Interplanetary Magnetic Field Data Research Articles

Applicability of Bernoulli's theorem to upstream and downstream of the bow shock

In this research, the validity of Bernoulli's theorem for the solar wind crossing the Earth's bow shock has been investigated based on space explorations. Using solar wind plasma and interplanetary magnetic field data from the OMNI database, along with magnetosheath plasma and magnetic field data measured by the magnetospheric multiscale satellite in 2016 and 2018, 95 events were selected to study the Bernoulli invariant from the upstream to the downstream of Earth's bow shock (from the unshocked solar wind region to the shocked magnetosheath region). Statistical investigations show that the differences in the characteristic energy of plasmas between the solar wind region and the magnetosheath region are less than 5%, regardless of the upstream solar wind speed and the satellite's location in the magnetosheath. These results quantitatively support the generalization of Bernoulli's equation to the solar wind flowing through the bow shock, including the contribution of magnetic field energy.

Report on the effects of the May 2024 Mother's day geomagnetic storm observed from Chile

This study investigates the extreme geomagnetic storm of May 10–15, 2024, utilizing data from the SER ground-based station in Chile and the DSCOVR satellite. The methodology involves calculating the horizontal magnetic field (H), filtering geomagnetic data using a Butterworth filter, and conducting cross-correlation analysis between solar wind parameters and Pc5 pulsations. The storm, starting with a sudden storm commencement triggered by a coronal mass ejection around 18:00 UT on May 10, exhibited a main phase lasting about 8 h, followed by a recovery phase starting on May 11. The extreme storm exhibited abrupt fluctuations in the interplanetary magnetic field data and solar wind parameters, inducing a depression in the geomagnetic field H-component reaching ΔH ∼ −551 nT at the SER station. Throughout the storm, solar wind parameters such as density, speed, and temperature exhibited varied ranges, with significant changes observed in all storm phases. Notably, during the initial and main phases, cross-correlation analysis unveiled robust associations between Pc5 pulsations and solar wind parameters such as density and speed, with maximum R values reaching 0.98 for both phases.

On the northward shift of the heliospheric current sheet at the end of solar cycle 24

ABSTRACT Since solar cycle 16, the heliospheric current sheet (HCS) has been found to be shifted southward during the late declining to minimum phase. However, this trend is broken at the end of solar cycle 24. In this paper, we analyse the shift of the HCS by using information obtained from coronal model and in situ data provided by the near-Earth OMNI data base and the Parker Solar Probe (PSP). Coronal potential field source surface modelling results show that the northward shift is established at the beginning of 2018 and remains stable for about 2 yr. Interplanetary magnetic field data obtained from and within 1 au also support the northward shift, as the southern polarity T appears more frequently than the northern polarity A between 2018 and 2020. Both model results and in situ observation obtained by PSP imply that the HCS shift is established in the corona, and then propagates into the heliosphere. The quadrupole term still has a significant influence on the formation of the HCS shift.

The Response of the Earth Magnetosphere to Changes in the Solar Wind Dynamic Pressure: 1. Plasma and Magnetic Pressures

AbstractIn the present study, the influence of the solar wind dynamic pressure on the plasma and magnetic pressures of the magnetosphere is studied. We use 11‐year Time History of Events and Macroscale Interactions during Substorms (THEMIS) instruments for plasma and magnetic field measurements in the magnetosphere and the OMNI database for solar wind dynamic pressure and IMF data. We focus on the effects of the solar wind dynamic pressure (PSW) and consider only times in which the interplanetary magnetic field (IMF) components are within ±5 nT. We find that the plasma pressure inside the magnetosphere follows the solar wind dynamic pressure and that an increase in PSW also influence the day‐night pressure asymmetry. Our analysis also reveals the existence of ion and electron drifts from midnight toward the dusk and dawn sectors, respectively. We observe a local magnetic pressure minimum located near a plasma pressure maximum at around 11 RE on the nightside. Comparing the effect of PSW on both plasma and magnetic pressures, we observe trends which are consistent with the diamagnetic properties of plasmas. In general, the distribution of plasma pressure within the Earth's magnetosphere is an important criterion for evaluating the magnetostatic equilibrium and electric current system. The outcome of this study should provide additional methodologies for the characterization of key plasma characteristics within the magnetosphere.

