Search Results (7)

Accurate thermospheric mass density (TMD) prediction is critical for applications in solar-terrestrial physics, spacecraft safety, and remote sensing systems. While existing deep learning (DL)-based TMD models are predominantly data-driven, their performance remains constrained by observational data limitations. This study proposes ResNet-MSIS, a novel hybrid framework that integrates prior knowledge from the empirical NRLMSIS-2.1 model into a residual network (ResNet) architecture. The incorporation of NRLMSIS-2.1 enhanced the performance of ResNet-MSIS, yielding a lower root mean squared error (RMSE) of 0.2657 × ${10}^{-12}$ kg/m³ in TMD prediction compared with 0.2750 × ${10}^{-12}$ kg/m³ from ResNet, along with faster convergence during training and better generalization on Gravity Recovery and Climate Experiment (GRACE-A) data, which was trained and validated on the CHAllenging Minisatellite Payload (CHAMP) TMD data (2000–2009, altitude of 305–505 km, avg. 376 km) under quiet geomagnetic conditions (Kp ≤ 3). The DL model was subsequently tested on the remaining CHAMP-derived TMD observations, and the results demonstrated that ResNet-MSIS outperformed both ResNet and NRLMSIS-2.1 on the test dataset. The model’s robustness was further demonstrated on GRACE-A data (2002–2009, altitude of 450–540 km, avg. 482 km) under magnetically quiet conditions, with the RMSE decreasing from 0.3352 × ${10}^{-12}$ kg/m³ to 0.2959 × ${10}^{-12}$ kg/m³, indicating improved high-altitude prediction accuracy. Additionally, ResNet-MSIS effectively captured the horizontal TMD variations, including equatorial mass density anomaly (EMA) and midnight density maximum (MDM) structures, confirming its ability to learn complex spatiotemporal patterns. This work underscores the value of merging data-driven methods with domain-specific prior knowledge, offering a promising pathway for advancing TMD modeling in space weather and atmospheric research. Full article

Thermospheric mass densities are investigated to explore their responses to solar irradiance and geomagnetic activity during the period from 31 October to 7 November 2021. Utilizing data from the Global Navigation Satellite System (GNSS) payload and an ionization gauge mounted on the Orbital Neutral Atmospheric Detector (OAD) payload onboard the QQ-Satellite, thermospheric mass densities are derived through two independent means: precise orbit determination (POD) and pressure measurements. For the first time, observations of these two techniques are compared and analyzed in this study to demonstrate similarities and differences. Both techniques exhibit similar spatial–temporal variations, with clear dependences on local solar time (LT). However, the hemispheric asymmetry is almost absent in simulations from the NRLMSISE-00 and DTM94 models compared with observations. At high latitudes, density enhancements of observations and simulations are shown, characterized by periodic bulge structures. In contrast, only the OAD-derived densities exhibit wave-like disturbances that propagate from two poles to lower latitudes during geomagnetic storm periods, suggesting a connection to traveling atmospheric disturbances (TADs). Over the long term, thermospheric mass densities derived from the two means of POD and the OAD show good agreements, yet prominent discrepancies emerge during specific periods and under different space-weather conditions. We propose possible interpretations as well as suggestions for utilizing these two means. Significantly, neutral winds should be considered in both methods, particularly at high latitudes and under storm conditions. Full article

A comparison of three types of ionosonde data from Europe during an interplanetary coronal mass ejection (ICME)- and a corotating interaction region (CIR)-driven geomagnetic storm event is detailed in this study. The selected events are 16–20 March 2015 for the ICME-driven storm and 30 May to 4 June 2013 for the CIR-driven one. Ionospheric data from three European ionosonde stations, namely Pruhonice (PQ), Sopron (SO) and Rome (RO), are investigated. The ionospheric F2-layer responses to these geomagnetic events are analyzed with the ionospheric foF2 and h’F2 parameters, the calculated deltafoF2 and deltahF2 values, the ratio of total electron content (rTEC) and Thermosphere, Ionosphere, Mesosphere, Energetics and Dynamics (TIMED) satellite Global Ultraviolet Imager (GUVI) thermospheric [O]/[N₂] measurement data. The storm-time and the quiet-day mean values are also compared, and it can be concluded that the quiet-day curves are similar at all the stations while the storm-time ones show the latitudinal dependence during the development of the storm. As a result of the electron density comparison, during the two events, it can be concluded that the sudden storm commencement (SSC) that characterized the ICME induced a traveling atmospheric disturbance (TAD) seen in the European stations in the main phase, while this is not seen in the CIR-driven ionospheric storm, which shows a stronger and more prolonged negative effect in all the stations, probably due to the season and the depleted O/N₂ ratio. Full article

