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2019
Authors: Aseev N. A., and Shprits Y Y
Title: Reanalysis of Ring Current Electron Phase Space Densities Using Van Allen Probe Observations, Convection Model, and Log‐Normal Kalman Filter
Abstract: 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 . . .
Date: 04/2019 Publisher: Space Weather Pages: 619 - 638 DOI: 10.1029/2018SW002110 Available at: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2018SW002110
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Authors: Aseev N.A., and Shprits Y.Y.
Title: Reanalysis of ring current electron phase space densities using Van Allen Probe observations, convection model, and log‐normal Kalman filter
Abstract: 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 erro. . .
Date: 04/2019 Publisher: Space Weather DOI: 10.1029/2018SW002110 Available at: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2018SW002110
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2018
Authors: Coleman Tim, McCollough James, Young Shawn, and Rigler E. J.
Title: Operational Nowcasting of Electron Flux Levels in the Outer Zone of Earth's Radiation Belt
Abstract: 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 mode. . .
Date: 04/2018 Publisher: Space Weather DOI: 10.1029/2017SW001788 Available at: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2017SW001788
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