# Towards a Real-Time Description of the Ionosphere:
## 1 Introduction  
Space Weather events can have a deep negative impact on the technological systems, such as power systems, satellites, Global Positioning System (GPS), pipelines, and communication cables. The damages suffered by these systems, on which our society is nowadays greatly dependent, besides leading to very high costs, can also significantly affect human life [1, 2]. Therefore, in this context, in recent years the near real-time specification of the ionosphere has become more and more important to nowcast and possibly mitigate the adverse consequences of Space Weather events. For this purpose, several models able to assimilate real-time ionospheric measurements have been recently proposed [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]. Some climatological models already existent were adapted for the quasi real-time assimilation of ionospheric data. One outstanding example is the IRI-based Real-Time Assimilative Model (IRTAM) [17, 18] that, by ingesting ionosonde-derived F2-layer peak parameters values, updates the underlying empirical global climatological knowledge of the ionosphere provided by the International Reference Ionosphere (IRI) model [19, 20], thus supplying a global real-time representation of the ionosphere. The assimilation of real-time measurements in a background empirical model is one of the most applied and fruitful methodologies for the real-time specification of the ionospheric electron density. Inthis context, the knowledge of the large-scale climatological behavior of the ionosphere provided by the underlying background empirical model is complemented with the small-scale weather information provided by real-time assimilated data. The effectiveness and quality of such data-assimilation procedures is critically dependent on the applied algorithm, on the quality, spatial distribution, and availability of assimilated data, and of course on the underlying background empirical model. Empirical models, such as IRI, are based on analytical formulations whose numerical coefficients are obtained on the basis of the underlying datasets; as a consequence, when new datasets are released, it is of utmost importance to validate the model against new data and eventually recalculate the model's coefficients with the inclusion of the newest data. This validation and recalculation scheme is an ongoing process for empirical models and leads to the continuous improvement of the model itself. Over the years, IRI underwent many validation studies and comparisons with other ionospheric models [21; 22; 23; 24; 25; 26; 27; 28]; on the contrary, validations of the IRTAM model are restricted to the works by [PERSON] [29] and [PERSON] et al. [18] for specific locations and conditions. Due to the ever-growing importance that IRTAM is gaining as the most used and affirmed real-time specification of the ionosphere, it is important to validate its performances against large and different datasets to quantify the improvement made by IRTAM in the description of ionospheric weather when compared to the climatological representation made by IRI. In the present paper, a global validation of the ionosphere F2-layer peak characteristics as modeled by IRI and IRTAM is presented. Specifically, the IRI and IRTAM models, the latter assimilating both the F2-layer ordinary critical frequency (\(f\)oF2) and the F2-layer peak height (\(hm\)F2) from ground-based ionosondes, have been validated according to two different datasets: (1) \(f\)oF2 and \(hm\)F2 ground-based ionosonde observations recorded from 1 January 2000 to 31 December 2019 at 40 ionospheric stations spread in both hemispheres; (2) \(f\)oF2 and \(hm\)F2 derived from Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC/FORMOSAT-3) radio occultation (RO) observations from 22 April 2006 to 31 December 2018. In order to assess the performances of both IRI and IRTAM, different statistical metrics have been estimated.