Interplanetary Magnetic Field Parameters Research Articles

Daily geomagnetic variations under variable IMF/solar conditions and their connection with underground conductivity changes in Japan

Fluctuations in Solar Wind (SW) and Interplanetary Magnetic Field (IMF) significantly affect the Geomagnetic Field (GF) measurements particularly during extreme space weather events. The current study investigates the variations of horizontal (H), vertical (Z) geomagnetic components under quiet and disturbed IMF and solar conditions as well as the effect of underground conductivity on the stationary geomagnetic measurements in Japan. Results of data analysis show that the response of the GF to IMF and solar parameters fluctuations is variable. The H- and Z-components were in good harmony with high visual correlation along a latitudinal profile across Japan during quiet times. On the other hand, during the disturbed times related to a Coronal Mass Ejection (CME) launched on 13 May 2005, the GF components varied with the disturbance of IMF and solar parameters. The H-components showed highly correlated variations with a significant reduction along the examined profile due to intensified ionospheric currents, while the Z-components recorded in the northern part of Japan showed abnormal daily variations pattern (positive daily variations) with an enhanced amplitude which is opposite to the normal behavior of daily variations recorded in the central and southern parts of Japan (negative daily variations). The observed enhanced and abnormal daily variation of Z-components in north Japan, which we consider a remarkable observation here, is possibly linked with underground conductivity anomaly in this region.

Relativistic electron ([formula omitted]) flux driving parameters over geostationary orbit using 30 years of data

The relativistic electron population at MeV energy in the Earth’s outer radiation belt at geostationary (GOE) orbit largely varies in association with solar wind disturbances. Although more researches have been done to understand the influence of different mechanisms that cause the variations in the relativistic electron flux in the region, but still the underlying physics is often complex and unclear. The present study uses relativistic electron (E>2MeV) fluxes data of Los Alamos National Laboratory (LANL) geosynchronous satellites to understand the variations and its relationship with the driving parameters. The study examines the relativistic electron fluxes variations associated with the interplanetary magnetic field (IMF) and solar wind parameters using daily averaged data from 01 January 1990 to 31 December 2018. The results indicated that the information transfer from solar wind and IMF parameters into relativistic electrons flux is dependent on the kind of driving parameter. The result identifies the rank of driving parameters based on their maximum values in the cross correlation and information transfer analysis. Thus, AE index and vsw are the dominant driving parameters for the dynamics of relativistic electrons flux at GEO orbit. The peak values of coefficient (correlation coefficients, mutual information and entropy transfer) are observed at similar lag times (τpeak). For instance, in the case of AE and Je, we have observed the maximum coefficient (associated with lag time) as follows; maximum correlation coefficient; CCmax=0.846(5days), maximum mutual information; MImax=0.803(5days), maximum transfer entropy; TEmax=0.892(5days). Such dependency analysis can help to select the model parameters that influence the relativistic electrons fluxes and improve the existed models of the dynamical system.

Prediction of the SYM‐H Index Using a Bayesian Deep Learning Method With Uncertainty Quantification

AbstractWe propose a novel deep learning framework, named SYMHnet, which employs a graph neural network and a bidirectional long short‐term memory network to cooperatively learn patterns from solar wind and interplanetary magnetic field parameters for short‐term forecasts of the SYM‐H index based on 1‐ and 5‐min resolution data. SYMHnet takes, as input, the time series of the parameters' values provided by NASA's Space Science Data Coordinated Archive and predicts, as output, the SYM‐H index value at time point t + w hours for a given time point t where w is 1 or 2. By incorporating Bayesian inference into the learning framework, SYMHnet can quantify both aleatoric (data) uncertainty and epistemic (model) uncertainty when predicting future SYM‐H indices. Experimental results show that SYMHnet works well at quiet time and storm time, for both 1‐ and 5‐min resolution data. The results also show that SYMHnet generally performs better than related machine learning methods. For example, SYMHnet achieves a forecast skill score (FSS) of 0.343 compared to the FSS of 0.074 of a recent gradient boosting machine (GBM) method when predicting SYM‐H indices (1 hr in advance) in a large storm (SYM‐H = −393 nT) using 5‐min resolution data. When predicting the SYM‐H indices (2 hr in advance) in the large storm, SYMHnet achieves an FSS of 0.553 compared to the FSS of 0.087 of the GBM method. In addition, SYMHnet can provide results for both data and model uncertainty quantification, whereas the related methods cannot.

