Geomagnetic Indices Research Articles

Empirical Model of SSUSI‐Derived Auroral Ionization Rates

AbstractWe present an empirical model for auroral (90–150 km) electron–ion pair production rates, ionization rates for short, derived from Special Sensor Ultraviolet Spectrographic Imager electron energy and flux data. Using the Fang et al. (2010, https://doi.org/10.1029/2010gl045406) parametrization for mono‐energetic electrons, and the NRLMSISE‐00 neutral atmosphere model (Picone et al., 2002, https://doi.org/10.1029/2002ja009430), the calculated ionization rate profiles are binned in 2‐hr magnetic local time and 3.6°geomagnetic latitude to yield time series of ionization rates at 5‐km altitude steps. We fit each of these time series to the geomagnetic indices Kp, PC, and Ap, the 81‐day averaged solar radio flux index, and a constant term. The resulting empirical model can easily be incorporated into coupled chemistry–climate models to include particle precipitation effects.

Long-term geomagnetic activities and stratospheric winter temperature

In this research, impact of long term solar forcing on stratospheric winter temperature is checked. The 11- year sunspot activity and geomagnetic indices (AE, Kp, Dst) are used as an indicator for solar forcing, as geomagnetic activity indices show good correlation with solar variability. To understand the impact of solar forcing through high latitude, stratospheric winter (November to March) time North Polar Region (60N–90N) temperature anomalies are considered. The findings showed that temperature changes in the stratosphere are significantly correlated with solar activity, as evidenced by a significant positive correlation between the 11-year moving mean of stratospheric (10 hPa) temperature anomalies and sunspot number. Approximately from 1970 to 2000, the North Polar Region saw positive anomalous stratospheric winter temperatures. During the same time, the geomagnetic activity also showed a substantial increase. The year-to-year correlation between stratospheric pole temperature and geomagnetic activity is significant (about 0.5). The Empirical Mode Decomposition analysis reveals a highly significant correlation (around 0.9) between the long-term component of stratospheric winter temperature (IMF-4) and the long-term component of geomagnetic activity (IMF-3 and IMF-4). One of the reasons for the increase in lower stratospheric temperature is an increase in ozone concentration during the same period when geomagnetic activity is higher. Empirical orthogonal function (EOF) and correlation analysis of stratospheric winter temperature with large-scale circulation patterns are also carried out. The spatial correlation is checked for stratospheric winter temperature at North Pole and lower atmospheric levels (250 hPa and 850 hPa) followed by pre-monsoon and monsoon season. This study includes statistical analysis, however, also highlights the necessity of in-depth dynamical analysis to improve our understanding of how solar activity impacts Earth's atmospheric layers, which may be helpful in predicting the weather and climate.

МГД-волны в области предфронта межпланетных ударных волн 6 и 7 сентября 2017 г.

We analyze strong space weather disturbances during first ten days of September 2017, using the geomagnetic Dst index, parameters of normals to interplanetary shock fronts, direct measurements of interplanetary magnetic field, solar wind, and cosmic ray parameters. By applying spectral analysis methods to interplanetary medium data, we analyze MHD waves at the pre-front of two interplanetary shocks responsible for geomagnetic disturbances on September 6 and 7, 2017. The main results are as follows: the contribution of three branches of MHD waves (Alfvén, fast and slow magnetosonic) to the observed spectrum of the interplanetary magnetic field modulus has been established. We have confirmed the conclusion that the generation of Alfvén waves and fast magnetosonic waves is due to the presence of low-energy proton fluxes (Ep~1 MeV) at the pre-front of interplanetary shocks. We have also discovered a predominant contribution of slow magnetosonic waves to the observed spectrum of the interplanetary magnetic field modulus, but its reason is yet unknown. It is noted that different orientations of the normals to the interplanetary shock fronts and to the direction of the interplanetary magnetic field average vector on spacecraft located fairly close to each other may indicate waviness of the shock front structure.

