Demonstration of Ensemble Kalman Filter Data Assimilation Technique for Application to Surface-Flux Transport Modeling

The Ensemble Kalman Filter (EnKF) data assimilation technique is a method used to make predictions from a system with many state variables. Typically, observational data points are used to constrain the advancement in time of a set of equations that govern a physical system. Such predictive schemes are widely used throughout society, including with weather forecasting, cruise control, epidemiology (disease forecasting), etc. In this poster, we demonstrate using a “toy model” the EnKF technique using a simple harmonic oscillator having a randomly-varying restoring force. We show how various parameters, such as the size of the ensemble and the frequency at which observations are incorporated, affect the predictive ability of the technique. We additionally show how such EnKF methods, when used with a surface-flux transport model that captures the evolution of the solar photospheric magnetic field, can be used to make predictions of meridional flow variations.