Rotation of a Stealth CME on 2012 October 5 Observed in the Inner Heliosphere

Coronal mass ejections (CMEs) are subject to changes in their direction of propagation, tilt, and other properties. This is because CMEs interact with the ambient solar wind and other large-scale magnetic field structures. In this work, we report on the observations of the 2012 October 5 stealth CME using coronagraphic and heliospheric images. We find clear evidence of a continuous rotation of the CME, i.e., an increase in the tilt angle, estimated using the graduated cylindrical shell (GCS) reconstruction at different heliocentric distances, up to 58 R ⊙. We find a further increase in the tilt at L1 estimated from the toroidal and cylindrical flux rope fitting on the in situ observations of interplanetary magnetic field (IMF) and solar wind parameters. This study highlights the importance of observations of Heliospheric Imager (HI), on board the Solar Terrestrial Relations Observatory. In particular, the GCS reconstruction of CMEs in the HI field of view promises to bridge the gap between the near-Sun and in situ observations at the L1. The changes in the CME tilt have significant implications for the space weather impact of stealth CMEs.

Latitudinal dependence of Pc3-4 amplitudes across the dip equator along the 210º Magnetic Meridian

Studying Pc3-4 geomagnetic pulsations at equatorial and very low latitude regions is an important issue to understand their generation and propagation mechanisms. Pc3-4 amplitudes and their latitudinal dependency across the dip equator up to low latitudes (± 25°) are investigated using geomagnetic data simultaneously obtained by the MAGDAS/CPMN stations along the 210° Magnetic Meridian (MM) chain. Forty-five Pc3 events and thirty-two Pc4 events were selected for this study. Our results show a clear dependence of Pc3-4 amplitudes on geomagnetic latitudes. At the dip equator, most of the selected Pc3 events (~75%) showed an enhancement in amplitudes, while the rest (~25%) showed an attenuation. After that, the amplitudes decreased gradually by increasing latitudes. These results suggest mixed generation and propagation mechanisms for the equatorial and very low latitudes Pc3s. For better understanding, the Interplanetary Magnetic Field (IMF) and solar conditions are examined during the selected events. Results indicate that Pc3 events with enhanced amplitudes at dip equator are mainly occurred in daytime with no preference to IMF (magnitude and direction) and solar parameters, which suggests the ionospheric currents model as a generation and propagation mechanism for these events. While the attenuation observed in the other Pc3 events was associated with intense and abrupt fluctuations in the IMF and solar parameters, which in turn suggests the compressional wave model for generating these Pc3 events. On the other hand, these two models can explain the observed enhancement in the Pc4 amplitudes at the dip equator. Therefore, our obtained results clarified the origin of equatorial Pc3-4 pulsations.

Ionospheric electric potential as an alternative indicator of solar effect on the lower atmosphere

We have explored the possibility of applying the ionospheric electric potential (EP) as a parameter describing the effects of solar activity on the troposphere. We calculated EP, using the semi-empirical model, where the potential spatial distribution is determined by solar wind, interplanetary magnetic field parameters, and the geomagnetic activity index AL. We have carried out a comparative analysis of EP and the commonly used geomagnetic activity indices in a high-latitude region for 1975–2019. It has been shown that EP can be used as an indicator of solar activity since it describes both short-period disturbances and long-term variations. The revealed similar trends in long-term EP variations and near-surface temperature suggest that the changes in climate system parameters are induced by slower changes in the Sun’s large-scale magnetic field. The performed analysis of EP and near-surface temperature correlation maps has revealed that the atmospheric static stability conditions have an effect on spatial distribution of tropospheric response to solar impact.

