On divergence- and gradient-preserving coarse-graining for finite volume primitive equation ocean models. (February 2022)

• Record Type: Journal Article
• Authors: Patching, Stuart
• Is Part Of: Ocean modelling. Volume 170(2022)
• Publication Date: 2022-02
• Subjects: Coarse-graining -- Finite volume -- Primitive equations; Oceanography -- Periodicals; Océanographie -- Périodiques; Oceanography; Periodicals; 551.46
• DOI: 10.1016/j.ocemod.2021.101941
• Languages: English
• ISSNs: 1463-5003

Abstract

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. Highlights: Coarse-graining for finite volume methods can be chosen to preserve properties of variables. We may derive general conditions for the preservation of such properties. Divergence is preserved by an equally-weighted average over neighbouring cells. Gradient is preserved by direct projection.