Reconstruction of the Radiation Belts for Solar Cycles 17–24 (1933–2017)

• Published in: Space Weather Vol. 19; no. 3
• Main Authors: Saikin, A. A., Shprits, Y. Y., Drozdov, A. Y., Landis, D. A., Zhelavskaya, I. S., Cervantes, S.
• Format: Journal Article
• Language: English
• Published: Washington John Wiley & Sons, Inc 01.03.2021
• Subjects: Boundary conditions; Electron density; Electron flux; Electron radiation; Empirical models; Environment models; Evolution; Neural networks; Pitch (inclination); Radiation; Radiation belts; Reconstruction; Simulation; Solar cycle
• ISSN: 1542-7390; 1539-4964; 1542-7390
• DOI: 10.1029/2020SW002524

We present a reconstruction of the dynamics of the radiation belts from solar cycles 17 to 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 (January 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 (January 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. Key Points Developed nonlinear auto regressive network with exogenous inputs neural network predicts an upper radial boundary condition used to reconstruct the evolution of the radiation belts Solar cycle 24 (2009–2017) was an unusual solar cycle and shows much weaker radiation belts which may not be representative The presented long‐term reconstruction may be used for the development of empirical models of the radiation belt environment