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Found 3 papers, 2 papers with code
Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling
In this work, we propose a downscaling method based on the Fourier neural operator.
Hard-Constrained Deep Learning for Climate Downscaling
In order to conserve physical quantities, here we introduce methods that guarantee statistical constraints are satisfied by a deep learning downscaling model, while also improving their performance according to traditional metrics.
ClimART: A Benchmark Dataset for Emulating Atmospheric Radiative Transfer in Weather and Climate Models
Numerical simulations of Earth's weather and climate require substantial amounts of computation.
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