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Inner magnetosphere coupling
(2017)
The dynamics of the inner magnetosphere is strongly governed by the interactions between different plasma populations that are coupled through large-scale electric and magnetic fields, currents, and wave-particle interactions. Inner magnetospheric plasma undergoes self-consistent interactions with global electric and magnetic fields. Waves excited in the inner magnetosphere from unstable particle distributions can provide energy exchange between different particle populations in the inner magnetosphere and affect the ring current and radiation belt dynamics. The ionosphere serves as an energy sink and feeds the magnetosphere back through the cold plasma outflow. The precipitating inner magnetospheric particles influence the ionosphere and upper atmospheric chemistry and affect climate. Satellite measurements and theoretical studies have advanced our understanding of the dynamics of various plasma populations in the inner magnetosphere. However, our knowledge of the coupling processes among the plasmasphere, ring current, radiation belts, global magnetic and electric fields, and plasma waves generated within these systems is still incomplete. This special issue incorporates extended papers presented at the Inner Magnetosphere Coupling III conference held 23–27 March 2015 in Los Angeles, California, USA, and includes modeling and observational contributions addressing interactions within different plasma populations in the inner magnetosphere (plasmasphere, ring current, and radiation belts), coupling between fields and plasma populations, as well as effects of the inner magnetosphere on the ionosphere and atmosphere.
We present the PINE (Plasma density in the Inner magnetosphere Neural network‐based Empirical) model ‐ a new empirical model for reconstructing the global dynamics of the cold plasma density distribution based only on solar wind data and geomagnetic indices. Utilizing the density database obtained using the NURD (Neural‐network‐based Upper hybrid Resonance Determination) algorithm for the period of 1 October 2012 to 1 July 2016, in conjunction with solar wind data and geomagnetic indices, we develop a neural network model that is capable of globally reconstructing the dynamics of the cold plasma density distribution for 2≤L≤6 and all local times. We validate and test the model by measuring its performance on independent data sets withheld from the training set and by comparing the model‐predicted global evolution with global images of He+ distribution in the Earth's plasmasphere from the IMAGE Extreme UltraViolet (EUV) instrument. We identify the parameters that best quantify the plasmasphere dynamics by training and comparing multiple neural networks with different combinations of input parameters (geomagnetic indices, solar wind data, and different durations of their time history). The optimal model is based on the 96 h time history of Kp, AE, SYM‐H, and F10.7 indices. The model successfully reproduces erosion of the plasmasphere on the nightside and plume formation and evolution. We demonstrate results of both local and global plasma density reconstruction. This study illustrates how global dynamics can be reconstructed from local in situ observations by using machine learning techniques.