Gotowe bibliografie tematyczne / Downscaling

Gotowa bibliografia na temat „Downscaling”

Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 7 lipca 2024

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Artykuły w czasopismach na temat "Downscaling"

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Yano, J. I. "Downscaling, parameterization, decomposition, compression: a perspective from the multiresolution analysis". Advances in Geosciences 23 (22.06.2010): 65–71. http://dx.doi.org/10.5194/adgeo-23-65-2010.

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Abstract. Geophysical models in general, and atmospheric models more specifically, are always limited in spatial resolutions. Due to this limitation, we face with two different needs. The first is a need for knowing (or "downscaling") more spatial details (e.g., precipitation distribution) than having model simulations for practical applications, such as hydrological modelling. The second is a need for "parameterizing" the subgrid-scale physical processes in order to represent the feedbacks of these processes on to the resolved scales (e.g., the convective heating rate). The present article begins by remarking that it is essential to consider the downscaling and parametrization as an "inverse" of each other: downscaling seeks a detail of the subgrid-scale processes, then the parameterization seeks an integrated effect of the former into the resolved scales. A consideration on why those two closely-related operations are traditionally treated separately, gives insights of the fundamental limitations of the current downscalings and parameterizations. The multiresolution analysis (such as those based on wavelet) provides an important conceptual framework for developing a unified formulation for the downscaling and parameterization. In the vocabulary of multiresolution analysis, these two operations may be considered as types of decompression and compression. A new type of a subgrid-scale representation scheme, NAM-SCA (nonhydrostatic anelastic model with segmentally-constant approximation), is introduced under this framework.
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Yhang, Yoo-Bin, Soo-Jin Sohn i Il-Won Jung. "Application of Dynamical and Statistical Downscaling to East Asian Summer Precipitation for Finely Resolved Datasets". Advances in Meteorology 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/2956373.

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Various downscaling approaches have been developed to overcome the limitation of the coarse spatial resolution of general circulation models (GCMs). Such techniques can be grouped into two approaches of dynamical and statistical downscaling. In this study, we investigated the performances of different downscaling methods, focusing on East Asian summer monsoon precipitation to obtain more finely resolved and value added datasets. The dynamical downscaling was conducted by the Regional Model Program (RMP) of the Global/Regional Integrated Model system (GRIMs), while the statistical downscaling was performed through coupled pattern-based simple linear regression. The dynamical downscaling resulted in a better representation of the spatial distribution and long-term trend than the GCM produced; however, it tended to overestimate precipitation over East Asia. In contrast, the application of the statistical downscaling resulted in a bias in the amount of precipitation, due to low variance that is inherent in regression-based downscaling. A combination of dynamical and statistical downscaling produced the best results in time and space. This study provides a guideline for determining the most effective and robust downscaling method in the hydrometeorological applications, which are quite sensitive to the accuracy of downscaled precipitation.
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Zhang, Xunchang, Mingxi Shen, Jie Chen, Joel W. Homan i Phillip R. Busteed. "Evaluation of Statistical Downscaling Methods for Simulating Daily Precipitation Distribution, Frequency, and Temporal Sequence". Transactions of the ASABE 64, nr 3 (2021): 771–84. http://dx.doi.org/10.13031/trans.14097.

