Geomagnetism and Aeronomy最新文献

This study aims to investigate the energy transfer mechanisms and the behavior of thermal conductivity of this region by examining the thermal conductivity coefficients calculated for critical altitudes in the F region of the ionosphere. Electron-ion collisions and the geometry of the magnetic field affect these coefficients. The thermal conductivity in the ionosphere can exhibit anisotropic properties (different values in different directions) due to the directional dependence of the Earth’s magnetic field. Theoretical approaches have been used and numerical calculations have been performed to analyze the thermal conductivity of the ionosphere. The findings indicate that the magnitudes of the thermal conductivity coefficients were at the level of electrical conductivity and the tensor elements (K_zx, K_xz, K_yz, K_zy) were negative, while the K_yx, K_xy elements were positive up to the equator and then became negative. This phenomenon, called effective thermal conductivity, is not actually a negative value for thermal conductivity, but rather an unusual situation resulting from the direction-dependent effect of the magnetic field. It has been determined that the magnitudes of the tensor elements on March 21 are slightly greater than those on September 23.

The present study investigates the subauroral ionospheric response to geomagnetically disturbed conditions across different seasons of 2012, using Total electron content (TEC) and S4 index data derived from a Global Positioning System (GPS) receiver installed at the Indian Antarctic station Maitri (geographic coordinates: 70.76° S, 11.74° E). TEC and S4-index measurements for January, March, and June 2012 were analysed alongside the corresponding Auroral Electrojet (AE) index and the interplanetary magnetic field (IMF) Bz component to assess seasonal variability in ionospheric behaviour. The results reveal that the subauroral ionosphere exhibits a negative response (i.e., TEC depletion) during periods of southward IMF Bz orientation, whereas a positive response is generally observed during northward IMF Bz, particularly during the summer and equinoctial periods. In contrast, this trend appears to reverse during the winter season. The observed negative ionospheric responses are attributed to a combination of equatorward plasma transport and thermospheric compositional changes. Additionally, poleward compression of the auroral oval and enhanced molecular precipitation are believed to contribute to these depletions. Furthermore, the study examines the occurrence characteristics of amplitude scintillations under disturbed geomagnetic conditions. It is observed that the intensity of amplitude scintillation during the winter (polar night) is significantly higher compared to that during summer and equinox periods, suggesting enhanced small-scale ionospheric irregularities under such conditions.

We analyzed the occurrence and characteristics of various types of magnetic storms during solar cycle 24. The annual mean total sunspot number (SSN) was used to quantify solar cycle activity. The intensity and classification of magnetic storms, by type and rank, were assessed using two geomagnetic indices: Dst (Disturbance Storm Time Index) and aa (global geomagnetic activity index), respectively. Based on the minimum Dst values, we identified a total of 130 magnetic storm events, comprising 104 moderate and 26 intense storms. Using the maximum aa values, we further classified these events by type and rank. Among them, 54 storms displayed sudden commencement (S-storms), while 76 storms exhibited gradual commencement (G-storms). Additionally, the types of storms were categorized by five ranks. According to established literature, the main common sources of storms are issued from interplanetary coronal mass ejections (ICMEs) and corotating interaction regions (CIRs). Our findings revealed that 76% of storms associated with ICME sources were S-storms, typically occurring near the peak of solar activity. Conversely, 60% of storms related to CIR sources were G-storms, most commonly observed during the declining phase of the solar cycle. This study contributes to the broader understanding of magnetic storm behavior during solar cycle 24, in terms of both intensity and classification. Lastly, we compared the distribution of storms in solar cycle 24 with those of previous cycles to contextualize the overall activity level.

Estimating with low uncertainty the parameters of time and magnitude of upcoming earthquakes is necessary to create an earthquake warning system. Nowadays, by using different satellite data, it is possible to monitor a large number of earthquake precursors. Multi-precursor analysis, along with multi-method analysis, has made it possible to detect a large number of LAI (lithospheric atmospheric ionospheric) seismic anomalies in the study of strong earthquake-affected areas. In this study, the deviation values of 898 LAI anomalies detected using 20 implemented predictor algorithms around the time and location of 21 powerful earthquakes that occurred in recent years have been considered. Using different scenarios, various functions were fitted on the collected data, including the day of anomaly observation, anomaly intensity, geographic latitude of epicenter and real magnitude of the earthquake, and functions were developed to estimate magnitude parameters with RMSE of about 0.53 (M_W) and the day of the earthquake with about RMSE of 8.27 day. In addition, by using an MLP neural network, and training it using the detected LAI anomalies, accuracies of 0.21 and 9.29 were obtained, respectively, for estimating the magnitude and time of an impending earthquake. Therefore, by comparing the two functional and machine learning-based methods proposed in this study, it can be concluded that the proposed functions are efficient for estimating magnitude and time of forthcoming strong earthquakes. Although the accuracy of predicting the magnitude of the earthquake is acceptable, the accuracy of about 8 days for predicting the day of the earthquake can be efficient for relatively short-time earthquake prediction.

