Disturbance Storm Time Research Articles

Ionospheric irregularities observed during the geomagnetic storm of March 2015 and June 2015 over Ethiopia

In this paper we studied the occurrence of ionospheric irregularities over Ethiopia as the consequence of the intense geomagnetic storm of March 2015 and June 2015. Total Electron Content (TEC) data obtained from six Ethiopian GPS reciever stations and those data were used to derive the ionospheric irregularities proxy indices, e.g.,rate of change of TEC (ROT) and ROT index (ROTI). These indices were used to characterize the intensity of the fluctuation of total electron content. ROTI was used as a criterion of ionospheric irregularities that took place during the storm. These indices were characterized along side with the disturbance storm time (Dst),the Interplanetary Electric Field (IEFy) and the SYM-H index (high resolution version of Dst) from ground-based magnetometers to see the effect of geomagnetic storm on horizontal component of geomagnetic field. Irregularities manifested in the form of fluctuations in TEC. Prompt penetration of electric field (PPEF) modulated the behaviour of irregularities during the initial and recovery phases of the geomagnetic storms. As a result,it was observed that the irregularities in the ionosphere of Ethiopia was in the range from weak to moderate. These irregularities generally occurred during the initial and recovery phases of the storm at all selected stations. The effect of electric field is found to depend on the local time at which the IMF Bz turned into southward. Therefore,east - west fluctuating Ey in March storm main phase is associated with suppression of irregularities in all stations and eastward Ey in the main phase of June 2015 is associated with small scale irregularity formation. The generation (inhibition) of irregularities is related to the effect of eastward (westward) storm time electric field disturbance dynamo electric fields and prompt penetration electric fields that created favourable (unfavourable) conditions for the generation of irregularities by uplifting (lowering) the F region.

Multi‐Hour‐Ahead Dst Index Prediction Using Multi‐Fidelity Boosted Neural Networks

AbstractThe Disturbance storm time (Dst) index has been widely used as a proxy for the ring current intensity, and therefore as a measure of geomagnetic activity. It is derived by measurements from four ground magnetometers in the geomagnetic equatorial region. We present a new model for predicting Dst with a lead time between 1 and 6 hr. The model is first developed using a Gated Recurrent Unit (GRU) network that is trained using solar wind parameters. The uncertainty of the Dst model is then estimated by using the Accurate and Reliable Uncertainty Estimate method (Camporeale & Carè, 2021, https://doi.org/10.1615/int.j.uncertaintyquantification.2021034623). Finally, a multi‐fidelity boosting method is developed in order to enhance the accuracy of the model and reduce its associated uncertainty. It is shown that the developed model can predict Dst 6 hr ahead with a root‐mean‐square‐error of 13.54 nT. This is significantly better than a persistence model or a single GRU model.

CORRELATIVE STUDY OF SOLAR TRANSIENT PARAMETERS ASSOCIATED WITH DST INDEX FOR SOLAR CYCLE 23 AND 24

Geomagnetic storms and solar coronal mass ejections (CMEs) have been shown to be associated since the discovery of CMEs in solar activity (Yurchyshyn et al., 2004). An intense geomagnetic storm can either be stemmed from a CME, or from a coronal hole. However, a great geomagnetic storm can only be sourced from a CME (Le et al., 2012). The aim of this paper is to study the relation between the coronal mass ejections (CMEs), and their associated solar flares and correlate this with the Disturbance storm time (Dst) index. We have studied the count rate of soft X-rays class of X, M and C type through a histogram type plot for studying the number of X-rays of different class observed during solar cycle 23 and 24. We have also studied the angular width, speed of coronal mass ejections (CMEs) and disturbance storm time (Dst) index of solar cycle 23 and 24 for the whole cycle (Pant et al., 2021).

