasons via an iterative process. The direct and indirect effects of the variables left in the final models indicate that for either season, three main predictors exhibit direct effects on the daily precipitation amounts: the meridional velocity at the 850 HPa level, the vorticity at the 500 HPa level, and the specific humidity at the 500 HPa level. Each of these variables is heavily loaded onto the first three principal components respectively. Further, a key fact emerges: From season to season, the same seven significant large-scale NCEP predictors exhibit a similar model structure when the daily precipitation amounts at Dorval Airport were used as a dependent variable. This fact indicated that the covariance structural model was physically more consistent than the stepwise regression one since different model structures with different sets of significant variables could be identified when a stepwise procedure is employed.
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Lin, Liao-Fan. "Data assimilation and dynamical downscaling of remotely-sensed precipitation and soil moisture from space". Diss., Georgia Institute of Technology, 2016. http://hdl.handle.net/1853/54974.
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Environmental monitoring of Earth from space has provided invaluable information for understanding the land-atmosphere water and energy exchanges. However, the use of satellite observations in hydrologic applications is often limited by coarse space-time resolutions. This study aims to develop a data assimilation system that integrates remotely-sensed precipitation and soil moisture observations into physically-based models to produce fine-scale precipitation, soil moisture, and other relevant hydrometeorological variables. This is particularly useful with the active Global Precipitation Measurement and Soil Moisture Active Passive missions. The system consists of two major components: (1) a framework for dynamic downscaling of satellite precipitation products using the Weather Research and Forecasting (WRF) model with four-dimensional variational data assimilation (4D-Var) and (2) a variational data assimilation system using spatio-temporally varying background error covariance for directly assimilating satellite soil moisture data into the Noah land surface model coupled with the WRF model. The WRF 4D-Var system can effectively assimilate and downscale six-hour precipitation products of a spatial resolution of about 20 km (i.e., those derived from the National Centers for Environmental Prediction Stage IV data and the Tropical Rainfall Measuring Mission (TRMM) 3B42 dataset) to hourly precipitation with a spatial resolution of less than 10 km. The system is able to assimilate and downscale daily soil moisture products at a gridded 36-km resolution obtained from the Soil Moisture and Ocean Salinity (SMOS) mission to produce hourly 4-by-4 km surface soil moisture forecasts with a reduction of mean absolute error by 35% on average. The results from the system with coupled components show that assimilation of the TRMM 3B42 precipitation improves the quality of both downscaled precipitation and soil moisture analyses, while the effect of SMOS soil moisture data assimilation is largely on the soil moisture analyses. The downscaled WRF precipitation, with and without assimilation of TRMM precipitation, was preliminarily tested with a spatially distributed simulation of streamflow using the TIN (Triangular Irregular Network)-based Real-time Integrated Basin Simulator (tRIBS).
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Rodriguez, Jesus, i Jesus Rodriguez. "Downscaling Modis Evapotranspiration via Cokriging in Wellton-Mohawk Irrigation and Drainage District, Yuma, AZ". Diss., The University of Arizona, 2016. http://hdl.handle.net/10150/621782.
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Evapotranspiration (ET) is a key parameter for irrigation planning and management, and it is a crucial factor for water conservation practices considering the challenges associated with agricultural water availability. Field ET determination is the most accurate, but remains to be expensive and limited in scope. On the other hand, remote sensing is becoming an alternative tool for the estimation of ET. Operational ET algorithms, like the Moderate Resolution Imaging Spectroradiometer (MODIS)-based ET, are now successful at generating ET estimates globally at 1km resolution, however their intent is not management of agriculture irrigation. This research was done to develop an integrated method for downscaling MODIS ET appropriate for farm-level applications using geostatistical and remote sensing techniques. The proposed methodology was applied in the Wellton-Mohawk Irrigation and Drainage District of Yuma, Arizona. In a first effort, ET data was downscaled from standard 1-km-MODIS to a medium 250-m-spatial resolution via cokriging using Land Surface Temperature and Enhanced Vegetation Index as covariates. Results showed consistent downscaled ET with a variance greater than the variance of the coarse scale input and nearly similar mean values. This 250m product can serve larger irrigation districts in developed countries, where plot size is fairly large and regular. However, the size and shapes of most farms in developing countries makes the 250m ET challenging. For this reason, the second part of this work was done to downscale global scale 1km ET to 30m farm level application for irrigation use. This approach involved the generation of daily vegetation indices (VI) at 30m in order to support the downscaling of MODIS 1km ET. Landsat and MODIS reflectances were combined with the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm and the resulting VI data was used as a covariate to downscale ET with the cokriging approach. The results showed that the MODIS ET data seriously underestimates ET over irrigated areas. To correct this problem the MODIS data was then adjusted using field measured values to make it useful for operational purposes. The proposed geospatial method was applied to different growth stages of cotton and results were validated with actual ET from The Arizona Meteorological Network (AZMET) and published consumptive use of water for the area. The adjusted downscaled ET was comparable to these two published data (maximum error of 33%). This methodology is a practical alternative in areas where there is no ancillary data to estimate ET and it is expected to help in the planning of irrigation agriculture that will lead to improved agricultural productivity and irrigation efficiency.
