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In simulations of multiscale dynamical systems, not all relevant processescan be resolved explicitly. Taking the effect of the unresolved processes intoaccount is important, which introduces the need for paramerizations. We presenta machine-learning method, used for the conditional resampling of observationsor reference data from a fully resolved simulation. It is based on theprobabilistic classiffcation of subsets of reference data, conditioned onmacroscopic variables. This method is used to formulate a parameterization thatis stochastic, taking the uncertainty of the unresolved scales into account. Wevalidate our approach on the Lorenz 96 system, using two different parametersettings which are challenging for parameterization methods.

Resampling with neural networks for stochastic parameterization in multiscale systems