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Coupled online learning as a way to tackle instabilities and biases in neural network parameterizations
[article]
2019
arXiv
pre-print
Over the last couple of years, machine learning parameterizations have emerged as a potential way to improve the representation of sub-grid processes in Earth System Models (ESMs). So far, all studies were based on the same three-step approach: first a training dataset was created from a high-resolution simulation, then a machine learning algorithms was fitted to this dataset, before the trained algorithms was implemented in the ESM. The resulting online simulations were frequently plagued by
arXiv:1907.01351v3
fatcat:nvcmx2cmyncf3ia2mkzhqld3nu