Occurrence Of Geomagnetic Storms Research Articles

Assessment of Seasonal Distribution and Characterisation of Geomagnetic Storm Occurrence during Solar Cycles 21–24

Geomagnetic storms (GMSs) are an important space weather phenomenon that poses serious threats to the advancement of space technology, power transmission lines, oil pipelines, and other infrastructure. This study investigates seasonal patterns of GMSs due to recent reports on the prominence of large storms (Dst ? -50 nT) during equinox conditions. Hourly Dst index data provided by the World Data Center, Kyoto, Japan, for solar cycles 21–24 (1976–2019) were employed. Storm occurrences in each solar cycle considered were identified using the minimum Dst value. The identified storms were categorized and analyzed statistically. Results revealed that storm occurrence varied from month to month, season to season, and solar cycle to solar cycle based on storm categories. Furthermore, the observed seasonal distribution of GMS occurrence decreases in the following order: autumn, spring, winter, and summer. This indicates that equinox conditions are more likely to have GMSs, consistent with the Russell-McPherron effect, compared to solstice conditions. The findings suggest that the distribution and characterization of storm occurrence vary seasonally due to solar activity. The insights on storm occurrence, distribution, and characterization may serve as a guide to space scientists to avert the impacts of GMSs while exploring space.

Modeling Field Line Curvature Scattering Loss of 1–10 MeV Protons During Geomagnetic Storms

AbstractThe proton radiation belt contains high fluxes of adiabatically trapped protons varying in energy from ∼one to hundreds of megaelectron volts (MeV). At large radial distances, magnetospheric field lines become stretched on the nightside of Earth and exhibit a small radius of curvature RC near the equator. This leads protons to undergo field line curvature (FLC) scattering, whereby changes to the first adiabatic invariant accumulate as field strength becomes nonuniform across a gyroorbit. The outer boundary of the proton belt at a given energy corresponds to the range of magnetic L shell over which this transition to nonadiabatic motion takes place, and is sensitive to the occurrence of geomagnetic storms. In this work, we first find expressions for nightside equatorial RC and field strength Be as functions of Dst and L* to fit the TS04 field model. We then apply the Tu et al. (2014, https://doi.org/10.1002/2014ja019864) condition for nonadiabatic onset to solve the outer boundary L*, and refine our expression for RC to achieve agreement with Van Allen Probes observations of 1–50 MeV proton flux over the 2014–2018 era. Finally, we implement this nonadiabatic onset condition into the British Antarctic Survey proton belt model (BAS‐PRO) to solve the temporal evolution of proton fluxes at L ≤ 4. Compared with observations, BAS‐PRO reproduces storm losses due to FLC scattering, but there is a discrepancy in mid‐2017 that suggests a ∼5 MeV proton source not accounted for. Our work sheds light on outer zone proton belt variability at 1–10 MeV and demonstrates a useful tool for real‐time forecasting.

Thermospheric Density Estimation Method Using a First-Order Gauss–Markov Process

Low-Earth-orbit (LEO) spacecraft are significantly influenced by atmospheric drag. Accurately estimating thermospheric density is pivotal for the precise calculation of drag acceleration. However, thermospheric density along a specific orbit, computed using existing thermospheric models, has certain inaccuracies. In this work, a first-order Gauss–Markov process is used to model the deviation of atmospheric drag acceleration. With the Markov parameter of the initial state iteratively computed through sequential estimation and the smoothing method, the thermospheric density is derived from high-precision GPS measurements. In simulation scenarios, the root-mean-square error and relative error of the estimated thermospheric density reduce by about 45 and 50% relative to the prior density, respectively. Using the estimated density for orbit propagation, satellite trajectories’ one-day position and velocity error are, respectively, within 100 m and 0.1 m/s, and an average improvement in orbit precision is over 80%. The proposed method has been applied to the real Tsinghua Science Satellite (Q-SAT) GPS measurements for effectiveness verification. It shows strong adaptability under extreme space weather and during the occurrence of geomagnetic storms. Due to the estimated Markov parameter of the initial state obeying the Langevin dynamics properties, the proposed method also offers short-term thermospheric density forecasting potential.

