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.

中文翻译：

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太阳能循环辐射带的重建17–24（1933–2017）

我们提出了从太阳周期17到24的辐射带动力学的重构，这使我们能够研究辐射带活动在不同太阳周期之间如何变化。辐射带模拟是使用通用电子辐射带（VERB）-3D代码生成的。VERB-3D代码模拟结合了径向，能量和俯仰角扩散，以重现辐射带。我们的模拟使用Kp的历史测量值（自太阳周期17，即1933年以来可用）对L *  = 1–6.6之间的演化辐射带动力学进行建模。使用GOES 15测量（2011年1月至2014年3月）训练了带有外来输入的非线性自回归网络（NARX）神经网络，并用于提供上限条件（L * = 6.6）在太阳周期17–24（即1933–2017）的过程中。该模型与Van Allen Probes和CRRES的长期观测结果的比较表明，在NARX边界的驱动下，我们的模型可以重建辐射带通量的一般演变。太阳周期24（2008年1月至2017年1月）是所考虑的太阳周期中最活跃的周期，这导致异常低的电子通量。我们的结果表明，太阳周期24不应该用作开发长期环境模型的代表太阳周期。已开发的通量重建可用于开发或改进辐射带的经验模型。