Электрический потенциал ионосферы — альтернативный индикатор солнечного воздействия на нижнюю атмосферу

We have explored the possibility of applying the ionospheric electric potential (EP) as a parameter describing the effects of solar activity on the troposphere. We calculated EP, using the semi-empirical model, where the potential spatial distribution is determined by solar wind, interplanetary magnetic field parameters, and the geomagnetic activity index AL. We have carried out a comparative analysis of EP and the commonly used geomagnetic activity indices in a high-latitude region for 1975–2019. It has been shown that EP can be used as an indicator of solar activity since it describes both short-period disturbances and long-term variations. The revealed similar trends in long-term EP variations and near-surface temperature suggest that the changes in climate system parameters are induced by slower changes in the Sun’s large-scale magnetic field. The performed analysis of EP and near-surface temperature correlation maps has revealed that the atmospheric static stability conditions have an effect on spatial distribution of tropospheric response to solar impact.

Helium Abundance Decrease in ICMEs in 23–24 Solar Cycles

Based on the OMNI database, the influence of the solar activity decrease in solar cycles (SCs) 23–24 on the behavior of the relative helium ions abundance Nα/Np inside interplanetary coronal mass ejections (ICMEs) is investigated. The dependences of the helium abundance on the plasma and interplanetary magnetic field parameters in the epoch of high solar activity (SCs 21–22) and the epoch of low activity (SCs 23–24) are compared. It is shown that Nα/Np significantly decreased in SCs 23–24 compared to SCs 21–22. The general trends of the dependences have not changed with the change of epoch, but the helium abundance dependences on some parameters (for example, the magnitude of the interplanetary magnetic field) have become weaker in the epoch of low activity than they were in the epoch of high activity. In addition, the dependence of the helium abundance on the distance from spacecraft to the ICME axis was revealed; the clearest dependence is observed in magnetic clouds. The Nα/Np maximum is measured at the minimum distance, which confirms the hypothesis of the existence of a helium-enriched electric current inside an ICME.

Investigation of the relationship between geomagnetic activity and solar wind parameters based on a novel neural network (potential learning)

Predicting geomagnetic conditions based on in-situ solar wind observations allows us to evade disasters caused by large electromagnetic disturbances originating from the Sun to save lives and protect economic activity. In this study, we aimed to examine the relationship between the Kp index, representing global magnetospheric activity level, and solar wind conditions using an interpretable neural network known as potential learning (PL). Data analyses based on neural networks are often difficult to interpret; however, PL learns by focusing on the “potentiality of input neurons” and can identify which inputs are significantly utilized by the network. Using the full advantage of PL, we extracted the influential solar wind parameters that disturb the magnetosphere under southward Interplanetary magnetic field (IMF) conditions. The input parameters of PL were the three components of the IMF (Bx, By, Bz), solar wind flow speed (Vx), and proton number density (Np) in geocentric solar magnetospheric (GSM) coordinates obtained from the OMNI solar wind database between 1998 and 2019. Furthermore, we classified these input parameters into two groups (targets), depending on the Kp level: Kp = 6–9 (positive target) and Kp = 0 to 1 + (negative target). Negative target samples were randomly selected to ensure that numbers of positive and negative targets were equal. The PL results revealed that solar wind flow speed is an influential parameter for increasing Kp under southward IMF conditions, which was in good agreement with previous reports on the statistical relationship between the Kp index and solar wind velocity, and the Kp formulation based on the IMF and solar wind plasma parameters. Based on this new neural network, we aim to construct a more correct and parameter-dependent space weather forecasting model.Graphical

Peculiarities of the Heliospheric State and the Solar-Wind/Magnetosphere Coupling in the Era of Weakened Solar Activity

Based on the data of the solar wind (SW) measurements of the OMNI database for the period 1976–2019, we investigate the behavior of SW types, as well as plasma and interplanetary magnetic field (IMF) parameters, for 21–24 solar cycles (SCs). Our analysis shows that with the beginning of the period of low solar activity (SC 23), the number of all types of disturbed events in the interplanetary medium decreased, but the proportion of magnetic storms initiated by CIR increased. In addition, a change in the nature of SW interaction with the magnetosphere could occur due to a decrease in the density, temperature, and IMF of solar wind.

