Search Results (24)

This study focuses on the mid- and low-midlatitude ionospheric response to the 2024 Mother’s Day superstorm, utilizing ground-based and Swarm satellite observations. The ground-based ionosonde measured F1, F2-layer, B0 and B1 parameters, as well as isodensity data, were used. The ionospheric absorption was investigated with the so-called amplitude method, which is based on ionosonde data. Auroral sporadic E-layer was the first time ever recorded at Sopron. Moreover, the auroral F-layer appeared at exceptionally low latitude (35° mlat, over San Vito) during the storm main phase. These unprecedented detections were confirmed by optical all-sky cameras. The observations revealed that these events were linked to the extreme equatorward shift of the auroral oval along with the midlatitude trough. As a result, the midlatitude ionosphere became confined to the trough itself. Three stages of F2-layer uplift were identified during the night of 10/11 May, each caused by different mechanisms: most probably by the effect of prompt penetration electric fields (PPEFs) (1), the travelling ionospheric disturbances (TIDs) (2) and the combination of electrodynamic processes and decreased O/N₂ ratio (3). After a short interval of G-condition, an unprecedented extended disappearance of the layers was observed during daytime hours on 11 May, which was further confirmed by Swarm data. This phenomenon appeared to be associated with a reduced O/N₂ along with the influence of disturbance dynamo electric fields (DDEFs) and it cannot be explained only by the increased ionospheric absorption according to the results of the amplitude method. Full article

The impact of solar flares on the Earth’s ionosphere has been studied for many decades using both experimental and theoretical approaches. However, the accuracy of predicting ionospheric layer dynamics in response to variations in solar radiation remains limited. In particular, understanding the vertical redistribution of charged particles in the ionosphere during flares with different spectral characteristics presents a significant challenge. In this study, a method is presented for reconstructing the temporal evolution of the vertical electron concentration ($Ne$) profile based on GNSS (Global Navigation Satellite Systems) measurements of total electron content along partially illuminated satellite-receiver paths. Using this method, vertical profiles of $Ne$ were reconstructed during various phases of the X13.3-class solar flare that occurred on 6 September 2017. The resulting profiles correctly respond to the observed variations in solar extreme ultraviolet and X-ray radiation. This indicates that the method can be effectively applied to analyse other powerful solar events. Full article

Equatorial plasma bubbles (EPBs) are regions of depleted electron density that form in the Earth’s ionosphere due to Rayleigh–Taylor instability. These bubbles can cause signal scintillation, leading to signal loss and errors in position calculations. EPBs can be detected in images captured by All-Sky Imager (ASI) systems. This study proposes a low-cost automatic detection method for EPBs in ASI data that can be used for both real-time detection and classification purposes. This method utilizes Two-Dimensional Principal Component Analysis (2DPCA) with Recursive Feature Elimination (RFE), in conjunction with a Random Forest machine learning model, to create an Explainable Artificial Intelligence (XAI) model capable of extracting image features to automatically detect EPBs with the lowest possible dimensionality. This led to having a small-sized and extremely fast-trained model that could be used to identify EPBs within the captured ASI images. A set of 2458 images, classified into two categories—Event and Empty—were used to build the database. This database was randomly split into two subsets: a training dataset (80%) and a testing dataset (20%). The produced XAI model demonstrated slightly higher detection accuracy compared to the standard 2DPCA model while being significantly smaller in size. Furthermore, the proposed model’s performance has been evaluated and compared with other deep learning baseline models (ResNet18, Inception-V3, VGG16, and VGG19) in the same environment. Full article

