# On the Feasibility of Interhemispheric Patch Detection Using Ground-Based GNSS Measurements
## 2 Methodology and Data Set
They, in turn, are responsible for the faster convergence of polynomial fitting to background STEC variations, which correspond to sub-daily changes of ionospheric conditions and the impact of the elevation angle. The initial fitting provides the preliminary information on the background ionosphere, and is treated as final for very small variations in the magnitude of STEC. The last two-step iterative mode allows the approximation of background STEC level for disturbed conditions. In comparison to earlier analysis aimed at the detection of different ionospheric structures [37], the thresholds applied here are highly restrictive. This corresponds to the results of Polar Cap Products ([[https://www.mn.uio.no/fysikk/english/research/projects/swarm/](https://www.mn.uio.no/fysikk/english/research/projects/swarm/)]([https://www.mn.uio.no/fysikk/english/research/projects/swarm/](https://www.mn.uio.no/fysikk/english/research/projects/swarm/))), which indicated the occurrence of weak patches during the test period (an example of such structures is given in Section 4). Finally, the relative STEC values are computed as a difference between geometry-free data at a particular epoch and the fitted polynomial. The above algorithm was applied to GNSS data with an elevation mask set to \(10^{\circ}\) and arcs longer than three hours. The second criterion ensures the proper work of the algorithm and prevents a situation wherein the patch or their sequence could fill the entire time series. As the low-elevated data are expected to be less accurate, the main part of multi-station analyses was performed with the cut-off angle set to \(30^{\circ}\). Figure 1 depicts the performance of the algorithm, including three-step fitting, for an \(L_{4}\) time series from the northern and southern hemispheres (left and right column, respectively). The first panel (station ALRT, satellite GPS 31) depicts the situation with signatures of patches observed for low-elevated data. In this case, the structures are properly extracted from the \(L_{4}\) time series, although the orientation of the GNSS signal was poor. On the other hand, it should be remarked that STEC enhancement for almost horizontal direction may significantly differ from the vertical one due to the long path of the signal in the ionosphere. The remaining arcs that are shown in Figure 1 correspond to the period with extreme patch activity. Comparing the results for the northern (station SRMP, satellite GPS; 32, bottom left panel) and southern hemispheres (station CAS1, satellites GPS 5 and GLONASS; six top and bottom panels on the right side, respectively), one can observe the significant difference in STEC enhancement, which is discussed in detail in Section 4.2. Furthermore, Figure 1 indicates two issues related to the algorithm results. The first is artificial convexity in a fitted polynomial, which is clearly observable in top right panel. It mostly occurs for elevation below 30\({}^{\circ}\), and in particularly adverse conditions (partially outside extended structures), it may lead to the overestimation of relative STEC values. The application of an appropriate mask practically eliminates it from the final solution. The second effect, which is depicted in results for the southern hemisphere, is the lack of simple dependence of the STEC enhancement on the elevation angle. Comparing figures in the right panel, one can see that the strongest effect (up to \(\sim\)80 TECu) is observed at 30\({}^{\circ}\) and 60\({}^{\circ}\) of elevation, respectively. The results for the other structures are even more ambiguous, i.e., the signatures at low elevations are both overestimated and underestimated in reference to high-elevated data. This effect is related to the different spatial orientation of GNSS signals. The similar analysis for other periods confirms the independence of an applied indicator on the elevation angle; thus, further analyses used relative enhancement in a slant direction.