Scale-Dependent Data Assimilation of Solar Photospheric Magnetic Field

Modeling the evolution of the solar photospheric magnetic flux, typically used to drive coronal and solar wind models, is a key challenge to forecasting near earth space weather variability. Accurate estimations of the solar global magnetic field are paramount in predicting space weather events that effect terrestrial communication and guidance systems. The magnetic flux is difficult to model due to the emergence of magnetic active regions which arise from unobservable zones below the photosphere. For this reason the model used in our forecast has severe bias at the scale of emerging active regions. We use wavelet based multiresolution analysis to separate scales in model and observations during the application of an ensemble Kalman filter. Our method of assimilation for the photospheric flux demonstrates a unique version of a scale-dependent EnKF. We demonstrate that our assimilation method allows accurate data assimilation of observed active regions despite large, scale-dependent model bias.