# Global GNSS-RO Electron Density in the Lower Ionosphere
## 4 Discussions
### TEC Comparisons and Maps  
The \(TEC\) retrieval methods described in Figure 7 are applied to the real RO data and compared with the \(TEC\) measurements from ground-based GPS receivers. To validate the \(TEC\) retrieval methods, we use the MetOp-A data from 9-18 June 2020 when it acquired the high-rate RO data with the top up to \(\sim\)290 km. To find collocated RO profiles with ground-based \(TEC\) observations, the RO \(TEC\) measurements are required within \(1^{\circ}\times 1^{\circ}\) latitude-longitude and 5 min windows. Figure 18 shows the RO \(TEC\) retrievals for the same four methods as illustrated in Figure 7. Similar to the conclusion in Figure 7, the method from Equation (12) works best, showing the lowest bias and standard deviation. The RO-\(TEC\) and ground-\(TEC\) are in generally good agreement along the 1:1 line. The standard deviations from the MetOp-A retrieval are higher than the simulation from the synthetic data, which may be caused by additional variability and differences in their observing times and spatial averaging where horizontal inhomogeneity can play a role. To develop a generalized method beyond Equations (11) and (12), we obtained a set of regression coefficients for the \(TEC\) retrieval from the RO profiles with different top cutoffs, using the synthetic IRI-2016 data. This method uses all \(\Delta hTEC\) measurements below the RO top, and the retrieval coefficients for different RO top cutoffs are stored in a lookup table. For the RO \(\Delta hTEC\) profiles with the top below 180 km, the \(TEC\) retrieval error tends to increase. We found that the RO profiles with a top above 120 km can still produce a scientifically useful \(TEC\) retrieval, but the retrieval error becomes too large if the top reaches only to 90 km, such as in the normal operation with MetOp-A/B/C. Figure 17: Average daytime (LST = 0800–1600) \(N_{e}\) for the GPS-RO and ionosonde techniques for (a) unshifted and (b) shifted profiles. The average \(f_{min}\) is displayed as a vertical line, while the average \(h_{m}E\) is displayed as a horizontal line. One of the advantages with the high-rate RO data is to provide a better spatiotemporal coverage for the _TEC_ measurement. Spatiotemporal sampling is critical for characterizing the ionosphere with large diurnal and geographical variabilities. Because of the recent high number of high-rate RO profiles (see Appendix B), the 2-hourly sampling of global _TEC_ maps can be obtained on a monthly basis from GNSS-RO observations. Figure 19 demonstrates the latest capability of dense sampling from the combined Spire and COSMIC-2 constellations, plus a few additional GNSS-RO satellites in October 2021. More examples of monthly _TEC_ maps can be found in Figure S6. While the daily sampling from the GNSS-RO observations is still sparse, the monthly sampling from these RO observations can provide nearly global coverage over a 4\({}^{\circ}\)\(\times\) 8\({}^{\circ}\) latitude-longitude grid every 2 h. Thanks to the COSMIC-2 constellation, there is little data gap in the tropical/subtropical latitudes (\(<\)45\({}^{\circ}\)N/S). In addition, a small number of gaps are found in extratropical and polar latitudes largely because of the coverage from the Spire constellation. FY3D helps to fill in a small sampling gap in the Spire constellation around nontime at mid- to high latitudes (Appendix B). Further reduction in data gaps at high latitudes is a benefit from other polar-orbiting satellites (MetOp-A, TSX, Kompsat-3, FY3C, PAZ). Figure 18: **(a–d)** RO-_TEC_ retrievals using the methods as labeled for the same panels as in Figure 7 and Equations (11) and (12), and their comparison with Ground-_TEC_. The MetOp-A RO data from 9–18 June 2020 are used because its high-rate ROs had a top up to \(h_{t}\)\(\sim\) 290 km. The 1:1 line is plotted to guide the comparisons where daytime and nighttime measurements are in red and blue, respectively.