8 citations · 8 across the 1 of their papers we have counts for
2 papers
physics.ao-ph2024★ 8 cited
A Deep Learning Earth System Model for Efficient Simulation of the Observed Climate
Nathaniel Cresswell-Clay, Bowen Liu, Dale Durran +4
A key challenge for computationally intensive state-of-the-art Earth System models is to distinguish global warming signals from interannual variability. Here we introduce DLESyM,…
physics.ao-ph2023
Advancing Parsimonious Deep Learning Weather Prediction using the HEALPix Mesh
Matthias Karlbauer, Nathaniel Cresswell-Clay, Dale R. Durran +5
We present a parsimonious deep learning weather prediction model to forecast seven atmospheric variables with 3-h time resolution for up to one-year lead times on a 110-km global m…