Effect of Solar and Interplanetary Disturbances on Geomagnetic Field: A Comprehensive Review

The Sun, a G-type main-sequence star, is the central engine of the solar system, continually emitting electromagnetic radiation, energetic particles, and plasma. While its radiant energy supports life on Earth, the non-radiative emissions, primarily in the form of the solar wind, can interact profoundly with the Earth’s magnetosphere, giving rise to a wide range of space weather phenomena. The solar wind a stream of charged particles (mainly electrons and protons) flowing outward from the Sun’s corona carries embedded magnetic fields referred to as the Interplanetary Magnetic Field (IMF) (Parker, 1958). This interaction is not uniform; rather, it fluctuates with the 11-year solar activity cycle, during which the frequency and intensity of solar eruptions such as coronal mass ejections (CMEs), solar flares, and high speed solar wind streams (HSSs) vary dramatically (Schrijver & Siscoe, 2010).

When these solar emissions particularly southward-oriented IMF components (negative Bz) interact with the Earth's magnetic field, they can trigger geomagnetic storms by enabling magnetic reconnection on the dayside magnetopause (Dungey, 1961; Gonzalez et al., 1994). This reconnection allows large quantities of energy and plasma to be injected into the magnetosphere and ionosphere, initiating disturbances that may last from hours to several days. These events are measured using indices such as Dst (Disturbance Storm Time), which reflects the strength of the ring current; Kp, which quantifies planetary magnetic activity; and AE (Auroral Electrojet), which tracks auroral zone disturbances (Rostoker, 1972; Sugiura & Kamei, 1991).

One of the most significant consequences of solar-induced geomagnetic disturbances is their disruption of technological systems. For instance, satellite electronics, GPS-based navigation, radio communication, and even ground-based power grids are vulnerable to intense geomagnetic activity (Boteler et al., 1998; Pulkkinen, 2007). Notably, the March 1989 geomagnetic storm induced by a CME led to the collapse of the Hydro-Québec power grid in Canada, leaving millions without power (Bolduc, 2002). As modern society increasingly depends on sensitive technological systems, understanding and forecasting space weather has become an urgent scientific and operational priority (Baker et al., 2008).

Over the past few decades, space science has made significant progress through dedicated observational missions, such as SOHO (Solar and Heliospheric Observatory), ACE (Advanced Composition Explorer), WIND, STEREO, SDO (Solar Dynamics Observatory), and Parker Solar Probe, which provide real-time data on solar wind conditions, IMF parameters, and solar eruptive events (Domingo et al., 1995; Stone et al., 1998; Luhmann et al., 2008). These missions have enabled more precise measurements of CME speeds, shock arrival times, plasma density, temperature, and magnetic field orientation, all of which are critical inputs for space weather modeling.

Simultaneously, theoretical and empirical models ranging from magnetohydrodynamic (MHD) simulations to empirical coupling functions like the Kan-Lee (1979) and Newell et al. (2007) coupling functions have evolved to better represent the solar wind magnetosphere interaction. These models, however, still face limitations in accurately predicting the onset, duration, and intensity of geomagnetic storms, especially for extreme events (Tsurutani et al., 2006; Zhang et al., 2007).

In recent years, the integration of machine learning (ML) and artificial intelligence (AI) approaches into space weather forecasting has opened new frontiers. Techniques such as neural networks, support vector machines, and deep learning models have shown promise in forecasting Dst and Kp indices by learning complex, nonlinear relationships between solar wind inputs and geomagnetic outputs (Tan et al., 2018; Camporeale, 2019). These data driven models benefit from the growing volume of continuous multi-decadal data from ground-based observatories and satellite missions, enabling real-time predictions and improving lead time for issuing geomagnetic storm alerts.