Statistical analysis on the ionospheric response over South American mid- and near high-latitudes during 70 intense geomagnetic storms occurred in the period of two decades

The first-time statistical response of the positive and negative ionospheric storms phases using Vertical Total Electron Content (VTEC) measurements during 70 geomagnetic storms at near high- and mid-latitudes regions in the Antarctic and Argentine/Chilean sectors in the Southern hemisphere are investigated. The study covers the years between 1999 and 2018 of solar cycles 23 and 24, using the Dst ≤ −100 nT as a criterion for all 70 storms selected. Significant features of solar cycle, seasonal and local time of ionospheric storms are showed. Our results indicate that the occurrence of geomagnetic storms follows a pattern of solar activity dependence, and also indicate a predominance of positive and positive-negative phases during autumn, winter, and spring at mid-latitudes and winter at near high-latitudes. Negative and negative-positive phases occur during all seasons at near high- and mid-latitudes. In addition, positive phases occur more frequently during the daytime while the negative phases occur predominantly in nighttime. There is also a predominance of positive and positive-negative phases simultaneously at near high- and mid-latitudes in the Antarctic and Argentine/Chilean sectors. The percentages of occurrence of positive and positive-negative phases are of 50% and 19%, respectively, at mid-latitude and 60% and 22%, respectively, at near high-latitudes. Negative and negative-positive phases are below 9% at both latitudes.

Statistical analysis of the occurrence rate of geomagnetic storms during solar cycles 20–24

This study examines the occurrences rate of geomagnetic storms during the solar cycles (SCs) 20–24. It also investigates the solar sources at SCs 23 and 24. The Disturbed storm time (Dst) and Sunspot Number (SSN) data were used in the study. The study establishes that the magnitude of the rate of occurrences of geomagnetic storms is higher (lower) at the descending phases (minimum phases) of solar cycle. It as well reveals that severe and extreme geomagnetic storms (Dst < -250 nT) seldom occur at low solar activity but at very high solar activity and are mostly associated with coronal mass ejections (CMEs) when occurred. Storms caused by CME + CH-HSSW are more prominent during the descending phase than any other phase of the solar cycle. Solar minimum features more CH-HSSW- associated storms than any other phase. It was also revealed that all high intensity geomagnetic storms (strong, severe and extreme) are mostly associated with CMEs. However, CH-HSSW can occasionally generate strong storms during solar minimum. The results have proven that CMEs are the leading cause of geomagnetic storms at the ascending, maximum and the descending phases of the cycles 23 and 24 followed by CME + CH-HSSW. The results from this study indicate that the rate of occurrence of geomagnetic storms could be predicted in SC phases.

Development of Coronal Holes Index For Study of Coronal Hole Geo-Effectiveness

Coronal holes are expansion regions in the solar corona with lower density and temperature than their surroundings. Coronal holes have open magnetic field structure that radiates out into interplanetary space as the solar wind. During minimum solar activity, coronal holes play important role in space weather because they are sources of high speed-streams. Therefore, studying the geo-effectiveness of coronal holes is very important. This paper presented a development of the coronal holes geo-effectiveness index related to the occurrence of geomagnetic storms. The index which was represented as “G(CH)”, is based on the combination of center position and cross-sectional length of coronal holes surface on latitudinal direction. Using 5 years of data of coronal holes (2015-2020), we analyzed the threshold combinations of the two parameters that can be used to scale the geo-effectiveness of a coronal hole. The results of all CH events with G(CH) > 0 should be considered as geomagnetic storm generator; CH events having the same characteristics as the CME event, i.e. although they have the potential to generate geomagnetic storms, the determining factor is influenced by the behavior and conditions of the solar wind and IMF Bz (-) during reconnection. So, the intensity of geomagnetic storm is strongly influenced by behavior and conditions (value and duration) both solar wind and Bz(-); the duration of CH event with G(CH) > 0 that have the potential to generate geomagnetic storms is a minimum of 6 days after the CH event; CH events accompanied by CIR affect the intensity and duration of the geomagnetic storms they generate, and the minimum value of G(CH) = 2.21.

