Bibliography



Found 5 entries in the Bibliography.


Showing entries from 1 through 5


2021

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 de ...

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

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

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

2020

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 ...

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

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

2019

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

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

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 convec ...

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

YEAR: 2019     DOI: 10.1029/2018SW002110

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

2018

Operational Nowcasting of Electron Flux Levels in the Outer Zone of Earth\textquoterights Radiation Belt

We describe a lightweight, accurate nowcasting model for electron flux levels measured by the Van Allen probes. Largely motivated by Rigler et al. [2004], we turn to a time-varying linear filter of previous flux levels and Kp. We train and test this model on data gathered from the 2.10 MeV channel of the Relativistic Electron-Proton Telescope (REPT) sensor onboard the Van Allen probes. Dynamic linear models are a specific case of state space models, and can be made flexible enough to emulate the nonlinear behavior of particl ...

Coleman, Tim; McCollough, James; Young, Shawn; Rigler, E.;

YEAR: 2018     DOI: 10.1029/2017SW001788

forecasting; Kalman Filter; Van Allen Probes



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