# Towards a Real-Time Description of the Ionosphere:
## 9 Final Analyses and Comparisons between IRI and IRTAM
Figures 45 and 46 confirm the general picture outlined by the analyses described in Sections 5-8, i.e., the comparison with ionosonde data highlights how IRTAM significantly improves the _fo_F2 prediction made by IRI, while for _hm_F2 the performances are quite similar between the two models. Since IRTAM assimilates both _fo_F2 and _hm_F2 from the GIRO network, we would have expected a similar improvement also in the _hm_F2 prediction. Besides the obvious differences due to the application of the newest [PERSON] et al. [44] IRI _hm_F2 default option, two important points need to be highlighted. First, the IRTAM _hm_F2 description is based on the mapping procedure introduced by [PERSON] et al. [53], which introduces residuals in the range from \(-\)10 to 10 km when compared to the original _hm_F2 values obtained from the [PERSON] et al. [41] formulation. The second point is inherent to the _hm_F2 derivation from ionograms. In fact, while _fo_F2 is a parameter that is directly obtained from ionograms as the maximum ordinary frequency reflected by the ionosphere, _hm_F2 has to be derived trough a mathematical inversion procedure that, starting from critical frequencies measured at different virtual heights, allows obtaining the electron density values at real heights [72]. This inversion procedure is sensitive to different possible error sources due to the E-valley presence, the interpretation of the F2-layer cusp made by ARTIST, and in general the quality of the ionogram echo traces. All of these matters may represent possible sources of error, that are estimated in the order of 10 km [73]. From the above considerations, it clearly emerges that to obtain reliable _hm_F2 values is more difficult than to get _fo_F2 ones, even with data assimilation. This is a point that requires further improvements and refining of both the data assimilation procedure and the measurement technique itself. The comparison with the F2-layer peak characteristics derived from COSMIC RO showed a general worsening of the IRTAM performances in comparison with the IRI ones. Specifically, the comparison with the COSMIC dataset (Figures 20 and 41) highlighted that IRTAM improves the IRI _fo_F2 and _hm_F2 predictions mainly in regions characterized by a dense ionosonde network. This suggests the extent to which IRTAM is tied to data assimilated by ionosondes and to the corresponding spatial distribution. Since assimilated data are used by IRTAM to update the coefficients of the spherical harmonic analysis underlying the IRI description, we would have expected that the improvements were not restricted to the ionosondes' locations but would embrace at least the whole latitudinal sector where assimilated ionosondes are located. Moreover, the IRTAM description should fade towards that of the IRI in regions where the effect of the assimilated data can be considered negligible. These two points are very important, impacting on IRTAM global performances, and need to be deeply investigated for future versions of IRTAM. Currently, IRTAM assimilates data from about 60 GIRO Digisondes. With the ever-increasing number of available Digisondes, able to provide real-time data, and as a consequence of their more homogeneous spatial distribution, a continuous improvement of the IRTAM performance is expected. However, even if in the future the availability of ionosonde data should increase for both the time resolution and the spatial coverage, the fact that IRTAM is so tied to the underlying IRI model (i.e., to the URSI formalism) represents a limit for the improvement and development of IRTAM itself. In fact, IRTAM through NECTAR, to minimize at assimilation sites the mismatch between measured and IRI modeled values, calculates the corrections to be added to the URSI coefficients, but the order of the diurnal and spatial harmonics is left unchanged.