Search Results (99)

This study investigates pre-seismic ionospheric Total Electron Content (TEC) disturbances preceding the Mw 6.9 Northern Aegean Sea earthquake of 24 May 2014 using observations from 13 Global Navigation Satellite System (GNSS) stations. A pronounced negative TEC disturbance was identified on 22 May 2014, approximately two days before the earthquake, under comparatively quiet solar and geomagnetic conditions. Station-wise Z-score analysis, which expresses the TEC departure from the reference mean in units of standard deviation, revealed significant negative deviations across the network, while inter-station correlations indicated a temporally coherent but spatially heterogeneous ionospheric response. To characterize the disturbance beyond peak-based measures, the TEC Anomaly Significance Index (TASI) was developed by integrating the mean absolute Z-score, coefficient of variation, and Shannon entropy. TASI showed strong agreement with the maximum absolute Z-score ranking (Spearman’s $\rho =0.89$, $p<0.001$) while providing greater sensitivity to cumulative and persistent anomaly behaviour. It exhibited stronger associations than maximum $\mid Z\mid$ for six of the seven evaluated temporal descriptors, particularly those representing anomaly duration and consecutive persistence. Among the analyzed GNSS stations, KASI recorded the highest TASI despite not being the nearest station to the epicenter, indicating that anomaly significance was not governed solely by epicentral distance. The proposed framework provides a multidimensional, network-based approach for characterizing potential pre-seismic ionospheric disturbances and establishes a basis for future multi-event and control-period validation. Full article

Magnetic-field effects have been reported in controlled biological systems, but the relevance of weak geomagnetic variability to vegetation under natural conditions remains uncertain. We examined temporal associations between satellite-derived solar-induced chlorophyll fluorescence (SIF) and geomagnetic disturbance derived from the Disturbance Storm Time (Dst) index. We defined the sign-inverted Dst index, SII, as the sign-inverted daily mean Dst, so that stronger negative Dst excursions corresponded to larger positive SII values. The primary exposure was the trailing 28-day mean SII, excluding the day of the SIF observation. The primary analysis used Orbiting Carbon Observatory-2 (OCO-2) SIF at 771 nm with leave-one-year-out harmonic adjustment for the annual cycle and linear calendar-time trend. Sensitivity and robustness analyses examined a 21-day exposure, a more flexible cyclic-spline seasonal adjustment, spatial cluster bootstrap, and three temporal-surrogate null models. We also examined temperature and vegetation strata, land-cover and geographic controls, adjustment for an ECMWF Reanalysis version 5 (ERA5) surface solar radiation downwards (SSRD)-derived photosynthetically active radiation (PAR) energy proxy and vapour-pressure deficit, and comparisons with planetary Kp index, 10.7 cm solar radio flux index (F10.7), and supplementary SIF wavelengths. In the primary analysis, the 28-day trailing mean of SII was weakly negatively associated with SIF anomalies (Spearman ρ = −0.051; 95% cluster-bootstrap CI, −0.053 to −0.050), with an estimated linear change of −0.0381 residual-SIF units per 100 nT. Empirical p-values were 0.084 for year permutation, 0.011 for circular shift, and 0.001 for 30-day block permutation. The association remained negative at 21 days and in persistently vegetated cells, but its magnitude was substantially reduced with cyclic-spline adjustment (ρ = −0.013 in the pairwise-matched sample). Negative estimates were found in several vegetated land-cover classes, whereas estimates for the Barren land-cover class and Sahara geographic control were close to zero. The contribution of SII to explained variance remained below one percentage point across the examined temperature regimes. Overall, the results show a weak temporal association whose magnitude depends on analytical choices. Independent observational replication and controlled experiments are needed to determine whether it reflects a biological response to natural geomagnetic variability. Full article