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Zanetti, Stefano. "Downscaling della pioggia di punto attraverso modelli stocastici di precipitazione: Sviluppi teorici ed applicazioni". Doctoral thesis, Università degli studi di Padova, 2009. http://hdl.handle.net/11577/3426473.
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The identification of general relationships linking statistical properties of rainfall aggregated at different temporal and spatial scales possesses clear theoretical and practical
relevance. A wide and recent literature has developed around downscaling techniques. These methods allow to estimate high resolution temporal series or precipitation fields on the basis of coarse resolution output obtained by numerical models. Nevertheless these methodologies concentrate on spatial downscaling, while relatively few attention is put on temporal downscaling of rainfall series, particularly hourly precipitation, that are of principal interest in the hydrological studies. Among other properties it is important to characterize the scale dependence of rainfall variability, which may, for many purposes, be summarized by its variance. Previous theoretical results are revised to characterize the connection of the temporal variance of rainfall to the scale of observation under the assumption of second-order stationarity. Here we present a new method for downscaling rainfall in time using theoretically-based estimates of rainfall variability at the hourly scales from daily statistics. In particular, we review non-scaling scale relations which imply a non-power-law form of the second order moment. The method is validated on a wide data set representative of different rainfall regimes and produces unbiased estimates of rainfall variance at the hourly scale when a power-law-tailed autocorrelation is used for the rainfall process. We then demonstrate how the downscaling method together with a Bartlett-Lewis rainfall stochastic model may be used to generate hourly rainfall sequences which reproduce the observed small-scale variability uniquely from daily statistics. Finally, we compare the results obtained by the application of our new downscaling method and by a commonly used approach, which assumes a power-law structure of the statistical moments. We show that non-scaling procedures outperform those based on power-law expressions of the statistical moments in the estimation of rainfall variability at the hourly scale on the basis of daily observations.
Another important section presented in this work concerns long historical precipitation series. Daily rainfall observations in Padova (Italy) arguably constitute one of the longest observed rainfall time series in the world. Observations started in 1725 and were regularly annotated by the scientists who headed the Astronomic Observatory of Padova over more than two centuries. A patient work of data recovery from the original registries allowed the reconstruction of the entire precipitation time series, characterized by a very limited amount of missing data. Here we present some preliminary statistical analysis aimed at identifying the possible presence of trends, periodicities and characteristic scales, with particular attention to extreme events. The results indeed show the presence of trends in yearly amounts, average intensities, and extreme values. Cyclicities are also detected reflecting large-scale forcings as expressed by global atmospheric index (NAO, etc.), with implications for the documentation of past climatic change. The last section of this work focuses on the application of tools developed for the integration of spatial and temporal stochastic rainfall models with geomorphic models of the hydrologic response. Answering the need of reliable tools for water resources management and flood mitigation we develop and apply an approach to generate space-time rainfall fields reproducing the relevant characters of observed rainfall events, with specific applications to two catchments located in Northern Italy. The stochastic rainfall model relies on the Bartlett-Lewis formulation for the reproduction of the temporal variability of rainfall whereas it adopts a convective cells formulation (based on Cox and Isham, 1987) to describe the spatial variability and the correlation structure of rainfall fields. We show that the generated rainfall fields are respectful of the statistical characters of the observed rainfall both in space and time and allow for the reproduction of extreme events which are coherent with observed ones for the relatively low