## 1 Introduction
Moreover, the solar activity variability can be described when long time series become available. Furthermore, the cost of this measurement technique strongly limits the number and distribution of ISRs worldwide. As a consequence, for modeling purposes, both in-situ satellites and remote sensing ISRs observations are very important and complement each other. An outstanding example is represented by the International Reference Ionosphere (IRI) model [9], which is the empirical climatological model of the ionosphere used as reference by the ionospheric community, and recently recognized as the official ISO (International Organization for Standardization) standard for the ionosphere [10]. Through the exploitation of different measurement datasets of ionospheric parameters collected by ionosondes, ISRs, LEO satellites, Global Positioning System (GPS) radio occultation, and rockets, IRI describes the hourly monthly median behavior of the electron density, electron and ion temperature, and ion composition in the ionospheric altitude range, on a global basis, for different levels of solar and magnetic activity. Due to its empirical nature, IRI evolves and improves when new datasets become available and are used to derive the empirical coefficients of its analytical formulations. In this framework, the ability to model the changes of the ionospheric ground state as a function of the different climatological conditions of both the interplanetary medium and the Earth's seasons is also fundamental in relation to space weather science. Despite a long series of measurements acquired by both satellites and ISRs, the major current gap in modeling and studying ionospheric \(T_{\rm e}\) consists in the lack of an accurate description of its dependence on the most important geographic and geophysical parameters. There is still insufficient knowledge of the global distribution and relevant description of \(T_{\rm e}\) longitudinal variations as well as of its seasonal and hemispheric asymmetries, despite some attempts [11; 12; 13]. A difference between long-term and short-term \(T_{\rm e}\) variations with F10.7 has also been noted [14], which further complicates the modeling of \(T_{\rm e}\). Empirical models still struggle to describe small-scale structures such as the morning overshoot [15; 16; 17] and its dependence on solar and magnetic activity or the mid-latitude \(T_{\rm e}\) enhancement [18; 19]. The dependence on the magnetic activity and the choice of an appropriate magnetic activity proxy for \(T_{\rm e}\) modeling is still an open problem; for example, [PERSON] and [PERSON] [20] found no clear dependence of \(T_{\rm e}\) on the _ap_ index, with exception of perhaps a slight systematic increase of \(T_{\rm e}\) with _ap_ at polar latitudes. Apart from modeling, there are still gaps in the fundamental theory. For instance, [PERSON] et al. [21] introduced an anomalous conductivity to obtain an agreement between theory and EXOS-D data in the plasmasphere. Even very advanced physics-based models still encounter problems with reproducing observational data (e.g., the post-sunset \(T_{\rm e}\) enhancement) and must be driven by measured data [22]. Another tricky point concerns the existing problems associated with the frequent \(T_{\rm e}\) differences among satellite data from different missions, and between in-situ data and ground-based ISR measurements [23; 24], which require in many cases a cross calibration and/or a correction of these data. Because of this, studying and global modeling of \(T_{\rm e}\) has become a very challenging problem. From this point of view, \(T_{\rm e}\) observations collected by LPs on-board the European Space Agency (ESA) Swarm satellites [25] are very promising for a future inclusion in the IRI \(T_{\rm e}\) model [26]. In fact, the three Swarm satellites launched at the end of 2013 and still operating provide \(T_{\rm e}\) data in the topside ionosphere region on a global basis, for different local times, at a 2-Hz rate.