Analysis of the Occurrence of the First Most Intense Geomagnetic Storm (G4) to Hit the Earth in Solar Cycle 25; A Multiple-Step Geomagnetic Storm

A study of the occurrence of the first intense Geomagnetic storm (G4) to hit the Earth since the start of the Solar Cycle 25 is carried out. This study analyzed the Geomagnetic Storm that occurred between the 28th of October and the 7th of November 2021. The values of the Disturbance storm time (Dst) index of the storm reach -115 nT (G4) which occurred on the 4th of November at 1300UT. The storm is the first intense magnetic storm that occurred in the solar cycle 25. It is also a multiple-step storm with moderate two-step storm occurrence which occurred on the 5th and 6th of November at 1500UT and 0600UT respectively towards the recovery phase after the intense storm. The interplanetary magnetic field (IMF) Bz (nT) during these storm events decreases. Our analysis shows that the rise in solar wind speed, temperature and the enhancement of pressure plays a significant role in the occurrence of the first intense Geomagnetic storm of Solar Cycle 25.

Pheromone Trap Catch of Fruit Pest Moths Influenced by the Geomagnetic Disturbance Storm Time (Dst)

Pheromone Trap Catch of Fruit Pest Moths Influenced by the Geomagnetic Disturbance Storm Time (Dst)

Storm-Time Relative Total Electron Content Modelling Using Machine Learning Techniques

Accurately predicting total electron content (TEC) during geomagnetic storms is still a challenging task for ionospheric models. In this work, a neural-network (NN)-based model is proposed which predicts relative TEC with respect to the preceding 27-day median TEC, during storm time for the European region (with longitudes 30°W–50°E and latitudes 32.5°N–70°N). The 27-day median TEC (referred to as median TEC), latitude, longitude, universal time, storm time, solar radio flux index F10.7, global storm index SYM-H and geomagnetic activity index Hp30 are used as inputs and the output of the network is the relative TEC. The relative TEC can be converted to the actual TEC knowing the median TEC. The median TEC is calculated at each grid point over the European region considering data from the last 27 days before the storm using global ionosphere maps (GIMs) from international GNSS service (IGS) sources. A storm event is defined when the storm time disturbance index Dst drops below 50 nanotesla. The model was trained with storm-time relative TEC data from the time period of 1998 until 2019 (2015 is excluded) and contains 365 storms. Unseen storm data from 33 storm events during 2015 and 2020 were used to test the model. The UQRG GIMs were used because of their high temporal resolution (15 min) compared to other products from different analysis centers. The NN-based model predictions show the seasonal behavior of the storms including positive and negative storm phases during winter and summer, respectively, and show a mixture of both phases during equinoxes. The model’s performance was also compared with the Neustrelitz TEC model (NTCM) and the NN-based quiet-time TEC model, both developed at the German Aerospace Agency (DLR). The storm model has a root mean squared error (RMSE) of 3.38 TEC units (TECU), which is an improvement by 1.87 TECU compared to the NTCM, where an RMSE of 5.25 TECU was found. This improvement corresponds to a performance increase by 35.6%. The storm-time model outperforms the quiet-time model by 1.34 TECU, which corresponds to a performance increase by 28.4% from 4.72 to 3.38 TECU. The quiet-time model was trained with Carrington averaged TEC and, therefore, is ideal to be used as an input instead of the GIM derived 27-day median. We found an improvement by 0.8 TECU which corresponds to a performance increase by 17% from 4.72 to 3.92 TECU for the storm-time model using the quiet-time-model predicted TEC as an input compared to solely using the quiet-time model.

