Abstract
Abstract. Data assimilation using Kalman filters provides an effective way of understanding both spatial and temporal variations in the outer electron radiation belt. Data assimilation is the combination of in situ observations and physical models, using appropriate error statistics to approximate the uncertainties in both the data and the model. The global magnetic field configuration is one essential element in determining the adiabatic invariants for the phase space density (PSD) data used for the radiation belt data assimilation. The lack of a suitable global magnetic field model with high accuracy is still a long-lasting problem. This paper employs a physics-based magnetic field configuration for the first time in a radiation belt data assimilation study for a moderate storm event on 19 December 2002. The magnetic field used in our study is the magnetically self-consistent inner magnetosphere model RAM-SCB, developed at Los Alamos National Laboratory (LANL). Furthermore, we apply a cubic spline interpolation method in converting the differential flux measurements within the energy spectrum, to obtain a more accurate PSD input for the data assimilation than the commonly used linear interpolation approach. Finally, the assimilation is done using an ensemble Kalman filter (EnKF), with a localized adaptive inflation (LAI) technique to appropriately account for model errors in the assimilation and improve the performance of the Kalman filter. The assimilative results are compared with results from another assimilation experiment using the Tsyganenko 2001S (T01S) magnetic field model, to examine the dependence on a magnetic field model. Results indicate that the data assimilations using different magnetic field models capture similar features in the radiation belt dynamics, including the temporal evolution of the electron PSD during a storm and the location of the PSD peak. The assimilated solution predicts the energy differential flux to a relatively good degree when compared with independent LANL-GEO in situ observations. A closer examination suggests that for the chosen storm event, the assimilation using the RAM-SCB predicts a better flux at most energy levels during storm recovery phase but is slightly worse in the storm main phase than the assimilation using the T01S model.
Highlights
Data assimilation has recently become an increasingly important tool applied by the magnetospheric physics community for understanding the dynamics of the outer electron radiation belts
A more accurate assimilative result, we will use the above physics-based global magnetic field model combined with a more sophisticated interpolation technique to improve the inversion of observed differential flux into phase space density (PSD), which will lead in turn to less biased data assimilation output
This study carried out a radiation belt data assimilation based on a 1-D radial diffusion model and electron phase space density data obtained from Los Alamos National Laboratory (LANL)-GEO satellites and Polar spacecraft
Summary
Data assimilation has recently become an increasingly important tool applied by the magnetospheric physics community for understanding the dynamics of the outer electron radiation belts. A more accurate assimilative result, we will use the above physics-based global magnetic field model combined with a more sophisticated interpolation technique to improve the inversion of observed differential flux into PSD, which will lead in turn to less biased data assimilation output. We perform another radiation belt data assimilation using the empirical magnetic field model T01S and compare both assimilation results to observations, to examine the dependence of the assimilative state on a magnetic field model. The comparison with results using empirical magnetic field models will help us identify in the global sense whether a physics-based magnetic field model represents the more realistic magnetospheric configuration
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