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HighlightsNine statistical downscaling methods from three downscaling categories were evaluated.Weather generator-based methods had advantages in simulating non-stationary precipitation.Differences in downscaling performance were smaller within each category than between categories.The performance of each downscaling method varied with climate conditions.Abstract. Spatial discrepancy between global climate model (GCM) projections and the climate data input required by hydrological models is a major limitation for assessing the impact of climate change on soil erosion and crop production at local scales. Statistical downscaling techniques are widely used to correct biases of GCM projections. The objective of this study was to evaluate the ability of nine statistical downscaling methods from three available statistical downscaling categories to simulate daily precipitation distribution, frequency, and temporal sequence at four Oklahoma weather stations representing arid to humid climate regions. The three downscaling categories included perfect prognosis (PP), model output statistics (MOS), and stochastic weather generator (SWG). To minimize the effect of GCM projection error on downscaling quality, the National Centers for Environmental Prediction (NCEP) Reanalysis 1 data at a 2.5° grid spacing (treated as observed grid data) were downscaled to the four weather stations (representing arid, semi-arid, sub humid, and humid regions) using the nine downscaling methods. The station observations were divided into calibration and validation periods in a way that maximized the differences in annual precipitation means between the two periods for assessing the ability of each method in downscaling non-stationary climate changes. All methods were ranked with three metrics (Euclidean distance, sum of absolute relative error, and absolute error) for their ability in simulating precipitation amounts at daily, monthly, yearly, and annual maximum scales. After eliminating the poorest two performers in simulating precipitation mean, distribution, frequency, and temporal sequence, the top four remaining methods in ascending order were Distribution-based Bias Correction (DBC), Generator for Point Climate Change (GPCC), SYNthetic weather generaTOR (SYNTOR), and LOCal Intensity scaling (LOCI). DBC and LOCI are bias-correction methods, and GPCC and SYNTOR are generator-based methods. The differences in performances among the downscaling methods were smaller within each downscaling category than between the categories. The performance of each method varied with the climate conditions of each station. Overall results indicated that the SWG methods had certain advantages in simulating daily precipitation distribution, frequency, and temporal sequence for non-stationary climate changes. Keywords: Climate change, Climate downscaling, Downscaling method evaluation, Statistical downscaling.
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Njuki, Sammy M., Chris M. Mannaerts i Zhongbo Su. "An Improved Approach for Downscaling Coarse-Resolution Thermal Data by Minimizing the Spatial Averaging Biases in Random Forest". Remote Sensing 12, nr 21 (25.10.2020): 3507. http://dx.doi.org/10.3390/rs12213507.

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Land surface temperature (LST) plays a fundamental role in various geophysical processes at varying spatial and temporal scales. Satellite-based observations of LST provide a viable option for monitoring the spatial-temporal evolution of these processes. Downscaling is a widely adopted approach for solving the spatial-temporal trade-off associated with satellite-based observations of LST. However, despite the advances made in the field of LST downscaling, issues related to spatial averaging in the downscaling methodologies greatly hamper the utility of coarse-resolution thermal data for downscaling applications in complex environments. In this study, an improved LST downscaling approach based on random forest (RF) regression is presented. The proposed approach addresses issues related to spatial averaging biases associated with the downscaling model developed at the coarse resolution. The approach was applied to downscale the coarse-resolution Satellite Application Facility on Land Surface Analysis (LSA-SAF) LST product derived from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor aboard the Meteosat Second Generation (MSG) weather satellite. The LSA-SAF product was downscaled to a spatial resolution of ~30 m, based on predictor variables derived from Sentinel 2, and the Advanced Land Observing Satellite (ALOS) digital elevation model (DEM). Quantitatively and qualitatively, better downscaling results were obtained using the proposed approach in comparison to the conventional approach of downscaling LST using RF widely adopted in LST downscaling studies. The enhanced performance indicates that the proposed approach has the ability to reduce the spatial averaging biases inherent in the LST downscaling methodology and thus is more suitable for downscaling applications in complex environments.
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Wu, Yichen, Zhihua Zhang, M. James C. Crabbe i Lipon Chandra Das. "Statistical Learning-Based Spatial Downscaling Models for Precipitation Distribution". Advances in Meteorology 2022 (7.06.2022): 1–12. http://dx.doi.org/10.1155/2022/3140872.

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The downscaling technique produces high spatial resolution precipitation distribution in order to analyze impacts of climate change in data-scarce regions or local scales. In this study, based on three statistical learning algorithms, such as support vector machine (SVM), random forest regression (RF), and gradient boosting regressor (GBR), we proposed an efficient downscaling approach to produce high spatial resolution precipitation. In order to demonstrate efficiency and accuracy of our models over traditional multilinear regression (MLR) downscaling models, we did a downscaling analysis for daily observed precipitation data from 34 monitoring sites in Bangladesh. Validation revealed that R 2 of GBR could reach 0.98, compared with RF (0.94), SVM (0.88), and multilinear regression (MLR) (0.69) models, so the GBR-based downscaling model had the best performance among all four downscaling models. We suggest that the GBR-based downscaling models should be used to replace traditional MLR downscaling models to produce a more accurate map of high-resolution precipitation for flood disaster management, drought forecasting, and long-term planning of land and water resources.
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Liu, X., P. Coulibaly i N. Evora. "Comparison of data-driven methods for downscaling ensemble weather forecasts". Hydrology and Earth System Sciences Discussions 4, nr 1 (1.02.2007): 189–210. http://dx.doi.org/10.5194/hessd-4-189-2007.

