A new solar wind‐driven global dynamic plasmapause model: 1. Database and statistics

Abstract

A large database, possibly the largest plasmapause location database, with 49,119 plasmapause crossing events from the in situ observations and 3957 plasmapause profiles (corresponding to 48,899 plasmapause locations in 1 h magnetic local time (MLT) intervals) from optical remote sensing from 1977 to 2015 by 18 satellites is compiled. The responses of the global plasmapause to solar wind and geomagnetic changes and the diurnal, seasonal, solar cycle variations of the plasmapause are investigated based on this database. It is found that the plasmapause shrinks toward the Earth globally and a clear bulge appears in the afternoon to premidnight MLT sector as the solar wind or geomagnetic conditions change from quiet to disturbed. The bulge is clearer during storm times or southward interplanetary magnetic field. The diurnal variations of the plasmapause are most probably the result of the difference between the magnetic dipole tilt and the Earth's spin axis. The seasonal variations of the plasmapause are characterized by equinox valleys and solstice peaks. It is also found that the plasmapause approaches the Earth during high solar activity and expands outward during low solar activity. This database will help us study and understand the evolution properties of the plasmapause shape and the interaction processes of the plasmasphere, the ring current, and the radiation belts in the magnetosphere.

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

“…The plasma flow that generates the electric field in the magnetosphere (Vasyliūnas, ) is mainly controlled by solar wind and the IMF ( Darrouzet et al, ; Goldstein et al, ; Goldstein et al, ; Goldstein et al, ; Goldstein et al, ; Katus et al, ; Sandel et al, ; and references therein). At the same time, the geomagnetic effects (storms and substorms) may be different events under the same solar wind and IMF conditions, and variations in the plasmapause shape are also different (Goldstein et al, ; Goldstein et al, ; Liemohn et al, ; Liemohn et al, ; He et al, ; Zhang et al, ; and references therein). Adding the proxies of SYM‐H for storms and for substorms may improve the accuracy of the model, and these two indices are also widely used in previous empirical models (e.g.,Moldwin et al, ;Ober et al, ;Liu et al, ).…”

“…Constructing a three‐dimensional (3‐D) plasmapause model is important to the space community. In this study, based on the database compiled by Zhang et al (), a three‐dimensional solar wind‐driven global dynamic plasmapause model (3‐D‐SWGDP) is developed with a back‐propagation neural network (BPNN) method. The data set and methodology will be described in sections and , respectively.…”

Quantifying the relationship between the plasmapause and the inner boundary of small-scale field-aligned currents, as deduced from Swarm observations

“…The in situ electron density data used in this study are inferred from the upper hybrid resonance frequency f uh observations by the plasma wave High Frequency Receiver (Waves HFR) instrument of the Electric and Magnetic Field Instrument Suite and Integrated Science (EMFISIS) suite. We used the electron number densities N e observed between 1 January 2014 and 1 July 2016 by VAP A as derived by Zhelavskaya et al (2016) through the Neural-network-based Upper hybrid Resonance Determination (NURD) algorithm. These in situ electron density data are openly available at the ftp://rbm.epss.ucla.edu/ftpdisk1/ NURD/ ftp site.…”

A new solar wind‐driven global dynamic plasmapause model: 2. Model and validation

“…The first is external convective driving from the solar wind and IMF [Goldstein et al, 2003a[Goldstein et al, , 2003b[Goldstein et al, , 2005a[Goldstein et al, , 2005bSandel et al, 2003;Darrouzet et al, 2009;Katus et al, 2015, and references therein], which modifies large-scale convection in the inner magnetosphere that drives the dynamic distribution of plasmaspheric plasma through E × B drifts. The second is internal driving due to the dynamics of magnetospheric energetic particles and the ionosphere [Goldstein et al, 2003c[Goldstein et al, , 2007Liemohn et al, 2004Liemohn et al, , 2006He et al, 2016;Zhang et al, 2017], especially by auroral substorms that produce strong ion and electron precipitation in the ionosphere [Akasofu, 1964;McPherron et al, 1973] and dipolarization in the magnetosphere [Runov et al, 2009;Ge et al, 2012]. In the following sections, we will investigate the correlations of the plasmapause location with geomagnetic indices and solar wind parameters to optimize the parameters that drive the NSW-GDP model.…”