On Divergence- and Gradient-Preserving Coarse-Graining for Finite Volume Primitive Equation Ocean Models

TL;DR

This paper develops a method for coarse-graining in finite-volume ocean models that preserves divergence and gradient operators, improving the accuracy of low-resolution simulations by carefully choosing averaging weights.

Contribution

The authors derive conditions for averaging weights that preserve divergence and gradient in coarse-graining, specifically applied to triangular meshes in ocean models.

Findings

Derived specific averaging weights that preserve divergence.

Identified weights that preserve the gradient.

Demonstrated reduced error in FESOM2 simulation data.

Abstract

We consider the problem of coarse-graining in the context of finite-volume fluid models. If a variable is defined on a high-resolution grid it may be coarse-grained so that it is defined on a grid of lower resolution. In general this will cause some information about the variable to be lost. In particular, horizontal divergences, gradients or other operators calculated on the coarse grid after projecting may differ from those calculated on the fine grid. In some cases we are able to choose averaging weights for coarse-graining such that the coarse-grid operators will give a result approximating that of the corresponding fine-grid operators applied on the fine grid. In this work we derive general conditions on the averaging weights that allow the divergence and gradient to be preserved. These conditions are applied to a regular triangular mesh with B-grid variable placement in which the fine-grid resolution is some integer multiple $N$ of the coarse-grid resolution. For this case we find particular values for the averaging weights that preserve the divergence, and a set of different averaging weights that preserve the gradient. We observe that the vertical component of the curl is also preserved by the same coarse-graining that preserves divergence. These coarse-grainings are applied to data from FESOM2 simulations and we demonstrate that using the particular coarse-grainings derived herein gives an overall reduction in $L^1$ error when compared with other methods.