The present review aims to provide a comprehensive synthesis of the physical mechanisms, observational datasets, and modeling approaches related to the influence of solar and interplanetary disturbances on Earth's geomagnetic field. It further highlights the role of key interplanetary structures such as magnetic clouds, sheath regions, corotating interaction regions (CIRs), and heliospheric current sheets (HCS) in storm generation (Richardson & Cane, 2012). Special emphasis is placed on the variability and complexity observed across solar cycles 24 and 25, where atypical solar minimum conditions and rising trends in weak CMEs have posed new challenges for prediction (Gopalswamy et al., 2015; Kilpua et al., 2017).

In essence, by synthesizing classical theories, recent empirical findings, and modern computational techniques, this review seeks to bridge gaps in our understanding of Sun Earth connections and to evaluate the effectiveness of emerging technologies in forecasting geomagnetic activity, with the ultimate goal of enhancing space weather resilience in the era of global digital infrastructure.

The Earth’s geomagnetic field is a fundamental component of the planet’s defense system against extraterrestrial hazards. Generated by the geodynamo action of convective movements within the planet’s liquid iron-nickel outer core, the geomagnetic field resembles a dipole on the planetary scale, but with notable multipolar features and spatial-temporal variability (Glatzmaier & Roberts, 1995). This geomagnetic field extends into space and forms the magnetosphere a complex, dynamic cavity that shields the Earth from continuous bombardment by solar wind particles and cosmic rays (Kivelson & Russell, 1995). On the dayside, the magnetosphere is compressed by the solar wind to approximately 10 Earth radii (RE), while on the nightside, it is stretched into a vast magnetotail extending over 100 RE downstream (Baumjohann & Treumann, 1996).

The structure and behavior of the geomagnetic field are not static. The solar wind, a supersonic plasma stream primarily composed of electrons and protons emitted by the Sun, exerts continuous dynamic pressure on the magnetosphere. This pressure is influenced by parameters such as solar wind speed, density, temperature, and the interplanetary magnetic field (IMF) embedded in the solar wind (Parker, 1958). These solar wind conditions are in turn modulated by various forms of solar activity, including coronal mass ejections (CMEs), solar flares, and high-speed streams emanating from coronal holes.

Of particular importance is the Bz component of the IMF its north-south orientation relative to the Earth's magnetic field. When the IMF Bz is oriented northward, it tends to reinforce the Earth's magnetic shield, acting in a relatively stable configuration. However, when the IMF Bz turns southward, it becomes antiparallel to the Earth’s dayside magnetic field, a configuration that leads to magnetic reconnection at the dayside magnetopause (Dungey, 1961). This reconnection process facilitates the direct entry of solar wind energy and particles into the magnetosphere, triggering a sequence of dynamic responses that can culminate in geomagnetic storms and substorms.

Such disturbances begin with the injection of energetic particles into the inner magnetosphere, leading to intensification of current systems such as the ring current, field aligned currents, and the magnetotail current sheet (Akasofu, 1981). These alterations manifest as fluctuations in the Dst (Disturbance Storm Time) index, which quantifies the strength of the ring current, and other indices like Kp and AE, which track planetary-scale and auroral activity, respectively (Sugiura & Kamei, 1991; Rostoker, 1972).

The vulnerability of the geomagnetic field to these external perturbations highlights the Sun Earth coupling as a complex, non-linear system. Extreme events such as the Carrington Event of 1859, the March 1989 storm, and more recent disturbances in October 2003 (“Halloween storms”) demonstrate the susceptibility of Earth’s magnetic environment to severe disruptions driven by solar variability (Tsurutani et al., 2003; Cliver & Svalgaard, 2004).

Understanding the conditions under which the geomagnetic field becomes vulnerable particularly during prolonged southward IMF Bz, high-speed solar wind, or enhanced dynamic pressure is essential for forecasting space weather events and protecting critical infrastructure, including power grids, satellite systems, and navigation networks. The interdisciplinary nature of geomagnetic studies, combining geophysics, solar physics, and space plasma physics, ensures a holistic understanding of these vulnerabilities and continues to inform the development of predictive tools and mitigation strategies.