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Abstract. This study investigates dynamically different data-driven methods, specifically a statistical downscaling model (SDSM), a time lagged feedforward neural network (TLFN), and an evolutionary polynomial regression (EPR) technique for downscaling numerical weather ensemble forecasts generated by a medium range forecast (MRF) model. Given the coarse resolution (about 200-km grid spacing) of the MRF model, an optimal use of the weather forecasts at the local or watershed scale, requires appropriate downscaling techniques. The selected methods are applied for downscaling ensemble daily precipitation and temperature series for the Chute-du-Diable basin located in northeastern Canada. The downscaling results show that the TLFN and EPR have similar performance in downscaling ensemble daily precipitation as well as daily maximum and minimum temperature series whatever the season. Both the TLFN and EPR are more efficient downscaling techniques than SDSM for both the ensemble daily precipitation and temperature.
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Coulibaly, P., i N. Evora. "Comparison of data-driven methods for downscaling ensemble weather forecasts". Hydrology and Earth System Sciences 12, nr 2 (20.03.2008): 615–24. http://dx.doi.org/10.5194/hess-12-615-2008.

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Abstract. This study investigates dynamically different data-driven methods, specifically a statistical downscaling model (SDSM), a time lagged feedforward neural network (TLFN), and an evolutionary polynomial regression (EPR) technique for downscaling numerical weather ensemble forecasts generated by a medium range forecast (MRF) model. Given the coarse resolution (about 200-km grid spacing) of the MRF model, an optimal use of the weather forecasts at the local or watershed scale, requires appropriate downscaling techniques. The selected methods are applied for downscaling ensemble daily precipitation and temperature series for the Chute-du-Diable basin located in northeastern Canada. The downscaling results show that the TLFN and EPR have similar performance in downscaling ensemble daily precipitation as well as daily maximum and minimum temperature series whatever the season. Both the TLFN and EPR are more efficient downscaling techniques than SDSM for both the ensemble daily precipitation and temperature.
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Coulibaly, Paulin, Yonas B. Dibike i François Anctil. "Downscaling Precipitation and Temperature with Temporal Neural Networks". Journal of Hydrometeorology 6, nr 4 (1.08.2005): 483–96. http://dx.doi.org/10.1175/jhm409.1.

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Abstract The issues of downscaling the outputs of a global climate model (GCM) to a scale that is appropriate to hydrological impact studies are investigated using a temporal neural network approach. The time-lagged feed-forward neural network (TLFN) is proposed for downscaling daily total precipitation and daily maximum and minimum temperature series for the Serpent River watershed in northern Quebec (Canada). The downscaling models are developed and validated using large-scale predictor variables derived from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis dataset. Atmospheric predictors such as specific humidity, wind velocity, and geopotential height are identified as the most relevant inputs to the downscaling models. The performance of the TLFN downscaling model is also compared to a statistical downscaling model (SDSM). The downscaling results suggest that the TLFN is an efficient method for downscaling both daily precipitation and temperature series. The best downscaling models were then applied to the outputs of the Canadian Global Climate Model (CGCM1), forced with the Intergovernmental Panel on Climate Change (IPCC) IS92a scenario. Changes in average precipitation between the current and the future scenarios predicted by the TLFN are generally found to be smaller than those predicted by the SDSM model. Furthermore, application of the downscaled data for hydrologic impact analysis in the Serpent River resulted in an overall increasing trend in mean annual flow as well as earlier spring peak flow. The results also demonstrate the emphasis that should be given in identifying the appropriate downscaling tools for impact studies by showing how a future climate scenario downscaled with different downscaling methods could result in significantly different hydrologic impact simulation results for the same watershed.
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Xu, Mengchao, Qian Liu, Dexuan Sha, Manzhu Yu, Daniel Q. Duffy, William M. Putman, Mark Carroll, Tsengdar Lee i Chaowei Yang. "PreciPatch: A Dictionary-based Precipitation Downscaling Method". Remote Sensing 12, nr 6 (23.03.2020): 1030. http://dx.doi.org/10.3390/rs12061030.

