A state-space model of radiation belt electron flux dynamics

Rigler, E. J.; Baker, D. N.

doi:10.1016/j.jastp.2008.01.019

Cited by 4 publications

(14 citation statements)

References 25 publications

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“…Statistical models, which rely on dynamically fit linear and simple nonlinear filters. These typically have a more opaque interpretation than the models in (1) but tend to be easily fit and updated (e.g., Baker et al, 1990;Balikhin et al, 2011;Boynton et al, 2013;Denton et al, 2016Denton et al, , 2015Rigler & Baker, 2008, 2004Rigler et al, 2004). Note that some empirical models can still be based on physical principles, such as in Chen et al (2016).…”

Section: Radiation Beltsmentioning

confidence: 99%

“…This work was later expanded upon in Hartley et al (2014). Rigler et al (2004) and Rigler and Baker (2008) used solar wind, magnetic field strength, and ion density as the inputs for a nonstationary linear filter. Denton et al (2015) used bilinear interpolation between fluxes of particles at various energy levels to fill in for potential coverage gaps for a geostationary satellite.…”

Section: Radiation Beltsmentioning

confidence: 99%

“…These are typically fluid‐dynamical approximation models (e.g., Fok et al, ). Statistical models, which rely on dynamically fit linear and simple nonlinear filters. These typically have a more opaque interpretation than the models in (1) but tend to be easily fit and updated (e.g., Baker et al, ; Balikhin et al, ; Boynton et al, ; Denton et al, , ; Rigler & Baker, , ; Rigler et al, ). Note that some empirical models can still be based on physical principles, such as in Chen et al (). Models with forecasts driven by data‐mining techniques, typically neural networks.…”

Section: Introductionmentioning

confidence: 99%

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Operational Nowcasting of Electron Flux Levels in the Outer Zone of Earth's Radiation Belt

et al. 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. (, https://doi.org/10.1029/2003SW000036), 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 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.

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Section: Radiation Beltsmentioning

confidence: 99%

Section: Radiation Beltsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Operational Nowcasting of Electron Flux Levels in the Outer Zone of Earth's Radiation Belt

et al. 2018

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“…State vectors for the magnetosphere and for the solar wind have been used in prior mathematical analysis of the magnetospheric system as driven by the solar wind. Solar wind state vectors have been used as inputs to analyses of magnetospheric variables (Balikhin et al, 2011;Borovsky & Funsten, 2003;Boynton et al, 2013;Rigler & Baker, 2008;Sakaguchi et al, 2013); in other studies magnetospheric state vectors have been used as outputs of single solar wind variables (Horton et al, 1999;Vassiliadis et al, 2002). For studies of the evolution of the electron radiation belt, mixed magnetospheric and solar-wind state vectors have been used (Borovsky, 2017;Simms et al, 2016;Wei et al, 2011) and mixed solar wind-magnetosphere state vectors have also been used to categorize, parameterize, and query radiation belt models (Fung, 1996;Fung et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Exploration of a Composite Index to Describe Magnetospheric Activity: Reduction of the Magnetospheric State Vector to a Single Scalar

Borovsky

Denton

2018

JGR Space Physics

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Geomagnetic activity is usually gauged by a single time‐dependent geomagnetic index. One drawback is that an individual geomagnetic index measures only one aspect of the activity in the Earth's magnetosphere. Here we construct a time‐dependent 11‐element magnetospheric state vector E for Earth that consists of measures of high‐latitude currents, polar cap current, magnetospheric convection, plasma pressure, ion and electron precipitation rates, the intensity of substorm‐injected electrons, and the elapsed time since the last substorm onset. At the same time we construct an eight‐element time‐dependent solar‐wind state vector S from the measured properties of the solar wind at Earth. In this S → E picture a time‐dependent “magnetospheric‐activity index” E(1) is created from a weighted sum of the 11 variables in the magnetospheric state vector, with the weights chosen by the vector correlation properties between the magnetospheric state vector E and the solar wind state vector S. The properties and advantages of this E(1) index are examined for the years 1991–2007. Comparing fits made on different subsets of the data demonstrates the robustness of the definition of E(1); tests using out‐of‐sample data demonstrate the accuracy of solar wind predictions of E(1). The advantages of the composite index E(1) include a more direct association with the solar wind, linearity, high predictability, and versatility with respect to (a) storm‐versus‐quiet intervals, (b) solar maximum versus solar minimum, and (c) the various types of solar wind plasma. Defining magnetospheric storms with the composite index E(1) is explored, and categorizing magnetospheric storms using the magnetospheric state vector E is considered.

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“…Vassiliadis et al [14] extended this technique spatially by incorporating SAMPEX/PET electron f ux data over a broad range of L-shells from 1.1 to 10 Earth Radii (RE). The f rst self-consistent spatiotemporal state space models were then constructed [11], providing "one-step ahead" predictions for the nonlinear impulse-reponse of the radiation belts and, in particular, provided new and important clues to the timescales (typically days) involved in the dissipation of f ux.…”

Section: Introductionmentioning

confidence: 99%

Volterra network modeling of the nonlinear finite-impulse reponse of the radiation belt flux

Taylor¹,

Daglis²,

Anastasiadis³

et al. 2011

AIP Conference Proceedings

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We show how a general class of spatio-temporal nonlinear impulse-response forecast networks (Volterra networks) can be constructed from a taxonomy of nonlinear autoregressive integrated moving average with exogenous inputs (NAR-MAX) input-output equations, and used to model the evolution of energetic particle f uxes in the Van Allen radiation belts. We present initial results for the nonlinear response of the radiation belts to conditions a month earlier. The essential features of spatio-temporal observations are recovered with the model echoing the results of state space models and linear f nite impulse-response models whereby the strongest coupling peak occurs in the preceding 1-2 days. It appears that such networks hold promise for the development of accurate and fully data-driven space weather modelling, monitoring and forecast tools.

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A state-space model of radiation belt electron flux dynamics

Cited by 4 publications

References 25 publications

Operational Nowcasting of Electron Flux Levels in the Outer Zone of Earth's Radiation Belt

Operational Nowcasting of Electron Flux Levels in the Outer Zone of Earth's Radiation Belt

Exploration of a Composite Index to Describe Magnetospheric Activity: Reduction of the Magnetospheric State Vector to a Single Scalar

Volterra network modeling of the nonlinear finite-impulse reponse of the radiation belt flux

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