# Towards a Real-Time Description of the Ionosphere:
## 6 The _f_oF2 statistics  
Low solar activity  
Figure 16: Same as Figure 4 but for Thule. Figure 17: Same as Figure 16 but for MSA level. In order to simplify the IRI-IRTAM comparison to the reader, all figures have been generated with the same scale. Since the main goal of the paper is the comparison between IRI and IRTAM performances, we will focus on the differences between the statistical metrics provided by IRI and IRTAM, without going into detail about the corresponding physical and theoretical explanation of the observed trends. Figures 4-6 show that at Jicamarca for LSA, IRTAM models _fo_F2 more accurately than IRI, especially during pre-sunrise and post-sunset hours. An improvement in the _fo_F2  
Figure 18: Same as Figure 16 but for HSA level. modeling accuracy made by IRTAM is still observed for MSA around sunrise hours for equinoctial and summer months and during daytime hours for some equinoctial months. For both LSA and MSA, at sunrise both models show high percentages of error (at least 25%). Nevertheless, during nighttime, a significant reduction in the percentage error made by IRTAM can be appreciated, which means a good improvement in the _fo_F2 modeling precision. For HSA, around sunrise in equinoctial and summer months, IRTAM performs better than IRI in terms of accuracy. The relatively high absolute errors (at least 2.0 MHz) made by IRI in equinoctial and summer months are strongly smoothed by IRTAM. Except for the hours around sunrise, where the percentage error remains pretty high (at least 25%), a very strong reduction in the percentage error made by IRTAM is observed in all seasons, which means a considerable improvement in the _fo_F2 modeling precision. Figures 7-9 show that at Ascension Island overall, for LSA, during daytime hours and for all seasons, IRI models _fo_F2 less accurately than IRTAM. A very similar behavior of the two models is observed for the absolute error. However, during the central hours of the day, IRTAM provides a greater _fo_F2 modeling precision, being its percentage errors lower than those of IRI. Overall, for both MSA and HSA, the comparison between IRI and IRTAM _Res. Mean_ grids suggests that IRI is less accurate than IRTAM. The IRTAM model reduces both the absolute and the percentage error, during the first hours of the night and daytime hours, which highlights an improvement in the IRI _fo_F2 modeling precision. Both IRI and IRTAM provide bad performance during post-sunset hours, for which extremely high percentage errors, up to 30% and more, are observed. Figures 10-12 show that at Rome for LSA, a better performance in terms of _fo_F2 modeling accuracy is clearly observed in favour of IRTAM, while _RMSE_ and _NRMSE_ grids show very similar patterns, thus indicating that the precision with which IRI and IRTAM model _fo_F2 is comparable. Overall, for MSA, IRI and IRTAM performances are comparable in terms of accuracy. For HSA, a greater accuracy is provided by IRTAM, during pre-sunrise hours and daytime hours for summer months. For both MSA and HSA, the IRTAM model lowers the values of the absolute and percentage errors, which means that an improvement in the _fo_F2 modeling precision is achieved. Figures 13-18 show that at Sondrestrom and Thule for the three considered solar activity levels a remarkable reduction in the _Res. Mean_, _RMSE_, and _NRMSE_ values is observed when passing from IRI to IRTAM, which means that a significant improvement in the _fo_F2 modeling is achieved in terms of both accuracy and precision. The improvement brought by IRTAM is remarkable at equinoctial and winter months. From a visual inspection of _fo_F2 grids obtained for the three solar activity ranges, some general considerations on how the IRI and IRTAM performances depend on solar activity can be made: no clear _Res.