The plasmasphere, which is located above the ionosphere, is a significant component of Earth’s atmosphere, and the plasmasphere electron content (PEC) distribution is determined by different physical mechanisms to those of the ionosphere electron content (IEC). However, the observation for the PEC is very limited. In this study, we introduced a methodology (called zero assumption method, which is based on the assumption that PEC can reach zero) to extract the PEC over TOPEX/JASON (T/J) and global navigation satellite system (GNSS) overlapping areas. Results show that the daily systematic bias (T/J vertical TEC > GNSS-derived vertical TEC) for both low (2009) and high (2011) solar activity condition is consistent, and the systematic bias for JASON2 and JASON1 is different. We suggest that systematic biases predominantly arise from the sea state bias (SSB), especially the tracker bias. After removing the systematic bias, we extracted reliable PEC inferred from differences between GNSS-derived vertical TEC and T/J vertical TEC data. Finally, the characteristics of the plasmaspheric component distribution for different local times, latitudes, and seasons were investigated.

The prevalence of real-time, low-cost, single-frequency, decimeter-level positioning has increased with the development of global navigation satellite systems (GNSSs). Ionospheric delay accounts for most errors in real-time single-frequency GNSS positioning. To eliminate ionospheric interference in real-time single-frequency precise point positioning (RT-SF-PPP), global ionospheric vertical total electron content (VTEC) product is designed in the next stage of the International GNSS Service (IGS) real-time service (RTS). In this study, real-time generation of a global ionospheric map (GIM) based on IGS RTS is proposed and assessed. There are three crucial steps in the process of generating a real-time global ionospheric map (RTGIM): estimating station differential code bias (DCB) using the precise point positioning (PPP) method, deriving slant total electron content (STEC) from PPP with raw observations, and modeling global vertical total electron content (VTEC). Experiments were carried out to validate the algorithm’s effectiveness. First, one month’s data from 16 globally distributed IGS stations were used to validate the performance of DCB estimation with the PPP method. Second, 30 IGS stations were used to verify the accuracy of static PPP with raw observations. Third, the modeling of residuals was assessed in high and quiet ionospheric activity periods. Afterwards, the quality of RTGIM products was assessed from two aspects: (1) comparison with the Center for Orbit Determination in Europe (CODE) global ionospheric map (GIM) products and (2) determination of the performance of RT-SF-PPP with the RTGIM. Experimental results show that DCB estimation using the PPP method can realize an average accuracy of 0.2 ns; static PPP with raw observations can achieve an accuracy of 0.7, 1.2, and 2.1 cm in the north, east, and up components, respectively. The average standard deviations (STDs) of the model residuals are 2.07 and 2.17 TEC units (TECU) for moderate and high ionospheric activity periods. Moreover, the average root-mean-square (RMS) error of RTGIM products is 2.4 TECU for the one-month moderate ionospheric period. Nevertheless, for the high ionospheric period, the RMS is greater than the RMS in the moderate period. A sub-meter-level horizontal accuracy and meter-level vertical accuracy can be achieved when the RTGIM is employed in RT-SF-PPP.

The global 3‐dimensional structure of the concentric traveling ionospheric disturbances (CTIDs) triggered by 2022 Tonga volcano was reconstructed by using the 3‐dimensional computerized ionospheric tomography (3DCIT) technique and extensive global navigation satellite system (GNSS) observations. This study provides the first estimation of the CTIDs vertical wavelengths, ∼736 km, which was much larger than the gravity wave (GW) vertical wavelength, 240–400 km, estimated using ICON neutral wind observations. Notable trend with the variation of azimuth was also found in horizontal speeds at 200 and 500 km altitudes and differences between them. These results imply that (a) the global propagation of Lamb waves determined the arrival time of local ionospheric disturbances, and (b) the arriving Lamb waves caused vertical atmospheric perturbations that are not typical of GWs, resulting in local thermospheric horizontal wave propagation which is faster than the Lamb wave propagation at lower altitudes.

Using the Global Navigation Satellite System (GNSS) differenced total electron content (dTEC) series, the traveling ionosphere disturbances (TIDs) of 22 typhoons registered in Taiwan/Japan between 2013 and 2016 were studied. The horizontal speed of the first TID during a typhoon landing can be estimated by a two-station method with the ionosphere anomaly indicator in total electron count units (TECUs) (|dTEC| ≥ 0.15 TECU). The horizontal speed of the TIDs was from 155 to 210 m/s and with an average speed of 168.70 m/s. The estimated TID speeds of Typhoons Soudelor (205.93 m/s) and Megi (158.47 m/s) are not consistent with each other, even though they had very similar trajectories when cross through Taiwan Island. Moreover, the propagation velocity of the typhoon ionospheric anomaly showed a significant positive correlation (r = 0.78, α = 0.05) with the change rate of the typhoon central air pressure and a negative correlation (r = -0.52, α = 0.05) with the central pressure before landing. Gravity waves were generated by land friction, terrain blocking, and strong wind shear transport energy into the atmosphere from the near surface to the mesosphere and thermosphere, which is the main cause of ionosphere disturbances during typhoon landing.