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Climate and weather data such as precipitation derived from Global Climate Models (GCMs) and satellite observations are essential for the global and local hydrological assessment. However, most climatic popular precipitation products (with spatial resolutions coarser than 10km) are too coarse for local impact studies and require “downscaling” to obtain higher resolutions. Traditional precipitation downscaling methods such as statistical and dynamic downscaling require an input of additional meteorological variables, and very few are applicable for downscaling hourly precipitation for higher spatial resolution. Based on dynamic dictionary learning, we propose a new downscaling method, PreciPatch, to address this challenge by producing spatially distributed higher resolution precipitation fields with only precipitation input from GCMs at hourly temporal resolution and a large geographical extent. Using aggregated Integrated Multi-satellitE Retrievals for GPM (IMERG) data, an experiment was conducted to evaluate the performance of PreciPatch, in comparison with bicubic interpolation using RainFARM—a stochastic downscaling method, and DeepSD—a Super-Resolution Convolutional Neural Network (SRCNN) based downscaling method. PreciPatch demonstrates better performance than other methods for downscaling short-duration precipitation events (used historical data from 2014 to 2017 as the training set to estimate high-resolution hourly events in 2018).
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Sachindra, D. A., F. Huang, A. Barton i B. J. C. Perera. "Statistical downscaling of general circulation model outputs to catchment scale hydroclimatic variables: issues, challenges and possible solutions". Journal of Water and Climate Change 5, nr 4 (15.07.2014): 496–525. http://dx.doi.org/10.2166/wcc.2014.056.

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The aim of this paper is to discuss the issues and challenges associated with statistical downscaling of general circulation model (GCM) outputs to hydroclimatic variables at catchment scale and also to discuss potential solutions to address these issues and challenges. Outputs of GCMs (predictors of statistical downscaling models) suffer a considerable degree of uncertainty, mainly due to the lack of theoretical robustness caused by the limited understanding of various physical processes of the atmosphere and the incomplete mathematical representation of those processes in GCMs. The presence of several future GHG emission scenarios with equal likelihood of occurrence leads to scenario uncertainty. Outputs of a downscaling study are dependent on the quality and the length of the record of field observations, as statistical downscaling models are calibrated and validated against these observations of the hydroclimatic variables (predictands of statistical downscaling models). The downscaled results vary from one statistical downscaling technique to another due to different representations of the predictor–predictand relationships. Also different techniques used in selecting the predictors for statistical downscaling models influence the model outputs. Although statistical downscaling faces these issues, it is still considered as a potential method of predicting the catchment scale hydroclimatology from GCM outputs.
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Rozprawy doktorskie na temat "Downscaling"

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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 Fisica
Apresenta-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 &amp 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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Książki na temat "Downscaling"

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Inger, Hanssen-Bauer, i Chen Deliang, red. Empirical-statistical downscaling. New Jersey: World Scientific Pub Co Inc., 2008.

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Benestad, Rasmus E. Empirical-statistical downscaling. New Jersey: World Scientific Pub Co Inc., 2008.

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Kathy, Babbitt, red. Downscaling: Simplify and enrich your lifestyle. Chicago: Moody Press, 1993.

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Bierkens, Marc F. P., 1965-, Finke Peter A i Willigen P. de, red. Upscaling and downscaling methods for environmental research. Dordrecht: Kluwer Academic Publishers, 2000.

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Dehn, Martin. Szenarien der klimatischen Auslösung alpiner Hangrutschungen: Simulation durch Downscaling allgemeiner Zirkulationsmodelle der Atmosphäre. Sankt Augustin: In Kommission bei Asgard-Verlag, 1999.