An Analysis on the Occurrence of Geomagnetic Storms During Solar Cycle 22 and 23

An Analysis on the Occurrence of Geomagnetic Storms During Solar Cycle 22 and 23

Risk assessment of power system oscillation instability based on geomagnetic storm events in the 24th solar cycle

When studying the harm of geomagnetic storms to the power system, the focus is often placed on the influence of transformers reactive power loss (GIC-Q) caused by geomagnetic induced current (GIC) on voltage fluctuation. GIC-Q, as a reactive load, can cause power flow changes, and the small disturbance stability is affected by the power flow distribution before disturbance, so GIC-Q connects geomagnetic storms with small disturbance stability. In this study, the probability distribution function of induced geoelectric fields is established, and the sample data is obtained by sampling. Combined with the GIC benchmark network model, multiple sets of GIC-Q in each substation are calculated and added to the system one by one to study the influence of geomagnetic storms on the oscillation characteristic parameters and establish the risk index of small disturbance instability. The result shows that the risk index value of oscillation instability in the occurrence of geomagnetic storms increases both in the AC system and the AC/DC hybrid system, but the increased range of risk value of local oscillation instability decreases with the addition of DC systems. The research results can provide a reference for evaluating the influence of geomagnetic storms on the stable operation of power systems.

Geomagnetic Storm Occurrence and Their Relation With Solar Cycle Phases

AbstractUsing a time series of geomagnetic storm events between 1957 and 2019, obtained by selecting storms where −50 nT, we have analyzed the probability of occurrence of moderate, intense, and severe events. Considering that geomagnetic storms can be modeled as stochastic processes with a log‐normal probability distribution over their minimum index, the dataset was separated according to solar cycle (SC) and SC phases, and the distributions of events were fitted through maximum likelihood method in order to characterize the occurrence of storms in each cycle and phase, and then compare those occurrences to the SC24. Our results show that there is a strong dependence between the occurrence of intense storms, with −100 nT, and the strength of the SC measured by the sunspot numbers. In particular, SC24 is very similar to SC20. However, when comparing the occurrence of storms by SC phases, events tend to show similar activity toward the minimum phase and have significant differences in the maximum phases. By looking at the value—the fit log‐normal distribution “width” parameter—characteristic of the occurrence rate of storms, we have found that the (the sigma value in the descending phase of one cycle) shows the highest correlation () with (the sigma value in the maximum phase of the next cycle) which allows us to estimate the occurrence rate of storms for SC25 to be similar to those of SC21 and SC22, suggesting a more intense cycle than the one that just ended.

The Effectiveness of E-CALLISTO System in Predicting Geomagnetic Disturbance

This study focuses on analyzation of solar radio burst (SRB) data obtained by e-CALLISTO system in order to predict the occurrence of geomagnetic storm. E-CALLISTO is a global network of solar spectrometer that continually generates the observed radio signals in a form of spectrographs. Previous studies have strongly proved that the source of geomagnetic disturbance turned out to be type IV SRB which can be detected by CALLISTO instrument. Therefore, we selected 4 stations located at different locations to study the effectiveness and consistency of this system in detecting type IV bursts associated to solar storm during solar maximum. The data chosen was on 10th September 2014 where type IV bursts were formed at 1727 UT until 1745 UT within a frequency range of 135MHz to 390MHz. Accompanying the bursts was a halo CME prior to the bursts’ formation and an X1.6 flare was registered. From the results obtained by all stations, the pattern of the bursts depicts the same characteristic as theory says, by which, they emit a broadband continuum in a zebra pattern with varying fine structures. The formation of the bursts is due to magnetic reconnection and disruption of magnetic loops during large flares on Sept. 10th. As a consequence to type IV bursts associated to a vigorous CME, a major G2 storm was reported by NOAA a couple of days later. The presented results have shown a parallel correlation between type IV bursts detected by those 4 stations and the commencement of geomagnetic disturbance which took place 2 days afterwards.

