Open access
Sep 2026
Probabilistic multi-fidelity climate model parameterization for better generalization
Machine-learning-based parameterizations (i.e. representation of sub-grid processes) of global climate models have recently been proposed as a powerful alternative to physical, but empirical, representations, offering a lower computational cost and higher accuracy. Yet, those approaches still suffer from a lack of gene...
B. Balogh, M. A. Bhouri, L. Peng et al.
· Machine Learning: Earth · 0 citations