Boundary conditions control for a Shallow-Water model

TL;DR

This paper demonstrates that controlling boundary discretization in a shallow-water model via variational data assimilation improves model accuracy and climate variability representation, validated through experiments with artificial and real data.

Contribution

It introduces a novel boundary control method using variational data assimilation to optimize discretization near boundaries in shallow-water models.

Findings

Boundary control improves model-data fit during assimilation window.

Control of boundary discretization enhances climate variability simulation.

Method effective with both artificial and real observational data.

Abstract

A variational data assimilation technique was used to estimate optimal discretization of interpolation operators and derivatives in the nodes adjacent to the rigid boundary. Assimilation of artificially generated observational data in the shallow-water model in a square box and assimilation of real observations in the model of the Black sea are discussed. It is shown in both experiments that controlling the discretization of operators near a rigid boundary can bring the model solution closer to observations as in the assimilation window and beyond the window. This type of control allows also to improve climatic variability of the model.