Geomagnetic Parameters Research Articles

Navigation by magnetic signatures in a realistic model of Earth's magnetic field.

Certain animal species use the Earth's magnetic field (i.e. magnetoreception) alongside their other sensory modalities to navigate long distances that include continents and oceans. It is hypothesized that several animals use geomagnetic parameters, such as field intensity and inclination, to recognize specific locations or regions, potentially enabling migration without a pre-surveyed map. However, it is unknown how animals use geomagnetic information to generate guidance commands, or where in the world this type of strategy would maximize an animal's fitness. While animal experiments have been invaluable in advancing this area, the phenomenon is difficult to studyin vivoorin situ, especially on the global scale where the spatial layout of the geomagnetic field is not constant. Alongside empirical animal experiments, mathematical modeling and simulation are complementary tools that can be used to investigate animal navigation on a global scale, providing insights that can be informative across a number of species. In this study, we present a model in which a simulated animal (i.e. agent) navigates via an algorithm which determines travel heading based on local and goal magnetic signatures (here, combinations of geomagnetic intensity and inclination) in a realistic model of Earth's magnetic field. By varying parameters of the navigation algorithm, different regions of the world can be made more or less reliable to navigate. We present a mathematical analysis of the system. Our results show that certain regions can be navigated effectively using this strategy when these parameters are properly tuned, while other regions may require more complex navigational strategies. In a real animal, parameters such as these could be tuned by evolution for successful navigation in the animal's natural range. These results could also help with developing engineered navigation systems that are less reliant on satellite-based methods.

Cross-Correlation Analysis of Cosmic Ray Intensity with Interplanetary and Geomagnetic Parameters during Disturbed and Quiet Periods

Cross-Correlation Analysis of Cosmic Ray Intensity with Interplanetary and Geomagnetic Parameters during Disturbed and Quiet Periods

Application of bioinspired geomagnetic sensor measurements and geomagnetic map modeling based on neural networks in simulated navigation

A geomagnetic field is a vector field in which the strength and direction are related to geographical location. Geomagnetic navigation technology, which uses collected geomagnetic field information to achieve positioning and navigation, has the advantages of reliability, stability, accuracy, and concealment. With the deepening research on geomagnetic navigation, bioinspired geomagnetic navigation technology has also been developed, which mainly studies and imitates the magnetic sensing mechanism and navigation behavior of animals, providing new research ideas for geomagnetic navigation technology. The magnetic particle hypothesis and free radical pair hypothesis are two mainstream mechanisms of biological sensing using the geomagnetic field, and studies have shown that these two mechanisms may be coupled within organisms. In this study, we propose a bioinspired weak magnetic vector (BWMV) sensor based on the joint sensing mechanism of magnetic particles and free radicals. It consists of a magnetic rod made of soft magnetic material and a tunnel magnetoresistance (TMR) sensor array. A magnetic rod was used to simulate magnetic particles to convert magnetic field angle information into magnetic field intensity distribution information, and the TMR sensor array was used to simulate the perception of the magnetic field distribution by free radicals. In addition, artificial neural networks (ANNs) were used for BWMV sensors to obtain the mapping relationship between the magnetic field distribution and parameters, which can be used for geomagnetic navigation. To verify the navigation effect of the BWMV sensor in the laboratory, a simulated geomagnetic navigation device was built, and the high-precision mapping relationship from geomagnetic parameters to latitude and longitude information of the selected navigation area was obtained through another ANN. Finally, the effectiveness of the BWMV sensor based on ANNs for geomagnetic navigation is verified using simulated navigation experiments.

Monitoring the electrical potential difference of pine tree

The subject of the study is the time series of the difference in electrical potentials along the trunk of a pine tree and the time series of meteorological and geomagnetic parameters consistent with them, measured by hardware and software complexes of the geophysical observatory "Petrozavodsk" and the Botanical Garden of Petrozavodsk State University in 2017-2022, as well as the time series of the K-index database global geomagnetic activity. The existence of characteristic regimes in the difference in electrical potentials of pine trees is shown, depending on the season, temperature, precipitation, humidity, determined by physical environmental factors and physiological processes in the tree. The reaction of the electrical potential difference of pine trees to meteorological parameters, thunderstorms, and space weather phenomena: magnetic storms and solar eclipses is analyzed. The results of these observations are qualitatively interpreted using known features of electric field of the Earth.

