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Showing 1 to 6 of 6 for “"spatial downscaling"”.

  1. Spatial Downscaling and Forecasting of Atmospheric Variables over Complex Terrain Using Machine Learning

    … Models (GCMs), however, typically have a coarse spatial resolution that is unable to capture local scale variations, leading to limitations in regional climate applications. This thesis addresses these limitations by downscaling atmospheric variables over mountainous regions, utilizing machine …

    trento Repository record for Spatial Downscaling and Forecasting of Atmospheric Variables over Complex Terrain Using Machine Learning (opens in a new tab)

  2. 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 …

    texas-state Repository record for Geo-Informed Deep Learning for Spatial Downscaling of Solute Transport in Heterogeneous Porous Media (opens in a new tab)

  3. Advances in spatial modeling for complex data with applications to symbolic data and spatial transcriptomics.

    Spatial statistical modeling is commonly used to analyze and draw inference from data collected across geographic space, providing insight into underlying spatial processes across environmental, biological, epidemiological, and other scientific applications. Spatial data are increasingly complex, …

    baylor Repository record for Advances in spatial modeling for complex data with applications to symbolic data and spatial transcriptomics. (opens in a new tab)

  4. 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 …

    uconn-diss Repository record for Bias Correction and Downscaling of Climate Model Outputs Required for Impact Assessments of Climate Change in the U.S. Northeast (opens in a new tab)

  5. Rural Land Management Impacts on Catchment Scale Flood Risk

    … understanding this effect requires capturing the spatial resolution associated with field-scale hydrological processes simultaneously with the upscaling of these processes to the downstream locations where flood risk is of concern. Most approaches to this problem aim to upscale from individual …

    durham Repository record for Rural Land Management Impacts on Catchment Scale Flood Risk (opens in a new tab)