Явления гистерезиса в отклике геомагнитной активности и параметров космических лучей на вариации межпланетной среды во время магнитной бури

The dynamics of the intensity of cosmic rays is known to be different on the ascending and descending branches of the 11-year solar cycle, i.e., hysteresis phenomena are observed. Recently, it has been obtained that at shorter intervals on the scale of magnetic storms there are also signs of hysteresis in dependences of cosmic ray cutoff rigidities R (geomagnetic thresholds) on heliosphere and geosphere parameters. R is the rigidity below which a particle flux is cut off due to geomagnetic shielding. In this paper, we have analyzed the dependence of the geomagnetic storm index Dst and the variation of the ΔR thresholds on interplanetary magnetic field (IMF) and solar wind (SW) parameters during the two-step magnetic storm on September 7–8, 2017. We have found hysteresis phenomena in the following paired series: (1) dependences of Dst on SW and IMF parameters, and (2) dependences of ΔR on SW and IMF parameters. We have established that the dependence curves in the storm descending phase (main phase) and ascending phase (recovery phase) do not coincide — hysteresis loops are formed. A specific feature of the storm under study is the second lowering of Dst in the recovery phase. The hysteresis pattern reflects this specific storm dynamics, forming two hysteresis loops in response to the two Dst drops.

MHD waves at the pre-front of interplanetary shocks on September 6 and 7, 2017

We analyze strong space weather disturbances during first ten days of September 2017, using the geomagnetic Dst index, parameters of normals to interplanetary shock fronts, direct measurements of interplanetary magnetic field, solar wind, and cosmic ray parameters. By applying spectral analysis methods to interplanetary medium data, we analyze MHD waves at the pre-front of two interplanetary shocks responsible for geomagnetic disturbances on September 6 and 7, 2017. The main results are as follows: the contribution of three branches of MHD waves (Alfvén, fast and slow magnetosonic) to the observed spectrum of the interplanetary magnetic field modulus has been established. We have confirmed the conclusion that the generation of Alfvén waves and fast magnetosonic waves is due to the presence of low-energy proton fluxes (Ep~1 MeV) at the pre-front of interplanetary shocks. We have also discovered a predominant contribution of slow magnetosonic waves to the observed spectrum of the interplanetary magnetic field modulus, but its reason is yet unknown. It is noted that different orientations of the normals to the interplanetary shock fronts and to the direction of the interplanetary magnetic field average vector on spacecraft located fairly close to each other may indicate waviness of the shock front structure.

Hysteresis phenomena in the response of geomagnetic activity and cosmic ray parameters to variations in the interplanetary medium during a magnetic storm

The dynamics of the intensity of cosmic rays is known to be different on the ascending and descending branches of the 11-year solar cycle, i.e., hysteresis phenomena are observed. Recently, it has been obtained that at shorter intervals on the scale of magnetic storms there are also signs of hysteresis in dependences of cosmic ray cutoff rigidities R (geomagnetic thresholds) on heliosphere and geosphere parameters. R is the rigidity below which a particle flux is cut off due to geomagnetic shielding. In this paper, we have analyzed the dependence of the geomagnetic storm index Dst and the variation of the ΔR thresholds on interplanetary magnetic field (IMF) and solar wind (SW) parameters during the two-step magnetic storm on September 7–8, 2017. We have found hysteresis phenomena in the following paired series: (1) dependences of Dst on SW and IMF parameters, and (2) dependences of ΔR on SW and IMF parameters. We have established that the dependence curves in the storm descending phase (main phase) and ascending phase (recovery phase) do not coincide — hysteresis loops are formed. A specific feature of the storm under study is the second lowering of Dst in the recovery phase. The hysteresis pattern reflects this specific storm dynamics, forming two hysteresis loops in response to the two Dst drops.

