Towards Time-Dependent Data Assimilation of Ionospheric Electrodynamics

Data assimilation is essential for studying ionospheric electrodynamics, as no single data set offers a complete representation of the system. By combining multiple incomplete data sets, data assimilation techniques provide valuable insights into this complex system. However, existing methods typically rely on a steady-state assumption, reducing the ionospheric electric field to a potential electric field.

While this simplification is often useful, it imposes limitations on studying temporal evolution, as the system is modeled independently at each time step. Consequently, interpreting changes between time steps in a physically meaningful way becomes challenging.

We present initial efforts to extend the Lompe data assimilation framework by incorporating the ionospheric induction electric field, thereby introducing time dependence into the model. This is achieved through the implementation of a Kalman filter, enabling the co-estimation of the potential and induction electric fields. By accounting for the temporal dynamics of the system, this approach seeks to provide deeper insights into ionospheric electrodynamics and enhance the interpretation of time-evolving processes.