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Das, Someshwar. Simulation of seasonal monsoon rainfall over the SAARC region by dynamical downscaling using WRF model. Dhaka: SAARC Meteorological Research Centre, 2012.

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Busuioc, Aristita, i Alexandru Dumitrescu. Empirical-Statistical Downscaling: Nonlinear Statistical Downscaling. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.770.

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This is an advance summary of a forthcoming article in the Oxford Research Encyclopedia of Climate Science. Please check back later for the full article.The concept of statistical downscaling or empirical-statistical downscaling became a distinct and important scientific approach in climate science in recent decades, when the climate change issue and assessment of climate change impact on various social and natural systems have become international challenges. Global climate models are the best tools for estimating future climate conditions. Even if improvements can be made in state-of-the art global climate models, in terms of spatial resolution and their performance in simulation of climate characteristics, they are still skillful only in reproducing large-scale feature of climate variability, such as global mean temperature or various circulation patterns (e.g., the North Atlantic Oscillation). However, these models are not able to provide reliable information on local climate characteristics (mean temperature, total precipitation), especially on extreme weather and climate events. The main reason for this failure is the influence of local geographical features on the local climate, as well as other factors related to surrounding large-scale conditions, the influence of which cannot be correctly taken into consideration by the current dynamical global models.Impact models, such as hydrological and crop models, need high resolution information on various climate parameters on the scale of a river basin or a farm, scales that are not available from the usual global climate models. Downscaling techniques produce regional climate information on finer scale, from global climate change scenarios, based on the assumption that there is a systematic link between the large-scale and local climate. Two types of downscaling approaches are known: a) dynamical downscaling is based on regional climate models nested in a global climate model; and b) statistical downscaling is based on developing statistical relationships between large-scale atmospheric variables (predictors), available from global climate models, and observed local-scale variables of interest (predictands).Various types of empirical-statistical downscaling approaches can be placed approximately in linear and nonlinear groupings. The empirical-statistical downscaling techniques focus more on details related to the nonlinear models—their validation, strengths, and weaknesses—in comparison to linear models or the mixed models combining the linear and nonlinear approaches. Stochastic models can be applied to daily and sub-daily precipitation in Romania, with a comparison to dynamical downscaling. Conditional stochastic models are generally specific for daily or sub-daily precipitation as predictand.A complex validation of the nonlinear statistical downscaling models, selection of the large-scale predictors, model ability to reproduce historical trends, extreme events, and the uncertainty related to future downscaled changes are important issues. A better estimation of the uncertainty related to downscaled climate change projections can be achieved by using ensembles of more global climate models as drivers, including their ability to simulate the input in downscaling models. Comparison between future statistical downscaled climate signals and those derived from dynamical downscaling driven by the same global model, including a complex validation of the regional climate models, gives a measure of the reliability of downscaled regional climate changes.
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Mearns, Linda, Katharine Hayhoe i Rao Kotamarthi. Downscaling Techniques for High-Resolution Climate Projections. University of Cambridge ESOL Examinations, 2021.

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Singh, Vijay P., i Taesam Lee. Statistical Downscaling for Hydrological and Environmental Applications. Taylor & Francis Group, 2018.

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Lee, Taesam, i Vijay P. Singh. Statistical Downscaling for Hydrological and Environmental Applications. CRC Press, 2018. http://dx.doi.org/10.1201/9780429459580.

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Części książek na temat "Downscaling"

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Yoosefdoost, Arash, Omid Bozorg-Haddad, Jie Chen, Kwok Wing Chau i Fahmida Khan. "Downscaling Methods". W Climate Change in Sustainable Water Resources Management, 179–278. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1898-8_7.

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Wilby, Robert L., i Hayley J. Fowler. "Regional climate downscaling". W Modelling the Impact of Climate Change on Water Resources, 34–85. Chichester, UK: John Wiley & Sons, Ltd, 2010. http://dx.doi.org/10.1002/9781444324921.ch3.

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Khanna, Vinod Kumar. "Downscaling Classical MOSFET". W NanoScience and Technology, 45–72. New Delhi: Springer India, 2016. http://dx.doi.org/10.1007/978-81-322-3625-2_4.