Ionospheric scintillation is a pressing issue in space weather studies due to its diverse effects on positioning, navigation, and timing (PNT) systems. Developing an accurate and timely prediction model for this event is crucial. In this work, we developed two machine learning models for the prediction of ionospheric scintillation events at the equatorial anomaly during the maximum and minimum phases of solar cycle 24. The models developed in this study are the Random Forest (RF) algorithm and the eXtreme Gradient Boosting (XGBoost) algorithm. The models take inputs based on the solar wind parameters obtained from the OMNI Web database from the years 2010–2017 and Pc5 wave power obtained from the Bear Island (BJN) magnetometer station. We retrieved data from the Scintillation Network and Decision Aid (SCINDA) receiver in Egypt from which the S4 index was computed to quantify amplitude scintillations that were utilized as the target in the model development. Out-of-sample model testing was performed to evaluate the prediction accuracy of the models on unseen data after training. The similarity between the observed and predicted scintillation events, quantified by the R² score, was 0.66 and 0.74 for the RF and XGBoost models, respectively. The corresponding Root Mean Square Errors (RMSEs) associated with the models were 0.01 and 0.01 for the RF and XGBoost models, respectively. The similarity in error shows that the XGBoost model is a good and preferred choice for the prediction of ionospheric scintillation events at the equatorial anomaly. With these results, we recommend the use of ensemble learning techniques for the study of the ionospheric scintillation phenomenon. Full article

The Lithospheric–Atmospheric–Ionospheric Coupling (LAIC) mechanism stands as the leading model for the prediction of seismic activities. It consists of a cascade of physical processes that are initiated days before a major earthquake. The onset is marked by the discharge of ionized gases, such as radon, through subterranean fissures that develop in the lead-up to the quake. This discharge augments the ionization at the lower atmospheric layers, instigating disturbances that extend from the Earth’s surface to the lower ionosphere. A critical component of the LAIC sequence involves the distinctive perturbations of Extremely Low Electromagnetic Frequencies (ELF) within the Schumann Resonances (SR) spectrum of 2 to 50 Hz, detectable days ahead of the seismic event. Our study examines 10 earthquakes that transpired over a span of 3.5 months—averaging nearly three quakes monthly—which concurrently generated 45 discernible potential precursor seismic signals. Notably, each earthquake originated in Southern Greece, within a radius of 30 to 250 km from the observatory on Mount Parnon. Our research seeks to resolve two important issues. The first concerns the association between specific ELF signals and individual earthquakes—a question of significant importance in seismogenic regions like Greece, where earthquakes occur frequently. The second inquiry concerns the parameters that determine the detectability of an earthquake by a given station, including the requisite proximity and magnitude. Initial findings suggest that SR signals can be reliably linked to a particular earthquake if the observatory is situated within the earthquake’s preparatory zone. Conversely, outside this zone, the correlation becomes indeterminate. Additionally, we observe a differentiation in SR signals based on whether the earthquake took place over land or offshore. The latter category exhibits unique signal behaviors, potentially attributable to the water layers above the epicenter acting as a barrier to the ascending gases, thereby affecting the atmospheric–ionospheric ionization process. Full article

The ionospheric quasi-periodic wave is a type of typical and common electromagnetic wave phenomenon occurring in extremely low-frequency (ELF) and very low-frequency ranges (VLF). These emissions propagate in a distinct whistler-wave mode, with varying periodic modulations of the wave intensity over time scales from several seconds to a few minutes. We developed an automatic detection model for the QP waves in the ELF band recorded by the China Seismo-Electromagnetic Satellite. Based on the 827 QP wave events, which were collected through visual screening from the electromagnetic field observations, an automatic detection model based on the Transformer architecture was built. This model, comprising 34.27 million parameters, was trained and evaluated. It achieved mean average precision of 92.3% on the validation dataset, operating at a frame rate of 39.3 frames per second. Notably, after incorporating the proton cyclotron frequency constraint, the model displayed promising performance. Its lightweight design facilitates easy deployment on satellite equipment, significantly enhancing the feasibility of on-board detection. Full article