Forecasting Geomagnetic Storm Disturbances and Their Uncertainties Using Deep Learning

AbstractSevere space weather produced by disturbed conditions on the Sun results in harmful effects both for humans in space and in high‐latitude flights, and for technological systems such as spacecraft or communications. Also, geomagnetically induced currents (GICs) flowing on long ground‐based conductors, such as power networks, potentially threaten critical infrastructures on Earth. The first step in developing an alarm system against GICs is to forecast them. This is a challenging task given the highly non‐linear dependencies of the response of the magnetosphere to these perturbations. In the last few years, modern machine‐learning models have shown to be very good at predicting magnetic activity indices. However, such complex models are on the one hand difficult to tune, and on the other hand they are known to bring along potentially large prediction uncertainties which are generally difficult to estimate. In this work we aim at predicting the SYM‐H index characterizing geomagnetic storms multiple‐hour ahead, using public interplanetary magnetic field (IMF) data from the Sun‐Earth L1 Lagrange point and SYM‐H data. We implement a type of machine‐learning model called long short‐term memory (LSTM) network. Our scope is to estimate the prediction uncertainties coming from a deep‐learning model in the context of forecasting the SYM‐H index. These uncertainties will be essential to set reliable alarm thresholds. The resulting uncertainties turn out to be sizable at the critical stages of the geomagnetic storms. Our methodology includes as well an efficient optimization of important hyper‐parameters of the LSTM network and robustness tests.

Prediction of ionospheric total electron content data using spatio-temporal residual network

Since the ionospheric total electron content (TEC) exhibits complex spatial and temporal behaviour including the seasonal and solar activity dependencies, resulting in large scale differences across different regions of the world, developing an effective deep-learning-based predictive model, capturing the complex behaviour of TEC is much needed. We present a novel deep learning-based model to predict the TEC using the spatio-temporal residual network (ST-ResNet). The TEC data used in the study were obtained from 185 GPS receiver locations, covering the latitude region from 10° N to 80° N and the longitude region from 110° W to 34° E. We considered TEC data of 154 days each corresponding to the solar maximum (2014) and the solar minimum (2020) years for training the network together with the exogeneous interplanetary magnetic field (IMF) data, including the magnetic field components By, Bz, plasma flow speed (Vp), and proton density (Np). A total of 44,352 maps (collected at the rate of 288 maps per day) were pre-processed by grouping them into closeness, period, and trend channels, which associate with the near-time, short-term, and long-term trends in the TEC data, respectively. After training, the model was tested for one day. The objectives of the study are: i) To develop and evaluate the performance of the ST-ResNet model, ii) to assess its performance on TEC data corresponding to mid-latitude region, under different solar conditions and iii) to compare the ST-ResNet model with other predicted models. For the entire study region, the root mean squared error (RMSE) was found to be 3.13 TECU for the solar maximum year and 1.23 TECU for the solar minimum year. Similar trends were observed in the mid-latitude region (30° N-60° N), with RMSE values of 2.31 TECU and 1.58 TECU for the solar maximum and solar minimum years, respectively. ST-ResNet model showed an improved accuracy over the long short-term memory (LSTM) deep learning network by about 23% and 31% for the entire study region and by 49% and 19% for only mid-latitude region, during the solar maximum and minimum years respectively, suggesting the superior performance of ST-ResNet over the LSTM network. A comparative observation of results of ST-ResNet model with those of back propagation and international reference ionosphere-2016 (IRI-2016) model also showed the superiority of the former. Results confirm the suitability of the ST-ResNet model for predicting the spatial and temporal behavior of TEC across different locations during various solar conditions.

