Impact of Thermospheric Mass Density on the Orbit Prediction of LEO Satellites

Abstract

Many thermospheric mass density (TMD) variations have been recognized in observationsand physical simulations; however, their impact on the low-Earth-orbit satellites has not been fully evaluated. The present study investigates the quantitative impact of periodic spatiotemporal TMD variations modulated by the empirical DTM2013 model. Also considered are two small-scale variations, that is, the equatorial mass anomaly and the midnight density maximum, which are reproduced by the Thermosphere-Ionosphere-Electrodynamics General Circulation Model. This investigation is performed through a 1-day orbit prediction (OP) simulation for a 400-km circular orbit. The results show that the impact of TMD variations during solar maximum is 1 order of magnitude larger than that during solar minimum. The dominant impact has been found in the along-track direction. Semiannual and semidiurnal variations in TMD exert the most significant impact on OP among the intra-annual and intradiurnal variations, respectively. The zero mean periodic variations in TMD may not significantly affect the predicted orbit but increase the orbital uncertainty if their periods are shorter than the time span of OP. Additionally, the equatorial mass anomaly creates a mean orbit difference of 50 m (5 m) with a standard deviation of 30 m (3 m) in 1-day OP during high (low) solar activity. The midnight density maximum exhibits a stronger impact in the order of 150 ± 30 and 15 ± 6 m during solar maximum and solar minimum, respectively. This study makes clear that careful selection of TMD variations is of great importance to balance the trade-off between efficiency and accuracy in OP problems.where ⃗ v is the velocity of the satellite and ⃗ v r is the relative velocity of the satellite with respect to the wind ⃗ v w . In the orbit prediction (OP) simulation performed in this study, these equations are evaluated in the Earth-centered inertial frame.Empirical thermospheric mass density (TMD) models can be used for atmospheric drag calculations, for example, NRLMSISE-00 (Naval Research Laboratory Mass Spectrometer Incoherent Scatter Radar Series) (Picone et al., 2002), JB2008 (Jacchia-Bowman) (Bowman et al., 2008), and DTM2013 (Drag Temperature Model) (Bruinsma, 2015). Accurate TMD prediction is critical in the tracking and collision avoidance of LEO satellites. The error of empirical TMD models is one of the largest sources of uncertainty in OP for LEO

The Geomagnetic Kp Index and Derived Indices of Geomagnetic Activity

“…Based on one year of data from 10 subauroral and 1 auroral observatory, IAGA finally endorsed four algorithms that achieved at least 69.9% accordance to hand-scaled K indices, with differences in derived K indices greater than one amounting to less than 2% (Menvielle et al, 1995). The four endorsed algorithms are the Adaptive Smoothing Method (ASM; Nowozynski et al, 1991), Finnish Meteorological Institute method (FMI; Sucksdorff et al, 1991), linear-phase robust non-linear smoothing (LRNS; Hattingh et al, 1989) and United States Geological Survey method (USGS; Wilson, 1987). The algorithms in use today at the various Kp-stations are FMI (for observatories NGK, UPS, WNG), semiautomatic LRNS (for CNB), USGS (for FRD, SIT), two modified ASM algorithms of which one is BGS (for HAD, ESK, LER) described in Clark (1992) and the other one is used for MEA and OTT, and additionally the NZ algorithm (McNoe, 1989) for EYR.…”

“…(1997) used Kp to describe and forecast the intensity, latitude and local time of irregularities with the Wideband Ionospheric Scintillation Model and Kp has high skill and impact to machine learning‐based predictions of high‐latitude scintillations (e.g., McGranaghan et al., 2018). Geomagnetic activity affects thermospheric density (e.g., Bruinsma, 2015) and Kp plays an outstanding role in low earth orbit space safety and space traffic management (Berger et al., 2020; He et al., 2020). Finally, Boteler (2001) showed a relation between Kp and modeled peak electric field magnitudes due to geomagnetically induced currents (GIC).…”

Improving the Extraction Ability of Thermospheric Mass Density Variations From Observational Data by Deep Learning

“…Therefore, grasping solar‐terrestrial physics requires a thorough understanding of the thermospheric feature (Koskinen et al., 2017). In addition, the variations of the thermospheric mass density (TMD) are closely related to the orbit prediction of the Low Earth Orbit (LEO) objects via atmospheric drag (C. He et al., 2020; Mehta et al., 2022; Qian & Solomon, 2012). The number of LEO objects will increase rapidly (K. Tobiska, Bowman, Pilinski, et al., 2021).…”

Application of a Multi‐Layer Artificial Neural Network in a 3‐D Global Electron Density Model Using the Long‐Term Observations of COSMIC, Fengyun‐3C, and Digisonde

“…The ionosphere is populated with large numbers of electrically charged particles, and the charged particles ionized by the action of extraterrestrial radiation can affect the propagation of radio waves. Therefore, a comprehensive understanding of the physical transportation, photoionization and chemical recombination of charged particles in three dimensions has an important role in the ionospheric delay effect, physical mechanism explanation (He et al., 2020; Jin et al., 2017; Li, Yue, Guo, et al., 2018; Li, Yue, Yang, et al., 2018). Currently, the space‐borne and ground‐based systems fail to provide information about the ionospheric electron density at any time and for any region; thus, it is essential to develop three‐dimensional electron density models for ionospheric applications.…”