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

Empirical modeling of the plasmasphere dynamics using neural networks

We propose a new empirical model for reconstructing the global dynamics of the cold plasma density distribution based only on solar wind data and geomagnetic indices. Utilizing the density database obtained using the NURD (Neural-network-based Upper hybrid Resonance Determination) algorithm for the period of October 1, 2012 - July 1, 2016, in conjunction with solar wind data and geomagnetic indices, we develop a neural network model that is capable of globally reconstructing the dynamics of the cold plasma density distribution for 2<=L<=6 and all local times. We validate and test the model by measuring its performance on independent datasets withheld from the training set and by comparing the model predicted global evolution with global images of He+ distribution in the Earth\textquoterights plasmasphere from the IMAGE Extreme UltraViolet (EUV) instrument. We identify the parameters that best quantify the plasmasphere dynamics by training and comparing multiple neural networks with different combinations of input parameters (geomagnetic indices, solar wind data, and different durations of their time history). The optimal model is based on the 96-hour time history of Kp, AE, SYM-H, and F10.7 indices. The model successfully reproduces erosion of the plasmasphere on the night side and plume formation and evolution. We demonstrate results of both local and global plasma density reconstruction. This study illustrates how global dynamics can be reconstructed from local in-situ observations by using machine learning techniques.

Zhelavskaya, Irina; Shprits, Yuri; c, Maria;

Published by: Journal of Geophysical Research: Space Physics      Published on: 10/2017

YEAR: 2017     DOI: 10.1002/2017JA024406

inner magnetosphere; Machine learning; Models; neural networks; plasmasphere; Van Allen Probes

Automated determination of electron density from electric field measurements on the Van Allen Probes spacecraft

We present the Neural-network-based Upper hybrid Resonance Determination (NURD) algorithm for automatic inference of the electron number density from plasma wave measurements made on board NASA\textquoterights Van Allen Probes mission. A feedforward neural network is developed to determine the upper hybrid resonance frequency, fuhr, from electric field measurements, which is then used to calculate the electron number density. In previous missions, the plasma resonance bands were manually identified, and there have been few attempts to do robust, routine automated detections. We describe the design and implementation of the algorithm and perform an initial analysis of the resulting electron number density distribution obtained by applying NURD to 2.5 years of data collected with the Electric and Magnetic Field Instrument Suite and Integrated Science (EMFISIS) instrumentation suite of the Van Allen Probes mission. Densities obtained by NURD are compared to those obtained by another recently developed automated technique and also to an existing empirical plasmasphere and trough density model.

Zhelavskaya, I.; Spasojevic, M.; Shprits, Y; Kurth, W.;

Published by: Journal of Geophysical Research: Space Physics      Published on: 05/2016

YEAR: 2016     DOI: 10.1002/2015JA022132

electron number density; neural networks; Van Allen Probes