Data assimilation for stratified convection

TL;DR

This paper demonstrates how 3DVAR data assimilation can effectively estimate the state of stratified convection flows in astrophysics, significantly reducing observational noise through numerical simulations.

Contribution

It applies 3DVAR data assimilation to astrophysical convection, showing its effectiveness in a new context with scalar covariance matrices.

Findings

3DVAR reduces observational noise by up to three orders of magnitude.

The method provides accurate error estimates of the system state.

Successful application in synthetic solar convection data.

Abstract

We show how the 3DVAR data assimilation methodology can be used in the astrophysical context of a two-dimensional convection flow. We study the way this variational approach finds best estimates of the current state of the flow from a weighted average of model states and observations. We use numerical simulations to generate synthetic observations of a vertical two-dimensional slice of the outer part of the solar convection zone for varying noise levels and implement 3DVAR when the covariance matrices are scalar. Our simulation results demonstrate the capability of 3DVAR to produce error estimates of system states between up to tree orders of magnitude below the original noise level present in the observations. This work exemplifies the importance of applying data assimilation techniques in simulations of the stratified convection.