Successive Interacting Coronal Mass Ejections: How to Create a Perfect Storm

Coronal mass ejections (CMEs) are the largest type of eruptions on the Sun and the main driver of severe space weather at the Earth. In this study, we implement a force-free spheromak CME description within 3D magnetohydrodynamic simulations to parametrically evaluate successive interacting CMEs within a representative heliosphere. We explore CME–CME interactions for a range of orientations, launch time variations, and CME handedness and quantify their geo-effectiveness via the primary solar wind variables and empirical measures of the disturbance storm time index and subsolar magnetopause standoff distance. We show how the interaction of two moderate CMEs between the Sun and the Earth can translate into extreme conditions at the Earth and how CME–CME interactions at different radial distances can maximize different solar wind variables that induce different geophysical impacts. In particular, we demonstrate how the orientation and handedness of a given CME can have a significant impact on the conservation and loss of magnetic flux, and consequently B z , due to magnetic reconnection with the interplanetary magnetic field. This study thus implicates the identification of CME chirality in the solar corona as an early diagnostic for forecasting geomagnetic storms involving multiple CMEs.

A Study of the Relationship Between Southward Bz > -10 nT and Storm Time Disturbance Index During Solar Cycle 23

Magnetic reconnection can be used for studying the geoeffective processes in the coupled Sun–Solar wind – Magnetosphere dynamics leading to geomagnetic disturbance. In this study, 1-hour resolution solar wind plasma parameters from OMNIweb were used to investigate the relationship between moderate southward interplanetary magnetic field, IMF-Bz (i.e., Bz > -10 nT) and geomagnetic storm time disturbance, Dst , during the ascending, maximum and descending phases of solar cycle 23. Occurrences of different classes of geomagnetic storms during moderate southward Bz are reported. The occurrence of weak and moderate geomagnetic storms is more predominant during maximum solar activity than intense and super intense storms. It was found that 10.11 % (181) of all the classes of the storm were intense, and 0.17 % (3) were super intense storms. Furthermore, it was found that 4 (2.2 %) out of the 181 intense storms were caused by southward Bz > -10 nT which were associated with the complex structure due to the high-speed solar wind stream and corotating interacting region. In such a complex structure and Bz > -10 nT, we observed that an intense geomagnetic storm rarely occurs and if it does, would be predominant around solar maximum. It was found that long-duration (\Delta t > 6 hrs) of southward Bz (i.e., -10 nT < Bz <= -3.6 nT ) can also lead to an intense geomagnetic storm during the solar maximum and descending phase (moderate solar activity) of a solar cycle. The complex structure of intense geomagnetic storms associated with the Bz > -10 nT is rare and possesses a special configuration of magnetic field and solar wind parameters structures which are CIR manifestations.

English

To better understand the geomagnetic conditions and the particularities of solar cycles 23 and 24, we present analysis from morphological variations of solar and geomagnetic activities parameters IMF-Bz, the interplanetary electric field (E), the proton density (N), Dst (disturbance storm time), Polar cap (PC) indices (PCN and PCS), and geomagnetic activity index aa. The occurrences of the different classes of geomagnetic activity established by Zerbo et al. (2012), through the daily averages of the Aa index are also presented for the period 1996-2019 covering the solar cycle 23 and 24. This investigation reveals: (1) a decrease in solar activity and geomagnetic activity during the last decade; (2) the solar cycle 23 was a magnetically disturbed solar cycle with 41.52% disturbed days versus 72.35% very quiet conditions for the solar cycle 24; (3) the most important numbers of intense and severe storm or magnetic substorm conditions (E, PC index, Dst < -100 nT) are recorded during the solar cycle 23; (4) solar cycle 24 presents similar characteristics as solar cycles 12 and 14 and has also experienced spotless days at its maximum phase; (5) solar cycle 24 experienced low solar activity compared to solar cycle 23. The two solar cycles can be qualified as antagonistic cycles. Key words: Solar cycle, storms, solar activity, geomagnetic activity, antagonist cycle.

