Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 151 for “"Downscaling."”.
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FLOOD PROJECTION AND ANALYSIS THROUGH STOCHASTIC DOWNSCALING
… this end, this study applies a novel stochastic downscaling method that was originally developed for rainfall nowcasting using radar data. In this study, the stochastic model was first modified to adapt the tropical weather data. Singapore’s radar data were utilized to form the basis of the …
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WRF-based dynamical downscaling over High Mountain Asia
… thesis. The HAR v2 was generated by dynamical downscaling of ERA5 reanalysis data using the Weather Research and Forecasting Model (WRF). The HAR v2 provides atmospheric data at 10 km grid spacing and hourly temporal resolution. It is currently available from 2000 to 2020 and will be extended …
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Deep learning for downscaling GOES-18 measurements for wildfire detection
This thesis aims to address the challenge of accurate wildfire detection using satellite imagery. Despite the availability of various satellite-based fire products, real-time detection of fire perimeters remain difficult due to limitations in the spatio-temporal resolution of current satellite …
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Catchment Scale Downscaling of Hydroclimatic Variables from General Circulation Model Outputs
… studies. Therefore either dynamic or statistical downscaling techniques are used for linking GCM outputs to catchment scale hydroclimatic variables.
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A standardized framework for evaluating the skill of regional climate downscaling techniques
… techniques. Despite the essential role of downscaling in regional assessments, there is no standard approach to evaluating various downscaling methods. Hence, impact communities often have little awareness of limitations and uncertainties associated with downscaled projections. To develop a …
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Use of global datasets for downscaling soil moisture with the EMT+VS model
… model. The use of global datasets decreased downscaling performance and the spatial variability of soil moisture was underestimated. Overall, only 5 of the 16 parameters can be estimated from global datasets. However, the global model still provides more reliable soil moisture estimates than …
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Evaluation of sampling techniques to characterize topographically-dependent variability for soil moisture downscaling
… a linear dimension of 10 to 50 m) is difficult. Downscaling methods can be used to estimate catchment-scale soil moisture patterns from coarser resolution estimates or spatial average soil moisture values. These methods usually infer the fine-scale variability in soil moisture using variations in …
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UNCERTAINTY QUANTIFICATION IN DOWNSCALING OF PRECIPITATION EXTREMES AND ITS APPLICATION TO NEW ENGLAND
<p>Downscaling of precipitation extremes, while providing essential information for impact assessment of climate change and the development of adaptation strategies, is subject to significant uncertainty, particularly in connection with long-term predictions. The focus of this dissertation is to …
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Spatial Downscaling and Forecasting of Atmospheric Variables over Complex Terrain Using Machine Learning
… This thesis addresses these limitations by downscaling atmospheric variables over mountainous regions, utilizing machine learning (ML). The objectives of the study focused on downscaling ERA5-Land reanalysis daily mean temperature and precipitation data from a coarse resolution of 9 km to a …
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Regional dynamical downscaling and analysis of water balance across different regions and time scales
Employing the regional dynamical downscaling (RDD) technique, we can enhance the spatial and temporal resolution of global reanalysis and general circulation model (GCM) data, providing improved data for studying ecological processes. This method ensures physical validity of the data, albeit at …
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Geo-Informed Deep Learning for Spatial Downscaling of Solute Transport in Heterogeneous Porous Media
… generated by the dual-branch autoencoder (i.e., downscaling). We train and test our framework using five solute transport cases with varying levels of heterogeneity and compare the results with standalone methods, namely the vanilla autoencoder and vanilla SRGAN in addition to ground truth …
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Data assimilation and dynamical downscaling of remotely-sensed precipitation and soil moisture from space
… 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 …
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Extreme events over the contiguous United States portrayed in a CESM-WRF dynamical downscaling framework
A dynamical downscaling framework is adopted to explore historical (1950-1999) and projected (2050-2099) behavior of extreme precipitation (PR), maximum temperature (TMAX) and minimum temperature (TMIN) events within the contiguous United States. Compared to reanalysis data, simulations represent …
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On the use of Statistical Downscaling for the Study of Coastal Hazards in the Pacific
… are impossible to characterize. Statistical downscaling is a computationally efficient and reliable technique for obtaining hydrodynamic components based on the relationship between large scale predictors and local predictands. Firstly, based on the relationship between sea level pressure …
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Extreme value theory: Applications to estimation of stochastic traffic capacity and statistical downscaling of precipitation extremes
… results.</p><p>Next, we perform a statistical downscaling by applying a CDF transformation function to local-level daily precipitation extremes (from NCDC station data) and corresponding NARCCAP regional climate model (RCM) output to derive local-scale projections. These high-resolution …
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Climate change and hazardous convective weather in the United States: Insights from high-resolution dynamical downscaling
… the motivation for continued use of dynamical downscaling to overcome the limitations of the GCM-based environmental analysis.</p>
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Forecasting of rainfall using statistical downscaling model (SDSM) – general circulation model (GCM) for future estimation of rainwater harvesting
Changes in the spatial and temporal rainfall pattern affected by the climate change need to be investigated as its significant characteristics are often used for managing water resources. In this study, the impacts of climate change on rainfall variability in Johor was investigated by using General …
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Bias Correction and Downscaling of Climate Model Outputs Required for Impact Assessments of Climate Change in the U.S. Northeast
… impact analysis of climate change. Although downscaling of GCM outputs can be performed by dynamical downscaling using Regional Climate Models (RCMs), it requires large computational capacity. When daily climate data from multiple GCMs are required to be downscaled, dynamical downscaling may …
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