Improving Space Weather Specification Using Effective Indices

The variability of the near-Earth space environment is greater than that described by existing empirical or physicsbased models.Accordingly, data assimilation models are growing in both sophistication and importance in forecasting space weather.While traditional data assimilation uses the ionospheric state produced by a model as a background state, other approaches exist which modify the input drivers provided to the background as a mean to propagate information.This study will examine a simple data assimilation experiment where traditional data assimilation, a driveronly approach, and a mixed approach are compared.This experiment suggests that a mixed approach, where both the ionospheric state and the background drivers are modified, is able to outperform other options.