Spectral Features of Forbush Decreases during Geomagnetic Storms

Geomagnetic storms and Forbush decreases (FD) on Earth are primarily caused by interplanetary coronal mass ejections (ICMEs) and stream/corotating interaction regions (SIRs/CIRs) originating in the Sun, which are propagated as a low-energy plasma disturbance through the interplanetary magnetic field (IMF). In this paper, we study the variations of the solar-wind parameters (solar wind velocity, plasma density, and IMF-Bz component) and the Earth's disturbance storm-time index (Dst) in relation to cosmic ray flux measurements from 8 neutron monitor stations distributed over Canada, Russia, Finland, and Greenland, during 3 intense geomagnetic storms occurred during the 24th solar cycle (March 16–18, 2015, June 21–23, 2015, and September 7–9, 2017). The wavelet analysis of the cosmic ray intensity reveals the clear evolution of the classical two-step FD with a peak period of approximately 2.1 h. The correlation-delay analysis shows a very strong correlation (∼0.9) between the relative count rate changes in cosmic ray intensity and the indices of solar wind speed and Dst. We obtain similar time-delay responses to the solar wind speed for all the cases (∼4 h), but large discrepancies are seen for the Dst index between the storms. We, therefore, recommend not using the Dst index for predicting Forbush decreases. Finally, we employ the resulting delay times to parameterise the Forbush decreases in terms of the solar wind, and we obtain a predictive model with R2 parameter of an approximate value of 0.8. Moreover, we observe a possible dependence on solar wind proton density which modulates the magnitude of Forbush decreases under similar solar wind speed conditions. Our results verify the suitability of using solar wind parameters to predict Forbush decreases in the cosmic ray flux.

Intergroup Cascade Broad Learning System With Optimized Parameters for Chaotic Time Series Prediction

The prediction of chaotic time series can well describe the physical properties and influencing factors of a sequence of numerical data points in successive order and has played an increasingly crucial role in various scientific and engineering communities. These time series data require the prediction model to have strong dynamic feature representation ability, which often leads to excessive calculation burden. As an emerging and effective learning method, a broad learning system (BLS) can be trained quickly by incremental learning, and the system can be reconstructed without requiring a lengthy retraining process, which has been shown to facilitate superior time-saving performance. However, the historical information of time series data is often ignored, resulting in the lack of dynamic feature representation ability of the original BLS algorithm. In this article, a novel intergroup cascade BLS (ICBLS) method and its variant with optimized parameters, termed MOICBLS, are proposed, which can combine the previous knowledge with current information to determine the output results by constructing a novel intergroup cascade structure for the enhancement nodes. The new algorithms try to retain the time-saving advantage of BLS and greatly enhance the extraction ability of dynamic features of chaotic time series data. In addition, it cannot be ignored that the network structure parameters have an unquestionable impact on the performance of a BLS. In response, two conflicting goals of prediction accuracy and diversity are formulated as multiobjective optimization functions in the training phase to generate the optimized network structure parameters needed by multiple candidate models. The experimental results on two benchmark chaotic time series dataset, the Lorenz system and Rossler system, and a geomagnetic disturbance storm time chaotic time series dataset demonstrate that our proposed algorithms are effective for prediction of chaotic time series, and the prediction accuracy is better than some existing approaches.

Probabilistic Prediction of Dst Storms One‐Day‐Ahead Using Full‐Disk SoHO Images

AbstractWe present a new model for the probability that the disturbance storm time (Dst) index exceeds −100 nT, with a lead time between 1 and 3 days. Dst provides essential information about the strength of the ring current around the Earth caused by the protons and electrons from the solar wind, and it is routinely used as a proxy for geomagnetic storms. The model is developed using an ensemble of Convolutional Neural Networks that are trained using Solar and Heliospheric Observatory (SoHO) images (Michelson Doppler Imager, Extreme ultraviolet Imaging Telescope, and Large Angle and Spectrometric Coronagraph). The relationship between the SoHO images and the solar wind has been investigated by many researchers, but these studies have not explicitly considered using SoHO images to predict the Dst index. This work presents a novel methodology to train the individual models and to learn the optimal ensemble weights iteratively, by using a customized class‐balanced mean square error (CB‐MSE) loss function tied to a least‐squares based ensemble. The proposed model can predict the probability that Dst < −100 nT 24 hr ahead with a True Skill Statistic (TSS) of 0.62 and Matthews Correlation Coefficient (MCC) of 0.37. The weighted TSS and MCC is 0.68 and 0.47, respectively. An additional validation during non‐Earth‐directed CME periods is also conducted which yields a good TSS and MCC score.