Крупномасштабные связи геомагнитных индексов SYM-H и ASY-H с северо-южной компонентой ММП и бета-параметром солнечного ветра

Using annual average values, the relationships are examined of the geomagnetic indices SYM-H, ASY-H, and Dst with solar wind parameters in 1981–2015. The data used was divided into two samples according to the sign of the north-south component Bn of the interplanetary magnetic field (IMF). Variations in the annual average values of each of the Dst, SYM-H, and ASY-H indices for southward and northward IMF have been found to be similar and their linear correlation coefficients r to be high: 0.871, 0.863, and 0.943 respectively. The similarity between variations of the indices with different signs of Bn is probably due to their connection with the number of sunspots. It has been established that Dst, SYM-H, and ASY-H depend on the solar wind parameter β: their absolute values decrease with increasing β, regardless of Bn sign. The decrease in the indices with increasing β is likely to be caused by the transition of the magnetosphere to a quiet state due to the increasing predominance of thermal pressure over magnetic one in the solar wind and a decrease in the level of solar wind turbulence. SYM-H and ASY-H have been found to reveal the closest relationships with β, whereas SYM-H more strongly depends on β for southward IMF (r=0.744) than for northward IMF (r=0.677). On the contrary, for ASY-H r=–0.741 at northward IMF and r=–0.719 at southward IMF. Similar to SYM-H, Dst (to a lesser extent) significantly correlates with β at southward IMF (r=0.629) and weaker at northward IMF (r=0.456).

Large-scale relationships of the geomagnetic indices SYM-H and ASY-H with the north-south IMF component and the solar wind beta parameter

Using annual average values, the relationships are examined of the geomagnetic indices SYM-H, ASY-H, and Dst with solar wind parameters in 1981–2015. The data used was divided into two samples according to the sign of the north-south component Bn of the interplanetary magnetic field (IMF). Variations in the annual average values of each of the Dst, SYM-H, and ASY-H indices for southward and northward IMF have been found to be similar and their linear correlation coefficients r to be high: 0.871, 0.863, and 0.943 respectively. The similarity between variations of the indices with different signs of Bn is probably due to their connection with the number of sunspots. It has been established that Dst, SYM-H, and ASY-H depend on the solar wind parameter β: their absolute values decrease with increasing β, regardless of Bn sign. The decrease in the indices with increasing β is likely to be caused by the transition of the magnetosphere to a quiet state due to the increasing predominance of thermal pressure over magnetic one in the solar wind and a decrease in the level of solar wind turbulence. SYM-H and ASY-H have been found to reveal the closest relationships with β, whereas SYM-H more strongly depends on β for southward IMF (r=0.744) than for northward IMF (r=0.677). On the contrary, for ASY-H r=–0.741 at northward IMF and r=–0.719 at southward IMF. Similar to SYM-H, Dst (to a lesser extent) significantly correlates with β at southward IMF (r=0.629) and weaker at northward IMF (r=0.456).

Thermospheric Neutral Density Data Assimilation System Based on the Whole Atmosphere Model During the November 2003 Storm

AbstractThe Iterative Driver Estimation and Assimilation (IDEA) data assimilation technique was used with the Whole Atmosphere Model (WAM) to improve neutral density specification in the upper thermosphere. Two different neutral density data sources were examined to enhance the capability of simulating the global thermospheric state. The first were accelerometer estimates of neutral density from the Challenging Mini‐Satellite Payload (CHAMP) satellite. The second were neutral density estimates from the Global Ultraviolet Imager (GUVI) limb‐scan airglow observations aboard the Thermosphere Ionosphere Mesosphere Energetics and Dynamics satellite. Due to the intensity of the November 2003 storm, two changes were necessary in WAM. The first was allowing the Kp geomagnetic index to exceed 9 and the second was changing the relationship between Kp and the solar wind parameters used to drive the model. With these changes, results show that IDEA effectively captures the thermospheric neutral density at the CHAMP satellite altitude and follows the time‐dependence through the November 2003 storm period. Furthermore, a cross‐comparison was conducted with the GUVI dayside limb scan measurements. GUVI neutral densities within 270–320 km show the closest agreement with WAM when CHAMP data was assimilated by IDEA. We speculate on the potential for observations from GUVI at 300 km to be used as a data source in the IDEA‐WAM simulations. These simulations demonstrate the utility of the IDEA data assimilation technique with physical models and that using either accelerometer observations or ultraviolet airglow limb measurement during extreme storm periods could be used.

