Reconstructing the Dynamics of the Outer Electron Radiation Belt by Means of the Standard and Ensemble Kalman Filter With the VERB‐3D Code

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 obser...

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Main Authors: Angelica M. Castillo Tibocha, Jana deWiljes, Yuri Y. Shprits, Nikita A. Aseev
Format: Article
Language:English
Published: Wiley 2021-10-01
Series:Space Weather
Subjects:
Online Access:https://doi.org/10.1029/2020SW002672
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author Angelica M. Castillo Tibocha
Jana deWiljes
Yuri Y. Shprits
Nikita A. Aseev
author_facet Angelica M. Castillo Tibocha
Jana deWiljes
Yuri Y. Shprits
Nikita A. Aseev
author_sort Angelica M. Castillo Tibocha
collection DOAJ
description 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 nonlinear 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 is a computationally efficient data assimilation tool that delivers an accurate approximation of the optimal KF solution, and is suitable for real‐time forecasting. Future applications of the EnKF to direct assimilation of fluxes and nonlinear estimation of electron lifetimes are discussed.
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spelling doaj-art-fb0985fdc5f6472498d7ff34a72a7b8f2025-01-14T16:30:34ZengWileySpace Weather1542-73902021-10-011910n/an/a10.1029/2020SW002672Reconstructing the Dynamics of the Outer Electron Radiation Belt by Means of the Standard and Ensemble Kalman Filter With the VERB‐3D CodeAngelica M. Castillo Tibocha0Jana deWiljes1Yuri Y. Shprits2Nikita A. Aseev3GFZ German Research Centre for Geosciences Potsdam GermanyInstitute of Mathematics, University of Potsdam Potsdam GermanyGFZ German Research Centre for Geosciences Potsdam GermanyGFZ German Research Centre for Geosciences Potsdam GermanyAbstract 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 nonlinear 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 is a computationally efficient data assimilation tool that delivers an accurate approximation of the optimal KF solution, and is suitable for real‐time forecasting. Future applications of the EnKF to direct assimilation of fluxes and nonlinear estimation of electron lifetimes are discussed.https://doi.org/10.1029/2020SW002672Kalman filterEnsemble Kalman filterforecasting
spellingShingle Angelica M. Castillo Tibocha
Jana deWiljes
Yuri Y. Shprits
Nikita A. Aseev
Reconstructing the Dynamics of the Outer Electron Radiation Belt by Means of the Standard and Ensemble Kalman Filter With the VERB‐3D Code
Space Weather
Kalman filter
Ensemble Kalman filter
forecasting
title Reconstructing the Dynamics of the Outer Electron Radiation Belt by Means of the Standard and Ensemble Kalman Filter With the VERB‐3D Code
title_full Reconstructing the Dynamics of the Outer Electron Radiation Belt by Means of the Standard and Ensemble Kalman Filter With the VERB‐3D Code
title_fullStr Reconstructing the Dynamics of the Outer Electron Radiation Belt by Means of the Standard and Ensemble Kalman Filter With the VERB‐3D Code
title_full_unstemmed Reconstructing the Dynamics of the Outer Electron Radiation Belt by Means of the Standard and Ensemble Kalman Filter With the VERB‐3D Code
title_short Reconstructing the Dynamics of the Outer Electron Radiation Belt by Means of the Standard and Ensemble Kalman Filter With the VERB‐3D Code
title_sort reconstructing the dynamics of the outer electron radiation belt by means of the standard and ensemble kalman filter with the verb 3d code
topic Kalman filter
Ensemble Kalman filter
forecasting
url https://doi.org/10.1029/2020SW002672
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