Bibliography
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Found 6 entries in the Bibliography.
Showing entries from 1 through 6
| 2021 |
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Abstract Reconstruction and prediction of the state of the near-Earth space environment is important for anomaly analysis, development of empirical models and understanding of physical processes. Accurate reanalysis or predictions that account for uncertainties in the associated model and the observations, can be obtained by means of data assimilation. The ensemble Kalman filter (EnKF) is one of the most promising filtering tools for non-linear and high dimensional systems in the context of terrestrial weather prediction. In this study, we adapt traditional ensemble based filtering methods to perform data assimilation in the radiation belts. By performing a fraternal twin experiment, we assess the convergence of the EnKF to the standard Kalman filter (KF). Furthermore, with the split-operator technique, we develop two new three-dimensional EnKF approaches for electron phase space density that account for radial and local processes, and allow for reconstruction of the full 3D radiation belt space. The capabilities and properties of the proposed filter approximations are verified using Van Allen Probe and GOES data. Additionally, we validate the two 3D split-operator Ensemble Kalman filters against the 3D split-operator KF. We show how the use of the split-operator technique allows us to include more physical processes in our simulations and offers computationally efficient data assimilation tools that deliver accurate approximations to the optimal solution of the KF and are suitable for real-time forecasting. Future applications of the EnKF to direct assimilation of fluxes and non-linear estimation of electron lifetimes are discussed. Tibocha, A.; de Wiljes, J.; Shprits, Y; Aseev, N.; Published by: Space Weather Published on: 08/2021 YEAR: 2021 DOI: https://doi.org/10.1029/2020SW002672 Kalman Filter; Ensemble Kalman filter; forecasting; Van Allen Probes |
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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 |
| 2020 |
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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. 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 |
| 2019 |
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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 |
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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. 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 |
| 2018 |
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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 particle fluxes within the radiation belts. Real-time estimation of the parameters of the model is done using a Kalman Filter, where the state of the model is exactly the parameters. Nowcast performance is assessed against several baseline interpolation schemes. Our model demonstrates significant improvements in performance over persistence nowcasting. In particular, during times of high geomagnetic activity, our model is able to attain performance substantially better than a persistence model. In addition, residual analysis is conducted in order to assess model fit, and to suggest future improvements to the model. Coleman, Tim; McCollough, James; Young, Shawn; Rigler, E.; Published by: Space Weather Published on: 04/2018 YEAR: 2018 DOI: 10.1029/2017SW001788 |
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