# Improvement of Global Ionospheric TEC Derivation with Multi-Source Data in Modip Latitude
## 3 Simulation
### Fitting with Simulated IGS and Satellite TEC  
Next, we evaluated the fittings between ground-based GNSS and combined ground- and space-based data. The locations of the IGS receivers and all the IPs of the space-based observation data for the period of 16:00-18:00 UT, DOY 265, 2014, are shown in Figure 4, where the observation amounts of COSMIC occultation and JASON-2 altimetry only account for 0.15% and 6.37% of the ground-based observations. It should be pointed out that at a certain time, there were fewer space-based observations. The areas without observation still account for the vast majority with the space-based data added.  
Based on the actual IPP positions from ground-based observations, which are denoted as IRI(G), and from ground- and space-based observations including COSMIC occultation and JASON-2 altimetry, which are denoted as IRI(GAC), TEC data were generated by the IRI-2016 model. Residual maps of fitting with IRI(G) and IRI(GAC) data at 18:00 UT are shown in Figure 5, where the performance is much worse than when using the ED data. Due to the lack of ground-based observations, the extrapolation of TEC will lead to the appearance of abnormal values in the areas where observation data are limited, especially in the EA, SAMA regions and oceans. The addition of space-based observations has a slight impact on the global TEC derivation, where the mean residual d  
Figure 4: Locations of International GNSS Service (IGS) receivers and ionospheric piercing points (IPPs) of space-based observation data at 16:00–18:00 UT, DOY 265, 2014.  
Figure 3: Root-mean-square error (RMSE) in geomagnetic and modify coordinate systems over time (**top**) and different latitude bands (**bottom**) on DOY 265, 2014.  
geomagnetic latitude and 1.37 TECU in modify latitude, and the major differences occur in the areas with limited data in the Pacific, Atlantic and the equatorial region, which is consistent with the location of data we added. After modip latitude was used, the mean and peak values of the residuals dropped by 1.62 and 4.95 TECU, respectively.  
The RMSE results of fitting residuals with IRI(G) and IRI(GAC) data in geomagnetic and modip latitudes are shown in Figure 6, where only the extrapolated regions were statistically analyzed. The mean improvements of fitting in the modip latitude can reach 12.64% (G) and 11.47% (GAC). In the areas that lack data, the reduction of RMSE when using the IRI(GAC) data has mean values of 3.26% and 4.49% in geomagnetic and modip latitude, respectively, suggesting the improvement is limited and has a strong relation to the number of space-based observations. According to these results, obtaining relative reasonable TEC information from an ionospheric empirical model in areas lacking observation data could be another alternative to consider, due to the limited amount of space-based observations in the current situation.  
Data on DOVs 081, 173, 265 and 356 were used to analyze the variation in RMSE by using modip latitude and multi-source data in different months. The results are shown in Table 1, where the mean RMSE reduction is about 10.88%. According to the Table 1, the mean improvement is about 9.48% when using modip latitude and ground-based observations, and 3.54% in geomagnetic latitude and 1.40% in mo  
Figure 5: Residual maps of fitting in different coordinate systems (18:00 UT, DOY 265, 2014).  
Figure 6: RMSE of fitting residual in geomagnetic and modip latitude (DOY 265, 2014).  
space-based observations. The largest RMSE reductions from using modify latitude are 13.90% on DOY 081, 8.48% on DOY 265, 7.29% on DOY 356 and 6.89% on DOY 173.