Results 71 to 80 of about 109,140 (374)
Refining Coarse-grained Spatial Data using Auxiliary Spatial Data Sets with Various Granularities
, 2019 We propose a probabilistic model for refining coarse-grained spatial data by utilizing auxiliary spatial data sets. Existing methods require that the spatial granularities of the auxiliary data sets are the same as the desired granularity of target data.
Iwata, Tomoharu +5 more
core +1 more source
Errors in climate model daily precipitation and temperature output: time invariance and implications for bias correction [PDF]
, 2013 When correcting for biases in general circulation model (GCM) output, for example when statistically downscaling for regional and local impacts studies, a common assumption is that the GCM biases can be characterized by comparing model simulations and ...
Cayan, Daniel R. +2 more
core +3 more sources
Climate Dynamics, 2022 A hybrid statistical dynamical downscaling method intended to emulate regional climate models is described and applied to Western Europe. The method is based on a constructed analogues algorithm, already used for statistical downscaling.
J. Boé, A. Mass, Julie Deman
semanticscholar +1 more source
Colloidal Crack Sintering Lithography for Light‐Induced Patterning of Particle Assemblies
Advanced Functional Materials, EarlyView.Colloidal crack sintering lithography (CCSL) is a microfabrication technique that uses light‐induced photothermal heating to trigger sintering and controlled cracking in polymer colloidal assemblies. Local structural changes generate microchannels and patterns, enabling direct writing of diverse topographic motifs.
Marius Schoettle +2 more
wiley +1 more source
Analysis of the impact of climate change on groundwater related hydrological fluxes: a multi-model approach including different downscaling methods [PDF]
Hydrology and Earth System Sciences, 2011 Climate change related modifications in the spatio-temporal distribution of precipitation and evapotranspiration will have an impact on groundwater resources.
S. Stoll +3 more
doaj +1 more source
Downscaling SMAP Soil Moisture Products With Convolutional Neural Network
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Soil moisture (SM) downscaling has been extensively investigated in recent years to improve coarse resolution of SM products. However, available methods for downscaling are generally based on pixel-to-pixel strategy, which ignores the information among ...
Wei Xu +3 more
doaj +1 more source
Detail-Preserving Pooling in Deep Networks
, 2018 Most convolutional neural networks use some method for gradually downscaling the size of the hidden layers. This is commonly referred to as pooling, and is applied to reduce the number of parameters, improve invariance to certain distortions, and ...
Goesele, Michael +3 more
core +1 more source
Advanced Materials, EarlyView.AI‐Assisted Workflow for (Scanning) Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling. Abstract (Scanning) transmission electron microscopy ((S)TEM) has significantly advanced materials science but faces challenges in correlating precise atomic structure information with the functional properties of ...
Marc Botifoll +19 more
wiley +1 more source
Downscaling precipitation extremes [PDF]
Theoretical and Applied Climatology, 2009 A new method for predicting the upper tail of the precipitation distribution, based on empirical–statistical downscaling, is explored. The proposed downscaling method involves a re-calibration of the results from an analog model to ensure that the results have a realistic statistical distribution.
openaire +2 more sources
Earth and Space Science, 2022 Downscaling techniques are effective to bridge the scale gap between global circulation models and regional studies. Statistical downscaling methods are prevalent due to their advantages in high computational efficiency and accuracy. However, an implicit
Xintong Li +2 more
doaj +1 more source