Articles | Volume 27, issue 2
https://doi.org/10.5194/npg-27-209-2020
https://doi.org/10.5194/npg-27-209-2020
Research article
 | 
16 Apr 2020
Research article |  | 16 Apr 2020

Data-driven versus self-similar parameterizations for stochastic advection by Lie transport and location uncertainty

Valentin Resseguier, Wei Pan, and Baylor Fox-Kemper

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Cited articles

Bachman, S. D., Fox-Kemper, B., and Pearson, B.: A scale-aware subgrid model for quasi-geostrophic turbulence, J. Geophys. Res.-Oceans, 122, 1529–1554, 2017. a, b, c
Blumen, W.: Uniform potential vorticity flow: part I. theory of wave interactions and two-dimensional turbulence, J. Atmos. Sci., 35, 774–783, 1978. a
Blumen, W.: Wave-Interactions in Quasi-Geostrophic Uniform Potential Vorticity Flow, J. Atmos. Sci., 39, 2388–2396, 1982. a
Cai, S., Mémin, E., Dérian, P., and Xu, C.: Motion estimation under location uncertainty for turbulent fluid flows, Exp. Fluids, 59, 8, https://doi.org/10.1007/s00348-017-2458-z, 2018. a
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Geophysical flows span a broader range of temporal and spatial scales than can be resolved numerically. One way to alleviate the ensuing numerical errors is to combine simulations with measurements, taking account of the accuracies of these two sources of information. Here we quantify the distribution of numerical simulation errors without relying on high-resolution numerical simulations. Specifically, small-scale random vortices are added to simulations while conserving energy or circulation.
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