Found 3 entries in the Bibliography.
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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.
Published by: Space Weather Published on: 08/2021
YEAR: 2021   DOI: https://doi.org/10.1029/2020SW002672
AbstractWe present a reconstruction of the dynamics of the radiation belts from Solar Cycles 17 – 24 which allows us to study how radiation belt activity has varied between the different solar cycles. The radiation belt simulations are produced using the Versatile Electron Radiation Belt (VERB)-3D code. The VERB-3D code simulations incorporate radial, energy, and pitch angle diffusion to reproduce the radiation belts. Our simulations use the historical measurements of Kp (available since Solar Cycle 17, i.e., 1933) to model the evolution radiation belt dynamics between L* = 1 – 6.6. A nonlinear auto regressive network with exogenous inputs (NARX) neural network was trained off GOES 15 measurements (Jan. 2011 – March 2014) and used to supply the upper boundary condition (L* = 6.6) over the course of Solar Cycles 17 – 24 (i.e., 1933 – 2017). Comparison of the model with long term observations of the Van Allen Probes and CRRES demonstrates that our model, driven by the NARX boundary, can reconstruct the general evolution of the radiation belt fluxes. Solar Cycle 24 (Jan 2008 – 2017) has been the least active of the considered solar cycles which resulted in unusually low electron fluxes. Our results show that Solar Cycle 24 should not be used as a representative solar cycle for developing long term environment models. The developed reconstruction of fluxes can be used to develop or improve empirical models of the radiation belts.This article is protected by copyright. All rights reserved.
Published by: Space Weather Published on: 02/2021
YEAR: 2021   DOI: https://doi.org/10.1029/2020SW002524
We describe a lightweight, accurate nowcasting model for electron flux levels measured by the Van Allen probes. Largely motivated by Rigler et al. , 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.
Published by: Space Weather Published on: 04/2018
YEAR: 2018   DOI: 10.1029/2017SW001788