Identifying the radiation belt source region by data assimilation

Abstract

[1] We describe how assimilation of radiation belt data with a simple radial diffusion code can be used to identify and adjust for unknown physics in the model. We study the dropout and the following enhancement of relativistic electrons during a moderate storm on 25 October 2002. We introduce a technique that uses an ensemble Kalman filter and the probability distribution of the forecast ensemble to identify if the model is drifting away from the observations and to find inconsistencies between model forecast and observations. We use the method to pinpoint the time periods and locations where most of the disagreement occurs and how much the Kalman filter has to adjust the model state to match the observations. Although the model does not contain explicit source or loss terms, the Kalman filter algorithm can implicitly add very localized sources or losses in order to reduce the discrepancy between model and observations. We use this technique with multisatellite observations to determine when simple radial diffusion is inconsistent with the observed phase space densities indicating where additional source (acceleration) or loss (precipitation) processes must be active. We find that the outer boundary estimated by the ensemble Kalman filter is consistent with negative phase space density gradients in the outer electron radiation belt. We also identify specific regions in the radiation belts (L* % 5-6 and to a minor extend also L* % 4) where simple radial diffusion fails to adequately capture the variability of the observations, suggesting local acceleration/loss mechanisms.

Quantifying the effect of magnetopause shadowing on electron radiation belt dropouts

“…DREAM performs data assimilation using an ensemble Kalman filter technique (Koller et al, 2007) by combining PSD data with a 1-D radial diffusion model. The input PSD data are converted from flux observations from three LANL-GEO satellites, one GPS satellite (ns41), and the POLAR spacecraft in the second half of 2002.…”

Verification of SpacePy's radial diffusion radiation belt model

“…More recent 3-D diffusion models now include cross-diffusion terms and have shown that such terms can be important to the evolution of the radiation belts (Xiao et al, 2010;Subbotin et al, 2010). The Dynamic Radiation Environment Assimilation Model (DREAM, Reeves et al, 2005) incorporates data assimilation to drive radial diffusion results towards more realistic values (Koller et al, 2007). Other models, such as the Radiation Belt Environment (RBE) model (Fok et al, 2008), use bounce-averaged kinetic representations of the belts along with time-accurate magnetic and electric field specifications instead of simpler diffusion equations.…”

Electron radiation belt data assimilation with an ensemble Kalman filter relying on the Salammbô code

“…They are in most cases 1-D, i.e. according to L * , where only radial diffusion is considered (Naehr and Toffoletto, 2005;Koller et al, 2007;Kondrashov et al, 2007). In Koller et al (2007) and Shprits et al (2007), data assimilation with a 1-D physical model has been used to study radial profiles of phase space density.…”

“…according to L * , where only radial diffusion is considered (Naehr and Toffoletto, 2005;Koller et al, 2007;Kondrashov et al, 2007). In Koller et al (2007) and Shprits et al (2007), data assimilation with a 1-D physical model has been used to study radial profiles of phase space density. In the present paper, we describe a data assimilation tool based on the 3-D Salammbô code (Varotsou et al, 2005(Varotsou et al, , 2008 and an ensemble Kalman filter (Evensen, 1994).…”