Geomagnetic Disturbances That Cause GICs: Investigating Their Interhemispheric Conjugacy and Control by IMF Orientation

AbstractNearly all studies of impulsive geomagnetic disturbances (GMDs, also known as magnetic perturbation events MPEs) that can produce dangerous geomagnetically induced currents (GICs) have used data from the northern hemisphere. In this study, we investigated GMD occurrences during the first 6 months of 2016 at four magnetically conjugate high latitude station pairs using data from the Greenland West Coast magnetometer chain and from Antarctic stations in the conjugate AAL‐PIP magnetometer chain. Events for statistical analysis and four case studies were selected from Greenland/AAL‐PIP data by detecting the presence of >6 nT/s derivatives of any component of the magnetic field at any of the station pairs. For case studies, these chains were supplemented by data from the BAS‐LPM chain in Antarctica as well as Pangnirtung and South Pole in order to extend longitudinal coverage to the west. Amplitude comparisons between hemispheres showed (a) a seasonal dependence (larger in the winter hemisphere), and (b) a dependence on the sign of the By component of the interplanetary magnetic field (IMF): GMDs were larger in the north (south) when IMF By was >0 (<0). A majority of events occurred nearly simultaneously (to within ±3 min) independent of the sign of By as long as |By| ≤ 2 |Bz|. As has been found in earlier studies, IMF Bz was <0 prior to most events. When IMF data from Geotail, Themis B, and/or Themis C in the near‐Earth solar wind were used to supplement the time‐shifted OMNI IMF data, the consistency of these IMF orientations was improved.

Relationship between TEC jumps and auroral substorm in the high-latitude ionosphere

The influence of an auroral substorm on the total electron content (TEC) jumps and cycle slips on Global Positioning System (GPS) at high-latitudes is studied. For the first time, optical data from the all-sky imager, as well as interplanetary magnetic field and magnetometer data are used to complete the analysis of the slips occurrence and to monitor the substorm evolution. Two types of slips are considered: (i) instrumental slips including losses in the measured phase of the GPS signal and (ii) sharp TEC variations (TEC jumps) It is demonstrated that the jumps in TEC determined from the GPS signals are mainly related to the auroral particle precipitation that normally occurs during geomagnetic substorms in the polar ionosphere. The GPS frequency {L}_{2} is consistently subject to more slips than frequency {L}_{1} both for quiet and disturbed conditions. The probability of TEC jumps is higher than for cycle slips in phase at frequencies {L}_{1} and {L}_{2}. The maximum of TEC jumps is observed during the recovery phase of the auroral substorm. Our findings are based on a data set obtained for a particular event. A generalization of the obtained numerical estimates to other events requires additional research and further analysis.

An Analysis of Active Region as a Trigger of Solar Flares

Sunspot number, solar radio flux and solar wind involve in interpreting the movements of CMEs and solar flare. Rotating sunspots are an extremely efficient way to inject energy into the magnetic field of the sun’s atmosphere. A solar flare is essentially a blast on the surface of the sun going from minutes to hours long. Therefore, the objective of this study is to state that the active region sunspot waves as a trigger solar flare. Equally important, the energy efficiency associated with solar flares may take several hours or even days to build up, but most flares take only a matter of minutes to release their energy. Detailed analyzed of solar flare event on 2016 and 2017 based on e-CALLISTO, the higher the number of active region sunspots, the higher the class of flares produced and released. Four classes of solar are observed in categories of frequency, interplanetary magnetic field and sunspot number emitted by the solar. By using the interplanetary magnetic field data, the magnetic energy contained in the active region sunspots has been calculated which this magnetic energy triggered the emission of solar flare. This study exposed us to analyze the active region waves as a trigger of solar flares and also the factor influenced by it. The physical element that triggers the solar flare by measuring the magnetic energy in the flaring site.

