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In this study we investigate two distinct loss mechanisms responsible for the rapid dropouts of radiation belt electrons by assimilating data from Van Allen Probes A and B and Geostationary Operational Environmental Satellites (GOES) 13 and 15 into a 3‐D diffusion model. In particular, we examine the respective contribution of electromagnetic ion cyclotron (EMIC) wave scattering and magnetopause shadowing for values of the first adiabatic invariant μ ranging from 300 to 3,000 MeV G−1. We inspect the innovation vector and perform a statistical analysis to quantitatively assess the effect of both processes as a function of various geomagnetic indices, solar wind parameters, and radial distance from the Earth. Our results are in agreement with previous studies that demonstrated the energy dependence of these two mechanisms. We show that EMIC wave scattering tends to dominate loss at lower L shells, and it may amount to between 10%/hr and 30%/hr of the maximum value of phase space density (PSD) over all L shells for fixed first and second adiabatic invariants. On the other hand, magnetopause shadowing is found to deplete electrons across all energies, mostly at higher L shells, resulting in loss from 50%/hr to 70%/hr of the maximum PSD. Nevertheless, during times of enhanced geomagnetic activity, both processes can operate beyond such location and encompass the entire outer radiation belt. Key Points: We present a 4‐year reconstruction of the outer radiation belt based on data assimilationIn the outer region of the inner magnetosphere, loss due to outward radial diffusion dominatesAt multi‐MeV energies, our simulations indicate that loss due to EMIC waves can dominate at L < 4.5 [ABSTRACT FROM AUTHOR]

Models of ring current electron dynamics unavoidably contain uncertainties in boundary conditions, electric and magnetic fields, electron scattering rates, and plasmapause location. Model errors can accumulate with time and result in significant deviations of model predictions from observations. Data assimilation offers useful tools which can combine physics‐based models and measurements to improve model predictions. In this study, we systematically analyze performance of the Kalman filter applied to a log‐transformed convection model of ring current electrons and Van Allen Probe data. We consider long‐term dynamics of μ = 2.3 MeV/G and K = 0.3 G1/2RE electrons from 1 February 2013 to 16 June 2013. By using synthetic data, we show that the Kalman filter is capable of correcting errors in model predictions associated with uncertainties in electron lifetimes, boundary conditions, and convection electric fields. We demonstrate that reanalysis retains features which cannot be fully reproduced by the convection model such as storm‐time earthward propagation of the electrons down to 2.5 RE. The Kalman filter can adjust model predictions to satellite measurements even in regions where data are not available. We show that the Kalman filter can adjust model predictions in accordance with observations for μ = 0.1, 2.3, and 9.9 MeV/G and constant K = 0.3 G1/2RE electrons. The results of this study demonstrate that data assimilation can improve performance of ring current models, better quantify model uncertainties, and help deeper understand the physics of the ring current particles. Key Points: The Kalman filter is capable of decreasing uncertainties in ring current electron simulations, using Van Allen Probe observationsThe Kalman filter can adjust the model in accordance with observations even in MLT sectors where data are unavailableThe Kalman filter can improve model predictions for a typical range of ring current electron energies [ABSTRACT FROM AUTHOR]

Data assimilation aims to blend incomplete and inaccurate data with physics‐based dynamical models. In the Earth's radiation belts, it is used to reconstruct electron phase space density, and it has become an increasingly important tool in validating our current understanding of radiation belt dynamics, identifying new physical processes, and predicting the near‐Earth hazardous radiation environment. In this study, we perform reanalysis of the sparse measurements from four spacecraft using the three‐dimensional Versatile Electron Radiation Belt diffusion model and a split‐operator Kalman filter over a 6‐month period from 1 October 2012 to 1 April 2013. In comparison to previous works, our 3‐D model accounts for more physical processes, namely, mixed pitch angle‐energy diffusion, scattering by Electromagnetic Ion Cyclotron waves, and magnetopause shadowing. We describe how data assimilation, by means of the innovation vector, can be used to account for missing physics in the model. We use this method to identify the radial distances from the Earth and the geomagnetic conditions where our model is inconsistent with the measured phase space density for different values of the invariants μ and K. As a result, the Kalman filter adjusts the predictions in order to match the observations, and we interpret this as evidence of where and when additional source or loss processes are active. The current work demonstrates that 3‐D data assimilation provides a comprehensive picture of the radiation belt electrons and is a crucial step toward performing reanalysis using measurements from ongoing and future missions. Key Points: We perform radiation belt reanalysis with a Kalman filter, a 3‐D diffusion model, and spacecraft dataSeveral physical mechanisms are added to the model and their effect in reanalysis is exploredWe use the innovation vector to pinpoint when and where missing physics in the model become apparent [ABSTRACT FROM AUTHOR]