# Towards a Real-Time Description of the Ionosphere:
## 9 Final Analyses and Comparisons between IRI and IRTAM  
The results shown in the previous sections are here summarized through the residual deviation ratio \(R_{\text{cw}}\) defined in Section 4. \(R_{\text{cw}}\) is a statistical parameter that in general is very suitable to assess definitively the performance of one model over another. To this end, the IRI and IRTAM performances are evaluated analyzing the distributions (in a logarithmic scale) of the residuals' deviation ratio calculated on both the full _fo_F2 ionosonde/COSMIC dataset (Figure 45) and the full \(hm\)F2 ionosonde/COSMIC dataset (Figure 46). Figure 45: Probability distributions of the residuals’ deviation ratio in a logarithmic scale, \(\log_{10}(R_{\text{cw}})\), between IRI and IRTAM models calculated on the (**a**) entire ionosonde and (**b**) COSMIC _fo_F2 datasets. The mean, standard deviation, and counts values are reported in the upper left corner of each plot. The dashed vertical lines indicate, respectively, when the models are on par (in black), IRTAM improves IRI by a factor of 2 (in red), and IRTAM improves IRI by a factor of 10 (in blue). The \(\log_{10}(R_{\rm cw})\) distribution shown in Figure 45a is clearly \"shifted\" towards the positive values. Specifically, the mean value of the distribution equal to +0.212 highlights that overall IRTAM performs better than IRI by a factor of about 1.6 when considering the \(f\)0F2 ionosonde dataset. Instead, the \(\log_{10}(R_{\rm cw})\) distribution shown in Figure 45b is quite symmetric with respect to the zero value. This fact is strongly supported by the mean value of the distribution, which is equal to \(-0.001\). Therefore, the IRI and IRTAM performances can be considered equivalent when considering the COSMIC \(f\)0F2 dataset. The \(\log_{10}(R_{\rm cw})\) distribution shown in Figure 46a is quite symmetric around the zero. Specifically, the mean value of the distribution equal to +0.057 (which corresponds to an improvement factor of about 1.14) highlights that IRTAM and IRI provide quite comparable outputs when considering the _hm_F2 ionosonde dataset. The \(\log_{10}(R_{\rm cw})\) distribution shown in Figure 46b is clearly \"shifted\" towards the negative values. Specifically, the mean value of the distribution equal to \(-0.188\) points out that IRI performs better than IRTAM when considering the _hm_F2 COSMIC dataset by a factor of about 1.5. The same analysis based on \(\log_{10}(R_{\rm cw})\) distribution was applied by [PERSON] et al. [18] on a dataset of \(f\)0F2 values recorded by 59 ionosondes during May-June 2019. They found results very similar to the ones shown in Figure 45a, with IRTAM improving IRI by a factor of about two (about 0.3 in the logarithmic scale of Figure 45). The slight differences are due on the one hand to the fact that to test IRTAM [PERSON] et al. [18] used only data from assimilated stations, while in this study we used also non-assimilated stations, and on  
Figure 46: Same as Figure 45 but for _hm_F2. the other hand to the larger extension of our dataset covering different seasons and solar activity levels. Results similar to those of [PERSON] et al. [18] were obtained also by [PERSON] [29] by considering a larger dataset covering one solar cycle, but again using only data from assimilated stations to test the model. [PERSON] [29] investigated the IRTAM performance also for _hm_F2 and found that IRTAM improved IRI by a factor of about 1.8. However, in that analysis the oldest [PERSON] et al. [41]_hm_F2 IRI option was used as comparison. When using the newest [PERSON] et al. [44] default IRI _hm_F2 option, we find much lower differences between IRTAM and IRI, thus confirming the very important step forward made by IRI about the _hm_F2 modeling, as on the other hand recently outlined by different authors [69; 70; 71].