The interaction between solar emissions and Earth's space environment is primarily governed by dynamic solar and interplanetary phenomena. These include Coronal Mass Ejections (CMEs), solar flares, and High-Speed Solar Wind Streams (HSS), often modulated by the interplanetary magnetic field (IMF) and structures such as Corotating Interaction Regions (CIRs). These disturbances form the core of space weather, with the potential to initiate geomagnetic storms, auroral displays, and a cascade of ionospheric and magnetospheric effects.

Coronal Mass Ejections (CMEs)

CMEs are immense eruptions of magnetized plasma expelled from the solar corona, often associated with solar active regions, particularly during the solar maximum phase. These events can release up to 101310^{13}1013 kg of plasma into interplanetary space, with velocities reaching over 2000 km/s. The Earth-impacting CMEs, especially those classified as halo CMEs due to their full 360° appearance in coronagraph imagery (indicating they are headed toward or away from Earth), are of greatest concern for geomagnetic storm forecasting.

When CMEs are directed toward Earth, they can compress the magnetosphere, generate shock fronts, and create conditions favorable for magnetic reconnection, particularly if their internal magnetic field contains a strong southward Bz component (i.e., opposite to Earth’s northward magnetic field). This results in the transfer of energy from the solar wind into the magnetosphere, driving storm-time ring current intensification and negative Dst excursions (Zhang et al., 2003). Studies by Gopalswamy et al. (2007) have shown that fast CMEs with magnetic clouds are especially geoeffective, with the embedded helical magnetic fields (flux ropes) contributing to sustained geomagnetic activity.

CMEs can also result in interplanetary shocks, leading to sudden storm commencements (SSCs) followed by intense main and recovery phases. These events are associated with various geospace phenomena, including auroras, satellite charging, and geomagnetically induced currents (GICs) that can damage terrestrial power grids.

Solar Flares and Radiation Bursts

Solar flares are sudden, intense flashes of electromagnetic radiation, primarily in X-ray and extreme ultraviolet (EUV) wavelengths, caused by the rapid release of magnetic energy in the vicinity of sunspots. They are categorized by the GOES X-ray classification system into A, B, C, M, and X classes based on peak flux intensity, with X-class flares being the most powerful.

Though flares do not directly impact the geomagnetic field as CMEs do, they significantly influence the ionosphere, especially the dayside D-layer, by causing sudden ionospheric disturbances (SIDs). These disruptions interfere with high-frequency (HF) radio communication, navigation systems (GNSS/GPS), and radar operations. Importantly, solar flares often precede or accompany CMEs, and their combined occurrence tends to increase the likelihood of severe geomagnetic consequences (Harrison, 1995).

Kahler (1992) highlighted that flares are frequently part of the larger-scale energy release processes that result in both radiation bursts and plasma ejections. Their emission across the spectrum from gamma rays to radio waves can serve as precursors or indicators of imminent space weather events, especially when Type II or Type IV radio bursts are observed, which are often linked with shock waves and CME propagation.

High-Speed Solar Wind Streams and Corotating Interaction Regions (CIRs)

Unlike the transient and impulsive nature of CMEs and flares, high-speed solar wind streams (HSS) originate from coronal holes regions of open magnetic field lines in the Sun’s corona that allow continuous outflow of plasma. These streams can attain velocities of 500–800 km/s and persist over several solar rotations.

When a fast HSS overtakes a slower-moving solar wind, it forms a Corotating Interaction Region (CIR), characterized by a compression region and often a forward and reverse shock structure. These CIRs are recurrent and particularly geoeffective during the declining phase of the solar cycle, when coronal holes are prevalent at lower solar latitudes and persist over several rotations (Tsurutani et al., 2006).

CIR-induced geomagnetic storms are generally moderate in intensity, but their predictable recurrence makes them important for long-term space weather planning. They can cause periodic enhancements in the auroral electrojets and trigger substorms, especially when the embedded magnetic fields have a southward orientation. CIRs are also associated with enhanced fluxes of energetic particles, which can pose risks to satellite operations and astronauts.