ULF geomagnetic anomaly associated with the Sumatra-Pagai Islands earthquake swarm during 2020

Observations of earthquake precursors via geomagnetic anomalies from ultra-low frequency (ULF) could provide an expectation for short-term earthquake prediction. However, there are still several obstacles in determining this precursor, one of which is the presence of a precursor bias if the earthquake occurs at a close time and location. To cope with this problem, we analysed six earthquakes with a magnitude >5 on Pagai Island, Mentawai, Sumatra, during 2020. These earthquakes have epicentres close to each other and occur within a short time (one month). This study used geomagnetic data recorded by the Magnetic Data Acquisition System (MAGDAS) magnetometer network at Sicincin station (SCN), West Sumatra, and Kepahiyang station (KPY), Bengkulu, with a span of one month before the earthquake. The anomaly of ULF emission was analysed using the power spectral density method at a frequency of 0.012 Hz for the H and Z components of the geomagnetic data. The onset time of the ULF emission anomaly was determined by the standard deviation value (pz + σ, pz – σ) of the SZ/SH power ratio polarization. The disturbance storm time index (Dst) was used to ensure that the anomaly occurred was not caused by geomagnetic storm. Also, the single station transfer function was used to calculate the azimuth, and the empirical formula between the anomaly amplitude and the magnitude was used to calculate the earthquake magnitude as the validation of the anomaly source. Several ULF anomalies as earthquake precursors were observed, especially at the KPY station. The estimated azimuth shows a good accuracy compared to the earthquake's azimuth, with an average value of 97.8%. Furthermore, the earthquake magnitude calculated from the ULF anomaly shows a good agreement with the actual earthquake magnitude, with an average accuracy of 98.13%.

Assessment of solar wind driven ionospheric storm forecasts: The case of the Solar Wind driven autoregression model for Ionospheric Forecast (SWIF)

This paper reports the results of the most recent validation tests on the performance of the Solar Wind driven autoregression model for Ionospheric Forecast (SWIF). SWIF uses the Interplanetary Magnetic Field (IMF) observations at L1 point as a proxy for upcoming ionospheric storm time disturbances, issuing an alert in case certain quantitative criteria are met. These criteria apply to the IMF total magnitude and the IMF-Bz component. The validation tests address the SWIF alert detection efficiency in the period 2014–2020 and were designed to complement previous efforts and allow the comprehensive discussion of SWIF's alert ability over one and a half solar cycle. The model's efficiency is quantified through probability of detection, false alarm rate and the success ratio for SWIF's users. The results are further discussed in terms of the intensity and the interplanetary causes of the storms to reveal high forecasting ability for specific storm scenarios (i.e. intense storms that are usually driven by coronal mass ejections), but also certain limitations in the efficiency of SWIF's alert criteria in capturing storm related interplanetary structures at L1 point: mainly structures that are not related to coronal mass ejections and typically, drive storms of moderate intensity. Additional results obtained by superposed epoch analysis of IMF key components indicate the potential relevance of the IMF-By component in ionospheric forecasting applications. The consideration of the IMF-By in SWIF's formulation can drive future improvements in its forecasting efficiency in all different faces of solar wind forcing. The findings of this work may also help the better understanding of the processes that control the coupling of the solar wind-magnetosphere-ionosphere.