Adaptive Smoothness Constraint Ionospheric Tomography Algorithm

Ionospheric tomography reconstruction based on global navigation satellite system observations is usually an ill-posed problem. To resolve it, an adaptive smoothness constraint ionospheric tomography algorithm is proposed in this work. The new algorithm performs an adaptive adjustment for the constrained weight coefficients of the tomography system. The computational efficiency and the reconstructed quality of ionospheric imaging are improved by using the new algorithm. A numerical simulation experiment was conducted in order to validate the feasibility and superiority of the algorithm. The statistical results of the reconstructed errors and the comparisons of ionospheric profiles confirmed the superiority of the new algorithm. Finally, the new algorithm was successfully applied to reconstruct three-dimensional ionospheric images under geomagnetic quiet and geomagnetic disturbance conditions over Hunan province. The tomographic results are reasonable and consistent with the general behavior of the ionosphere. The positive and negative phase storm effects are found during geomagnetic storm occurrence.

Geoelectric Field Evaluation During the September 2017 Geomagnetic Storm: MA.I.GIC. Model

AbstractThe space environment near Earth is constantly subjected to changes in the solar wind flow generated at the Sun. Examples of this variability are the occurrence of powerful solar disturbances, such as coronal mass ejections (CMEs). The impact of CMEs on the Earth's magnetosphere perturbs the geomagnetic field causing the occurrence of geomagnetic storms. Such extremely variable geomagnetic fields trigger geomagnetic effects measurable not only in the geospace but also in the ionosphere, upper atmosphere, and on the ground. For example, during extreme events, rapidly changing geomagnetic fields generate intense geomagnetically induced currents (GICs). In recent years, GIC impact on the power networks at middle and low latitudes has attracted attention due to the expansion of large‐scale power networks into these regions. This paper presents a new model, called MA.I.GIC. (Magnetosphere‐Ionosphere‐Ground‐Induced Current), to derive the geoelectric field used to determine the magnitude of GICs. In addition, we discuss the results of the MA.I.GIC. model applied to the September 2017 Geomagnetic Storm with particular focus on the two sudden impulses occurring on 6 and 7 September 2017, and the two main phases on 7 and 8 September 2017.

Forbush Decreases and &lt;2 Day GCR Flux Non-recurrent Variations Studied with LISA Pathfinder

Abstract Non-recurrent short-term variations of the galactic cosmic-ray (GCR) flux above 70 MeV n−1 were observed between 2016 February 18 and 2017 July 3 on board the European Space Agency LISA Pathfinder (LPF) mission orbiting around the Lagrange point L1 at 1.5 × 106 km from Earth. The energy dependence of three Forbush decreases is studied and reported here. A comparison of these observations with others carried out in space down to the energy of a few tens of MeV n−1 shows that the same GCR flux parameterization applies to events of different intensity during the main phase. FD observations in L1 with LPF and geomagnetic storm occurrence are also presented. Finally, the characteristics of GCR flux non-recurrent variations (peaks and depressions) of duration &lt;2 days and their association with interplanetary structures are investigated. It is found that, most likely, plasma compression regions between subsequent corotating high-speed streams cause peaks, while heliospheric current sheet crossing causes the majority of the depressions.

