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20232026
most citedStable Machine-Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection-Permitting Simulations

6 citations · 19 across the 6 of their papers we have counts for

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cs.LG2023★ 2 cited

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…

physics.ao-ph2023★ 5 cited

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…