Furthermore, CIRs play a major role in modulating galactic cosmic ray (GCR) intensity, often leading to recurrent Forbush decreases, as well as long-duration auroral activity observable even at mid-latitudes.

The Interplanetary Magnetic Field (IMF) is a critical component of the solar wind that governs the interaction between the Sun and Earth’s magnetosphere. Originating from the solar corona and embedded within the solar wind plasma, the IMF is carried through the heliosphere as a spiral structure known as the Parker spiral as a result of the Sun’s rotation (Parker, 1958). The IMF plays a decisive role in driving geomagnetic activity, especially when its orientation and structure become conducive to magnetic reconnection with the Earth's intrinsic magnetic field.

Magnetic reconnection is a fundamental physical process whereby antiparallel magnetic field lines i.e., those pointing in opposite directions break and reconnect, allowing energy, momentum, and plasma from the solar wind to enter the Earth’s magnetosphere. The dayside magnetopause the boundary separating the magnetosphere from the solar wind is the primary site for this energy transfer. The orientation of the IMF Bz component (the north-south direction) is the most critical factor determining the efficiency of reconnection. When IMF Bz is oriented northward, it aligns with Earth’s magnetic field, minimizing reconnection and leading to relatively quiet geomagnetic conditions. Conversely, when IMF Bz is oriented southward, it becomes antiparallel to the Earth’s northward field, thus promoting dayside reconnection and enabling the influx of solar wind energy into the inner magnetosphere (Dungey, 1961; Gonzalez et al., 1994).

This process initiates a chain of geomagnetic responses including enhancement of the ring current, formation of substorms, and auroral intensification. The ring current, composed of trapped energetic ions and electrons circulating westward around the Earth, contributes significantly to the depression of the geomagnetic field during geomagnetic storms. This depression is quantitatively measured by the Dst (Disturbance Storm Time) index, which reflects the average magnetic field decrease observed at low-latitude ground stations. Studies such as Kane (2005) and Burton et al. (1975) have shown a strong correlation between prolonged southward IMF Bz and intensified negative excursions in Dst, validating the role of IMF orientation in controlling storm magnitude.

Moreover, the IMF's ability to drive geomagnetic activity is influenced not just by its Bz polarity, but also by its strength, duration, and the solar wind speed accompanying it. A high-speed solar wind with a strong and sustained southward Bz is particularly effective at triggering severe geomagnetic storms, as it increases the solar wind electric field (Ey = -V × Bz) and thus enhances energy transfer into the magnetosphere. This interaction is further modulated by dynamic features such as interplanetary shocks, sheath regions, and coronal mass ejection (CME) magnetic clouds, which carry structured and intense magnetic fields.

In summary, the IMF and the process of magnetic reconnection serve as the primary gateway for solar-terrestrial coupling. Their roles are pivotal in the generation of geomagnetic storms and associated space weather phenomena, affecting everything from satellite operations to terrestrial power grids. Understanding the dynamics of IMF variations, particularly the conditions that lead to efficient magnetic reconnection, remains a central theme in space weather prediction models and magnetospheric physics.

The quantification and classification of geomagnetic activity rely heavily on various geomagnetic indices and a range of space-based and ground-based observational platforms. These indices offer a standardized method to evaluate the severity, onset, and duration of geomagnetic disturbances and are essential for space weather forecasting, statistical analysis, and correlating interplanetary conditions with geophysical effects on Earth.

Dst Index (Disturbance Storm Time Index)

The Dst index is one of the most widely used geomagnetic storm indicators. It reflects the intensity of the symmetric ring current around the Earth’s equator by measuring the average deviation of the horizontal component of the geomagnetic field at four low-latitude ground stations (located in Japan, Hawaii, San Juan, and Germany). This index is expressed in nanoteslas (nT) and is calculated hourly.

