Ensemble Kalman filter data assimilation in a Babcock-Leighton solar dynamo model: an observation system simulation experiment for reconstructing meridional flow-speed

TL;DR

This study demonstrates that Ensemble Kalman Filter data assimilation within a solar dynamo model effectively reconstructs meridional flow-speed variations, especially with sufficient observations and ensemble size, aiding understanding of solar cycle dynamics.

Contribution

The paper introduces the application of EnKF data assimilation in a Babcock-Leighton solar dynamo model to accurately reconstruct meridional flow-speed variations.

Findings

Reconstruction improves with ≥10 observations and 15-day update intervals.

Increasing ensemble size from 16 to 160 enhances accuracy.

EnKF effectively reconstructs spatio-temporal meridional circulation patterns.

Abstract

Accurate knowledge of time-variation in meridional flow-speed and profile is crucial for estimating a solar cycle's features, which are ultimately responsible for causing space climate variations. However, no consensus has been reached yet about the Sun's meridional circulation pattern observations and theories. By implementing an Ensemble Kalman Filter (EnKF) data assimilation in a Babcock-Leighton solar dynamo model using Data Assimilation Research Testbed (DART) framework, we find that the best reconstruction of time-variation in meridional flow-speed can be obtained when ten or more observations are used with an updating time of 15 days and a $\le 10\%$ observational error. Increasing ensemble-size from 16 to 160 improves reconstruction. Comparison of reconstructed flow-speed with "true-state" reveals that EnKF data assimilation is very powerful for reconstructing meridional flow-speeds and suggests that it can be implemented for reconstructing spatio-temporal patterns of meridional circulation.