return periods accessible through existing time series.L’identificazione di relazioni di carattere generale che leghino le proprietà statistiche della precipitazione aggregata a diverse scale, sia nel tempo che nello spazio, possiede una indiscutibile rilevanza sia dal punto di vista teorico, sia per quanto riguarda le applicazioni. Un’ampia e relativamente recente letteratura si è sviluppata in riferimento alle tecniche di downscaling. Questi metodi consentono di stimare serie temporali ad alta risoluzione o campi di precipitazione nello spazio sulla base degli output a risoluzione più grossolana ottenuti dai modelli numerici di previsione climatica o meteorologica. Tuttavia queste metodologie si concentrano prevalentemente sul downscaling spaziale, mentre relativamente poca attenzione è stata riservata al downscaling temporale delle sequenze di pioggia, in particolare la precipitazione oraria, che rivestono primaria importanza negli studi di tipo idrologico e ambientale. Tra le altre proprietà risulta importante caratterizzare la dipendenza di scala della variabilità della precipitazione, che può, per diversi motivi, essere rappresentata mediante la sua varianza. Studi teorici precedenti [Marani, 2003] e [Marani, 2005], sono stati analizzati e rivisti in modo da caratterizzare la connessione tra la varianza temporale della precipitazione e la scala di osservazione sotto l’ipotesi di stazionarietà del secondo ordine. Nella prima parte di questo lavoro viene presentato un nuovo metodo per il downscaling della varianza nel tempo, il quale utilizza stime teoricamente basate della varianza alla scala oraria sulla base di statistiche giornaliere. In particolare vengono prese in considerazione relazioni analitiche che non prevedono comportamento di scala e che implicano una forma diversa da quella di una legge di potenza per i momenti del secondo ordine al variare dell’aggregazione. Il metodo è stato validato su un ampio campione di dati rappresentativi di diversi regimi di precipitazione e produce stime della varianza oraria della pioggia non affette da errore sistematico, in particolare nel caso in cui si consideri una autocorrelazione con coda di legge di potenza per il processo di pioggia. E’ stato quindi dimostrato come il metodo di downscaling accoppiato con un modello stocastico della precipitazione del tipo Bartlett-Lewis possa essere usato per la generazione di lunghe sequenze di precipitazioni orarie che riproducano la variabilità osservata alle piccole scale di aggregazione sulla base unicamente di statistiche giornaliere. Infine, i risultati ottenuti dall’applicazione del nuovo metodo di downscaling proposto sono stati confrontati con quelli ricavati da un approccio comunemente utilizzato, che assume una struttura di legge di potenza dei momenti statistici al variare della scala di aggregazione. Si dimostra come procedure che non assumano comportamento di scala forniscano performance migliori rispetto a quelle basate su espressioni del tipo legge di potenza per i momenti nella stima della variabilità della precipitazione alla scala oraria sulla base di osservazioni giornaliere. Una sezione importante presentata in questo lavoro riguarda lo studio di lunghe sequenze di precipitazioni storiche registrate. Le osservazione della pioggia giornaliera a Padova costituiscono indiscutibilmente una delle serie storiche di precipitazione osservata più lunghe al mondo. Le osservazioni, iniziate a partire dal 1725, sono state regolarmente annotate dagli studiosi dell’Osservatorio Astronomico di Padova per più di due secoli. Un paziente lavoro di raccolta dati dai manoscritti originali ha permesso la ricostruzione dell’intera serie temporale di precipitazione, caratterizzata da pochissime e limitate interruzioni. Vengono qui presentate alcune analisi statistiche preliminari, prodotte con lo scopo di identificare la possibile presenza di trend, periodicità e scale carat teristiche, con particolare attenzione agli eventi estremi. I risultati mostrano effettivamente l’esistenza di trend nelle cumulate annuali, nelle intensità medie, e nei valori estremi. Sono state inoltre rilevate delle ciclicità, che possono riflettere l’influenza di forzanti su larga scala, come espresso dagli indici atmosferici globali (NAO, etc.), con importanti implicazioni per la documentazione di cambiamenti climatici nel passato. L’ultima sezione di questo lavoro si focalizza sull’applicazione di strumenti sviluppati per l’integrazione di modelli stocastici di precipitazione spaziotemporali con modelli geomorfologici della risposta idrologica. In