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Identifying Radiation Belt Electron Source and Loss Processes by Assimilating Spacecraft Data in a Three‐Dimensional Diffusion Model
Author(s) -
Cervantes S.,
Shprits Y. Y.,
Aseev N. A.,
Drozdov A. Y.,
Castillo A.,
Stolle C.
Publication year - 2020
Publication title -
journal of geophysical research: space physics
Language(s) - English
Resource type - Journals
eISSN - 2169-9402
pISSN - 2169-9380
DOI - 10.1029/2019ja027514
Subject(s) - data assimilation , van allen radiation belt , van allen probes , spacecraft , computational physics , kalman filter , physics , plasmasphere , radiation , diffusion , electromagnetic spectrum , geophysics , aerospace engineering , meteorology , statistical physics , computer science , magnetosphere , magnetic field , optics , astronomy , quantum mechanics , artificial intelligence , thermodynamics , engineering
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.