Interaction of global electron content with the Sun and solar wind during intense geomagnetic storms

Assessment of solar and solar wind parameters driving the ionosphere model is essential for prediction of the ionospheric weather. In the present paper impact of the different solar, interplanetary and geomagnetic parameters on the global electron content (GEC) during intense space weather storms is investigated. Hourly GEC values are calculated from JPL global maps of total electron content GIM-TEC from 1995 to 2023. The sample comprises 90 intense storms from 1995 to 2023 associated with monthly peak of the weighted accumulation of the geomagnetic Apo(τ, t) index exceeding 90 nT. The 27 day weighted accumulation of the solar sunspot numbers SSN2(τ), solar radio flux F10.7(τ), the solar hydrogen emission Lyman_α(τ) and the composite magnesium MgII(τ) indices are explored as precursors of GEC enhancements. As distinct from the positive ionosphere storm, the solar wind speed Vsw, the solar wind electric field Ey, merging electric field Em and Apo(τ, t) indices proved to be effective as potential drivers of the negative GEC depletion. The positive and negative dGEC deviations from hourly GEC are produced by subtracting a quiet reference GECav averaged during 24h prior the storm normalized by GECav. The hourly storm profiles Vsw(t), Em(t), Ey(t), Apo(τ, t), Dst(t), GEC(t) and dGEC(t) were reduced by method of superposed epochs. The zero epoch t0 = 0 was taken at the peak Apo*(τ, t0) and the storm time lasted for 48h from −12h prior t0 and 35h afterwards. The best correlation of the positive storm dGECp amplitude is obtained with MgII(τ) and the negative storm dGECn with Em* and Apo*(τ, t0) which are used to derive characteristics of five key points of storm-time dGEC(t) model: 1 – onset of the storm profile t1 = t(dGECp); 2 – the amplitude dGECpmax and its time t2(dGECpmax); 3 – the time of transition t3(dGEC = 0) from the positive to negative (±) GEC storm; 4 – minimum negative disturbance dGECnmin and its time t4(dGECnmin), 5 – the end of the storm profile t5(dGECn). Analytical model of dGEC(t) is derived with Epstein step functions fitting 5 key points. Deviations dGEC(t) are inverted to GEC(t) using quiet reference pre-storm GECav. The model is validated for three intense storms on 26–28 February, 23–25 March and 23–25 April 2023. The results show improvement of dGEC forecast with RMS error reduced from 45 to 80% compared to results produced by the international reference ionosphere−plasmasphere model IRI-Plas.

A Comparative Assessment of the Distribution of Joule Heating in Altitude as Estimated in TIE‐GCM and EISCAT Over One Solar Cycle

AbstractDuring geomagnetically active times, Joule (or frictional) heating in the Lower Thermosphere‐Ionosphere is a significant source of thermal energy, greatly affecting density, temperature, composition and circulation. At the same time, Joule heating and the associated Pedersen conductivity are amongst the least known parameters in the upper atmosphere in terms of their quantification and spatial distribution, and their parameterization by geomagnetic parameters shows large discrepancies between estimation methodologies, primarily due to a lack of comprehensive measurements in the region where they maximize. In this work we perform a long‐term statistical comparison of Joule heating as calculated by the NCAR Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model (TIE‐GCM) and as obtained through radar measurements by the European Incoherent Scatter Scientific Association (EISCAT). Statistical estimates of Joule heating and Pedersen conductivity are obtained from a simulation run over the 11 year period spanning from 2009 until 2019 and from radar measurements over the same period, during times of radar measurements. The results are statistically compared in different Magnetic Local Time sectors and Kp level ranges in terms of median values and percentiles of altitude profiles. It is found that Joule heating and Pedersen conductivity are higher on average in TIE‐GCM than in EISCAT for low Kp and are lower than EISCAT for high Kp. It is also found that neutral winds cannot account for the discrepancies between TIE‐GCM and EISCAT. Comparisons point toward the need for a Kp‐dependent parameterization of Joule heating in TIE‐GCM to account for the contribution of small scale effects.

