Estimation of a Long-Term Variation of a Magnetic-Storm Index Using the Merging Particle Filter

Abstract

The Dst index is the most popular measure of a scale of magnetic storms, and it is widely used as a monitor of the conditions of the Earth's magnetosphere. Since the Dst index contains contributions from multiple magnetospheric phenomena, it is important to distinguish each of the contributions in order to obtain meaningful information about the conditions of the magnetosphere. There have been several efforts which modeled temporal evolution of the Dst index empirically, and these empirical models considers some contributions separately. However, they take only short-term varations into accout, and contributions from phenomena which show long-term variations are neglected. In the present study, we have developed a technique for estimating the component of long-term variations of the Dst index using solar wind data and a nonlinear empirical model. The newly-developed technique adopts an algorithm which is similar to the particle filter. This algorithm allows an on-line processing of a long sequence of Dst data, which would enable a real-time estimation of system variables in a nonlinear system model. The estimates of the long-term variations can be used for accurate estimation of other contributions to the Dst index, which would provide credible information about the conditions of the magnetosphere. The framework proposed in the present study could be applied for the purpose of continuous real-time monitoring of the environment of the magnetosphere.