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Matebesi, Sethulego, Thomas Stewart, Maléne Campbell i Oupa Kale. "Koffiefontein mine downscaling". W Local Responses to Mine Closure in South Africa, 93–103. London: Routledge, 2023. http://dx.doi.org/10.4324/9781003403326-9.

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Mearns, Linda O., Melissa S. Bukovsky, Sarah C. Pryor i Victor Magaña. "Downscaling of Climate Information". W Regional Climate Studies, 201–50. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03768-4_5.

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Kucina, Ivan. "Belgrade/Upgrading by Downscaling". W Uropean Urbanity. Europan 7 and 8, 40–51. Vienna: Springer Vienna, 2007. http://dx.doi.org/10.1007/978-3-211-68145-9_3.

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Mearns, L. O., M. Bukovsky, S. C. Pryor i V. Magaña. "Downscaling of Climate Information". W Climate Modelling, 199–269. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-65058-6_8.

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Kim, Heewon, Myungsub Choi, Bee Lim i Kyoung Mu Lee. "Task-Aware Image Downscaling". W Computer Vision – ECCV 2018, 419–34. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01225-0_25.

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Srinivasa Raju, Komaragiri, i Dasika Nagesh Kumar. "Downscaling Techniques in Climate Modeling". W Springer Climate, 77–105. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6110-3_3.

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Visconti, Guido. "Downscaling, Regional Models and Impacts". W Climate, Planetary and Evolutionary Sciences, 31–99. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74713-8_2.

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Streszczenia konferencji na temat "Downscaling"

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Goly, Aneesh, i Ramesh S. V. Teegavarapu. "Assessment of Various Statistical Downscaling Methods for Downscaling Precipitation in Florida". W World Environmental and Water Resources Congress 2013. Reston, VA: American Society of Civil Engineers, 2013. http://dx.doi.org/10.1061/9780784412947.105.

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Liu, Yumin, Auroop R. Ganguly i Jennifer Dy. "Climate Downscaling Using YNet". W KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3394486.3403366.

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Jang, S., i M. L. Kavvas. "Statistical Downscaling versus Dynamic Downscaling: An Assessment Based upon a Sample Study". W World Environmental and Water Resources Congress 2014. Reston, VA: American Society of Civil Engineers, 2014. http://dx.doi.org/10.1061/9780784413548.062.

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Karamouz, M., S. Nazif i M. Fallahi. "Rainfall Downscaling Using Statistical Downscaling Model and Canonical Correlation Analysis: A Case Study". W World Environmental and Water Resources Congress 2010. Reston, VA: American Society of Civil Engineers, 2010. http://dx.doi.org/10.1061/41114(371)465.

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Vandal, Thomas, Evan Kodra, Sangram Ganguly, Andrew Michaelis, Ramakrishna Nemani i Auroop R. Ganguly. "Generating High Resolution Climate Change Projections through Single Image Super-Resolution: An Abridged Version". W Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/759.

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The impacts of climate change are felt by most critical systems, such as infrastructure, ecological systems, and power-plants. However, contemporary Earth System Models (ESM) are run at spatial resolutions too coarse for assessing effects this localized. Local scale projections can be obtained using statistical downscaling, a technique which uses historical climate observations to learn a low-resolution to high-resolution mapping. The spatio-temporal nature of the climate system motivates the adaptation of super-resolution image processing techniques to statistical downscaling. In our work, we present DeepSD, a generalized stacked super resolution convolutional neural network (SRCNN) framework with multi-scale input channels for statistical downscaling of climate variables. A comparison of DeepSD to four state-of-the-art methods downscaling daily precipitation from 1 degree (~100km) to 1/8 degrees (~12.5km) over the Continental United States. Furthermore, a framework using the NASA Earth Exchange (NEX) platform is discussed for downscaling more than 20 ESM models with multiple emission scenarios.
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Ashry, Mohammed H. "Downscaling Interest In Interest Rates". W 2014 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2014. http://dx.doi.org/10.1109/csci.2014.160.