Modulation of Solar Wind Impact on the Earth’s Magnetosphere during the Solar Cycle

The understanding of extreme geomagnetic storms is one of the key issues in space weather. Such phenomena have been receiving increasing attention, especially with the aim of forecasting strong geomagnetic storms generated by high-energy solar events since they can severely perturb the near-Earth space environment. Here, the disturbance storm time index Dst, a crucial geomagnetic activity proxy for Sun–Earth interactions, is analyzed as a function of the energy carried by different solar wind streams. To determine the solar cycle activity influence on Dst, a 12-year dataset was split into sub-periods of maximum and minimum solar activity. Solar wind energy and geomagnetic activity were closely correlated for both periods of activity. Slow wind streams had negligible effects on Earth regardless of their energy, while high-speed streams may induce severe geomagnetic storming depending on the energy (kinetic or magnetic) carried by the flow. The difference between the two periods may be related to the higher rate of geo-effective events during the maximum activity, where coronal mass ejections represent the most energetic and geo-effective driver. During the minimum period, despite a lower rate of high energetic events, a moderate disturbance in the Dst index can be induced.

Forecasting the Disturbance Storm Time Index with Bayesian Deep Learning

The disturbance storm time (Dst) index is an important and useful measurement in space weather research. It has been used to characterize the size and intensity of a geomagnetic storm. A negative Dst value means that the Earth's magnetic field is weakened, which happens during storms. In this paper, we present a novel deep learning method, called the Dst Transformer, to perform short-term, 1-6 hour ahead, forecasting of the Dst index based on the solar wind parameters provided by the NASA Space Science Data Coordinated Archive. The Dst Transformer combines a multi-head attention layer with Bayesian inference, which is capable of quantifying both aleatoric uncertainty and epistemic uncertainty when making Dst predictions. Experimental results show that the proposed Dst Transformer outperforms related machine learning methods in terms of the root mean square error and R-squared. Furthermore, the Dst Transformer can produce both data and model uncertainty quantification results, which can not be done by the existing methods. To our knowledge, this is the first time that Bayesian deep learning has been used for Dst index forecasting.

Study of the Mass-loss Rate from the Sun

We investigate the temporal evolution of the yearly total mass-loss rate (YTMLR) from the Sun through coronal mass ejections (CMEs) over solar cycles 23 and 24. The mass determination of CMEs can be subject to significant uncertainty. To minimize this problem, we have used extensive statistical analysis. For this purpose, we employed data included in the Coordinated Data Analysis Workshop (CDAW) catalog. We estimated the contributions to mass loss from the Sun from different subsamples of CMEs (selected on the basis of their masses, angular widths, and position angles). The temporal variations of the YTMLR were compared to those of the sunspot number (SSN), X-ray flare flux, and the Disturbance Storm Time (Dst) index. We show that the CME mass included in the CDAW catalog reflects with high accuracy the actual mass-loss rate from the Sun through CMEs. Additionally, it is shown that the CME mass distribution in the log-lin representation reflects the Gaussian distribution very well. This means that the CMEs included in the CDAW catalog form one coherent population of ejections that have been correctly identified. Unlike the CME occurrence rate, it turns out that the YTMLR is a very good indicator of solar activity (e.g., SSN) and space weather (e.g., Dst index) consequences. These results are very important, since the YTMLR, unlike the mass loss through solar wind, significantly depends on solar cycles.

A Multi-Network based Hybrid LSTM model for ionospheric anomaly detection: A case study of the Mw 7.8 Nepal earthquake

