Assimilating data into an {\alpha}{\omega} dynamo model of the sun: A variational approach

TL;DR

This paper introduces a variational data assimilation method applied to a toy solar { extalpha}{ extOmega} dynamo model, demonstrating its potential for improving solar magnetic field modeling.

Contribution

It develops the first variational data assimilation framework for the solar dynamo using a physically based model and derives the adjoint code for parameter inversion.

Findings

The variational technique effectively inverts key physical parameters.

Encouraging results suggest applicability to more realistic solar dynamo models.

The adjoint mean-field induction equation is derived in the appendix.

Abstract

We have developed a variational data assimilation technique for the Sun using a toy {\alpha}{\Omega} dynamo model. The purpose of this work is to apply modern data assimilation techniques to solar data using a physically based model. This work represents the first step toward a complete variational model of solar magnetism. We derive the adjoint {\alpha}{\Omega} dynamo code and use a minimization procedure to invert the spatial dependence of key physical ingredients of the model. We find that the variational technique is very powerful and leads to encouraging results that will be applied to a more realistic model of the solar dynamo. We also note that the continuous adjoint mean-field induction equation is derived in the appendix.