Effect of solar wind disturbances on small-amplitude simultaneous Forbush events during solar cycle 23

The transient and rapid reduction in galactic cosmic ray (GCR) flux intensity, commonly referred to as Forbush decrease (FD), is widely used as a mediator variable in searching for solar-terrestrial weather linkages. Whereas literature in the field is replete with high-amplitude (FD(%) ⩾3) FD-based analyses, the empirical connections between low-amplitude (FD(%) ⩽3) events and Sun-Earth variables are seldom tested. It is recently observed that the dearth of such studies might be due to the daunting task of accurate detection/precise timing of FDs and other transient astrophysical events. Using a sensitive version of the FD detection algorithm, we selected 264, 267, and 206 low amplitude (FD(%) ⩽3) FDs from the daily GCR data of Hermanus (HRMS), Newark (NWRK) and Oulu (OULU) neutron monitor stations respectively during solar cycle 23. A total of 80 simultaneous small-amplitude FDs were identified from the three stations with a simple coincident computer code. Statistically significant correlations at 95% confidence level exist between the planetary K-index (Kp), disturbance storm time index (Dst), planetary A-index (Ap), and the corresponding simultaneous small-amplitude FDs at OULU station, but not found in HRMS and NWRK data. These significant links between the weak FDs and the geomagnetic indices (Kp, Dst, and Ap) at OULU station suggest that the parameters (Kp, Dst, and Ap) are likely connected with some common solar activities.

Interplanetary Influence on Thermospheric Mass Density: Insights From Deep Learning Analyses

AbstractIn this study, the thermospheric mass density (TMD) features observed by the CHAllenging Minisatellite Payload between 2002 and 2010 were extracted using deep learning (DL) technology; the TMD features were then mapped and modeled with the Interplanetary environment information (IEI), solar radiation, and geomagnetic indices. The DL model was used to simulate the TMD features during Day of Year (DOY) 222–241 in 2014, a period that experienced complex solar‐terrestrial environmental variations. We explore the TMD features under different solar‐terrestrial environmental conditions and discuss the effects of various inputs by comparing the DL simulation results with satellite observations from Gravity Recovery and Climate Experiment‐A and Swarm‐A, as well as the simulation results from Jacchia‐Bowman 2008, Naval Research Laboratory Mass Spectrometer Incoherent Scatter radar model 2.1, and Drag Temperature Model 2013. These results show that the DL model can better capture the TMD features after adding IEI. Part of these TMD features, including the high‐latitude TMD enhancement during the space hurricane event (DOY 232, 2014) and global TMD variations under complex solar‐terrestrial environmental disturbances (DOY 222–225, 2014), cannot be well described by the geomagnetic indices. The DL model indicates that the east‐west component of the interplanetary magnetic field (IMF By) has a great impact on TMD variations, and its modulation is different from the typical energy injection process during storms. Our results emphasize the crucial influence of IEI on TMD under both geomagnetic disturbances and quiet conditions.

Ionospheric TEC Prediction in China during Storm Periods Based on Deep Learning: Mixed CNN-BiLSTM Method

Applying deep learning to high-precision ionospheric parameter prediction is a significant and growing field within the realm of space weather research. This paper proposes an improved model, Mixed Convolutional Neural Network (CNN)—Bidirectional Long Short-Term Memory (BiLSTM), for predicting the Total Electron Content (TEC) in China. This model was trained using the longest available Global Ionospheric Maps (GIM)-TEC from 1998 to 2023 in China, and underwent an interpretability analysis and accuracy evaluation. The results indicate that historical TEC maps play the most critical role, followed by Kp, ap, AE, F10.7, and time factor. The contributions of Dst and Disturbance Index (DI) to improving accuracy are relatively small but still essential. In long-term predictions, the contributions of the geomagnetic index, solar activity index, and time factor are higher. In addition, the model performs well in short-term predictions, accurately capturing the occurrence, evolution, and classification of ionospheric storms. However, as the predicted length increases, the accuracy gradually decreases, and some erroneous predictions may occur. The northeast region exhibits lower accuracy but a higher F1 score, which may be attributed to the frequency of ionospheric storm occurrences in different locations. Overall, the model effectively predicts the trends and evolution processes of ionospheric storms.

