Relativistic electrons of the outer radiation belt and methods of their forecast (review)

Potapov, A. S.; Потапов, А. С.

doi:10.12737/article_58f9703837c248.84596315

Cited by 5 publications

(1 citation statement)

References 61 publications

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“…Zhao et al. (2017) correlated daily averaged radiation belt electron phase space density (PSD) with n sw and also obtained τ = 1 day for MeV electrons ( μ > 700 MeV G −1 ; see also Potapov, 2017). However, if the effect of V sw is properly removed, τ shifts to 0 day (Wing et al., 2016).…”

Section: Introductionmentioning

confidence: 99%

Untangling the Solar Wind and Magnetospheric Drivers of the Radiation Belt Electrons

et al. 2022

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Many solar wind parameters correlate with one another, which complicates the causal‐effect studies of solar wind driving of the magnetosphere. Conditional mutual information (CMI) is used to untangle and isolate the effect of individual solar wind and magnetospheric drivers of the radiation belt electrons. The solar wind density (nsw) negatively correlates with electron phase space density (PSD; average energy ∼1.6 MeV) with time lag (τ) = 15 hr. The effect of nsw has been attributed to magnetopause shadowing or other loss mechanisms, but when the effect of solar wind velocity (Vsw) is removed, τ shifts to 7–11 hr, which is a more accurate time scale for this process. The peak correlation between Vsw and PSD shifts from τ = 30–50 to 44–56 hr, when the effect of nsw is removed. This suggests that the time scale for electron acceleration to 1–2 MeV is about 44–56 hr following Vsw enhancements. The effect of nsw is significant only at L* = 4.5–6 (L* > 6 is highly variable), whereas the effect of Vsw is significant only at L* = 3.5–6.5. The peak response of PSD to Vsw is the shortest and most significant at L* = 4.5–5.5. As time progresses, the peak response broadens and shifts to higher τ at higher and lower L*, consistent with local acceleration at L* = 4.5–5.5 followed by outward and inward diffusion. The outward radial diffusion time scale at L* = 5–6 is ∼40 hr per RE.

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Section: Introductionmentioning

confidence: 99%

Untangling the Solar Wind and Magnetospheric Drivers of the Radiation Belt Electrons

et al. 2022

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Determining the timing of driver influences on 1.8-3.5 MeV electron flux at geosynchronous orbit using ARMAX methodology

Simms

Engebretson

Reeves

2022

Preprint

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Classifier Neural Network Models Predict Relativistic Electron Events at Geosynchronous Orbit Better than Multiple Regression or ARMAX Models

Simms

Engebretson

2020

JGR Space Physics

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To find the best method of predicting when daily relativistic electron flux (>2 MeV) will rise at geosynchronous orbit, we compare model predictive success rates (true positive rate or TPR) for multiple regression, ARMAX, logistic regression, a feed-forward multilayer perceptron (MLP), and a recurrent neural network (RNN) model. We use only those days on which flux could rise, removing days when flux is already high from the data set. We explore three input variable sets: (1) ground-based data (Kp, Dst, and sunspot number), (2) a full set of easily available solar wind and interplanetary magnetic field parameters (|B|, Bz, V, N, P, Ey, Kp, Dst, and sunspot number, and (3) this full set with the addition of previous day's flux. Despite high validation correlations in the multiple regression and ARMAX predictions, these regression models had low predictive ability (TPR < 45%) and are not recommended for use. The three classifier model types (logistic regression, MLP, and RNN) performed better (TPR: 50.8-74.6%). These rates were increased further if the cost of missing an event was set at 4 times that of predicting an event that did not happen (TPR: 73.1-89.6%). The area under the receiver operating characteristic curves did not, for the most part, differ between the classifier models (logistic, MLP, and RNN), indicating that any of the three could be used to discriminate between events and nonevents, but validation suggests a full RNN model performs best. The addition of previous day's flux as a predictor provided only a slight advantage. Citation:Simms, L. E., & Engebretson, M. J. (2020). Classifier neural network models predict relativistic electron events at geosynchronous orbit better than multiple regression or ARMAX models.

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Relativistic electrons of the outer radiation belt and methods of their forecast (review)

Abstract: _________________________________________________________________________________________

Cited by 5 publications

References 61 publications

Untangling the Solar Wind and Magnetospheric Drivers of the Radiation Belt Electrons

Untangling the Solar Wind and Magnetospheric Drivers of the Radiation Belt Electrons

Determining the timing of driver influences on 1.8-3.5 MeV electron flux at geosynchronous orbit using ARMAX methodology

Classifier Neural Network Models Predict Relativistic Electron Events at Geosynchronous Orbit Better than Multiple Regression or ARMAX Models

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