risposta alla necessità di strumenti utili per la gestione delle risorse idriche e per la mitigazione delle piene, è stato sviluppato e applicato un nuovo approccio per la generazione di campi di precipitazione nello spazio e nel tempo in grado di riprodurre i caratteri principali degli eventi di pioggia osservati, con specifiche applicazioni a due bacini localizzati nel Nord Italia. Il modello stocastico proposto si basa su una formulazione di Bartlett-Lewis per la riproduzione della variabilità temporale della precipitazione, mentre adotta una formulazione a celle convettive [Cox et al., 1988] per descrivere la variabilità spaziale e la struttura di correlazione dei campi di pioggia. Si mostra come i campi generati rispettino i caratteri statistici delle piogge sperimentali sia nello spazio che nel tempo e permettano la riproduzione di eventi estremi coerenti con quelli misurati relativamente ai tempi di ritorno riproducibili sulla base delle serie storiche esistenti.
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Shrestha, Alen. "ANALYZING THE PAST AND FUTURE DROUGHT SITUATIONS USING HIGH RESOLUTION DROUGHT INDEX". OpenSIUC, 2020. https://opensiuc.lib.siu.edu/theses/2757.
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Regional assessments of droughts are limited and meticulous assessment of droughts over larger spatial scales are often not substantial. Understanding drought variability on a regional scale is crucial for enhancing resiliency and adaptive ability of water supply and distribution systems. Moreover, it can be essential for appraising the dynamics and predictability of droughts based on regional climate across various spatial and temporal scales. The drought analysis of the past was carried out with the development of a high-resolution dataset (1km×1km) for three drought-prone regions of India between 1950 and 2016. In the study the monthly values of self-calibrating Palmer Drought Severity Index (scPDSI), incorporating Penman–Monteith (PM) approximation, which is physically based on potential evapotranspiration. Climate data were statistically downscaled using the delta downscaling method and was formulated to form a timeline for characterizing major drought events that occurred in the past. The downscaled climate data were validated with the station observations. Major severe drought events that occurred between 1950 and 2016 were identified and studied with greater emphasis to the drought situation in smaller spatial extent such as districts, villages or localities. A timeline of drought events within the period of study was also prepared to have an understanding of the severity of drought in all three regions.Likewise, the future drought durations are projected for droughts of different levels of severity and assessed in the same regions of India. Coupled Model Intercomparison Project Phase 6 (CMIP6) simulated precipitation and climate data were used for near‐future (2015–2044) for different shared socio-economic pathways (SSPs). scPDSI, was used again based on its fairness in identifying drought conditions which accounts for the temperature as well. Gridded rainfall and temperature data of spatial resolution of 1km were used to bias correct the multi-model ensemble (MME) mean of 7 Global Climatic Models (GCMs) from CMIP6 project. Equidistant quantile-based mapping was adopted to remove the bias in the rainfall and temperature data and were corrected at the monthly scale. The downscaled climate data exhibited good statistical agreement with station data with correlation coefficient (R) ranging from 0.83 to 0.93 for both precipitation and temperature. Drought analysis indicated several major incidences over the analysis time period considered in this work, which truly adheres to the droughts recorded in qualitative reports of meteorological institutions in those regions. The drought study of the past helped to understand the situation in local levels and understand the necessities that can be opted for the future by proper management of water resources. While the outcome of the future prediction of drought duration suggests multiple severe to extreme drought events in all three study areas of appreciable durations mostly during the mid-2030s under the SSP2-4.5 scenario. The severe drought durations under the SSP2-4.5 scenario were found to be ranging around 25 to 30 months in 30 years period of the near future. The high-resolution drought index proved to be key to assess the drought situation for both the past and the future in three different drought-prone regions of India.