Unusual Forbush Decreases and Geomagnetic Storms on 24 March, 2024 and 11 May, 2024

As the current solar cycle 25 progresses and moves towards solar maxima, solar activity is increasing and extreme space weather events are taking place. Two severe geomagnetic storms accompanied by two large Forbush decreases in galactic cosmic ray intensity were recorded in March and May, 2024. More precisely, on 24 March 2024, a G4 (according to the NOAA Space Weather Scale for Geomagnetic Storms) geomagnetic storm was registered, with the corresponding geomagnetic indices Kp and Dst equal to 8 and −130 nT, respectively. On the same day, the majority of ground-based neutron monitor stations recorded an unusual Forbush decrease. This event stands out from a typical Forbush decrease because of its high amplitude decrease phase and rapid recovery phase, i.e., 15% decrease and an extremely rapid recovery of 10% within 1.5 h, as recorded at the Oulu neutron monitor station. Furthermore, on 10–13 May 2024, an unusual G5 geomagnetic storm (geomagnetic indices Kp = 9 and Dst = −412 nT) was registered (the last G5 storm had been observed in 2003). In addition, the polar neutron monitor stations recorded a Ground Level Enhancement (GLE74) during the recovery phase of a large Forbush decrease of 15%, which started on 10 May 2024. In this study, a detailed analysis of these two severe events in regard to the accompanying solar activity, interplanetary conditions and solar energetic particle events is provided. Moreover, the results of the NKUA “GLE Alert++ system”, the NKUA/IZMIRAN “FD Precursory Signals” method and the NKUA “ap Prediction tool” concerning these events are presented.

Global Thermospheric Infrared Response to the Mother's Day Weekend Extreme Storm of 2024

AbstractEarth experienced the strongest geomagnetic storm in 20 years over 10–13 May 2024. The Ap and Dst geomagnetic indices were 273 and −291.94 nT on 11 May. The Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument on the Thermosphere‐Ionosphere‐Mesosphere Energetics and Dynamics satellite observed significant enhancement in thermospheric infrared emission at 15, 5.3, 4.3, and 1.27 μm. On 11 May the daily global power radiated by nitric oxide (NO) at 5.3 μm was 1.41 TW and by carbon dioxide (CO2) at 15 μm was 1.35 TW. These are the largest single day power values observed by SABER in 22 years and the first time the daily power radiated by NO exceeded that of CO2. The total infrared power (above background) radiated during the storm was 2.64 TW (2.28 × 1017 J). Significant enhancement in limb radiance observed at 4.3 μm (to 250 km tangent height) is likely indicative of NO + formation during the storm.

Statistics and Models of the Electron Plasma Density From the Van Allen Probes

AbstractWe use the full NASA Van Allen Probes mission (2012–2019) to extract the electron plasma density from the Electric and Magnetic Field Instrument Suite and Integrated Science (EMFISIS) and Electric Field and Waves (EFW) instruments and discuss the evolution of the plasmasphere. We generate new statistics including mean and standard deviations of the plasma density with respect to L‐shell, magnetic local time (MLT), and various geomagnetic indices. These statistics are generated to be applied in radiation belt physics and space weather codes (with fits provided). The mean plasmasphere is circular around Earth with respect to MLT for Kp < 1. The mean 100 cm−3 level line is above L = 5 and mean 10 cm−3 level expands above the Van Allen Probes apogee for Kp < 1. The outer electron belt lies within the plasmasphere for 60% of all times. As activity increases (Kp > 2), a gradual MLT asymmetry forms with higher mean density in the afternoon sector due to plumes expanding outward. Conversely, the mean density decreases on the dawn and night sectors. The mean density is between ∼500 and ∼50 cm−3 between L ∼ 4 and L ∼ 6 during quiet and moderately active times (Kp < 3), representing ∼80% of all times. Statistics in regions of high density below L = 2 are underdefined for intense activity. The highest standard deviation of density represents a factor 2.5 to 3 times the mean above L = 5 and for active times. We find the percent difference between the EFW and EMFISIS densities is bounded by ±20% for quiet and moderate activity (Kp < 5) and goes up to ±100% for extreme activity.

