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DTSTART;TZID=Europe/Stockholm:20190612T114500
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UID:submissions.pasc-conference.org_PASC19_sess107_pap_jan110@linklings.co
 m
SUMMARY:Towards Data-Driven Dynamic Surrogate Models for Ocean Flow
DESCRIPTION:Paper\nComputer Science and Applied Mathematics, Climate and W
 eather\n\nTowards Data-Driven Dynamic Surrogate Models for Ocean Flow\n\nE
 deling, Crommelin\n\nCoarse graining of (geophysical) flow problems is a n
 ecessity brought upon us by the wide range of spatial and temporal scales 
 present in these problems, which cannot be all represented on a numerical 
 grid without an inordinate amount of computational resources. Traditionall
 y, the effect of the unresolved eddies is approximated by deterministic cl
 osure models, i.e. so-called parameterizations. The effect of the unresolv
 ed eddy field enters the resolved-scale equations as a forcing term, denot
 ed as the 'eddy forcing'. Instead of creating a deterministic parameteriza
 tion, our goal is to infer a stochastic, data-driven surrogate model for t
 he eddy forcing from a (limited) set of reference data, with the goal of a
 ccurately capturing the long-term flow statistics. Our surrogate modelling
  approach essentially builds on a resampling strategy, where we create a p
 robability density function of the reference data that is conditional on (
 time-lagged) resolved-scale variables. The choice of resolved-scale variab
 les, as well as the employed time lag, is essential to the performance of 
 the surrogate. We will demonstrate the effect of different modelling choic
 es on a simplified ocean model of two-dimensional turbulence in a doubly p
 eriodic square domain.<br /><br />https://doi.org/10.1145/3324989.3325713
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