Geomagnetic storms (GSs), driven by solar activity, produce significant disturbances in the Earth’s magnetic field—particularly in its horizontal component (H). This study investigates the response of the H-component to GSs during solar cycle 24 (2009–2019), using ground-based magnetometer data recorded at the TAM observatory in Tamanrasset, Algeria (22.79° N, 5.53° E), part of the INTERMAGNET network. A total of 130 storms were identified based on Dst-index thresholds and classified into 104 moderate (–100 nT < Dst ≤ –50 nT) and 26 intense (Dst ≤ –100 nT) events. The H-component was derived from the orthogonal north and east components (X, Y) of the geomagnetic field. The results reveal a gradual upward trend in the H-component over the solar cycle, consistent with secular geomagnetic field variations. However, during storm periods, the H-component exhibited significant decreases. These disturbances were quantified using the maximum deviation parameter ΔH_max, which displayed a statistically significant positive correlation with storm intensity (r = 0.71). Notably, the correlation was stronger for intense storms (r = 0.75) than moderate ones (r = 0.38). These results highlight the greater sensitivity of low-latitude geomagnetic observatories to high-intensity storms and demonstrate the diagnostic value of ΔH_max for space weather monitoring.

Taking into account the real magnetic field geometry of Earth in the northern hemisphere, this work produced the equations of real diffusion coefficients for the ionospheric F region (390, 410, 450, 500, 550, 600 km) at low latitudes. In a steady state, diffusion coefficients show real values, while in an unstable state, they show complex values with real and imaginary components. We performed numerical calculations at F region altitudes within the ionospheric plasma to determine the diffusion coefficients for both cases. The results show that in the steady state, the diffusion coefficients have values that are very close to the speed of light. In unstable conditions, on the other hand, the real parts are generally close to the conductivity values, while the imaginary parts are similar to the sound speed magnitudes. The fundamental focus of this technique is to demonstrate and calculate the complex structure of diffusion coefficients in the ionosphere, representing the first such instance in the literature.

Ground-based electron density measurements from ionosondes are used to evaluate the accuracy of ionospheric empirical models, such as the International Reference Ionosphere (IRI) and the NeQuick models. In the present study, the results obtained from ionosonde and empirical models (NeQuick2, IRI2016, and IRI2020) of the electron density at the Addis Ababa, Ethiopia ionosonde station, with a geographic latitude of 9.03° N and longitude 38.76° E on selected days in 2014 are presented. In the comparison of the NeQuick2, IRI2016, and IRI2020 models with the ionosonde data, the percentage deviation and the correlation coefficient (R) are used as measures of the performance of the models. The overall results show that the latest version of the IRI2020 model outperforms NeQuick2 and IRI2016 in ionospheric electron density value, with NeQuick2 showing slightly better performance than IRI2016. Mostly, the NeQuick2, IRI2016, and IRI2020 models show overestimation of the electron density values from the ionosonde data. The NeQuick2 model overestimates with a maximum percentage deviation of 38%; the IRI2016 model overestimates with a maximum percentage deviation of 40%; and the IRI2020 model overestimates with a maximum percentage deviation of 30% from the ionosonde data measurements, while underestimating with percentage deviations of 10, 18, and 9%, respectively. The average values of the correlation coefficients of the NeQuick2, IRI2016, and IRI2020 models are 0.79, 0.74, and 0.81, respectively.

While the link between coronal mass ejections (CMEs) and geomagnetic storms has been well established, the prediction of intensity and forecasting of the storms are necessary to notify the adverse effects in advance. In this work, we explore the relationship of the intensity of geomagnetic storm (Dst index) and southward magnetic component (B_s) with the magnetic parameters of the source active region (Space-weather HMI Active Region Patch, SHARP parameters) during 2011‒2017 to find the connection between the magnetic parameters of the source active region and geo-effectiveness. A set of 31 halo CMEs is found to have produced geomagnetic storms from 2011 to 2017. The preliminary analysis shows that these events erupted from active regions with strong and complex magnetic field structures and found to be associated with weak to intense storms (‒6 to ‒223 nT). The following important results are obtained from the detailed analysis: (i) Most of the storms are caused from the events near disk center to western longitudes except three. (ii) Moderate correlations are found between some magnetic parameters of the source active region with the intensity of the storm and southward magnetic field component. (iii) Empirical relations are derived for storm intensity and southward magnetic component in terms of important source region magnetic parameters. Furthermore, we got good correlation for the product of speeds of interplanetary coronal mass ejection (V_ICME) and B_s with the Dst index. These findings reveal the Sun–Earth connection of certain events and give some clues on improving our ability to connect the intensity of geomagnetic storms with CME kinematics and source region magnetic parameters.

Satellite communication and navigation systems are increasingly essential in modern society, making it crucial to understand the impact of solar activity on these technologies. Total electron content (TEC) significantly influences satellite performance, necessitating accurate forecasting to maintain operational reliability. This research focuses on predicting TEC during eleven distinct X-class solar flares that occurred in February, March, May, June, and August 2024, utilizing a long short-term memory (LSTM) model. The study employs a comprehensive dataset of TEC data sourced from the IONOLAB database, alongside important solar and geomagnetic parameters such as Kp, Ap, SSN, and F10.7 obtained from NASA OmniWeb. The model’s predictive performance was validated against the IRI-2017 model. Results demonstrate that the LSTM model effectively captures TEC variations during periods of extreme solar activity, consistently outperforming the IRI-2017 model. For instance, during significant solar events, the LSTM model achieved notable performance metrics, indicating its capability to provide precise TEC forecasts. This research contributes to the advancement of space weather forecasting models, enhancing the reliability of satellite-dependent systems critical for global communication and navigation.