Forecasting Geoffective Events from Solar Wind Data and Evaluating the Most Predictive Features through Machine Learning Approaches

This study addresses the prediction of geomagnetic disturbances by exploiting machine learning techniques. Specifically, the Long Short-term Memory recurrent neural network, which is particularly suited for application over long time series, is employed in the analysis of in situ measurements of solar wind plasma and magnetic field acquired over more than one solar cycle, from 2005 to 2019, at the Lagrangian point L1. The problem is approached as a binary classification aiming to predict 1 hr in advance a decrease in the SYM-H geomagnetic activity index below the threshold of −50 nT, which is generally regarded as indicative of magnetospheric perturbations. The strong class imbalance issue is tackled by using an appropriate loss function tailored to optimize appropriate skill scores in the training phase of the neural network. Beside classical skill scores, value-weighted skill scores are then employed to evaluate predictions, suitable in the study of problems, such as the one faced here, characterized by strong temporal variability. For the first time, the content of magnetic helicity and energy carried by solar transients, associated with their detection and likelihood of geoeffectiveness, were considered as input features of the network architecture. Their predictive capabilities are demonstrated through a correlation-driven feature selection method to rank the most relevant characteristics involved in the neural network prediction model. The optimal performance of the adopted neural network in properly forecasting the onset of geomagnetic storms, which is a crucial point for giving real warnings in an operational setting, is finally showed.

Establishment and Application of an Interplanetary Disturbance Index Based on the Solar Wind–Magnetosphere Energy Coupling Function and the Spectral Whitening Method

We develop a preliminary interplanetary disturbance index (J sW ) by applying the spectral whitening method to an energy coupling function with solar wind measurements during the years 1998–2014 as the input, which can be used as an indicator of perturbations in the near-Earth solar wind. The correlation and temporal variation between J sW and the geomagnetic disturbance index (J pG ) constructed from the same method have been analyzed in detail for 167 geomagnetic storms with the minimum D st index less than or equal to −50 nT. The time delay between J sW and J pG is clearly observable and varies for different events, according to which J sW is shifted backward with respect to J pG . We obtain a fairly good negative correlation between the shifted J sW and the J pG indices for the majority of events, and the significance level for 88% of the events (i.e., 147 events) does not exceed 0.05. A statistical analysis of the shifted J sW and the J pG indices for 147 selected events reveals that larger values of J sW and smaller magnitudes of J pG are commonly accompanied by enhanced southward magnetic fields, which implies that more solar wind energy is entering the magnetosphere and thus causing strong geomagnetic storms. Furthermore, a linear fit of the two indices suggests that the evolution of J pG can be predicted about 2 hr in advance based on J sW , indicating that J sW can provide early warnings of possible disturbances in the geomagnetic fields, which is crucial for space weather monitoring and operational forecasting.

Variability of Ionospheric Total Electron Content Over Morocco During the Godzilla Sand and Dust Storm of June 2020

