Datasets and source codes for the manuscript "Surrogate Downscaling of Mesoscale Wind Fields Using Ensemble Super-Resolution Convolutional Neural Networks" submitted to the journal "Artificial Intelligence for the Earth Systems" of the American Meteorological Society
Statistical downscaling methods seek to model the relationship between large scale atmospheric circu...
This paper describes a simple method, based on routine meteorological data, to produce high-resoluti...
In light of the success of superresolution (SR) applications in computer vision, recent studies have...
Datasets and source codes for the manuscript "Surrogate Downscaling of Mesoscale Wind Fields Using E...
Empirical-statistical downscaling (ESD) can be a computationally advantageous alternative to dynamic...
This datasets supports the paper "Stochastic Super-Resolution for Downscaling Time-Evolving Atmosphe...
In this paper, we present the novel approach for the downscaling of of near-surface winds in the Nor...
International audienceEstimating the impact of wind-driven snow transport requires modeling wind fie...
This paper presents a novel methodology for mesoscale‐to‐microscale downscaling of near‐surface wind...
SIGLEAvailable from British Library Document Supply Centre-DSC:DXN040194 / BLDSC - British Library D...
Numerical weather and climate simulations nowadays produce terabytes of data, and the data volume co...
Weather forecasts at high spatio-temporal resolution are of great relevance for industry and society...
The dataset for paper: Downscaling ERA5 Wind Speed Data: A Machine Learning approach considering Top...
Les champs de vent en montagne sont extrêmement complexes. De nombreuses interactions entre le vent ...
Inspired by the success of superresolution applications in computer vision, deep neural networks hav...
Statistical downscaling methods seek to model the relationship between large scale atmospheric circu...
This paper describes a simple method, based on routine meteorological data, to produce high-resoluti...
In light of the success of superresolution (SR) applications in computer vision, recent studies have...
Datasets and source codes for the manuscript "Surrogate Downscaling of Mesoscale Wind Fields Using E...
Empirical-statistical downscaling (ESD) can be a computationally advantageous alternative to dynamic...
This datasets supports the paper "Stochastic Super-Resolution for Downscaling Time-Evolving Atmosphe...
In this paper, we present the novel approach for the downscaling of of near-surface winds in the Nor...
International audienceEstimating the impact of wind-driven snow transport requires modeling wind fie...
This paper presents a novel methodology for mesoscale‐to‐microscale downscaling of near‐surface wind...
SIGLEAvailable from British Library Document Supply Centre-DSC:DXN040194 / BLDSC - British Library D...
Numerical weather and climate simulations nowadays produce terabytes of data, and the data volume co...
Weather forecasts at high spatio-temporal resolution are of great relevance for industry and society...
The dataset for paper: Downscaling ERA5 Wind Speed Data: A Machine Learning approach considering Top...
Les champs de vent en montagne sont extrêmement complexes. De nombreuses interactions entre le vent ...
Inspired by the success of superresolution applications in computer vision, deep neural networks hav...
Statistical downscaling methods seek to model the relationship between large scale atmospheric circu...
This paper describes a simple method, based on routine meteorological data, to produce high-resoluti...
In light of the success of superresolution (SR) applications in computer vision, recent studies have...
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