Characterization of the South Atlantic Anomaly

Abstract. This research intends to characterize the South Atlantic Anomaly (SAA) by applying the power spectrum analysis approach. The motivation to study the SAA region is due to its nature. A comparison was made between the stations in the SAA region and outside the SAA region during the geomagnetic storm occurrence (active period) and the normal period where no geomagnetic storm occurred. The horizontal component of the data of the Earth's magnetic field for the occurrence of the active period was taken on 11 March 2011 while for the normal period it was taken on 3 February 2011. The data sample rate used is 1 min. The outcome of the research revealed that the SAA region had a tendency to be persistent during both periods. It can be said that the region experiences these characteristics because of the Earth's magnetic field strength. Through the research, it is found that as the Earth's magnetic field increases, it is likely to show an antipersistent value. This is found in the high-latitude region. The lower the Earth's magnetic field, the more it shows the persistent value as in the middle latitude region. In the region where the Earth's magnetic field is very low like the SAA region it shows a tendency to be persistent.

Effect analysis of higher-order ionospheric corrections on quad-constellation GNSS PPP

With the development of global navigation satellite system (GNSS) precise point positioning (PPP) technology, higher positioning accuracy is required in some applications. An important error source in PPP is the residual higher-order (i.e. second- and third-order) ionospheric error after the first-order ionospheric error has been removed by dual-frequency observation combinations. Generally, higher-order ionospheric errors are negligible; but at high ionospheric activities, higher-order ionospheric errors can reach a few centimeters, which must be considered in high-precision positioning. In this study, a quad-constellation PPP approach with higher-order ionospheric corrections is proposed. The temporal variations of the higher-order ionospheric errors for GPS, GLONASS, BDS and Galileo are analyzed and their effects on quad-constellation PPP are evaluated. The GNSS observations collected at different solar activities are used to analyze the effect of the higher-order ionospheric errors and the results indicate that their magnitudes can reach almost 2 cm for their first frequency signals at high solar activity. The occurrence of geomagnetic storms further increases the higher-order ionospheric errors by a few millimeters. Multi-GNSS datasets collected at low, middle and high latitude stations at different ionospheric activities are processed and the results indicate that the higher-order ionospheric delay can affect the 3D position solutions of the quad-constellation PPP at a maximum of 6 mm.

On the Forecast Horizon of Magnetospheric Dynamics: A Scale‐to‐Scale Approach

AbstractThe Earth's magnetosphere is characterized by a complex dynamics resulting from the interaction of different multiscale processes which can be both directly driven/triggered by the interplanetary magnetic field and the solar wind, and due to internal processes of the magnetosphere. Recently, Alberti et al. (2017, https://doi.org/10.1002/2016JA023175) have shown that the fluctuations at distinct timescales of some geomagnetic indices differently respond to interplanetary changes during geomagnetic storms. In detail, using an information theory based approach it has been shown that geomagnetic indices fluctuations occurring at long timescales (typically longer than 200 min) are correlated with physical quantities characterizing the changes of the interplanetary conditions, while the short timescale ones (typically shorter than 200 min) do not seem to be directly related to the same physical quantities. The aim of this work is to identify the nature and character of the scale‐to‐scale fluctuations of two geomagnetic indices, AE and SYM‐H, capable of monitoring different geomagnetic current systems related to the occurrence of geomagnetic storms and substorms. By applying the theory of dynamical systems, we investigate the scale‐to‐scale correlation dimension D2 and the Kolmogorov entropy K2 showing the occurrence of a topological phase transition phenomenon between long and short‐timescale fluctuations. Furthermore, our results clearly show that the forecast horizon of fluctuations occurring at timescales shorter than 200 min dramatically decreases down to ∼2 min. The consequences of this result in the framework of Space Weather forecasting is briefly outlined and discussed.

A comparative study of geomagnetic storms for solar cycles 23 and 24

The aim of this paper is to investigate the association of the geomagnetic storms with the magnitude of interplanetary magnetic field IMF (B), solar wind speed (V), product of IMF and wind speed (\(V \cdot B)\), Ap index and solar wind plasma density (\(n_{\mathrm{p}})\) for solar cycles 23 and 24. A Chree analysis by the superposed epoch method has been done for the study. The results of the present analysis showed that \(V \cdot B\) is more geoeffective when compared to V or B alone. Further the high and equal anti-correlation coefficient is found between Dst and Ap index (− 0.7) for both the solar cycles. We have also discussed the relationship between solar wind plasma density (\(n_{\mathrm{p}})\) and Dst and found that both these parameters are weakly correlated with each other. We have found that the occurrence of geomagnetic storms happens on the same day when IMF, V, Ap and \(V \cdot B\) reach their maximum value while 1 day time lag is noticed in case of solar wind plasma density with few exceptions. The study of geomagnetic storms with various solar-interplanetary parameters is useful for the study of space weather phenomenon.