We propose a Multi-Network-based Hybrid Long Short Term Memory (N-LSTM) model for ionospheric anomaly detection to forecast highly irregular data of the ionospheric Total Electron Content (TEC). Previously purposed models were suffering from the problems of vanishing gradient, exploding gradient, uncertainty, and parameter bias. LSTM is used to overcome the vanishing and exploding gradient problems and the remaining two (uncertainty and parameter bias) are subjugated by N-LSTM. We have also discussed the implementation of the model by using the Mw 7.8 Nepal Earthquake (EQ) that occurred on April 25, 2015. The proposed model detects a significant negative anomaly about 14 days before the impending EQ. Furthermore, we find gradual increments in the TEC time-series, where the TEC values gradually increased, in terms of positive anomalies, for four continuous days (April 21–24, 2015) till the mainshock. Further analysis revealed that the nighttime TEC enhancements are more dominant than the daytimes, which persisted for 10 h (22:00–08:00 LT) in the absence of the solar flux, we named them the absolute seismic precursors. These gradual enhancements establish a perfect correlation with several atmospheric anomalies, reported in the previous studies, where the variations in different atmospheric parameters like Outgoing Longwave Radiation (OLR), air temperature, Ozone, and sub-ionospheric Very-Low Frequency (VLF), were reported for 4 consistent days till the EQ day. The N-LSTM result was compatible with a 30-day running median, which is the classical method of ionospheric anomaly detection. We also examined the planetary K-index (Kp), disturbance storm-time (Dst), solar radio flux (F10.7), and solar wind speed (VSW) indices to check the possible attribution of space weather on the ionosphere during the analysis. The results showed that the anomalies of the ionospheric TEC were more dominantly caused by the EQ phenomenon than the space weather during the nighttime hours. Also, N-LSTM provided sufficient short-term prediction performance of the ionospheric TEC and anomaly detection.

A Study of Possible Correlations between Seismo-Ionospheric Anomalies of GNSS Total Electron Content and Earthquake Energy

In this study, we conduct a correlation analysis between the daily occurrence times of the increase and decrease anomalies in the global total electron content (TEC) in the ionosphere, and the daily earthquake energy release within 110–130°E longitude over the following three latitude regions: A: 13°S–0.5°S (22.3°S–10°S geomagnetic), B: 0.5°S–19.5°N (10°S–10°N geomagnetic), and C: 19.5°N–32.1°N (10°N–22.5°N geomagnetic). The TEC data from global ionosphere maps (GIMs) during earthquake events of M ≥ 2.5 that occurred in 2015–2018 are used in this study. The time series of daily seismic wave energy releases within the three regions and the daily occurrence times of the TEC anomalies in each GIM grid are computed. By time-shifting the time series, the correlations are calculated and compared globally, and the temporal characteristics are also examined. The disturbance storm time (Dst) index, planetary geomagnetic index Kp, and daily observed 10.7 cm solar flux (F10.7) are used to remove data associated with space weather variations. Although the seismo-ionospheric precursor is not confirmed by the statistical investigations, the greater occurrence times of TEC decrease anomalies are observed in the southeast in Region A, and the conjugate point 13 days prior to a M6.9 earthquake in Region A, which occurred on 5 August 2018, in accordance with the statistical results. Therefore, it is required to apply more parameters to understand the causes of the ionospheric TEC variations and investigate whether ionospheric variations are caused by earthquakes.

Development of 24 hours Dst index prediction from solar wind data and IMF Bz using NARX

Disturbance Storm Time (Dst) index is an index which measured the decrease in the horizontal component of the Earth’s magnetic field near the magnetic equator due to increases in the magnetospheric ring current. The index shows the strength and duration of a geomagnetic storm. The geomagnetic storms themselves could harm technological systems on earth. Dst index prediction is needed as an effort to mitigate the impact of geomagnetic storms. This paper described a 24 hours Dst index prediction using a nonlinear autoregressive exogenous (NARX) method. Solar wind parameters and interplanetary magnetic field (IMF) Bz has been used as exogenous input for the model. By using 39 geomagnetic storms data during 1997 to 2000 as training data and 7 geomagnetic storms data during 2000 to 2001 as testing data that have not been used in the training, the correlation between target and ouput was 0.97. The model than being used to predict Dst index for operational purposes and obtained the RMSE from 1 August to 31 October 2021 was ‘∼5 – 20 nT during quiet days and ∼18 – 45 nT during disturb days.