A negative Dst value indicates a reduction in the magnetic field strength due to the buildup of charged particles in the magnetosphere during geomagnetic storms. Thresholds for storm categorization are as follows:

• –30 nT to –50 nT: weak storms.
• –50 nT to –100 nT: moderate storms.
• Below –100 nT: intense storms.
• Below –200 nT: superstorms (Turner et al., 2001; Gonzalez et al., 1994).

The Dst index provides insights into the storm-time dynamics of the inner magnetosphere, particularly the ring current's growth during the main phase and its decay during the recovery phase. It is a critical metric in storm classification and helps in understanding magnetospheric energy injection and dissipation processes.

Kp and AE Indices

The Kp index is a global measure of geomagnetic activity derived from data collected from a network of mid-latitude magnetic observatories. It is a quasi-logarithmic index with a scale from 0 to 9, representing quiet to extremely disturbed geomagnetic conditions. The Kp index is calculated in 3-hour intervals and is especially useful for monitoring planetary-scale disturbances, including sudden commencements and storm-time magnetic fluctuations.

The AE (Auroral Electrojet) index, on the other hand, is specifically designed to track high-latitude auroral zone activity. It represents the intensity of eastward and westward auroral electrojets and is calculated as:

AE = AU – AL

Where AU (upper) and AL (lower) indices indicate maximum and minimum perturbations observed in the horizontal magnetic field component.

The AE index is instrumental in studying magnetospheric substorms, which are localized and short-term enhancements of auroral activity. While the Dst index reflects large-scale current systems (ring current), the AE index reveals smaller-scale but intense processes in the high-latitude ionosphere (Akasofu, 1964; Davis & Sugiura, 1966).

These indices together offer a multi-scale, spatially-resolved view of geomagnetic dynamics, from low to high latitudes and from long-duration storms to short-lived substorms.

Data Sources: Satellites and Ground-Based Observatories

A robust understanding of solar–terrestrial coupling and space weather phenomena hinges on multi-platform observational capabilities, including both spaceborne satellites and ground-based stations.

Space-Based Observations

• SOHO (Solar and Heliospheric Observatory): Monitors solar irradiance, CMEs (via LASCO coronagraph), and solar wind conditions.
• ACE (Advanced Composition Explorer) and WIND: Positioned at the L1 Lagrangian point, these satellites provide near-real-time data on solar wind speed, density, temperature, and IMF critical for forecasting geomagnetic storms.
• STEREO (Solar Terrestrial Relations Observatory): Offers stereoscopic views of solar eruptions and helps track CME trajectories in three dimensions.

These missions are vital for detecting and characterizing the interplanetary precursors to geomagnetic activity, including shocks, magnetic clouds, and sheath regions.

Ground-Based Observations

• Kyoto World Data Center (WDC): Collects global magnetic data and computes Dst, AE, and other indices.
• Neutron Monitors: Located at high altitudes and polar regions, these instruments detect galactic cosmic rays (GCRs) and track Forbush decreases, which often coincide with CME-driven shocks.
• Magnetometer Networks: Arrays like INTERMAGNET provide real-time geomagnetic field measurements across the globe, essential for tracking local and global disturbances.

Together, these tools allow researchers to correlate interplanetary structures with geomagnetic responses, providing the foundation for both retrospective event studies and predictive modeling efforts.

The 11-year solar cycle, characterized by fluctuations in sunspot number, solar irradiance, and magnetic activity, plays a fundamental role in modulating space weather and geomagnetic conditions on Earth. These cycles are closely monitored through solar and geomagnetic indices, including sunspot number, solar flux (F10.7), and the Disturbance Storm Time (Dst) index. Comparing Solar Cycle 24 (SC24) and Solar Cycle 25 (SC25) provides valuable insights into the variability of solar-terrestrial interactions and the forecasting of geomagnetic disturbances.