Ionospheric TEC modeling using COSMIC-2 GNSS radio occultation and artificial neural networks over Egypt

Abstract The ionospheric delay significantly impacts GNSS positioning accuracy. To address this, an Artificial Neural Network (ANN) was developed using the high-quality COSMIC-2 ionospheric profile dataset to predict the Total Electron Content (TEC). ANNs are adept at addressing both linear and nonlinear challenges. For this research, eight distinct ANNs were cultivated. These ANNs were designed with the following inputs Year, Month, Day, Hour, Latitude, and Longitude. Along with solar and geomagnetic parameters such as the F10.7 solar radio flux index, the Sunspot Number (SSN), the Kp index, and the ap index. The goal was to discern the most influential parameters on ionosphere prediction. After pinpointing these key parameters, an enhanced model utilizing a pioneering technique of a secondary ANN was employed with the main ANN to predict TEC values for events in 2023. The study’s findings indicate that solar parameters markedly enhance the model’s accuracy. Notably, the augmented model featuring a prelude secondary network achieved a stellar correlation coefficient of 0.99. Distributionally, 41 % of predictions aligned within the (−1≤ ΔTEC ≤1) TECU spectrum, 28 % nestled within the (1< ΔTEC ≤2) and (−2≤ ΔTEC <−1) TECU ambit, while a substantial 30 % spanned the broader (2< ΔTEC ≤5) and (−5≤ ΔTEC <−2) TECU range. In essence, this research underscores the potential of incorporating solar parameters and advanced neural network techniques to refine ionospheric delay predictions, thus boosting GNSS positioning precision.

Ionospheric TEC prediction using FFNN during five different X Class solar flares of 2021 and 2022 and comparison with COKSM and IRI PLAS 2017

Abstract The Ionospheric Total Electron Content (TEC) measured in the ray path of the signals directly contributes to the Range Error (RE) of the satellite signals, which affects positioning and navigation. Employing the Co-Kriging-based Surrogate Model (COKSM) to predict TEC and RE correction has proven prolific. This research attempted to test and compare the prediction capability of COKSM with an Artificial Intelligence-based Feed Forward Neural Network model (FFNN) during five X-Class Solar Flares of 2021–22. Also, the results are validated by comparing them with the IRI PLAS 2017 model. TEC, solar, and geomagnetic parameters data for Hyderabad GPS station located at 17.31° N latitude and 78.55° E longitude were collected from IONOLAB & OMNIWEB servers. The COKSM uses six days of input data to predict the 7th day TEC, whereas prediction using the FFNN model is done using 45 days of data before the prediction date. The performance evaluation is done using RMSE, NRMSE, Correlation Coefficient, and sMAPE. The average RMSE for COKSM varied from 1.9 to 9.05, for FFNN it varied from 2.72 to 7.69, and for IRI PLAS 2017 it varied from 7.39 to 11.24. Likewise, evaluation done for three different models over five different X-class solar flare events showed that the COKSM performed well during the high-intensity solar flare conditions. On the other hand, the FFNN model performed well during high-resolution input data conditions. Also, it is notable that both models performed better than the IRI PLAS 2017 model and are suitable for navigational applications.

Локальная диагностика наличия полярных сияний на основе интеллектуального анализа геомагнитных данных

Despite the existing variety of approaches to monitoring space weather and geophysical parameters in the auroral oval region, the issue of effective prediction and diagnostics of auroras as a special state of the upper ionosphere at high latitudes remains virtually unresolved.
 In this paper, we explore the possibility of local diagnostics of auroras through mining of geomagnetic data from ground-based sources. We assess the significance of indicative variables and their statistical relationship.
 So, for example, the application of Bayesian inference to the data from the Lovozero geophysical station for 2012–2020 has shown that the dependence of a posteriori probability of observing auroras in the optical range on the state of geomagnetic parameters is logarithmic, and the degree of its significance is inversely proportional to the discrepancy between empirical data and approximating function.
 The accuracy of the approach to diagnostics of aurora presence based on the random forest method is at least 86 % when using several local predictors and ~80 % when using several global geomagnetic activity indices characterizing the geomagnetic field disturbance in the auroral zone.
 In conclusion, we discuss promising ways to improve the quality metrics of diagnostic models and their scope.