New Findings From Explainable SYM‐H Forecasting Using Gradient Boosting Machines

AbstractIn this work, we develop gradient boosting machines (GBMs) for forecasting the SYM‐H index multiple hours ahead using different combinations of solar wind and interplanetary magnetic field (IMF) parameters, derived parameters, and past SYM‐H values. Using Shapley Additive Explanation values to quantify the contributions from each input to predictions of the SYM‐H index from GBMs, we show that our predictions are consistent with physical understanding while also providing insight into the complex relationship between the solar wind and Earth's ring current. In particular, we found that feature contributions vary depending on the storm phase. We also perform a direct comparison between GBMs and neural networks presented in prior publications for forecasting the SYM‐H index by training, validating, and testing them on the same data. We find that the GBMs yield a statistically significant improvement in root mean squared error over the best published black‐box neural network schemes and the Burton equation.

О соответствии глобальной изолированной суббури статистической модели Мак-Феррона

It is shown that a diamagnetic structure (DS) of the slow solar wind (SW), the source of which on the Sun was a chain of streamers, arrived at Earth’s orbit on December 22, 2015. It interacted with Earth’s magnetosphere under conditions when the northward Bz component of the interplanetary magnetic field (IMF) remained for a long time in preceding undisturbed SW. The interaction and a sharp change in the direction of Bz to the south generated an isolated substorm whose duration depends on the duration of interaction with the DS. The substorm began at midday with the passage of the DS into the magnetosphere and spread to the east. All phases of the substorm — growth, expansion, and recovery — were observed for two hours. Variations in the SW and IMF parameters are shown to coincide for the isolated substorm whose energy source was the slow solar wind DS, and a trigger was the abrupt change in the direction of the vertical IMF component from north to south. The coincidence is justified by statistical generalizations of the same parameters in 40 % of cases of long-term observations of individual substorms whose trigger was a change in Bz direction.

Correspondence of a global isolated substorm to the McPherron statistical model

It is shown that a diamagnetic structure (DS) of the slow solar wind (SW), the source of which on the Sun was a chain of streamers, arrived at Earth’s orbit on December 22, 2015. It interacted with Earth’s magnetosphere under conditions when the northward Bz component of the interplanetary magnetic field (IMF) remained for a long time in preceding undisturbed SW. The interaction and a sharp change in the direction of Bz to the south generated an isolated substorm whose duration depends on the duration of interaction with the DS. The substorm began at midday with the passage of the DS into the magnetosphere and spread to the east. All phases of the substorm — growth, expansion, and recovery — were observed for two hours. Variations in the SW and IMF parameters are shown to coincide for the isolated substorm whose energy source was the slow solar wind DS, and a trigger was the abrupt change in the direction of the vertical IMF component from north to south. The coincidence is justified by statistical generalizations of the same parameters in 40 % of cases of long-term observations of individual substorms whose trigger was a change in Bz direction.

Variations of IMF Cone Angle and IMF Clock Angle to Solar Cycles and Geomagnetic Storms

Interplanetary magnetic field (IMF) carried by solar wind and rooted at solar corona. Variations of IMF depend on solar surface activities, e.g. sunspots. Moreover, solar magnetic storms are connected by the IMF. IMF parameters, sunspot number, and geomagnetic index for more than 55 years were downloaded from OMNIWeb, which the data were obtained from several spacecraft in geostasionary and L1 Lagrange orbits. Solar cycles are measured in terms of variations in the number of sunspots and their surface areas. Both magnetic events of IMF and sunspots indicate the relation between solar cycles and IMF orientation which is clock angle and cone angle. IMF also interacts with the Earth’s magnetosphere, and it can cause geomagnetic storms. Cone angle and clock angle are derived from IMF components on X-axis, Y-axis, and Z-axis. Their frequency distribution charts on each solar cycle of 20 to 24 and their maxima phase and minima phase are the key points for the analysis. The plot of clock angle to Dst index as a geomagnetic index for each solar cycle was also derived. Based on the analysis, solar cycles are indicated to give an impact on the magnitude of each IMF component. It makes the peak of the charts shifted from the minima phase to the maxima phase. Other than that, the reconnection in magnetopause mostly occurs when the clock angle is between 1400–2520 or when the clock angle directions are southward. But the 23rd solar cycle shows the northward clock angle can also cause extreme geomagnetic storms.