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Wonsook Ha, Prasanna H Gowda, Terry A Howell, George Paul, Jairo E Hernandez i Sukanta Basu. "Downscaling Surface Temperature Image with TsHARP". W 5th National Decennial Irrigation Conference Proceedings, 5-8 December 2010, Phoenix Convention Center, Phoenix, Arizona USA. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2010. http://dx.doi.org/10.13031/2013.35876.

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Zhang, Zixu, Kohei Inoue, Kenji Hara i Kiichi Urahama. "Image Downscaling Based on Neugebauer Model". W The 4th IIAE International Conference on Intelligent Systems and Image Processing 2016. The Institute of Industrial Applications Engineers, 2016. http://dx.doi.org/10.12792/icisip2016.079.

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"CORDEX – An international climate downscaling initiative". W 19th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2011. http://dx.doi.org/10.36334/modsim.2011.f5.evans.

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"The added value of dynamical downscaling". W 19th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2011. http://dx.doi.org/10.36334/modsim.2011.f5.katzfey.

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Raporty organizacyjne na temat "Downscaling"

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Mamaluy, Denis, Xujiao Gao i Brian David Tierney. The ultimate downscaling limit of FETs. Office of Scientific and Technical Information (OSTI), październik 2014. http://dx.doi.org/10.2172/1160288.

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Griffin, Sean. Spatial downscaling disease risk using random forests machine learning. Engineer Research and Development Center (U.S.), luty 2020. http://dx.doi.org/10.21079/11681/35618.

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van Haren, C., M. van Eupen, P. Verweij, M. Vittek, S. Islam, C. Terwisscha van Scheltinga, S. Hasan i in. Land use classification Bangladesh : combining and downscaling existing databases. Wageningen: Wageningen Environmental Research, 2022. http://dx.doi.org/10.18174/576671.

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Cayan, Dan. Developing Metrics to Evaluate the Skill and Credibility of Downscaling. Office of Scientific and Technical Information (OSTI), wrzesień 2020. http://dx.doi.org/10.2172/1656897.

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Maurer, Ed, i Alex Hall. Developing Metrics to Evaluate the Skill and Credibility of Downscaling. Office of Scientific and Technical Information (OSTI), wrzesień 2020. http://dx.doi.org/10.2172/1661196.

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Westley, Glenn D. Bancos comerciales en microfinanzas: Mejores prácticas y directrices para el diseño, seguimiento y evaluación de proyectos. Inter-American Development Bank, czerwiec 2007. http://dx.doi.org/10.18235/0009780.

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Este documento pretende proporcionar orientación al personal del BID sobre cómo preparar proyectos de downscaling, desde la fase de diseño hasta la fase de seguimiento y evaluación, utilizando cualquiera de los diversos instrumentos para proyectos del BID. Dada la importancia del compromiso de los bancos, el documento también hace hincapié en elegir bancos comprometidos a ofrecer servicios microfinancieros y en estructurar los proyectos de manera que se asegure este compromiso. Se discute justificación para la intervención de donantes o gobiernos y el diseño de los proyectos de downscaling en el BID.
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Moser, Lauren, i Manuel Alegre. Factores de éxito en financiamiento a la pequeña empresa: Estrategias y mejores prácticas internacionales en "downscaling". Inter-American Development Bank, październik 2008. http://dx.doi.org/10.18235/0007102.

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Johannesson, G. Accounting for Global Climate Model Projection Uncertainty in Modern Statistical Downscaling. Office of Scientific and Technical Information (OSTI), marzec 2010. http://dx.doi.org/10.2172/974391.

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Stenmark, Aurora, Jonas Olsson i Andreas Dobler. Downscaling climate projections – towards better adaptation strategies in the Nordic countries. Redaktor Bo Storrank. Nordic Council of Ministers, 2021. http://dx.doi.org/10.6027/na2021-901.

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Coleman, Michael L., i Jeffrey D. Niemann. Development and Application of a Soil Moisture Downscaling Method for Mobility Assessment. Fort Belvoir, VA: Defense Technical Information Center, maj 2011. http://dx.doi.org/10.21236/ada547252.

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