Local diagnostics of aurora presence based on intelligent analysis of geomagnetic data

Despite the existing variety of approaches to monitoring space weather and geophysical parameters in the auroral oval region, the issue of effective prediction and diagnostics of auroras as a special state of the upper ionosphere at high latitudes remains virtually unresolved.
 In this paper, we explore the possibility of local diagnostics of auroras through mining of geomagnetic data from ground-based sources. We assess the significance of indicative variables and their statistical relationship.
 So, for example, the application of Bayesian inference to the data from the Lovozero geophysical station for 2012–2020 has shown that the dependence of a posteriori probability of observing auroras in the optical range on the state of geomagnetic parameters is logarithmic, and the degree of its significance is inversely proportional to the discrepancy between empirical data and approximating function.
 The accuracy of the approach to diagnostics of aurora presence based on the random forest method is at least 86 % when using several local predictors and ~80 % when using several global geomagnetic activity indices characterizing the geomagnetic field disturbance in the auroral zone.
 In conclusion, we discuss promising ways to improve the quality metrics of diagnostic models and their scope.

Design and Implementation of 4WD Intelligent Car System Based on Integrated Navigation

Focuses on the basic principle of integrated navigation system based on geomagnetic sensor, three-axis gyroscope and three-axis accelerometer. A four-wheel independent driving car platform is designed with STM32F4 MCU as the control core and RZ7899 as the motor drive. The vehicle heading angle is obtained through the quaternion obtained by MPU6050, and combined with the geomagnetic parameters, the serial double PID control algorithm is used for navigation by correcting the deflection angle, the intelligent car can run in a straight line without other navigation systems within a certain range.

Koudougou (Burkina Faso, Africa), GPS-TEC Response to Recurrent Geomagnetic Storms during Solar Cycle 24 Declining Phase

In this paper, we presented the effect of moderate geomagnetic storms on the TEC variation at the Koudougou station (Geo Lat 12° 15′N; Geo Long: -2° 20′E) in Burkina Faso (Africa) during the descending phase of solar cycle 24. For this purpose, four moderate geomagnetic storms without storm sudden commencement (SSC) or sudden impulse (SI) that occurred on May 13, 2015 (Dst: -76 nT), June 08, 2015 (Dst: -73 nT), September 11, 2015 (Dst: -80 nT), and May 08-09, 2016 (Dst: -88nT), were considered. These moderate storms were found to be associated with transients induced by fast solar winds. At the Koudougou station, TEC variation shows a positive response to the different moderate geomagnetic storms studied, with increases of order of 2-21 TECU around 1300-1500 UT except for September 11, 2015, TEC variation which shows sometimes negative responses at a few hours (mainly at night). TEC increases observed are a function of geomagnetic parameter (magnitude and polarity) variation. Storm-induced electric field and neutral winds are the main drivers of TEC changes observed during the selected geomagnetic storms. In addition, it was found that the TEC peak on storm day behaves differently compared to the days before and after the storm depending on whether Dst is positive or negative before southward inversion. Indeed, a TEC small peak relative to the days before and after the storm is observed when Dst is negative before southward inversion, and a larger peak occurs in the opposite case. The reasons for these differences are not investigated in this paper.

Turning Noise Into Data: Characterization of the Van Allen Radiation Belt Using SDO Spikes Data

The Solar Dynamics Observatory (SDO) is a solar mission in an inclined geosynchronous orbit. Since commissioning, images acquired by Atmospheric Imaging Assembly (AIA) instrument on-board the SDO have frequently displayed “spikes,” pixel regions yielding extreme number of digital counts. These are theorized to occur from energetic electron collisions with the instrument detector system. These spikes are regularly removed from AIA Level 1.0 images to produce clean and reliable data. A study of historical data has found over 100 trillion spikes in the past decade. This project correlates spike detection frequency with radiation environment parameters in order to generate an augmented data product from SDO. We conduct a correlation study between SDO/AIA data and radiation belt activity within the SDO's orbit. By extracting radiation “spike” data from the SDO/AIA images, we produce a comprehensive data product which is correlated not only with geomagnetic parameters such as Kp, Ap, and Sym-H but also with the electron and proton fluxes measured by the GOES-14 satellite. As a result, we find that AIA spikes are highly correlated with the GOES-14 electrons detected by the magnetospheric electron detector and energetic proton, electron and alpha detectors instruments at the equator (where the two satellites meet) with Spearman's Correlation values of ρ = 0.73 and ρ = 0.53, respectively, while a weaker correlation of ρ = 0.47 is shown with magnetospheric proton detector protons for the 2 year period where both missions returned data uninterruptedly. This correlation proves that the SDO spike data can be proven useful for characterizing the Van Allen radiation belt, especially at areas where other satellites cannot.