(1) Introduction: The subject of the present study concerns the analysis of the existence and long time evolution of the solar ~200 yr (de Vries/Suess) and ~2400 yr (Hallstadt) cycles during the recent part of the Wurm ice epoch and the Holocene, as well as their forcing on the regional East European climate during the last two calendar millennia. The results obtained here are compared with those from our previous studies, as well as with the results obtained by other authors and with other types of data. A possible scenario of solar activity changes during the 21st century, as well as different possible mechanisms of solar–climatic relationships, is discussed. (2) Data and methods: Two types of indirect (historical) data series for solar activity were used: (a) the international radiocarbon tree ring series (INTCAL13) for the last 13,900 years; (b) the Schove series of the calendar years of minima and maxima and the magnitudes of 156 quasi 11 yr sunspot Schwabe–Wolf cycles since 296 AD and up to the sunspot cycle with number 24 (SC24) in the Zurich series; (c) manuscript messages about extreme meteorological and climatic events (Danube and Black Sea near-coast water freezing), extreme summer droughts, etc., in Bulgaria and adjacent territories since 296 and up to 1899 AD, when the Bulgarian meteorological dataset was started. A time series analysis and χ²-test were used. (3) Results and analysis: The amplitude modulation of the 200 yr solar cycle by the 2400 yr (Hallstadt) cycle was confirmed. Two groups of extremely cold winters (ECWs) during the last ~1700 years were established. Both groups without exclusion are concentrated near 11 yr sunspot cycle extremes. The number of ECWs near sunspot cycle minima is about 2 times greater than that of ECWs near sunspot cycle maxima. This result is in agreement with our earlier studies for the instrumental epoch since 1899 AD. The driest “spring-summer-early autumn” seasons in Bulgaria and adjacent territories occur near the initial and middle phases of the grand solar minima of the Oort–Dalton type, which relate to the downward phases and minima of the 200 yr Suess cycle. (4) Discussion: The above results confirm the effect of the Sun’s forcing on climate. However, it cannot be explained by the standard hypothesis for total solar irradiation (TSI) variations. That is why another hypothesis is suggested by the author. The mechanism considered by Svensmark for galactic cosmic ray (GCR) forcing on aerosol nuclei was taken into account. However, in the hypothesis suggested here, the forcing of solar X-ray flux changes (including solar flares) on the low ionosphere (the D-layer) and following interactions with the Earth’s lithosphere due to the terrestrial electric current systems play a key role for aerosol nuclei and cloud generation and dynamics during sunspot maxima epochs. The GCR flux maximum absorption layer at heights of 35–40 km replaces the ionosphere D-layer role during the sunspot minima epochs. Full article

Solar-terrestrial interaction is a dynamic process that manifests itself in the ionosphere. Interplanetary (IP) shocks or solar wind dynamic pressure pulses can generate enhanced brightening in dayside aurora. Foreshock transients are capable of inducing pressure changes, larger in magnitude than solar wind pressure pulses, which also contribute to intensifying dayside aurora. These pressure variations can accelerate particles into the ionosphere, generating field-aligned currents that produce magnetic impulse events and enhanced dayside auroral activity with periods of increased brightening. This study presents several dayside auroral brightening events that are not associated with IP shocks or solar wind dynamic pressure pulses. The dayside auroral brightening events are associated with a green (557.7 nm) to red (630.0 nm) ratio which is greater than 15. These extreme brightening events (EBEs) begin on the eastern or western end of a pre-existing dayside auroral arc. Periodic pulses of enhanced brightening are correlated with large sharp increases in the X-component (points toward the north-geographic pole) from ground magnetometers in the IMAGE network. EBEs occur predominately before magnetic noon and with X-component signatures from high-latitude stations. Ground-based data were obtained from the Kjell Henriksen Observatory in Longyearbyen and the IMAGE magnetometer network. Full article

Ionospheric scintillation often occurs in the polar and equator regions, and it can affect the signals of the Global Navigation Satellite System (GNSS). Therefore, the ionospheric scintillation detection applied to the polar and equator regions is of vital importance for improving the performance of satellite navigation. GNSS radio occultation is a remote sensing technique that primarily utilizes GNSS signals to study the Earth’s atmosphere, but its measurement results are susceptible to the effects of ionospheric scintillation. In this study, we propose an ionospheric scintillation detection algorithm based on the Sparrow-Search-Algorithm-optimized Extreme Gradient Boosting model (SSA-XGBoost), which uses power spectral densities of the raw signal intensities from GNSS occultation data as input features to train the algorithm model. To assess the performance of the proposed algorithm, we compare it with other machine learning algorithms such as XGBoost and a Support Vector Machine (SVM) using historical ionospheric scintillation data. The results show that the SSA-XGBoost method performs much better compared to the SVM and XGBoost models, with an overall accuracy of 97.8% in classifying scintillation events and a miss detection rate of only 12.9% for scintillation events with an unbalanced GNSS RO dataset. This paper can provide valuable insights for designing more robust GNSS receivers. Full article

