Development of a 3‐D Plasmapause Model With a Back‐Propagation Neural Network

Abstract

Several empirical models have been previously developed to study the characteristics of the global plasmasphere. A three-dimensional solar wind-driven global dynamic plasmapause model was developed in this study using a back-propagation neural network based on multisatellite measurements. Our database contains 37,859 plasmapause crossing events from 4 January 1995 to 31 December 2015 covering all 24 magnetic local times (MLTs) from −66 • to 79 • magnetic latitudes. This model is parameterized by solar wind speed V sw , interplanetary magnetic field B z , SYM-H, and AE indices with smooth MLTs and latitude dependence. As the first 3-D empirical model, this model corresponds to the true plasmapause structure and is useful for observing space weather and other applications especially for predicting the shape of the plasmapause by forecasting the input parameters. Moreover, the proposed model is not only highly sensitive to these parameters, but also to their changes over days, seasons, and years; this means that the diurnal, seasonal, and annual variations of the parameters can be captured by the model. Therefore, this model can provide a more accurate plasmapause position compared with previous models and can also be used as a basic reference to develop other models such as a global plasmaspheric density model. Key Points:• A new solar wind-driven 3-D dynamic plasmapause model based on multisatellites is constructed using a back-propagation neural network • This model can potentially be used to forecast the configuration of the plasmasphere • This model has higher precision than previous models

Relation of the Plasmapause to the Midlatitude Ionospheric Trough, the Sub‐Auroral Temperature Enhancement and the Distribution of Small‐Scale Field Aligned Currents as Observed in the Magnetosphere by THEMIS, RBSP, and Arase, and in the Topside Ionosphere by Swarm

“…The mean PP shapes agree both qualitatively and quantitatively with previous observations and empirical models (e.g., Zhang et al, 2017). The midnight PP position in our data agrees within a few tenths in L with the simplest Kp-based model of Carpenter and Anderson (1992), while the bulge motion follows the same geomagnetic activity dependent pattern as in the recent PP models (e.g., Zhang et al, 2017;Zheng et al, 2019) as well as global simulations (e.g., Goldstein et al, 2014). The mean MIT/SETE shapes also reproduce well the well-known MLT (see e.g., Karpachev, 2019;Whalen, 1989) and Kp dependence (e.g., Karpachev, 2021;Yang et al, 2015).…”

A Statistical Model of Inner Magnetospheric Electron Density: Van Allen Probes Observations

“…As shown in Section 1, many space weather parameters were used to develop the plasmaspheric model , especially Kp index (Berube et al., 2005; Carpenter & Anderson, 1992; Chu, Bortnik, Li, Ma, Denton, & Yue, 2017; Chu, Bortnik, Li, Ma, Angelopoulos, & Thorne, 2017; Gallagher et al., 2000; He et al., 2017; Huang et al., 2004; Moldwin et al., 2002; O'Brien, 2003; Sheeley et al., 2001; Tu et al., 2006; Zheng et al., 2019; Zhelavskaya et al., 2017, 2021). The dynamic distribution and evolution characteristics of the plasmaspheric electron density distribution in the MEP are also the results from the interaction of these space weather parameters.…”

“…The plasmapause is calculated according to a fixed density threshold of 40 cm −3 (the densities larger than the threshold are assumed to be inside the plasmasphere, otherwise, outside) (Zhelavskaya et al., 2021). As we know, the plasmasphere is a rather complicated region, influenced not only by the geomagnetic activity, but also by the solar and the ionosphere (Gallagher et al., 2021; Zhang et al., 2016, 2017; Zheng et al., 2019). Therefore, we develop the new model, by using Kp , SYM‐H , P dyn , and F10.7 as inputs.…”

“…Compared with the plasmaspheric density distribution models, the plasmapause models are more mature (Carpenter & Anderson, 1992; Cho et al., 2015; He et al., 2017; Liu et al., 2015; Moldwin et al., 2002; O'Brien, 2003; Verbanac et al., 2015; Zhang et al., 2017; Zheng et al., 2019). In terms of plasmaspheric density models, it can be divided into the following three categories: the density distribution models in the MEP (Berube et al., 2005; Carpenter & Anderson, 1992; Chu, Bortnik, Li, Ma, Angelopoulos, & Thorne, 2017; Sheeley et al., 2001; Zhelavskaya et al., 2017, 2021), the field‐aligned electron density distribution model (Huang et al., 2004; Ozhogin et al., 2012; Tu et al., 2006) and the global plasmaspheric electron density model (Chu, Bortnik, Li, Ma, Denton, et al., 2017; Gallagher et al., 2000).…”