Plasmaspheric Data Assimilation using LANL Satellite Plasmapause Crossings

Physics

Scientific paper

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[1910] Informatics / Data Assimilation, Integration And Fusion, [2722] Magnetospheric Physics / Forecasting, [2768] Magnetospheric Physics / Plasmasphere

Scientific paper

Accurately modeling the dynamics of the plasmasphere is important because the plasmasphere and in particular density variations in the plasmasphere are the sites of waves which contribute to acceleration and loss of radiation belt particles. The plasmasphere can be largely described in terms of plasma flow out of and into the ionosphere, and a convection electric field, with the electric field being primarily responsible for the dynamics. The plasmasphere has significant time history, meaning that the present state or configuration of the plasmasphere is determined by the time history of drivers, primarily the electric field. The current state of the plasmasphere is mostly independent of the current drivers. For that reason a conventional Kalman filter (3D-var) approach to plasmaspheric data assimilation is not expected to perform well. Because data are sparse both in space and in time, the 4D-var assimilation technique, which relies on linearizing the model, is also not expected to perform well. One approach which does perform well under non-linear conditions is the particle filter. In this paper we use a particle filter approach together with the Dynamic Global Core Plasma Model (DGCPM), assimilating LANL satellite plasmapause crossings into a simple Kp dependent electric field model. We will compare this to an open-loop run of the same electric field model, and an open-loop run using the AMIE electric field model. We will also make comparisons with VLF density measurements where available.

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