Analysis of Geoeffective Impulsive Events on the Sun During the First Half of Solar Cycle 24

A coronal mass ejection (CME) is an impulsive event that emerges rapidly from the Sun. We observed a quiet Sun without many spectacular episodes during the last decade. Although some fast halo and partial halo CMEs had taken place, among them was the backside CME on 23 July 2012. In this work, we verify the link between the variability of solar-wind, heliospheric and geomagnetic parameters and the transmission grid failures registered in southern Poland during 2010 – 2014 when many geomagnetic storms appeared, caused by halo and partial halo CMEs. We aim to apply three machine learning methods: Principal Components Analysis, Self-Organizing Maps, and Hierarchical Agglomerative Clustering to analyze sources on the Sun and the impacts of the intense geomagnetic storms in the first half of Solar Cycle 24. The conducted analyzes underline the importance of solar-wind proton temperature and point out other solar-wind and geomagnetic parameters independently indicated by all the methods used in this study.

Correlation Analysis of Geomagnetic Parameter with South Atlantic Anomaly Region

South Atlantic Anomaly (SAA) region is describe as a region with weak Earth magnetic field. The method apply is power spectrum analysis. By applying the power spectrum analysis method, the value of spectral exponent, β is obtained. From the spectral exponent, β, the Hurst exponent can be determined. The research is conducted by studying the SAA region where comparisons is made between the middle latitude region and high latitude region. 2 active period has been research, 11 March 2011 and 29 May 2011 and 2 normal period has been studied, 9 March 2011 and 12 May 2011. The research conduct indicate the SAA region tend to be persistent and as the vertical field intensity and total field intensity increase, the region tend to have a mixture of antipersistent and persistent characteristic. The high latitude region in this research conduct indicate a tendency to be antipersistent. By conducting research on the SAA region, it can provide knowledge of the dynamics and conditions of our Earth. The SAA region is a region where it is exposed to energetic particles. Satellites passing through the SAA region are also vulnerable to danger. Therefore, the region conducted on the SAA region, can provide knowledge on the characteristics of the SAA region. Research on SAA may increase knowledge of the Earth’s magnetic field.

Regional Ionosphere Delay Models Based on CORS Data and Machine Learning

<h3>Abstract</h3> The ionospheric refraction of GNSS signals can have an impact on positioning accuracy, especially in cases of single-frequency observations. Ionosphere models that are broadcasted by the satellite systems (e.g., Klobuchar, NeQuick-G) do not include enough details to permit them to correct single-frequency observations with sufficient accuracy. To address this issue, regional ionosphere models (RIMs) have been developed in several countries in the western Balkans based on dense Continuous Operating Reference Stations (CORS) observations. Subsequently, a RIM for the western Balkans was built using an artificial neural network that combined regional ionosphere parameters estimated from the CORS data with spatiotemporal (latitude, longitude, hour of day), solar (F10.7) and geomagnetic (Kp, Dst) parameters. The RIMs were tested at the solar maximum (March 2014), a geomagnetic storm (March 2015), and the solar minimum (March 2018). The new RIMs mimic the integrated electron density much more effectively than the Klobuchar model. Furthermore, RIMs significantly reduce the ionospheric effects on single-frequency positioning, indicating their necessity for use in positioning applications.

