EGU21-7539, updated on 04 Mar 2021
EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Rotating shallow water flow under location uncertainty with a structure-preserving discretization

Rüdiger Brecht1, Long Li2, Werner Bauer3, and Etienne Mémin2
Rüdiger Brecht et al.
  • 1Memorial University of Newfoundland, St. John's, Canada
  • 2Fluminance Group, Inria, Campus universitaire de Beaulieu, Rennes, France
  • 3Imperial College London, South Kensington Campus, London, United Kingdom

We introduce a new representation of the rotating shallow water equations based on a stochastic transport principle. The derivation relies on a decomposition of the fluid flow into a large-scale component and a noise term that models the unresolved small-scale flow. The total energy of such a random model is demonstrated to be preserved along time for any realization. Thus, we propose to combine a structure-preserving discretization of the underlying deterministic model with the discrete stochastic terms. This way, our method can directly be used in existing dynamical cores of global numerical weather prediction and climate models. For an inviscid test case on the f-plane we use a homogenous noise and illustrate that the spatial part of the stochastic scheme preserves the total energy of the system. Finally, using an inhomogenous noise, we show  that the proposed random model better captures the structure of a large-scale flow than a comparable deterministic model for a barotropically unstable jet on the sphere.

How to cite: Brecht, R., Li, L., Bauer, W., and Mémin, E.: Rotating shallow water flow under location uncertainty with a structure-preserving discretization, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7539,, 2021.

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