Bibliography

A combined neural network- and physics-based approach for modeling plasmasphere dynamics

AbstractIn recent years, feedforward neural networks (NNs) have been successfully applied to reconstruct global plasmasphere dynamics in the equatorial plane. These neural network-based models capture the large-scale dynamics of the plasmasphere, such as plume formation and erosion of the plasmasphere on the nightside. However, their performance depends strongly on the availability of training data. When the data coverage is limited or non-existent, as occurs during geomagnetic storms, the performance of NNs significantly decreases, as networks inherently cannot learn from the limited number of examples. This limitation can be overcome by employing physics-based modeling during strong geomagnetic storms. Physics-based models show a stable performance during periods of disturbed geomagnetic activity, if they are correctly initialized and configured. In this study, we illustrate how to combine the neural network- and physics-based models of the plasmasphere in an optimal way by using data assimilation. The proposed approach utilizes advantages of both neural network- and physics-based modeling and produces global plasma density reconstructions for both quiet and disturbed geomagnetic activity, including extreme geomagnetic storms. We validate the models quantitatively by comparing their output to the in-situ density measurements from RBSP-A for an 18-month out-of-sample period from 30 June 2016 to 01 January 2018, and computing performance metrics. To validate the global density reconstructions qualitatively, we compare them to the IMAGE EUV images of the He+ particle distribution in the Earth s plasmasphere for a number of events in the past, including the Halloween storm in 2003.This article is protected by copyright. All rights reserved.

Zhelavskaya, I.; Aseev, N.; Shprits, Y;

Published by: Journal of Geophysical Research: Space Physics      Published on: 02/2021

YEAR: 2021     DOI: https://doi.org/10.1029/2020JA028077

plasmasphere; plasma density; neural networks; data assimilation; Kalman Filter; Machine learning; Van Allen Probes

Quantifying the Effects of EMIC Wave Scattering and Magnetopause Shadowing in the Outer Electron Radiation Belt by Means of Data Assimilation

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.

Cervantes, S.; Shprits, Y; Aseev, N.; Allison, H.;

Published by: Journal of Geophysical Research: Space Physics      Published on: 08/2020

YEAR: 2020     DOI: https://doi.org/10.1029/2020JA028208

data assimilation; EMIC waves; magnetopause shadowing; innovation vector; Kalman Filter; radiation belt losses; Van Allen Probes

Reanalysis of Ring Current Electron Phase Space Densities Using Van Allen Probe Observations, Convection Model, and Log-Normal Kalman Filter

Aseev, N.; Shprits, Y;

Published by: Space Weather      Published on: 04/2019

YEAR: 2019     DOI: 10.1029/2018SW002110

data assimilation; inner magnetosphere; Kalman Filter; Reanalysis; ring current; Van Allen Probes

Reanalysis of ring current electron phase space densities using Van Allen Probe observations, convection model, and log-normal Kalman filter

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.

Aseev, N.A.; Shprits, Y.Y.;

Published by: Space Weather      Published on: 04/2019

YEAR: 2019     DOI: 10.1029/2018SW002110

data assimilation; inner magnetosphere; Kalman Filter; Reanalysis; ring current; Van Allen Probes

Simultaneous event-specific estimates of transport, loss, and source rates for relativistic outer radiation belt electrons

The most significant unknown regarding relativistic electrons in Earth\textquoterights outer Van Allen radiation belt is the relative contribution of loss, transport, and acceleration processes within the inner magnetosphere. Detangling each individual process is critical to improve the understanding of radiation belt dynamics, but determining a single component is challenging due to sparse measurements in diverse spatial and temporal regimes. However, there are currently an unprecedented number of spacecraft taking measurements that sample different regions of the inner magnetosphere. With the increasing number of varied observational platforms, system dynamics can begin to be unraveled. In this work, we employ in situ measurements during the 13\textendash14 January 2013 enhancement event to isolate transport, loss, and source dynamics in a one-dimensional radial diffusion model. We then validate the results by comparing them to Van Allen Probes and Time History of Events and Macroscale Interactions during Substorms observations, indicating that the three terms have been accurately and individually quantified for the event. Finally, a direct comparison is performed between the model containing event-specific terms and various models containing terms parameterized by geomagnetic index. Models using a simple 3/Kp loss time scale show deviation from the event-specific model of nearly 2 orders of magnitude within 72 h of the enhancement event. However, models using alternative loss time scales closely resemble the event-specific model.

Schiller, Q.; Tu, W.; Ali, A.; Li, X.; Godinez, H.; Turner, D.; Morley, S.; Henderson, M.;

Published by: Journal of Geophysical Research: Space Physics      Published on: 03/2017

YEAR: 2017     DOI: 10.1002/2016JA023093

CubeSat; data assimilation; electron; event specific; Modeling; Radiation belt; Van Allen Probes

Ring Current Pressure Estimation with RAM-SCB using Data Assimilation and Van Allen Probe Flux Data

Capturing and subsequently modeling the influence of tail plasma injections on the inner magnetosphere is important for understanding the formation and evolution of the ring current. In this study, the ring current distribution is estimated with the Ring Current-Atmosphere Interactions Model with Self-Consistent Magnetic field (RAM-SCB) using, for the first time, data assimilation techniques and particle flux data from the Van Allen Probes. The state of the ring current within the RAM-SCB model is corrected via an ensemble based data assimilation technique by using proton flux from one of the Van Allen Probes, to capture the enhancement of the ring current following an isolated substorm event on July 18, 2013. The results show significant improvement in the estimation of the ring current particle distributions in the RAM-SCB model, leading to better agreement with observations. This newly implemented data assimilation technique in the global modeling of the ring current thus provides a promising tool to improve the characterization of particle distribution in the near-Earth regions.

Godinez, Humberto; Yu, Yiqun; Lawrence, Eric; Henderson, Michael; Larsen, Brian; Jordanova, Vania;

Published by: Geophysical Research Letters      Published on: 11/2016

YEAR: 2016     DOI: 10.1002/2016GL071646

data assimilation; ring current; Van Allen Probes

Application of a new data operator-splitting data assimilation technique to the 3-D VERB diffusion code and CRRES measurements

In this study we present 3-D data assimilation using CRRES data and 3-D Versatile Electron Radiation Belt Model (VERB) using a newly developed operator-splitting method. Simulations with synthetic data show that the operator-splitting Kalman filtering technique proposed in this study can successfully reconstruct the underlying dynamic evolution of the radiation belts. The method is further verified by the comparison with the conventional Kalman filter. We applied the new approach to 3-D data assimilation of real data to globally reconstruct the dynamics of the radiation belts using pitch angle, energy, and L shell dependent CRRES observations. An L shell time cross section of the global data assimilation results for nearly equatorially mirroring particles and high and low values of the first adiabatic invariants clearly show the difference between the radial profiles of phase space density. At μ = 700 MeV/G cross section of the global reanalysis shows a clear peak in the phase space density, while at lower energy of 70 MeV/G the profiles are monotonic. Since the radial profiles are obtained from one global reanalysis, the differences in the profiles reflect the differences in the underlying physical processes responsible for the dynamic evolution of the radiation belt energetic and relativistic electrons.

Shprits, Yuri; Kellerman, Adam; Kondrashov, Dmitri; Subbotin, Dmitriy;

Published by: Geophysical Research Letters      Published on: 10/2013

YEAR: 2013     DOI: 10.1002/grl.50969

data assimilation; Modeling; Radiation belts