459 citations · 966 across the 23 of their papers we have counts for
4 papers · 1 filter
Navigating the Noise: Bringing Clarity to ML Parameterization Design with O(100) Ensembles
Jerry Lin, Sungduk Yu, Liran Peng +6
Machine-learning (ML) parameterizations of subgrid processes (here of turbulence, convection, and radiation) may one day replace conventional parameterizations by emulating high-re…
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…
Causally-informed deep learning to improve climate models and projections
Fernando Iglesias-Suarez, Pierre Gentine, Breixo Solino-Fernandez +4
Climate models are essential to understand and project climate change, yet long-standing biases and uncertainties in their projections remain. This is largely associated with the r…
Data-Driven Equation Discovery of a Cloud Cover Parameterization
Arthur Grundner, Tom Beucler, Pierre Gentine +1
A promising method for improving the representation of clouds in climate models, and hence climate projections, is to develop machine learning-based parameterizations using output…