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Modeling of unresolved scales with data-inferred stochastic processes

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I will discuss a data-driven stochastic approach to modeling unresolved scales, in which feedback from micro-scale processes is represented by a network of Markov processes. The Markov processes are conditioned on macro-scale model variables, and their properties are inferred from pre-computed high-resolution (micro-scale resolving) simulations. These processes are designed to emulate, in a statistical sense, the feedback observed in the high-resolution simulations, thereby providing a statistical-dynamical coupling between micro- and macro-scale models. This work is primarily aimed at applications in atmosphere-ocean science (stochastic parameterizations of atmospheric convection and of mesoscale oceanic eddies).

This talk is part of the Isaac Newton Institute Seminar Series series.

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