In this study, based on the OI135.6 nm night airglow data of the FY-3D Ionospheric Photometer (IPM) during the 2018-2021 geomagnetically quiet period, the global wavenumber 4 longitudinal structure of the equatorial ionization anomaly (EIA) at 2:00 local time was discovered, and the component of the wavenumber 4 was extracted from these structures. Compared with the OI135.6 nm night airglow data of the Special Sensor Ultraviolet Spectrographic Imager (SSUSI) F18 during 2011-2014, there were significant differences in the variation pattern of the relative amplitude of the two versus solar activity and the seasonal variation in the proportion of the component of the wavenumber 4. Based on the neutral wind speed observation results of Global High-Resolution Thermospheric Imaging (MIGHTI) on board the Ionospheric Connection Explorer (ICON) from 2020-2021, the longitudinal structures of the 4 ionospheric waves after midnight are related to the cross-equatorial meridional wind. We believe that the wavenumber 4 longitudinal structures after midnight originate from the eastward nonmigrating tidal semidiurnal wave (SE2) with a wavenumber of 2 in the cross-equatorial neutral wind rather than the eastward nonmigrating tidal semidiurnal wave (DE3) with a wavenumber of 3 in from the zonal wind, which modulates the daytime wavenumber 4 longitudinal structures.

The thermospheric density and its variations are crucial to aerospace activities as well as space weather research and operation. However, due to the difficulties to observe the thermosphere, there has been a lack of effective descriptions for the overall general characteristics of thermospheric density. In this present paper, the Two-Line-Element datasets (TLEs) from multi-target low Earth orbit (LEO) satellites are used to derive a proxy of the daily average atmospheric density in the thermospheric shell located in the vicinity of LEOs’ orbital altitude. It captures the overall characteristics of the thermosphere and exhibits good correlations (0.84) with modeled and observed thermospheric density. By applying the spectral whitening method (SWM) to this proxy, a new index JsT is derived to describe non-periodic perturbation of the density where the specific satellite passed by. The fact that the JsT obtained from different satellites within the same thermospheric shell presents significant consistency to each other means that the new index is a good indicator for the overall feature of the variations of thermospheric density, and it is possible to define a unified regional index JrT to describe density disturbances for the thermospheric shell where these satellites flying through. Moreover, the JrT at different altitudes also present good consistency suggesting the possibility of defining a global index JpT, capable of describing the density variation of the entire thermosphere.

Reliable short-time prediction of thermospheric mass density along the satellite orbit is always essential but challenging for the operation of Low-Earth orbit (LEO) satellites. In this paper, three machine-learning prediction algorithms are investigated, including the Bidirectional Long Short-Term Memory (Bi-LSTM), the Transformer, and the Light Gradient Boosting Machine (LightGBM) ensemble model of the above models. We use satellite data from CHAMP, GOCE, and SWARM-C to evaluate the robustness and accuracy of different density variations. The comparison demonstrates that all models achieve compelling predictions and are much better than NRLMSISE-00. The LightGBM ensemble model (LE-model) consistently outperforms others in accuracy and stability. Furthermore, when the obtained density data from the newly launched satellites are limited, the trained LE-model can provide a valid prediction for the new satellite orbit by transfer learning. This study offers a promising insight into the short-time prediction of thermospheric mass density using ensemble-transfer learning and may be advantageous to future research on space whether.

This paper describes the development of the fluxgate magnetometer on the Fengyun-4B satellite. The Fengyun-4 is the second generation of China’s geostationary orbit satellite with the function of monitoring the space environment in GEO orbit. A fluxgate magnetometer (FGM) is deployed on this satellite to observe the magnetic field as a necessary input to space weather forecasting. This payload adopts three 3-axis fluxgate sensors to obtain space magnetic field data by excluding the satellite’s interference. Each three-axis fluxgate sensor has an independent signal processing circuit. FGM uses digital signal processing technology to acquire magnetic field signals. First, the analog signal is oversampled using a high-speed ADC, then digital signal processing, such as phase-sensitive demodulation, integration, and filtering, is performed inside the FPGA, and the feedback signal is output to the feedback coil through the DAC. This signal processing loop constitutes an ADC system, and the quantization accuracy of the output digital quantity can reach 18 bits. the FGM performs in-orbit calibration during satellite rotation maneuver and Alfven wave events. Comparison with the GOES-16 satellite and Tsyganenko magnetic field model proves FGM to be effective in monitoring the magnetic field of the space environment. Through joint observations with GOES-16 satellite and geomagnetic stations, FY-4B describes the development of a typical magnetic storm on November 4, 2021. The Ground calibration and in-orbit preliminary results show the FY-4B satellite magnetometer outputs 20 bits of digital resolution data at a 30 Hz sampling rate, with the noise lower than 3×10-4nT2/Hz@1Hz in the ±600nT range.

Ionospheric Total Electron Content (TEC) prediction has important reference significance for the accuracy of global navigation satellite systems (GNSS) based global positioning system, satellite communications and other space communications applications. In the study, an available prediction model of global IGS-TEC map is established based on testing several different LSTM-based algorithms. We find that Multi-step auxiliary algorithm based prediction model performs the best. It can precisely predict the global ionospheric IGS-TEC in the next 6 days (the MAD and RMSE are 2.485 and 3.511 TECU, respectively). Then, the autoencoder network algorithm is adopted to construct an assimilation model that transforming IGS-TEC map to MIT-TEC map. In order to judge the validity of the assimilation model, the outputs of the assimilation model are evaluated and compared with the IRI2016 model in four different geomagnetic storm events. It seems that the assimilation model can accurately forecast MIT-TEC by inputting the predicted IGS-TEC value. The performance of assimilation model for the predicting MIT-TEC is better than that of IRI2016.

As there are strong crustal magnetic fields in some Martian concentrated regions. it has long been a goal of Martian science to understand how crustal magnetic field affects surrounding space environment. In the paper, using the data measured by MAVEN, the ratio of electron/CO2 density ( Ne/NCO2) in region with different levels of Martian ionospheric magnetic fields are studied. It seems that ratio of dayside Ne/NCO2 in region with stronger ionospheric magnetic field is larger while the altitude is more than 260 km. On the other hand, the effect of crustal magnetic field intensity on the nightside ratio of Ne/NCO2 is weak. Since the topological structure of magnetic field is very vulnerable to the solar wind, the correlation between Ne/NCO2 and solar wind parameters are analyzed. We find that there is obvious negative correlation between dayside ratio of Ne/NCO2 and solar wind dynamic pressure in the region with strong ionospheric magnetic field, which may imply that the ionospheric plasmas are significantly escaped in response to enhanced solar wind dynamic pressure pulses in the dayside region. However, the effect of solar wind on nightside ratio of Ne/NCO2 is very little. These results can be useful for understanding the dynamic process in the Martian ionosphere.