Исследование отклика среднеширотной ионосферы Северного полушария на магнитные бури в марте 2012

We have studied variations in ionospheric and geomagnetic parameters in the Northern Hemisphere during a series of magnetic storms in March 2012 by analyzing data from the Eurasian mid-latitude ionosonde chain, mid- and high-latitude chains of magnetometers of the global network INTERMAGNET. We have confirmed manifestations of the longitude inhomogeneity of ionospheric effects, which is associated with the irregular structure of the longitudinal variability of geomagnetic field components. The complex physics of the long magnetically disturbed period in March 2012 with switching between positive and negative phases of the ionospheric storm in the same period of the magnetic storm for different spatial regions is emphasized. The change in the effects of the ionospheric storm during this period might have been associated with the superposition in the mid-latitude region of the competing processes affecting the ionospheric ionization whose sources were in the auroral and equatorial ionosphere. We have compared the scenarios for the development of ionospheric disturbances under equinox conditions during magnetic storms in March 2012, October 2016, and March 2015.

Studying the response of the mid-latitude ionosphere of the Northern Hemisphere to magnetic storms in March 2012

We have studied variations in ionospheric and geomagnetic parameters in the Northern Hemisphere during a series of magnetic storms in March 2012 by analyzing data from the Eurasian mid-latitude ionosonde chain, mid- and high-latitude chains of magnetometers of the global network INTERMAGNET. We have confirmed manifestations of the longitude inhomogeneity of ionospheric effects, which is associated with the irregular structure of the longitudinal variability of geomagnetic field components. The complex physics of the long magnetically disturbed period in March 2012 with switching between positive and negative phases of the ionospheric storm in the same period of the magnetic storm for different spatial regions is emphasized. The change in the effects of the ionospheric storm during this period might have been associated with the superposition in the mid-latitude region of the competing processes affecting the ionospheric ionization whose sources were in the auroral and equatorial ionosphere. We have compared the scenarios for the development of ionospheric disturbances under equinox conditions during magnetic storms in March 2012, October 2016, and March 2015.

23rd solar cycle: Solar activity parameters and associated Forbush decreases

We report Forbush decreases (FD) in cosmic ray intensity from January 1996 to December 2008, the whole Solar Cycle 23rd. Statistical analysis is done for only 152 events for which associated solar flare position, flare classes, and Coronal Mass Ejections (CME) speed are given. We applied FD parameters taken from the Forbush Effects and Interplanetary Disturbances databases maintained by the Pushkov Institute of Terrestrial Magnetism, Ionosphere, and Radiowave Propagation (IZMIRAN), obtained by processing the data of the worldwide neutron monitor network using the global survey method (GSM) (A. Belov et al., 2018). For the said number of events, we examine their effect on interplanetary space and the decrease of the galactic cosmic rays (GCR) near Earth. We found that the 11–20° latitudinal belt shows more FD- associated flare events than the other latitudinal belts, and on this belt, the Southern hemisphere is more active. The results reveal that FDs and solar flares are well correlated. Statistical analysis is carried out for the magnitude of the CR decrease with solar and geomagnetic parameters.

The Study of Geomagnetic Activity from 2017 to 2020 Period

Geomagnetic storm has been extensively studied and observed because it can interfere satellite and radio communications. Some parameters, such as Dst index, Kp index, north-south interplanetary magnetic field (Bz) and speed of solar wind (Vsw), can be used to identify the level of geomagnetic storm and assess its potential risk. Therefore, this paper provides the analysis of geomagnetic parameters and their correlation to geomagnetic activities during 2017-2020 period. The geomagnetic data, i.e. Dst, Kp, Bz, and Vsw, were obtained from the website: https://omniweb.gsfc.nasa.gov. The results indicated that some significant geomagnetic storms occurred during 2017-2020, which were shown by the sharp increase of the Dst index, which was also followed by the increase of Kp index, the increase of Vsw, and the extreme fluctuations of Bz index. Three big events occurred on May 28th 2017, September 8th 2017, and August 26th 2018, which had the Dst increase to as high as -125 nT, -124 nT, and -174 nT (< -100 nT), respectively, followed by seventeen minor storm events with Dst between -45 nT until -76 nT. The geomagnetic storm events were probably driven by earth-directed coronal mass ejection (CME) or halo CMEs or high-speed solar wind flows from coronal holes. This result was also supported with the data of flux electron detected at outer radiation belt that have been increased. It can be concluded that the north-south interplanetary magnetic field (Bz) and speed of solar wind (Vsw) have a strong correlation to geomagnetic storm events in which the increasing of Bz followed the increasing of Vsw.