Ensemble Kalman Filter Data Assimilation Into Surface Flux Transport Model To Infer Surface Flows: An Observing System Simulation Experiment

TL;DR

This paper demonstrates how the Ensemble Kalman Filter combined with a surface flux transport model can improve predictions of the Sun's surface magnetic flows and magnetic field distribution, especially at high latitudes.

Contribution

It introduces a novel application of the Ensemble Kalman Filter to infer and forecast surface flows on the Sun using data assimilation within a flux transport model.

Findings

Ensemble Kalman Filter effectively reconstructs flow profiles.

Improved forecasts of magnetic field distribution at high latitudes.

Potential for better solar activity prediction.

Abstract

Knowledge of the global magnetic field distribution and its evolution on the Sun's surface is crucial for modeling the coronal magnetic field, understanding solar wind dynamics, computing the heliospheric open flux distribution and predicting solar cycle strength. As the far side of the Sun cannot be observed directly and high-latitude observations always suffer from projection effects, we often rely on surface flux transport simulations (SFT) to model long-term global magnetic field distribution. Meridional circulation, the large-scale north-south component of the surface flow profile, is one of the key components of the SFT simulation that requires further constraints near high latitudes. Prediction of the photospheric magnetic field distribution requires knowledge of the flow profile in the future, which demands reconstruction of that same flow at the current time so that it can be estimated at a later time. By performing Observing System Simulation Experiments, we demonstrate how the Ensemble Kalman Filter technique, when used with a SFT model, can be utilized to make ``posterior'' estimates of flow profiles into the future that can be used to drive the model forward to forecast photospheric magnetic field distribution.