Radiation belt data assimilation of a moderate storm event using a magnetic field configuration from the physics-based RAM-SCB model

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.

A new ionospheric electron precipitation module coupled with RAM‐SCB within the geospace general circulation model

“…Both simulations show quite similar temporal evolution of the trapped ring current electrons following the initiation of injection, suggesting that the influence of using different loss methods is very small. In other words, using pitch angle diffusion coefficients is almost equivalent to using lifetimes for solving the trapped electron flux distribution, which unambiguously supports previous studies on radiation belt dynamics that utilize lifetime scales [e.g., Ripoll et al, 2014Ripoll et al, , 2015Ripoll et al, , 2016Artemyev et al, 2015;Mourenas et al, 2012aMourenas et al, , 2012bMourenas et al, , 2014Yu et al, 2013Yu et al, , 2014b.…”

“…Methods of calculating the electron lifetimes [e.g., Albert and Shprits, 2009] has been validated and improved in the past decade and found to be a very good approximation to the exact lifetime [e.g. Artemyev et al, 2013] and thus being extensively and successfully employed in radiation belt studies [e.g., Ripoll et al, 2014Ripoll et al, , 2015Ripoll et al, , 2016Artemyev et al, 2015;Mourenas et al, 2012aMourenas et al, , 2012bMourenas et al, , 2014Yu et al, 2013Yu et al, , 2014b. In the ring current dynamics, the effect of using different electron loss models (i.e., different electron lifetimes) has been recently investigated by Chen et al [2015b] in the RCM-E model.…”

Application and testing of the L* neural network with the self‐consistent magnetic field model of RAM‐SCB

“…The reason for the two PSDs with the same adiabatic coordinates deviating from each other (i.e., the matching ratio R is not equal to one) can result from various error sources, including (1) inaccurate PSD conversion from flux observations especially in the fitting process (i.e., the fitting error source, as mentioned in Green and Kivelson []), (2) inadequate satellite intercalibration, (3) large substorm injection that can violate the Liouville's theorem based on which equation is derived, (4) errors in the K parameter due to the imperfection of the magnetic field model, and (5) errors in the L * calculation due to the magnetic field model as well as the training process of the neural network. The error resulting from (1) can be much improved by using a cubic spline interpolation method in fitting an energy spectrum as reported in Yu et al []. To avoid, to a large extent, the potential contribution from error sources (2) and (3), only quiet time periods with small AL index are chosen for the L * error estimation, because the intersatellite “fine‐tuned” calibration can produce trustworthy correction without large influence from substorm injection [ Chen et al , ].…”

“…Based on these magnetospheric configurations, L * is calculated from the neural network at several different locations ranging from 6.0 to 8.0 R E on the midnight Sun‐Earth line. The corresponding PSD at these positions is converted from nominal energy differential electron flux, which is obtained from the AE ‐8 radiation belt model with different energy and pitch angle for solar minimum conditions, following the conversion procedure described in Yu et al []. As an example, at location of (−6.5, 0, 0) R E L * and PSD both display a pseudo‐normal distribution (Figures a and b); the local magnetic field and PSD generally increase monotonically with L * (Figures c and d).…”

Ring current pressure estimation with RAM‐SCB using data assimilation and Van Allen Probe flux data

“…In this paper, we present for the first time the implementation of data assimilation methods in the Ring current‐Atmosphere Interactions Model with Self‐Consistent magnetic field (B) (RAM‐SCB) [ Jordanova et al , , ; Zaharia et al , ] in order to simulate more realistic global ring current particle distributions during geomagnetic disturbances. While the data assimilation has been extensively proven as a robust method for reconstructing/reanalyzing the energetic outer radiation belt environment [e.g., Bourdarie et al , ; Bourdarie and Maget , ; Koller et al , ; Shprits et al , ; Godinez and Koller , ; Schiller et al , ; Yu et al , ; Kellerman et al , ], little effort has been made in assimilating the global ring current environment. Garner et al [] made a first attempt at assimilating satellite data into a ring current model by directly inserting particle flux data.…”