On the Evaluation of Data Quality in the OMNI Interplanetary Magnetic Field Database

AbstractThe OMNI database is formed by propagating the solar wind measured at around Lagrange point L1, whose result may differ from the actual solar wind in the vicinity of the bow shock nose. To test the quality of the OMNI database, we cross‐correlate the 2‐hr intervals of 1‐min interplanetary magnetic field (IMF) data provided mostly by ACE and WIND spacecraft with Geotail measurements in front of the bow shock (10,409 cases in 1997–2016). We used two metrics: Pearson correlation coefficient (CC) and prediction efficiency (PE). Confirming previous studies, we found that the prediction quality of actual IMF degrades continuously with increasing distance of OMNI spacecraft from the Sun‐Earth line, with the amounts of poor and good predictions become nearly equal for RYZ ≥ 65 RE (they constitute ~12% of the entire database). In roughly 20% of the analyzed data, low CC and PE values were the consequence of low IMF variability (a low signal‐to‐noise ratio). The remaining data set includes 42% of very good data (CC ≥ 0.8), 33% of relatively good data (0.5 ≤ CC < 0.8 and PE ≥ 0), 10% of data having correct variability but wrong absolute values (0.5 ≤ CC < 0.8 and PE < 0), and 15% of poor data (CC < 0.5). We also discovered that the OMNI data are generally of a good quality when the PC index of geomagnetic activity correlates well with the solar wind‐magnetosphere coupling factor suggested by Kan and Lee (1979, https://doi.org/10.1029/GL006i007p00577).

Polar Ion Temperature Variations During the 22 January 2012 Magnetic Storm

AbstractWe study the 22 January 2012 magnetic storm, during which some localized neutral density (DN) increases developed, and its polar ion temperature (Ti) variations in terms of the earthward Poynting flux (S||) deposited into the coupled ionosphere‐thermosphere system. We investigate the storm's nature, some flow channel events that are the ionospheric signatures of flux transfer events triggered by magnetic reconnections, and the resultant thermospheric responses. We utilize solar and interplanetary magnetic field data and multi‐instrument topside ionospheric and thermospheric measurements. Results reveal the presence of antisunward propagating solar wind Alfven waves during the various storm phases triggering flux transfer events/flow channel events and launching atmospheric gravity waves whose Ti signatures appeared as episodic Ti variations. Various scenarios demonstrate the direct correlation between S|| and DN and the irregular Ti responses within/above flow channels that we explain in terms of frictional heating (Tfrc). We identify Ti responses during various flow channel events as (1) direct/intermittent and (2) opposite, with S|| and DN reflecting the respective underlying flow channel development at the time of detection as (1) early stage when Tfrc was high due to the still large velocity differences between ions and neutrals and as (2) late stage when Tfrc ceased since ions and neutrals moved together. Ti oscillations are observed to be originating from polar or auroral flow channels and propagating across the polar cap toward the equator. We conclude that antisunward solar wind Alfven waves had a significant impact on Ti variability during this storm by modulating magnetic reconnection and launching atmospheric gravity waves.

Relations Between vz and Bx Components in Solar Wind and their Effect on Substorm Onset

AbstractWe analyze two substorm onset lists, produced by different methods, and show that the (Bx·vz) product of the solar wind (SW) velocity and interplanetary magnetic field (IMF) components for two thirds of all substorm onsets has the same sign as IMF Bz. The explanation we suggest is the efficient displacement of the magnetospheric plasma sheet due to IMF Bx and SW flow vz, which both force the plasma sheet moving in one direction if the sign of (Bx·vz) correlates with the sign Bz. The displacement of the current sheet, in its turn, increases the asymmetry of the magnetotail and can alter the threshold of substorm instabilities. We study the SW and IMF data for the 15‐year period (which comprises two substorm lists periods and the whole solar cycle) and reveal the similar asymmetry in the SW, so that the sign of (Bx·vz) coincides with the sign of IMF Bz during about two thirds of all the time. This disproportion can be explained if we admit that about 66% of IMF Bz component is transported to the Earth's orbit by the Alfvén waves with antisunward velocities.