Atmospheric drag stands out as the predominant non-gravitational force acting on satellites in Low Earth Orbit (LEO), with altitudes below 2000 km. This drag exhibits a strong dependence on the thermospheric mass density (TMD), a parameter of vital significance in the realms of orbit determination, prediction, collision avoidance, and re-entry forecasting. A multitude of empirical TMD models have been developed, incorporating contemporary data sources, including TMD measurements obtained through onboard accelerometers on LEO satellites. This paper delves into three different TMD modelling techniques, specifically, Fourier series, spherical harmonics, and artificial neural networks (ANNs), during periods of geomagnetic quiescence. The TMD data utilised for modelling and evaluation are derived from three distinct LEO satellites: GOCE (at an altitude of approximately 250 km), CHAMP (around 400 km), and GRACE (around 500 km), spanning the years 2002 to 2013. The consistent utilisation of these TMD data sets allows for a clear performance assessment of the different modelling approaches. Subsequent research will shift its focus to TMD modelling during geomagnetic disturbances, while the present work can serve as a foundation for disentangling TMD variations stemming from geomagnetic activity. Furthermore, this study undertakes precise TMD modelling during geomagnetic quiescence using data obtained from the GRACE (at an altitude of approximately 500 km), CHAMP (around 400 km), and GOCE (roughly 250 km) satellites, covering the period from 2002 to 2013. It employs three distinct methods, namely Fourier analysis, spherical harmonics (SH) analysis, and the artificial neural network (ANN) technique, which are subsequently compared to identify the most suitable methodology for TMD modelling. Additionally, various combinations of time and coordinate representations are scrutinised within the context of TMD modelling. Our results show that the precision of low-order Fourier-based models can be enhanced by up to 10 % through the utilisation of geocentric solar magnetic coordinates. Both the Fourier- and SH-based models exhibit limitations in approximating the vertical gradient of TMD. Conversely, the ANN-based model possesses the capacity to capture vertical TMD variability without manifesting sensitivity to variations in time and coordinate inputs. Full article

In this study, we focused on the periodic variations of global average thermospheric density, derived from orbital decay measurements of about 5000 space objects from 1967 to 2013, by using the wavelet power spectrum method. The results demonstrated that the thermospheric density showed an ~11-year period, with semiannual and annual variations, while the seasonal variation was usually more significant under high solar activity conditions. Importantly, we investigated the possible link between the thermospheric density and the QBO, with the aid of the Global Average Mass Density Model (GAMDM) and the different density residuals method. The difference between the measured density and the GAMDM empirical model seemingly had QBO signal, but the ratio of them revealed that the QBO signal could not detect in the thermospheric density. Comprehensively, we found that the stratospheric QBO cannot impact on the thermosphere, and more data and numerical modeling are needed for further validation. Full article

The longitudinal distribution of upper atmospheric density has been broadly studied. However, the studies mostly focused on 24 h averaged distribution. This study presents the longitudinal distribution of thermospheric density at dawn and dusk, using observations collected by the atmospheric density detector onboard the Chinese satellite APOD (Atmospheric Density Detection and Precise Orbit Determination) during low solar activity. The APOD observations show a significant relative longitudinal variation of thermospheric density with global maxima (Δρ_rmax) near the geomagnetic pole, especially in the winter hemisphere. The annual maximum of Δρ_rmax appears in the Southern Hemisphere around the June solstices and reaches 26.3% and 39.6% at dawn and dusk, respectively. The auroral heating and meridional wind might play a significant role in the longitudinal variation of thermospheric density. We further compare the APOD observations with the semi-empirical atmospheric model MSIS (Mass Spectrometer Incoherent Scatter Radar) 2.0 predictions under low solar activity conditions. The MSIS 2.0 model reproduces similar longitudinal variations to the observations, with hemispheric asymmetry. The longitudinal variation of thermospheric density from APOD should be related to the distribution of the atmospheric average molecular weight from the model. More observational data are needed to verify the results of this study further. Full article

Atmospheric drag provides an indirect approach for evaluating atmospheric mass density, which can be derived from the Precise Orbit Determination (POD) of Low Earth Orbit (LEO) satellites. A method was developed to estimate nongravitational acceleration, which includes the drag acceleration of the thermospheric density model and empirical force acceleration in the velocity direction from the centimeter-level reduced-dynamic POD. The main research achievements include the study of atmospheric responses to geomagnetic storms, especially after the launch of the spherical Qiu Qiu (QQ)-Satellite (QQ-Satellite) with the global navigation system satellite (GNSS) receiver onboard tracking the Global Positioning System (GPS) and Beidou System (BDS) data. Using this derivation method, the high-accuracy POD atmospheric density was determined from these data, resulting in better agreement among the QQ-Satellite-derived densities and the NRLMSISE-00 model densities. In addition, the POD-derived density exhibited a more sensitive response to magnetic storms. Improved accuracy of short-term orbit predictions using derived density was one of the aims of this study. Preliminary experiments using densities derived from the QQ-Satellite showed promising and encouraging results in reducing orbit propagation errors within 24 h, especially during periods of geomagnetic activity. Full article