A data assimilation method for plasmasphere modeling using EUV imaging data

The structure of the plasmasphere is strongly controlled by the electric field in the inner magnetosphere and the feeding of ions from the ionosphere. In particular, the electric field plays a significant role in the dynamics of the plasmasphere. In order to grasp the dynamics of the plasmasphere, it is important to know the spatial distribution of the electric potential. However, due to the lack of observations, it is difficult to obtain the information on the spatial structure of the electric field in the inner magnetosphere. We are developing a data assimilation method for estimating the spatial structure of the plasmasphere and electric potential. The estimation is performed by incorporating imaging data from the IMAGE satellite into a simulation model of the plasmasphere. We treat the magnetospheric electric potential distribution as an unknown variable, and estimate it through the data assimilation. The plasmasphere ion distribution is estimated according to the estimated electric potential and the plasmasphere simulation model. In order to evaluate whether the present data assimilation method, we conducted data assimilation experiments using artificial imaging data sets which are generated from an independent simulation run under a plausible condition. The experimental result suggests that the data assimilation of the EUV imaging data well works for estimating the spatial structure of the plasmasphere and the electric field.