The process of manually checking, validating, and excluding data in an ionospheric very-low-frequency (VLF) analysis during extreme events is a labor-intensive and time-consuming task. However, this task can be automated through the utilization of machine learning (ML) classification techniques. This research paper employed the Random Forest (RF) classification algorithm to automatically classify the impact of solar flares on ionospheric VLF data and erroneous data points, such as instrumentation errors and noisy data. The data used for analysis were collected during September and October 2011, encompassing solar flare classes ranging from C2.5 to X2.1. The F1-score values obtained from the test dataset displayed values of 0.848; meanwhile, a more detailed analysis revealed that, due to the imbalanced distribution of the target class, the per-class F1-score indicated higher values for the normal data point class (0.69–0.97) compared to those of the anomalous data point class (0.31 to 0.71). Instances of successful and inadequate categorization were analyzed and presented visually. This research investigated the potential application of ML techniques in the automated identification and classification of erroneous VLF amplitude data points; however, the findings of this research hold promise for the detection of short-term ionospheric responses to, e.g., gamma ray bursts (GRBs), or in the analysis of pre-earthquake ionospheric anomalies. Full article

Over the last decade, thanks to the availability of historical satellite observations that have begun to be significantly large and thanks to the exponential growth of artificial intelligence techniques, many advances have been made in the detection of geophysical parameters such as seismic-related anomalies. In this study, the variations of the ionospheric Total Electron Content (TEC), one of the main parameters historically proposed as a seismic-connected indicator, are analyzed. To make a statistically robust analysis of the complex phenomena involved, we propose a completely innovative machine-learning approach developed in the R programming language. Through this approach, an optimal setting of the multitude of methodological inputs currently proposed for the detection of ionospheric anomalies is performed. The setting is optimized by analyzing, for the first time, multi-year—mostly twenty-year—time series of TEC satellite data measured by global navigation satellite systems (GNSS) over the Italian region, matched with the corresponding multi-year time series of seismic events. Seismic events including all the countries of the Mediterranean area, up to Turkey, are involved in the analysis. Tens of thousands of possible combinations of input methodological parameters are simulated and classified according to pre-established criteria. Several inputs examined return clear results. These results combined with each other highlight the presence of anomalous seismic-related sequences that have an extremely low probability of having been detected randomly (up to 2 out of 1 million). The anomalies identified represent the most anomalous behaviors of the TEC recorded during the entire period under investigation (e.g., 20 years). Some of the main conclusions are that, at mid-latitudes, ① the detection of seismic-TEC anomalies can be more efficient looking for punctual rather than persistent phenomena; ② the optimal thresholds for the identification of co-seismic anomalies can assume different values depending on type of anomaly (positive or negative) and type of observation; ③ single GNSS receiver data can be useful for capturing local earthquake-ionospheric effects and Global Ionospheric Maps (GIM) data can be functional in detecting large-scale earthquake-ionospheric effects; ④ earthquakes deeper than 50 km are less likely to affect the ionosphere. Full article

The strongest X-class solar flare (SF) event in 24th solar cycle, X9.3, occurred on 6 September 2017, accompanied by earthward-directed coronal mass ejections (CMEs). Such space weather episodes are known to cause various threats to human activities ranging from radio communication and navigation disturbances including wave blackout to producing geomagnetic storms of different intensities. In this study, SFs’ ionospheric impacts and effects of accompanied heliospheric disturbances on primary cosmic rays (CR) are investigated. This work offers the first detailed investigation of characteristics of these extreme events since they were inspected both from the perspective of their electromagnetic nature, through very low frequency (VLF) radio waves, and their corpuscular nature of CR by multi-instrumental approach. Aside data recorded by Belgrade VLF and CR stations, data from GOES and SOHO space probes were used for modeling and analysis. Conducted numerical simulations revealed a significant change of ionospheric parameters (sharpness and effective reflection height) and few orders of magnitude increase of electron density. We compared our findings with those existing in the literature regarding the ionospheric response and corresponding parameters. In addition, Forbush decrease (FD) magnitude, corrected for magnetospheric effect, derived from measurements, and one predicted from power exponents used to parametrize the shape of energetic proton fluence spectra at L1 were compared and found to be in good agreement. Presented findings could be useful for investigation of atmospheric plasma properties, particles’ modeling, and prediction of extreme weather impacts on human activities. Full article