Autocorrelation Study of Solar Wind Plasma and IMF Properties as Measured by the MAVEN Spacecraft

AbstractIt has long been a goal of the heliophysics community to understand solar wind variability at heliocentric distances other than 1 AU, especially at ∼1.5 AU due to not only the steepening of solar wind stream interactions outside 1 AU but also the number of missions available there to measure it. In this study, we use 35 months of solar wind and interplanetary magnetic field (IMF) data taken at Mars by the Mars Atmosphere and Volatile EvolutioN (MAVEN) spacecraft to conduct an autocorrelation analysis of the solar wind speed, density, and dynamic pressure, which is derived from the speed and density, as well as the IMF strength and orientation. We found that the solar wind speed is coherent, that is, has an autocorrelation coefficient above 1/e, over roughly 56 hr, while the density and pressure are coherent over smaller intervals of roughly 25 and 20 hr, respectively, and that the IMF strength is coherent over time intervals of approximately 20 hr, while the cone and clock angles are considerably less steady but still somewhat coherent up to time lags of roughly 16 hr. We also found that when the speed, density, pressure, or IMF strength is higher than average, the solar wind or IMF becomes uncorrelated more quickly, while when they are below average, it tends to be steadier. This analysis allows us to make estimates of the values of solar wind plasma and IMF parameters when they are not directly measured and provide an approximation of the error associated with that estimate.

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.

Large‐Scale Survey of the Structure of the Dayside Magnetopause by MMS

AbstractThis paper describes the generation and initial utilization of a database containing 80 vector and scalar quantities, for a total of 8,670 magnetopause and magnetosheath current sheet crossings by MMS1, using plasma and magnetic field data from the Fast Plasma Investigation, Fluxgate Magnetometer, and Hot Plasma Composition Analyzer instruments, augmented by solar wind and interplanetary magnetic field data from CDAWeb. Based on a determination of the current sheet width, measured and calculated vector and scalar quantities are stored for the two sides of the current sheet and for selected times within the current sheet. The only manual operations were the classification of the current sheets according to the type of boundary, the character of the magnetic field transition, and the quality of the current sheet fit. To characterize the database, histograms of selected key quantities are presented. We then give the statistics for the duration, motion, and thicknesses of the magnetopause current sheet, using single‐spacecraft techniques for the determination of the normal velocities, obtaining median results of 12.9 s, 38.5 km/s, and 705.4 km, respectively. When scaled to the ion inertial length, the median thickness became 12.6; there were no thicknesses less than one. Next, we apply the Walén relation to find crossings that are rotational discontinuities and thus may indicate ongoing magnetic reconnection. For crossings where the velocities in the outflow region exceed the velocity on the magnetosheath side by at least 250 km/s, 47% meet our rotational discontinuity criteria. If we require the outflow to exceed 250 km/s along the L direction, then the percentage rises to 68%.

A correlation study regarding the AE index and ACE solar wind data for Alfvénic intervals using wavelet decomposition and reconstruction

Abstract. The purpose of this study is to present a wavelet interactive filtering and reconstruction technique and apply this to the solar wind magnetic field components detected at the L1 Lagrange point ∼ 0.01 AU upstream of the Earth. These filtered interplanetary magnetic field (IMF) data are fed into a model to calculate a time series which we call AE∗. This model was adjusted assuming that magnetic reconnection associated with southward-directed IMF Bz is the main mechanism transferring energy into the magnetosphere. The calculated AE∗ was compared to the observed AE (auroral electrojet) index using cross-correlation analysis. The results show correlations as high as 0.90. Empirical removal of the high-frequency, short-wavelength Alfvénic component in the IMF by wavelet decomposition is shown to dramatically improve the correlation between AE∗ and the observed AE index. It is envisioned that this AE∗ can be used as the main input for a model to forecast relativistic electrons in the Earth's outer radiation belts, which are delayed by ∼ 1 to 2 days from intense AE events.

