Using MFACE as input in the UAM to specify the MIT dynamics

Abstract

The magnetosphere-ionosphere-thermosphere (MIT) dynamic system significantly depends on the highly variable solar wind conditions, in particular, on changes of the strength and orientation of the interplanetary magnetic field (IMF). The solar wind and IMF interactions with the magnetosphere drive the MIT system via the magnetospheric field-aligned currents (FACs). The global modeling helps us to understand the physical background of this complex system. With the present study, we test the recently developed high-resolution empirical model of field-aligned currents MFACE (a high-resolution Model of Field-Aligned Currents through Empirical orthogonal functions analysis). These FAC distributions were used as input of the time-dependent, fully self-consistent global Upper Atmosphere Model (UAM) for different seasons and various solar wind and IMF conditions. The modeling results for neutral mass density and thermospheric wind are directly compared with the CHAMP satellite measurements. In addition, we perform comparisons with the global empirical models: the thermospheric wind model (HWM07) and the atmosphere density model (Naval Research Laboratory Mass Spectrometer and Incoherent Scatter Extended 2000). The theoretical model shows a good agreement with the satellite observations and an improved behavior compared with the empirical models at high latitudes. Using the MFACE model as input parameter of the UAM model, we obtain a realistic distribution of the upper atmosphere parameters for the Northern and Southern Hemispheres during stable IMF orientation as well as during dynamic situations. This variant of the UAM can therefore be used for modeling the MIT system and space weather predictions.

The Global Numerical Model of the Earth’s Upper Atmosphere

“…The UAM successfully showed its ability to reproduce the general behavior of ionospheric and thermospheric parameters such as at low and high geomagnetic and solar activity conditions. A good agreement of the numerical simulations' results was achieved in comparison with observations by incoherent scatter radars located at various latitudes and longitudes (ISRs) [31], digital ionosonde CADI in the Voeykovo Main Geophysical Observatory [35], several chains of the ionospheric tomographical receivers [36][37][38], satellites, including CHAMP and GPS [3,4,26,30,[39][40][41][42], as well as with empirical models such as various types of IRI, MSIS, HWM [42][43][44][45][46][47], etc. and other theoretical models [42].…”

“…The main role of the thermospheric heating due to the soft electron precipitation was shown for the thermosphere substorm effects. The calculation results were presented in [25,26,30,[36][37][38][54][55][56][57][58][59] including the main ionospheric trough dynamics due to the ionosphere-magnetosphere convection and non-coincidence of the geographical and geomagnetic axes of the Earth. The physical mechanisms of the negative and positive F2-layer ionospheric storms (electron concentration decreases and increases, correspondingly) formation were described.…”

“…[28] and MFACE [29] in Ref. [30]. All these versions with various FACs take into account the dependence of FACs on the interplanetary magnetic field (IMF).…”

Diagnosing the Role of Alfvén Waves in Global Field‐Aligned Current System Dynamics During Southward IMF: Swarm Observations

“…This result certainly challenges the likely validity and accuracy of using data from either of the two Swarm spacecrafts alone to compute a single-spacecraft estimate of FAC. He et al, 2012, Prokhorov et al, 2014, Ritter et al, 2013. Perhaps even more significantly, Figure 3 suggests that processes, which result in significant magnetic field changes on timescales of onlỹ 10 s (the along-track separation between Swarms A and C), may represent a very strong component of the processes involved in MIC.…”

“…More recently, Lühr et al (2015) obtained a similar spatial scale of 150 km using data from the European Space Agency (ESA) Swarm mission in low Earth orbit, which separated quasi-static FAC structures from more dynamic structures at smaller scale. Results from such studies have influenced the approach used in the development of a number of empirical FAC models (e.g., He et al, 2012;Prokhorov et al, 2014), as well as the Swarm FAC data products (e.g., Ritter et al, 2013). In the case of the Swarm FAC data products, a low-pass filter on the order of 20 s, roughly corresponding to 150-to 200-km spatial scales in the frame of the Swarm spacecraft, was applied to the data in order to filter out the influence of any nonstationarities.…”