During sand and dust storm (SDS) events, atmospheric suspension and transport of sand and dust brings a reasonable amount of electrification in the atmosphere which plays a very important role in the atmosphere-ionosphere coupling. The Godzilla SDS began on 5<sup>th</sup> June 2020 in Algeria following a decrease in pressure and spread to other areas across the Sahara between 6<sup>th</sup> and 28<sup>th</sup> June 2020. Using SDS data from Copernicus Sentinel-5P satellite mission and Vertical Total Electron Content (VTEC) data from four GNSS receiver stations: IFR1 (Ifrane Seismic), MELI (Melilla), TETN (Tetouan) and OUCA (Ouca) over Morocco, we investigate the possible ionospheric TEC variability over the four GNSS receiver stations during the Godzilla SDS event which was tracked using the Sentinel-5P Satellite mission. Solar wind parameters: Horizontal component of Interplanetary Magnetic Field (IMF-Bz), interplanetary Electric Field (IEF-Ey) and solar wind speed (V) and geomagnetic indices: Disturbance Storm Time (Dst) and Planetary K (Kp) indices were examined and showed very minimal geomagnetic influence during the period. We observed major ionospheric disturbances over the four Global Navigation Satellite System (GNSS) receiver stations on 16<sup>th</sup>, 17<sup>th</sup>, 18<sup>th</sup>, 21<sup>st</sup>, 22<sup>nd</sup>, 23<sup>rd</sup> 25<sup>th</sup> and 26<sup>th</sup> June 2020: the period with the Sentinel-5P Aerosol Index (SAI) of more than 4 as recorded by the Sentinel-5P Satellite engine. The daily VTEC values over the four GNSS receiver stations recorded continuous electron density perturbations during these days. Apart from the ionospheric TEC perturbations, significant enhancements and decreases in daily maximum VTEC values over the four GNSS receiver stations were also noted. These were attributed to the changes in the atmospheric electric fields generated by the SDS event. The VTEC plots for each day exhibited similar trends, hence exhibited the same ionospheric dynamics. VTEC depletions of depths 3 to 6 TECU over all the four GNSS receiver stations were noted on 12<sup>th</sup>, 14<sup>th</sup>, 17<sup>th</sup>, 20<sup>th</sup> and 25<sup>th</sup> June 2020. Nighttime VTEC enhancements were also noted and majorly occurred between 20:00 and 21:00 UT on 9<sup>th</sup>, 13<sup>th</sup>, 15<sup>th</sup>, 17<sup>th</sup>, 19<sup>th</sup>, 20<sup>th</sup> and 21<sup>st</sup> June 2020. This was attributed to the development of the electron avalanche processes including dust and electron absorption or losses and the active conversion to electron dissociative attachment leading to electron excitation. In conclusion, the Godzilla SDS of June 2020 led to the electron density perturbations over Morocco.

Empirical orthogonal function based modelling of ionosphere using Turkish GNSS network

The study investigates ionospheric total electron content (TEC) variation over Turkey from the five selected global navigation satellite system (GNSS) stations situated in diverse parts of Turkey. The geomagnetic indices are used and observed TEC are modeled with the technique known as Empirical orthogonal function (EOF). It is valuable to note that the correlation coefficient between observed GNSS TEC values and EOF TEC values varies from 0.8020 to 0.9394. The root means square error (RMSE) values between observed GNSS TEC values and EOF TEC lie between 3.1665 TECU to 4.4220 TECU. These results show that the EOF model performs quite well in the Turkish region and can present the model TEC variations perfectly. Finally, these GNSS observed and EOF-predicted TEC values along with geomagnetic indices are studied with the tropospheric wind speed. The results showed that both observed and modeled TEC have very low correlations with tropospheric wind speed and do not provide any significant value. Hence, we concluded that the ionospheric region is not affected by tropospheric wind speed. It happens because the tropospheric wind speed is a matter of the lower troposphere and its atmospheric pressure while the ionosphere is far from the earth and depends upon the number of free electrons.

GRIMS: global and regional ionosphere monitoring system

The ionosphere shows regular changes such as daily, 27 days, seasonal, semi-annual, annual, and 11 years. These changes can be modeled and their effects largely determined. However, in addition to regular changes, irregular changes occur in the ionosphere due to space weather conditions, natural disasters, and human-induced irregularities. GNSS is one of the instruments along with many others that can give a piece of information on the ionospheric state. Various indices/parameters are used to determine the effect of space weather conditions. The well-known ones are solar activity indices, geomagnetic storm indices, magnetic field components, proton density, and proton flux parameters. It is important to take all of these indices into consideration when investigating the source of the anomaly. Considering only some of them may lead to incorrect inferences about the source of possible anomalies. To carry out comprehensive research in this field, it is necessary to analyze a very large data set. This indicates the requirement for an automatic system. With the Global and Regional Ionosphere Monitoring System (GRIMS) designed within the scope of this study, the ionosphere can be monitored globally and regionally. The GRIMS is online at https://www.online-grims.com/. By using Global ionospheric maps and GNSS receiver data, global, regional, and station-specific anomalies can be detected regularly through methods such as HDI (Highest Density Interval) and ARIMA (Autoregressive Integrated Moving Average). GRIMS gathers space weather-related parameters from ionospheric data centers to help users interpret the situation, and it allows users to download the results and request data for specific days. The details of the experimental results and output products of the system designed during the geomagnetic active days of March 17, 18, 2015 are given in this paper. Moreover, geomagnetic active days that occurred between 2000 and 2023 are given in the GRIMS.