Auroral Ionosphere Model (AIM-E) is designed to calculate chemical content in the high-latitude E region ionosphere and takes into account both the solar EUV radiation and the electron precipitation of magnetospheric origin. The latter is extremely important for auroral ionosphere chemistry especially in disturbed conditions. In order to maximize the AIM-E timing accuracy when simulating highly variable periods in the course of geomagnetic storms and substorms, we suggest to parameterize the OVATION-Prime empirical precipitation model with the ground-based Polar Cap (PC) index. This gives an advantage to: (1) perform ionospheric simulation with actual input, since PC index reflects the geoeffective solar wind conditions; (2) promptly assess the current geomagnetic situation, since PC index is available in real-time with 1 min resolution. The simulation results of AIM-E with OVATION-Prime (PC) demonstrate a good agreement with the ground-based incoherent scatter radar data (EISCAT UHF, Tromso) and with the vertical sounding data in the Arctic zone during events of intense particle precipitation. The model reproduces well the electron content calculated in vertical column (90–140 km) and critical frequency of sporadic E layer (f_OEs) formed by precipitating electrons. The AIM-E (PC) model can be applied to monitor the sporadic E layer in real-time and in the entire high-latitude ionosphere, including the auroral and subauroral zones, which is important for predicting the conditions of radio wave propagation. Full article

This paper studies extreme ionospheric events and their relations with geomagnetic and meteorological activity. With the long observation series at the Irkutsk (52° N, 104° E) and Kaliningrad (54° N, 20° E) ionosondes we obtained the datasets of ionospheric disturbances that were treated as relative deviations of the observed peak electron density values from their 27-day running median values. As the extreme disturbances, we considered cases when the disturbance was greater than 150%. As potential sources of extreme ionospheric disturbances, we considered sudden stratospheric warmings, geomagnetic storms by the criterion Dst ≤ −30 nT, and recurrent geomagnetic storms that did not necessarily satisfy the criterion Dst ≤ −30 nT. The morphological analysis showed that the extreme ionospheric disturbance was the nighttime phenomenon that occurs from late October to early March (mainly in December–January). Considering extreme ionospheric events as nights when disturbances were greater than 150%, we obtained 25 extreme ionospheric events (on average 1.8 events per year) from the 2003–2016 Irkutsk dataset and six extreme ionospheric events (on average 0.75 events per year) from the 2009–2016 Kaliningrad dataset. The year-by-year distribution of extreme events did not reveal a clear dependence on solar/geomagnetic activity in terms of yearly mean F10.7 and Ap values but showed a correlation between the number of events and the number of recurrent geomagnetic storms. The study of the relationship between extreme ionospheric events and manifestations of geomagnetic and meteorological activity revealed that about half of extreme ionospheric events may be related to geomagnetic storms by the criterion Dst ≤ −50 nT and/or sudden stratospheric warmings. Consideration of recurrent geomagnetic storms allowed us to find the sources of almost all extreme ionospheric events. Geomagnetic activity may be considered the main cause of extreme ionospheric events at Irkutsk (mainly associated with recurrent geomagnetic storms and partly with CME-storms); while the main cause of extreme ionospheric events at Kaliningrad is not clear (a comparable contribution of sudden stratospheric warmings and storms can be assumed). Full article

Strong radiation from solar X-ray flares can produce increased ionization in the terrestrial D-region and change its structure. Moreover, extreme solar radiation in X-spectral range can create sudden ionospheric disturbances and can consequently affect devices on the terrain as well as signals from satellites and presumably cause numerous uncontrollable catastrophic events. One of the techniques for detection and analysis of solar flares is studying the variations in time of specific spectral lines. The aim of this work is to present our study of solar X-ray flare effects on D-region using very low-frequency radio signal measurements over a long path in parallel with the analysis of X-spectral radiation, and to obtain the atmospheric parameters (sharpness, reflection height, time delay). We introduce a novel modelling approach and give D-region coefficients needed for modelling this medium, as well as a simple expression for electron density of lower ionosphere plasmas. We provide the analysis and software on GitHub. Full article