# Effects of the 12 May 2021 Geomagnetic Storm on Georeferencing Precision
## 3 Results  
In this section, we present the main results obtained after applying the methodology described in the previous section to the 868 stations. First, we isolated the period of time to analyze. We focused the study between 9 May and 17 May 2021 (DoY 129 to 137). Figure 2 presents an example of the signals analyzed for this work. We calculated VTEC, DVTEC, %DVTEC, and ROTI as described in the previous section. These variables can be seen in panels (g-j) of Figure 2, respectively, for a sample station, the Vegas de Iata station (known as vita station) located in Chile (36.42\({}^{\circ}\)S, 72.86\({}^{\circ}\)W). Then, by using the VTEC and the PPP-AR method (see Section 2.3), we were able to obtain the apparent position variation. In panels (d) to (f) in Figure 2, we present the root mean square (RMS) time series of the apparent position, after correcting using the common noise filter (see Equation (5)), per each component (East, North, and Up) also for the vita station. It is clear in the image that the period of the storm is the period with the larger uncertainties in the position estimation. From each station data, we can estimate the VTEC over each station during the period of the geomagnetic storm (14.50-14.75 UT) on 12 May 2021 as shown in Figure 3a. By using the ordinary Kriging interpolation, as described in Section 2.1, it is possible to obtain filled-in VTEC maps for the geomagnetic storm day (Figure 3c) and for the averaged VTEC (\(\overline{VTEC}\)). \(\overline{VTEC}\) is calculated with the VTEC of the previous days to the geomagnetic storm, DoYs 129, 130, and 131 (Figure 3b). Figure 3d,e show the differential VTEC (DVTEC) and perceptual DVTEC (%DVTEC) for the time of the geomagnetic storm, respectively, which are obtained by using Equations (1) and (2) with the data presented in (Figure 3b,c). From these figures, it is possible to notice that the South American sector is one of the most affected in terms of VTEC enhancement. Since this study focuses on the positioning accuracy of the stations during a moderated storm, we estimate the PPP-AR as described in Section 2.3. Figure 4 (left panel) and Figure 5 (left panel) show different 3D positioning error maps for various times, obtained using the CSRS-PPP service. The reports provided by CSRS had different reference values for each of the eight days requested. This provoked that more than 5% of the stations had baselines with differences over 100 cm, such as: lovj (67.89\({}^{\circ}\)N, 34.62\({}^{\circ}\)E), novm (55.03\({}^{\circ}\)N, 82.91\({}^{\circ}\)E), ieng (45.02\({}^{\circ}\)N, 7.64\({}^{\circ}\)E), mdvj (56.02\({}^{\circ}\)N, 37.21\({}^{\circ}\)E), meco (\(-\)29.18\({}^{\circ}\)S, \(-\)58.08\({}^{\circ}\)W), cosom (\(-\)52.78\({}^{\circ}\)S, \(-\)69.22\({}^{\circ}\)W) stations. Therefore, we processed the data to have an equal reference for all stations to facilitate the evaluation of the position variation time series. After calculating the apparent position variation for each station in the time period of the geomagnetic storm (DoY 132, 14.50-14.75 UT), we quantified the number of stations around the world that had errors that fell in certain intervals (<5 cm, [5-10] cm, [10-20] cm, [10-20] cm, [20-40] cm, [40-40] cm, [40-60] cm, [60-100] cm and >100 cm). It is important to highlight that the PPP-AR procedure includes the use of a common noise filter (see Equation (5)). Without the filter, we had periods of time where certain GNSS stations consistently had very high errorseven during quiet days. The results of the classification of the stations by their positioning errors is presented in Table 2. Each column on the table represents the percentage of stations with errors in a certain interval per each positioning component as well as the combination of these components in the 3D parameter.