Abstract

In this work we present the implementation of the ensemble Kalman filter (EnKF) data assimilation to the ring current-atmosphere interactions model with self-consistent magnetic and electric fields (RAM-SCBE) to correctly specify the particle distribution in Earth’s inner magnetosphere, as well as predict a number of space weather outputs including particle fluxes, magnetic field, and several relevant indices (Dst, SYM-H, deltaB, etc). The internal mechanism that RAM-SCBE uses to have a self-consistent magnetic field is to perform a fixed-point iteration of the magnetic field equations to agree with the prescribed pressure field. Hence, a simple data assimilation algorithm has the potential to disrupt the balance between the pressure and the magnetic field. In our presentation we show that, with proper consideration of the magnetic field in the assimilation framework, we are able to specify both the pressure and magnetic fields using the EnKF. For our experiments we simulate the enhancement storm event that took place on 7–10 September of 2017, where a large double-dip geomagnetic storm was observed. The assimilated results show a significant improvement in the estimation of the ring current particle distributions in the RAM-SCBE model, leading to better agreement with observations.