Investigation of nonlinearity effect during storm time disturbance

We examine the nonlinearity effect in the Disturbed Storm Time (Dst) signals during minor, moderate and major geomagnetic storm between the year 2008 and 2016 using Recurrence Plot (RP), Recurrence Rate (RR) and Approximate Entropy (ApEn). The dynamics of the Dst signals behave in stochastic path during the minor geomagnetic stormy period and in deterministic path during increasinggeomagnetic stormy period. As the geomagnetic storm increases, the RP showed more deterministic structure indicating that it possesses the potential to detect geomagnetic storm event even at the infinitesimal impact of the geomagnetic storm. The dynamical behaviour is reflected in the RR as the geomagnetic storm intensifies. The occurrence of geomagnetic storm associated with significant Dst signal and monthly average RR is prevalent during the beginning year (2012) of the high solar activity than the peak year (2014) of the high solar activity. Our results show that higher average RR is not consistent throughout the equinoctial season between the year 2008 and 2016.

Geomagnetic Index Kp Forecasting With LSTM

AbstractThrough making full use of the solar wind and interplanetary magnetic field data accumulated by ACE satellites we improve the prediction accuracy of the Kp geomagnetic index and accurately predict the occurrence of geomagnetic storms (Kp ≥ 5). Specially, we use long short‐term memory to train the Kp forecast model described in this study. Based on the large‐scale data, we build the Kp forecasting model with solar wind, interplanetary magnetic field parameters, and the historical Kp value as input. In this study, we first analyze the distribution of Kp and the effect of the data imbalance on the prediction of geomagnetic storms. Second, we analyze the correlation between the different input parameters and Kp. Thus, the input parameters of the model are selected by the results of the correlation. We consider two types of forecasting: one is the overall Kp forecasting and the other is the geomagnetic storm (Kp ≥ 5) forecasting. Hence, we design an integrated model which is then compared with other models. Some evaluation parameters are introduced: the root‐mean‐square error, the mean‐absolute error, and the correlation coefficient, as well as the measurement of geomagnetic storms (Kp ≥ 5) F1. The root‐mean‐square error and mean‐absolute error of our model are 0.4765 and 0.6382, respectively. The experimental results show that the proposed model with long short‐term memory improve the Kp forecasting.

Geomagnetically Induced Currents: Principles

The geospace, or the space environment near Earth, is constantly subjected to changes in the solar wind flow generated at the Sun. The study of this environment variability is called Space Weather. Examples of effects resulting from this variability are the occurrence of powerful solar disturbances, such as coronal mass ejections (CMEs). The impact of CMEs on the Earth’s magnetosphere very often greatly perturbs the geomagnetic field causing the occurrence of geomagnetic storms. Such extremely variable geomagnetic fields trigger geomagnetic effects measurable not only in the geospace but also in the ionosphere, upper atmosphere, and on and in the ground. For example, during extreme cases, rapidly changing geomagnetic fields generate intense geomagnetically induced currents (GICs). Intense GICs can cause dramatic effects on man-made technological systems, such as damage to high-voltage power transmission transformers leading to interruption of power supply, and/or corrosion of oil and gas pipelines. These space weather effects can in turn lead to severe economic losses. In this paper, we supply the reader with theoretical concepts related to GICs as well as their general consequences. As an example, we discuss the GIC effects on a North American power grid located in mid-latitude regions during the 13–14 March 1989 extreme geomagnetic storm. That was the most extreme storm that occurred in the space era age.