They possessed a real-time point positioning and real-time precision of single baselines. The measurements were affected by a geomagnetic storm (Dst peak = \(-\)80 nT and Kp-index = \(5^{+}\)), which made it difficult to separate the influence of the eclipse from the storm one. In the case of the 2017 eclipse, [PERSON] et al. [26] computed and compared the rate of change of the TEC (ROT) with respect to the day before and the day after the eclipse; and with a time window of 3 h, from 16 UT. They determined the means of positioning errors at four GNSS stations (localized in Oregon, United States) within the path of the total solar  
Figure 1: Instruments used in present work: 2337 GNSS stations (red dots), 4 digisondes (blue rhombuses), and 24 selected GNSS stations (green triangles). The magnetic equator (black line) and the annular eclipse path at ground level (blue line) are shown. Eclipse obscuration mask from P1–P4 time (shaded region), the annular solar eclipse path (magenta line), the maximum obscuration (magenta dot) at 350 km altitude are presented. The magenta dashed line starts from the maximum obscuration of the solar eclipse to its conjugate location in the Southern Hemisphere. eclipse which reached \(\sim\)32 cm. However, on reference days, the means of positioning errors were between 7-14 cm. The authors used the average length of the eight baselines, which was \(\sim\)270 km. On the eclipse day, the means of positioning results were \(-\)4 to 324% over the day before and the day after the eclipse. [PERSON] et al. [25] established the ionospheric eclipse factor method (IEFM) to model the ionospheric delay searching for the improvement of the GNSS positioning estimation. In this contest, the paper introduces the concept of the ionospheric eclipse factor method for the IPP for relatively precise separation of daytime from nighttime for the ionosphere. Although the ionospheric eclipse factor is not related to a solar eclipse as an astronomical phenomenon that occurs when the Moon obscures the Sun from Earth, this method could be used in future studies related to the impact of solar eclipses on GNSS positioning. In our case, we obtain the apparent position variation using the post-processing kinematic precise point positioning (PPP) with ambiguity resolution (PPP-AR) mode. We chose this method because PPP demonstrates a high ability to improve position estimation. PPP is used for calculating the coordinates of a single receiver without the need for a reference station nearby as a control station. In addition, we can find some free PPP services available online [29]. PPP-AR is an enhanced version of the PPP technique that resolves the carrier phase ambiguities, improving the PPP accuracy [29; 30; 31]. [PERSON] et al. [30] recently presented a comparison between PPP and PPP-AR. In order to offer the community the possibility of evaluating our analysis, we used an online service. Thus, we selected the CSRS-PPP service for this work because it is one of the most commonly used PPP online services in the field. We also applied the common noise filter to more than 2300 GNSS stations, to correct the time series of the North, East, and Up components of the GNSS receivers, as described in [29]. The regular ionospheric effects of solar eclipses are not yet fully understood. Studies of the eclipse-induced effects on the ionosphere are important because they provide a better understanding of the processes that control the ionosphere and that can cause GNSS positioning errors. In the present paper, we present the impact of the 10 June 2021 annular solar eclipse on ionospheric variations that also cause errors in GNSS positioning. Therefore, we first analyze the ionospheric behavior at a global scale based on 2337 dual-frequency (DF) GNSS stations, Swarm-A satellite, and four ionospheric stations. We used GNSS stations distributed around the world since they will allow us to evaluate the effects beyond the northern polar region with a higher spatial resolution than ever before.