Magnetospheric access for solar protons during the January 2005 SEP event

The early phase of the extraordinary solar energetic particle 20 January, 2005 event having the highest peak flux of any SEP in the past 50 years of protons with energies > 100 MeV is studied. Solar energetic particles (>16 MeV) entry to the Earth’s magnetosphere on January 20, 2005 under northward interplanetary magnetic field conditions is considered based on multi-satellite data analysis and magnetic field simulation. Solar wind parameters and interplanetary magnetic field data, as well as calculations in terms of the A2000 magnetospheric magnetic field model were used to specify conditions in the Earth’s environment corresponding to solar proton event. It was shown that during the early phase of the event energetic particle penetration into the magnetosphere took place in the regions on the magnetopause where the magnetospheric and interplanetary magnetic field vectors are parallel. Complex analysis of the experimental data on particle fluxes in the interplanetary medium (data from ACE spacecraft) and on low-altitude (POES) and geosynchronous (GOES) orbits inside the Earth’s magnetosphere show two regions on the magnetopause responsible for particle access to the magnetosphere: the near equatorial day-side region and open field lines window at the high-latitude magnetospheric boundary. Calculations in terms of A2000 magnetospheric magnetic field model and comparison with SuperDARN images support the link between high-latitude solar energetic particle precipitations and the region at the magnetopause where the magnetospheric field is coupled with northward IMF, allowing solar particles entrance into the magnetosphere and access to the northern polar cap.

Detection and analysis of short-period geomagnetic perturbations during increased solar activity and magnetic storms

The dynamics of geomagnetic field variations on the eve and during the periods of magnetic storms of 2015 and 2017 has been studied (data of the horizontal component of the Earth’s magnetic field of the terrestrial station network were used). The method developed by the authors based on wavelet transform and adaptive threshold functions was applied. Weak geomagnetic disturbances synchronously appearing at the stations and preceding the onset of strong magnetic storms was extracted. The correlation of the isolated geomagnetic disturbances with the AE-index is shown, both in the occurrence times and in intensities. On the basis of comparison with interplanetary environment data (interplanetary magnetic field data and the solar wind parameters were analyzed), and also based on the results of other author works [1, 2]. we assume that the isolated effects have solar nature. The research is supported by the grant of the Russian Science Foundation No. 14-11-00194.

Spatial Distribution and Semiannual Variation of Cold‐Dense Plasma Sheet

AbstractThe cold‐dense plasma sheet (CDPS) plays an important role in the entry process of the solar wind plasma into the magnetosphere. Investigating the seasonal variation of CDPS occurrences will help us better understand the long‐term variation of plasma exchange between the solar wind and magnetosphere, but any seasonal variation of CDPS occurrences has not yet been reported in the literature. In this paper, we investigate the seasonal variation of the occurrence rate of CDPS using Geotail data from 1996 to 2015 and find a semiannual variation of the CDPS occurrences. Given the higher probability of solar wind entry under stronger northward interplanetary magnetic field (IMF) conditions, 20 years of IMF data (1996–2015) are used to investigate the seasonal variation of IMF Bz under northward IMF conditions. We find a semiannual variation of IMF Bz, which is consistent with the Russell‐McPherron (R‐M) effect. We therefore suggest that the semiannual variation of CDPS may be related to the R‐M effect.

Characterization of the Complex Ejecta Measured In Situ on 19 – 22 March 2001 by Six Different Methods

This article proposes some traditional and newly developed methods to evaluate the properties of the magnetic cloud (MC) observed on 19 – 22 March 2001. We used physical and mathematical approaches to analyze the time series of solar wind plasma and interplanetary magnetic field data. Two methods that are commonly used to derive the MC properties, the minimum variance analysis and the Grad–Shafranov reconstruction, were applied to derive the properties of the MCs by fitting in situ measurements in the corresponding time intervals, as discussed in previous studies. Other methods developed by us (a travel time analysis of the interplanetary coronal mass ejection, spatio-temporal entropy, nonlinear fluctuation analysis, and wavelet analysis) are used as auxiliary tools to help identify whether the event consists of one or possibly two MCs. Our results suggest that the 19 – 22 March 2001 event was composed by two MCs. Our results agree with those of other researchers who used various fitting methods and identified the solar origin of this event as two CMEs.