**1. Introduction**

Space weather conditions impact the space environment, the most important for this chapter is the ionosphere of which its variation disrupts the communication links between ground and space systems. The ionosphere plays a vital role in sustaining communication links between space and ground satellite segments applied in aviation, remote sensing, navigation, and surveillance. Also, the variation in the ionosphere affects the propagation of high and medium-frequency radio and TV waves. The latitudinal, daily, and seasonal variation in the ionosphere depends on solar activity and related solar cycle effects and enhanced geomagnetic activity. The ionospheric variation shows repeatedly climatological conditions dependent on solar zenith angle and sun's activity over a long-term period of the solar cycle during quiet solar periods while it depends on space weather dynamics associated with enhanced geomagnetic activity during disturbed solar periods.

The delay effect induced by the ionosphere's dispersive, refractive, and scattering nature on the signal path is proportional to ionospheric Total Electron Content (TEC). Therefore, TEC is a measurement parameter for ionospheric variation calculated from the summation of electron density along any line of sight of a signal. In addition, ionospheric layers (D, E, F1, and F2) varies in altitude ranges and electron density concentrations which respond differently during space weather phenomena. While ionospheric TEC is calculated at F2 peak (350–550 km) from GPS measurements using a thin layer model [1], it's important to note that the plasmasphere at altitudes of 2000 km and above contributes to TEC with approximately 10–60% during the day and nighttime respectively [2]. Therefore, TEC and electron density variations determine the irregular ionospheric conditions with the associated space weather effects.

Other than TEC and electron density models, global TEC maps developed from GPS data provided by International GNSS Services (IGS) connecting a wide network of ground GPS receivers are powerful tools for space weather monitoring (**Figure 1**). Real-time Global Ionosphere Maps (GIMs) and Regional Ionosphere Maps (RIMs) define TEC distribution using several computational algorithms [3, 4] from which several empirical TEC models have been developed. GIMs and RIMs are important tools for validating existing models for performance improvement because a worldwide network of GPS receivers is associated with low-cost implications for maintenance and installation, high accuracy, easy use, continuous operation, accessibility, and high resolution of temporal and spatial variation in GPS-TEC measurements. Other data sources include ionosondes which monitor ionospheric parameters up to F2 peak, Incoherent Scatter Radars (ISR) which measure ionospheric parameters for the bottom and topside ionosphere, rocket measurements of ionospheric parameters for low altitude ionospheric regions, and satellite in-situ measurements of global

#### **Figure 1.**

*A map showing worldwide distribution of ground GNSS receiver stations extracted from https://igs.org/network/. The blue line is the geomagnetic equator.*

#### *Ionospheric Electron Density and Electron Content Models for Space Weather Monitoring DOI: http://dx.doi.org/10.5772/intechopen.103079*

ionospheric parameters important for evaluating model performances and correlation relationship with the measured parameters.

In this chapter, we discuss the latest developments and improvements in ionospheric models for TEC and electron density and the existing challenges. Several models show inconsistencies during storm periods and ionospheric plasma irregularities induced by either latitude or altitude variations. We also discuss the role of deep learning and neural networks techniques in improving space weather prediction capabilities.
