1.1k citations · 3k across the 33 of their papers we have counts for
3 papers · 1 filter
Assessing the Potential of Deep Learning for Emulating Cloud Superparameterization in Climate Models with Real-Geography Boundary Conditions
Griffin Mooers, Mike Pritchard, Tom Beucler +4
We explore the potential of feed-forward deep neural networks (DNNs) for emulating cloud superparameterization in realistic geography, using offline fits to data from the Super Par…
On the logarithmic profile of temperature in the atmospheric convective boundary layers
Yu Cheng, Qi Li, Pierre Gentine
Wall-bounded turbulent flows are widely observed in natural and engineering systems, such as air flows near the Earth's surface, water flows in rivers, and flows around a car or a…
Towards Physically-consistent, Data-driven Models of Convection
Tom Beucler, Michael Pritchard, Pierre Gentine +1
Data-driven algorithms, in particular neural networks, can emulate the effect of sub-grid scale processes in coarse-resolution climate models if trained on high-resolution climate…