28 citations · 53 across the 5 of their papers we have counts for
5 papers
Multi-fidelity climate model parameterization for better generalization and extrapolation
Mohamed Aziz Bhouri, Liran Peng, Michael S. Pritchard +1
Machine-learning-based parameterizations (i.e. representation of sub-grid processes) of global climate models or turbulent simulations have recently been proposed as a powerful alt…
ClimSim-Online: A Large Multi-scale Dataset and Framework for Hybrid ML-physics Climate Emulation
Sungduk Yu, Zeyuan Hu, Akshay Subramaniam +44
Modern climate projections lack adequate spatial and temporal resolution due to computational constraints, leading to inaccuracies in representing critical processes like thunderst…
Improving stratocumulus cloud amounts in a 200-m resolution multi-scale modeling framework through tuning of its interior physics
Liran Peng, Peter N. Blossey, Walter M. Hannah +4
High-Resolution Multi-scale Modeling Frameworks (HR) -- global climate models that embed separate, convection-resolving models with high enough resolution to resolve boundary layer…
Comparing Storm Resolving Models and Climates via Unsupervised Machine Learning
Griffin Mooers, Mike Pritchard, Tom Beucler +5
Global Storm-Resolving Models (GSRMs) have gained widespread interest because of the unprecedented detail with which they resolve the global climate. However, it remains difficult…
Climate-Invariant Machine Learning
Tom Beucler, Pierre Gentine, Janni Yuval +10
Projecting climate change is a generalization problem: we extrapolate the recent past using physical models across past, present, and future climates. Current climate models requir…