Principles and methods of scaling geospatial Earth science data
The properties of geographical phenomena vary with changes in the scale of measurement.
The information observed at one scale often cannot be directly used as information at …
The information observed at one scale often cannot be directly used as information at …
An intercomparison of statistical downscaling methods used for water resource assessments in the U nited S tates
Abstract Information relevant for most hydrologic applications cannot be obtained directly
from the native‐scale outputs of climate models. As a result the climate model output must …
from the native‐scale outputs of climate models. As a result the climate model output must …
Downscaling GRACE remote sensing datasets to high-resolution groundwater storage change maps of California's Central Valley
ME Miro, JS Famiglietti - Remote Sensing, 2018 - mdpi.com
NASA's Gravity Recovery and Climate Experiment (GRACE) has already proven to be a
powerful data source for regional groundwater assessments in many areas around the …
powerful data source for regional groundwater assessments in many areas around the …
ClimAlign: Unsupervised statistical downscaling of climate variables via normalizing flows
Downscaling is a common task in climate science and meteorology in which the goal is to
use coarse scale, spatio-temporal data to infer values at finer scales. Statistical downscaling …
use coarse scale, spatio-temporal data to infer values at finer scales. Statistical downscaling …
Improving the spatial resolution of GRACE-derived terrestrial water storage changes in small areas using the machine learning spatial downscaling method
Z Chen, W Zheng, W Yin, X Li, G Zhang, J Zhang - Remote Sensing, 2021 - mdpi.com
Gravity Recovery and Climate Experiment (GRACE) satellites can effectively monitor
terrestrial water storage (TWS) changes in large-scale areas. However, due to the coarse …
terrestrial water storage (TWS) changes in large-scale areas. However, due to the coarse …
Potential salinity and temperature futures for the Chesapeake Bay using a statistical downscaling spatial disaggregation framework
Estuaries are productive and ecologically important ecosystems, incorporating
environmental drivers from watersheds, rivers, and the coastal ocean. Climate change has …
environmental drivers from watersheds, rivers, and the coastal ocean. Climate change has …
Probabilistic Gaussian copula regression model for multisite and multivariable downscaling
Atmosphere–ocean general circulation models (AOGCMs) are useful to simulate large-scale
climate evolutions. However, AOGCM data resolution is too coarse for regional and local …
climate evolutions. However, AOGCM data resolution is too coarse for regional and local …
Intercomparison of multiple statistical downscaling methods: multi-criteria model selection for South Korea
A number of statistical downscaling methodologies have been introduced to bridge the gap
in scale between outputs of climate models and climate information needed to assess …
in scale between outputs of climate models and climate information needed to assess …
Estimating uncertainty in daily weather interpolations: a Bayesian framework for developing climate surfaces.
AM Wilson, JA Silander - International Journal of …, 2014 - search.ebscohost.com
Conservation of biodiversity demands comprehension of evolutionary and ecological
patterns and processes that occur over vast spatial and temporal scales. A central goal of …
patterns and processes that occur over vast spatial and temporal scales. A central goal of …
Multisite and multivariable statistical downscaling using a Gaussian copula quantile regression model
Statistical downscaling techniques are required to refine atmosphere–ocean global climate
data and provide reliable meteorological information such as a realistic temporal variability …
data and provide reliable meteorological information such as a realistic temporal variability …