Solar Cycle 24, spanning approximately from December 2008 to December 2019, was among the weakest solar cycles in over a century, with a maximum smoothed sunspot number of ~116, significantly lower than the ~180 observed in the preceding SC23 (Pesnell et al., 2018). The cycle exhibited reduced solar activity, including fewer X-class solar flares, Coronal Mass Ejections (CMEs), and a lower frequency of intense geomagnetic storms (Dst ≤ –100 nT). Despite its overall mild profile, SC24 produced a few significant space weather events, such as the March 2015 geomagnetic storm, driven by a fast CME, which temporarily impacted power systems and GPS signals.

In contrast, the onset of Solar Cycle 25, officially declared in December 2019, has shown early signs of heightened activity. Preliminary data from observatories like SDO (Solar Dynamics Observatory) and SOHO (Solar and Heliospheric Observatory) have detected an increasing number of Earth-directed CMEs, elevated sunspot counts, and several moderate geomagnetic storms. Some models forecast SC25 to be more active than SC24, though still below historical averages (Nguyen et al., 2019; NOAA SWPC, 2023). This resurgence in solar activity has implications for satellite operations, aviation safety, and long-term power grid planning.

Analysis of Dst trends, as well as solar wind parameters, suggests that while SC24 was relatively quiet, the potential for space weather surprises remains high due to the complex and often nonlinear nature of solar-terrestrial coupling. Understanding inter-cycle variability helps in refining forecast models, assessing technological vulnerability, and planning mitigation strategies for future geomagnetic storms.

The rapid increase in space weather-related data from space-based missions (e.g., ACE, STEREO, DSCOVR) and ground-based observatories (e.g., magnetometer arrays, neutron monitors) has enabled a shift toward data-driven methodologies for space weather prediction. Traditional empirical and physics-based models are increasingly being complemented by machine learning (ML) and artificial intelligence (AI) tools, offering improved forecasting accuracy and adaptability.

Machine learning models, such as Artificial Neural Networks (ANNs), Random Forests, Support Vector Machines (SVMs), and Deep Learning architectures, have demonstrated success in forecasting key geomagnetic parameters, including:

• Dst index prediction.
• Southward IMF (Bz) estimation.
• Solar wind propagation delays.
• Flare and CME occurrence probability.

For instance, Reiss et al. (2021) developed neural network models trained on historical solar wind and IMF data to predict Dst index variations up to 6 hours in advance. Similarly, Baumann & McCloskey (2021) introduced ensemble ML models to estimate solar wind arrival times, enhancing lead-time accuracy for CME-driven storms. These models leverage features such as solar X-ray flux, radio burst signatures, interplanetary shock characteristics, and Type II/IV emissions, improving both classification and regression performance in forecasting tasks.

Moreover, ML techniques have been applied to event detection in time series, including onset prediction of geomagnetic storms and Forbush decrease identification. Efforts are also underway to integrate human activity recognition algorithms originally developed for wearable sensors into solar time series to detect subtle precursors of solar events (Young et al., 2015).

Despite their promise, ML models face limitations including:

• Data sparsity for rare extreme events.
• Model interpretability challenges.
• Dependence on high-quality, real-time data.

However, with the increasing availability of open-access solar-terrestrial datasets (e.g., OMNIWeb, CDAWeb), and the incorporation of physics-informed neural networks, the field is advancing toward real-time, automated space weather prediction systems with broader practical applications in satellite operations, aviation management, and global infrastructure protection.

Despite the considerable progress made in understanding solar-terrestrial interactions and forecasting geomagnetic activity, several critical knowledge gaps remain that limit the accuracy, consistency, and timeliness of space weather prediction systems.

One of the primary challenges lies in the limited integration of multiple solar parameters such as coronal hole properties, CME kinematics, flare emissions, and interplanetary magnetic field orientation into cohesive forecasting frameworks. Most current models rely on isolated variables (e.g., IMF Bz or CME speed), often failing to account for nonlinear interactions and cumulative effects between solar wind components, shock fronts, and magnetic field structures. As a result, models may overpredict weak events or miss the onset of moderate to severe storms, particularly when multiple solar drivers are at play simultaneously.

