P59

Presentation Title: Application of Ensemble Kalman Filter Data Assimilation Technique to Infer the Surface Flows with Surface Flux Transport model
Author(s): Soumyaranjan Dash1, Marc DeRosa2, Mausumi Dikpati3, Xudong Sun1, Sushant Mahajan4, and Yang Liu4 1 Institute for Astronomy, University of Hawaii, USA 2 Lockheed Martin Solar and Astrophysics Laboratory, Palo Alto, CA, USA 3 NCAR/High Altitude Observatory (HAO), CO, USA 4 Stanford University, Stanford, CA, USA

Abstract:

Constraining solar surface flows is crucial to understanding solar magnetic activity at different time scales. Accurate global solar surface magnetic flux distribution is essenial for modelling coronal magnetic fields, solar wind dynamics, open flux distribution and solar cycle predictions. However, routine and direct observation of the whole photospheric magnetic field is still challenging due to our limited field of view and high projection effects. Data-driven numerical models play an important role in estimating the distribution of magnetic field distribution including polar fields more accurately. The north-south component of the Sun’s global flow, i.e. the meridional circulation that transports the flux from low latitudes and helps build up the polar field is utilized in surface flux transport (SFT) models to simulate photospheric magnetic field distribution. Since forecasting polar fields requires the forecast of the flow at a future time, it is necessary to reconstru ct the flow behavior at the current time, so that future flow-patterns can be estimated. Data assimilation techniques like Ensemble Kalman Filter (EnKF) which utilizes observational data points to constrain the time advancement of a set of equations that govern the evolution of the physical system, has been adopted to reconstruct the meridional flow speed with solar dynamo modeling. In this study, 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 ensemble estimates of flow into the future that will be used to drive the model forward to forecast solar surface magnetic field distribution.