The Drivers of the Martian Bow Shock Location: A Statistical Analysis of Mars Atmosphere and Volatile EvolutioN and Mars Express Observations.

The Martian interaction with the solar wind leads to the formation of a bow shock upstream of the planet. The shock dynamics appear complex, due to the combined influence of external and internal drivers. The extreme ultraviolet fluxes and magnetosonic Mach number are known major drivers of the shock location, while the influence of other possible drivers is less constrained or unknown such as crustal magnetic fields, solar wind dynamic pressure, or the Interplanetary Magnetic Field (IMF) intensity, and orientation. In this study, we compare the influence of the main drivers of the Martian shock location, based on several methods and published datasets from Mars Express (MEX) and Mars Atmosphere Volatile EvolutioN (MAVEN) missions. We include here the influence of the crustal fields, extreme ultraviolet fluxes, solar wind dynamic pressure, as well as (for MAVEN, thanks to magnetic field measurements) magnetosonic Mach number and Interplanetary Magnetic Field parameters (intensity and orientation angles). The bias due to the cross‐correlations among the possible drivers is investigated with a partial correlations analysis. Several model selection methods (Akaike Information Criterion and Least Absolute Shrinkage Selection Operator regression) are also used to rank the relative importance of the physical parameters. We conclude that the major drivers of the shock location are extreme ultraviolet fluxes and magnetosonic Mach number, while crustal fields and solar wind dynamic pressure are secondary drivers at a similar level. The IMF orientation also plays a significant role, with larger distances for perpendicular shocks rather than parallel shocks.

Average Ring Current Response to Solar Wind Drivers: Statistical Analysis of 61 Days of ENA Images

AbstractThis paper presents the first determination of the average global and regional response of ring current (RC) flux intensity versus four key solar wind (SW) and interplanetary magnetic field (IMF) parameters: SW electric field (ESW), IMF polarity (BZ,IMF), SW pressure (PSW), and SW speed (VSW). We analyze energetic neutral atom (ENA) images from Two Wide‐angle Imaging Neutral‐atom Spectrometers (TWINS) to measure the RC response, and use 5‐min downstream‐propagated data from the Advanced Composition Explorer (ACE) and Wind to measure SW driving. Our imaging data comprises 61 events, including 1,838 global 2D equatorial maps of line‐of‐sight (LOS) averaged ion flux derived from background‐subtracted, 15 min TWINS ENA images spanning 1–30 keV. Average spatial and spectral distributions were organized into four driver strength groups for each SW parameter. ESW has the strongest correlation with RC ion flux (coefficients are [0.95, 0.94, 0.88] at [1, 16, 30] keV, respectively), confirming that magnetospheric convection is a primary driver. In case studies, the global RC responds rapidly (≤15 min) to changes in ESW ranging from strong to weak. PSW is correlated with RC intensity, though not as strongly as ESW, indicating the secondary importance of PSW‐driven compression in RC injections. Strong ESW increases lower‐energy ions farther eastward and higher‐energy ions closer to midnight, consistent with an energy‐dependent interplay between magnetic drift and convection. The strongest SW driving preferentially increases the least energetic ions, suggesting that quasi‐steady convection can load the nightside RC with cooler ions.

Relationship between geomagnetic storm development and the solar wind parameter ß

We have analyzed the dynamics of solar wind and interplanetary magnetic field (IMF) parameters during the development of 933 isolated geomagnetic storms, observed over the period from 1964 to 2010. The analysis was carried out using the epoch superposition method at intervals of 48 hrs before and 168 hrs after the moment of Dst minimum. The geomagnetic storms were selected by the type of storm commencement (sudden or gradual) and by intensity (weak, moderate, and strong). The dynamics of the solar wind and IMF parameters was compared with that of the Dst index, which is an indicator of the development of geomagnetic storms. The largest number of storms in the solar activity cycle is shown to occur in the years of minimum average values (close in magnitude to 1) of the solar wind parameter β (β is the ratio of plasma pressure to magnetic pressure). We have revealed that the dynamics of the Dst index is similar to that of the β parameter. The duration of the storm recovery phase follows the characteristic recovery time of the β parameter. We have found out that during the storm main phase the β parameter is close to 1, which reflects the maximum turbulence of solar wind plasma fluctuations. In the recovery phase, β returns to background values β~2‒3.5. We assume that the solar wind plasma turbulence, characterized by the β parameter, can play a significant role in the development of geomagnetic storms.

