A geomagnetic storm is a disturbance on the Earth's magnetic field resulting from enhanced energy transfer from interplanetary space to the Earth's environment through the magnetosphere. The cause of geomagnetic storms is variation in the solar wind parameters, that is enhancement in solar wind speed and solar wind particles that increase solar wind pressure, and at least >3 hr sustained southward interplanetary magnetic field (IMF) on the day side magnetosphere (Gonzalez et al., 1994;Kumar & Kumar, 2019). The major solar driver of this disturbance is the coronal mass ejections (CMEs) embedded with large plasma emissions from the sun, taking several hours or days to reach the Earth's atmosphere. It should be noted that other sun's emissions like high solar wind speed from coronal holes, and corotating interaction regions also contribute to geomagnetic storms (Richardson, 2018;Richardson et al., 2006). The geomagnetic storm effects induce ionospheric disturbances characterized

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The least number of proton events and ground-level enhancements was recorded in the solar cycle 24 which corresponds with the least smoothed sunspot number compared to the last three previous solar cycles. This was attributed to the weak sun’s polar field and decreasing strength of the interplanetary magnetic field at the start of the solar cycle. The majority contribution to background radiation dose within our earth’s atmosphere is galactic cosmic rays and trapped particles in the Van Allen Belts. However, solar proton events cause sudden spikes in radiation doses, and this depends on the fluence and energy spectra of the events. While these doses are least detected in the lower atmosphere, they have significant radiation damage to spacecraft electronic components and astronauts on long space missions and at higher atmospheric altitudes. Therefore, the prediction of such events and estimation of their effective radiation damage is an important consideration for planning long space missions and spacecraft design materials.

Monitoring and prediction of space weather phenomena and associated effects requires an understanding of the ionospheric response related to ionospheric electron content and electron density redistribution. These ionospheric response effects to space weather over time have been quantified by ground station measurements (ionosondes, radars, and GPS), satellite and rocket measurements, and estimations from ionospheric models. However, the progressive development of ionospheric models has had inconsistences in trying to describe the redistribution of electron density in response to extreme space weather conditions. In this chapter, we review and discuss the recent developments, progress, improvements, and existing challenges in the developed ionospheric models for prediction and forecasting space weather events and the need for continuous validation. The utilization of deep learning and neural network techniques in developing more flexible, reliable, and accurate data-driven ionospheric models for space weather prediction is also discussed. We also emphasized the roles of International and national Organizations like COSPAR, URSI, ITU, CCIR, and other research and education institutions in supporting and maintaining observatories for real-time monitoring and measurements of ionospheric electron density and TEC.