A second gap involves the incomplete understanding of moderate-intensity geomagnetic storms (Dst between –50 nT and –100 nT) and the underlying substorm triggers in the magnetosphere-ionosphere system. While extreme storms have been extensively studied due to their clear space weather implications, moderate and recurrent geomagnetic activity, often associated with Co-rotating Interaction Regions (CIRs) and high-speed solar wind streams, remain undercharacterized. These events, though less dramatic, can still cause cumulative damage to satellite components and navigation systems over time. Substorm dynamics, especially their preconditioning and triggering mechanisms, also demand deeper analysis using both ground-based and satellite data.

Additionally, there is a noticeable underrepresentation of machine learning (ML) and artificial intelligence (AI) approaches in operational real-time forecasting systems. While numerous research studies have demonstrated the predictive capability of neural networks, decision trees, and deep learning algorithms for space weather parameters, the transition from experimental models to operational platforms remains limited. Challenges such as data imbalance, lack of explainability, and difficulty in handling rare/extreme events hinder broader adoption. Overcoming these issues requires the development of physics-informed ML models, curated training datasets, and real-time data pipelines that can feed into automated forecasting architectures.

Another key shortfall is the lack of standardized, comparative studies across solar cycles, particularly SC23, SC24, and the ongoing SC25. Variations in solar activity, magnetospheric response, and data resolution often make it difficult to establish longitudinal baselines or derive multi-cycle trends. The need for standardized metrics, uniform data preprocessing techniques, and consistent observational platforms is imperative to ensure comparability and reproducibility of results across different research groups.

Future Directions

To address these gaps, several future directions are recommended:

• Multi-parametric models that integrate solar irradiance, CME morphology, IMF structure, and geomagnetic indices should be prioritized for development.
• Enhanced ground-satellite coupling studies to better understand substorm triggers and moderate geomagnetic activity.
• Operational deployment of ML algorithms into real-time forecasting platforms such as NOAA’s SWPC, leveraging ensemble models and explainable AI techniques.
• Cross-cycle comparison studies using harmonized datasets from OMNIWeb, SOHO, ACE, and Kyoto WDC to understand cyclic trends in geomagnetic response.
• Investment in interdisciplinary approaches, combining physics, data science, and computational modeling, to unlock a holistic understanding of space weather processes.

By closing these knowledge gaps, the scientific community can move closer to robust, predictive, and timely space weather warning systems, which are vital for safeguarding modern infrastructure in an increasingly space-reliant technological era.

Solar and interplanetary disturbances exert a significant and dynamic influence on Earth’s geomagnetic field, shaping not only the behavior of the magnetosphere but also affecting a wide array of technological systems that modern society relies upon. From the expulsion of Coronal Mass Ejections (CMEs) and high-speed solar wind streams to the configuration of the interplanetary magnetic field (IMF), these solar-originated events interact with the Earth’s magnetospheric boundaries and internal current systems, often resulting in geomagnetic storms of varying intensity. As this review has outlined, a broad spectrum of mechanisms including magnetic reconnection, solar wind magnetosphere coupling, and plasma dynamics govern these interactions, and their effects are quantitatively observed through indices like Dst, Kp, and AE. The increasing availability of space-based observational data and ground-based instruments has led to significant empirical insights, while recent advances in machine learning and numerical modeling are pushing the boundaries of space weather forecasting. However, critical gaps remain particularly in understanding moderate geomagnetic events, integrating multiple solar parameters, and deploying real-time predictive models. Addressing these challenges will require interdisciplinary strategies that blend the rigor of space plasma physics with the scalability and adaptability of data-driven computational tools. Ultimately, enhancing our ability to monitor, predict, and respond to space weather events is not just a scientific endeavor it is a societal imperative, essential for protecting satellites, power grids, navigation systems, and the broader infrastructure of the digital age.