Связь развития геомагнитных бурь с параметром ß солнечного ветра

We have analyzed the dynamics of solar wind and interplanetary magnetic field (IMF) parameters during the development of 933 isolated geomagnetic storms, observed over the period from 1964 to 2010. The analysis was carried out using the epoch superposition method at intervals of 48 hrs before and 168 hrs after the moment of Dst minimum. The geomagnetic storms were selected by the type of storm commencement (sudden or gradual) and by intensity (weak, moderate, and strong). The dynamics of the solar wind and IMF parameters was compared with that of the Dst index, which is an indicator of the development of geomagnetic storms. The largest number of storms in the solar activity cycle is shown to occur in the years of minimum average values (close in magnitude to 1) of the solar wind parameter β (β is the ratio of plasma pressure to magnetic pressure). We have revealed that the dynamics of the Dst index is similar to that of the β parameter. The duration of the storm recovery phase follows the characteristic recovery time of the β parameter. We have found out that during the storm main phase the β parameter is close to 1, which reflects the maximum turbulence of solar wind plasma fluctuations. In the recovery phase, β returns to background values β~2‒3.5. We assume that the solar wind plasma turbulence, characterized by the β parameter, can play a significant role in the development of geomagnetic storms.

A Multiscale Approach to Geomagnetic Storm Morphology Analysis Based on DMA Activity Measures

The article is focused on the approach based on the discrete mathematical analysis conception and continues a series of studies related to the application of the previously developed methodology to geophysical data analysis. The main idea of the study is the modification of earlier conceptions regarding the interpreter’s logic that allows introducing a multiscale approach and performing the time series analysis using the activity measure plots, implying the vertical scale. This approach was used to study the morphology of several intense geomagnetic storms at the final stages of the 23rd and 24th solar activity cycles. Geomagnetic observatory data and interplanetary magnetic field parameters as well as the solar wind flux speed and proton density were analyzed for each of the studied storms using the activity measures. The developed methods, applied to geomagnetic storm morphological analysis, displayed good results in revealing the decreases and increases in various durations and intensities during storms, detecting low-amplitude disturbances, and storm sudden commencement recognition. The results provide an opportunity to analyze any physical data using a unified scale and, in particular, to implement this approach to geomagnetic activity studies.

Driving Parameters for Multi‐MeV Electrons Flux Variations in Outer Radiation Belt

AbstractThe Earth's outer radiation belt is highly dynamic and the variations of multi‐MeV electrons flux in the region depend on solar wind and interplanetary magnetic field () parameters. This study attempts to characterize the causality and timing of the solar wind and IMF parameter activities on the outer radiation belt environment based on three tools: time shifted cross correlation, mutual information, and transfer entropy statistical analysis. The Van Allen Probes mission measurements of multi‐MeV (3.4–6.3 MeV) electrons flux data in the period from January 01, 2017 to December 31, 2018 have been used. The results indicate that the information transfer from solar wind and IMF parameters into multi‐MeV electrons flux is dependent on the kind of driving parameter, range of L‐shell, and energy of electrons. The result identifies the rank of driving parameters based on their maximum values in the correlation and information transfer analysis. Thus, solar wind velocity is the dominant driving parameter for the variation of multi‐MeV electrons flux in the outer radiation belt. Given three ranges of L‐shell (, and ), with decreasing L‐shell values the maximum information transfer from driving parameters into electron flux decreases and the lag time increases. Such dependency analysis can help to select the model parameters that influence the variation of multi‐MeV electrons flux and improve the existed models of the dynamical system.