Accurate prediction of low-latitude total electron content, or TEC, is important for GNSS ionospheric sensing because pronounced diurnal variation and sensitivity to solar and geomagnetic activity make TEC highly nonlinear and nonstationary. This study proposes the RC-XGB-CNN-BiLSTM validation-constrained adaptive residual-fusion framework for one-hour-ahead TEC prediction. XGBoost provides the primary estimate from historical TEC, local-time factors, and solar–terrestrial inputs, while CNN-BiLSTM learns residuals generated from chronologically ordered out-of-fold predictions. Validation performance determines the residual-use mode and compensation strength. Experiments cover multivariate inputs, contrasting solar and geomagnetic activity conditions, module-wise ablation, and additional low-latitude grid points. In the 2019 multivariate experiment at $20°$ N, $110°$ E, the framework achieves an RMSE of 0.67 TECU, 20.2% lower than XGBoost, with concurrent reductions in MAE and P90AE. It also achieves the lowest errors among the compared models in both solar-activity experiments and at all three additional grid points. Grouped TreeSHAP identifies recent TEC as the dominant predictive information, while residual-gain analysis shows that validation-controlled fusion adapts correction to activity conditions and retains the primary prediction when additional correction is not beneficial. These results support validation-constrained residual fusion as an effective approach for low-latitude TEC prediction. Full article

The peak height of the F2 layer (hmF2) is a key parameter describing the vertical structure of the ionosphere. It is important for high-frequency radio communication planning and space-weather background assessment, particularly at high latitudes. To improve long-term hmF2 prediction, an empirical model-guided CatBoost model is developed. Predictions from the SHU and E-CHAIM empirical models are incorporated as prior predictors, while spatiotemporal periodicity, solar-activity, and geomagnetic-activity indices are jointly considered to represent the primary drivers of hmF2 variability. A two-stage feature selection procedure, combining stability-based selection and correlation-based redundancy pruning, is employed to identify informative and nonredundant features. The proposed model achieves consistently lower errors than both empirical models. Relative to SHU and E-CHAIM, the proposed model achieves relative root mean square error (RMSE) reductions of 22.67% and 17.70%, respectively, and relative mean relative error (MRE) reductions of 23.21% and 17.79%, respectively. Consistent improvements are observed across different time periods, seasons, and solar-activity conditions. The largest performance gains occur during spring and years of high solar activity. These results demonstrate that integrating empirical-model information with machine learning effectively improves the representation of hmF2 variability at high latitudes. The proposed model provides an effective empirical model-guided approach for long-term spatiotemporal prediction of hmF2 in high-latitude regions. Full article

This work proposes a multiscale spatial and temporal approach to assess the impacts of the ionosphere and neutrosphere (neutral atmosphere including both tropospheric and stratospheric) through an independent analysis of each component on Precise Point Positioning (PPP) accuracy and stability during selected representative geomagnetic events of Solar Cycle 25. Geomagnetically quiet and disturbed days were selected using the Kp index, with 21 multi-GNSS stations distributed across latitude bands. Kinematic PPP processing was performed using APPPOLO software (v1.0) with ionosphere-free dual-frequency combinations, precise products, and robust filtering, totaling 924 solutions. Results show improvements in geometry and satellite availability with multi-GNSS, achieving discrepancies within 0–10 cm in more than 89% of the solutions. The VMF3 model confirmed the deterministic behavior of ZHD and the latitudinal variability of ZWD, with increased stability in multi-GNSS solutions. Greater degradation was observed at high latitudes under disturbed geomagnetic conditions, particularly for GPS-only processing. Residual analysis indicated elevation-dependent effects and constellation-related differences. The analysis of ionospheric irregularities using ROTI revealed that PPP degradation is strongly associated with spatial distribution and satellite geometry, with enhanced effects at high latitudes and low elevation angles. Full article

Accurate modeling of topside ionospheric electron density is essential for improving GNSS positioning and understanding upper-atmosphere dynamics. A new four-dimensional (spatial and temporal) topside electron density model is developed using global GNSS radio occultation data within an L2-regularized artificial neural network framework. The model combines both empirical and physical variables, including geomagnetic coordinates, temporal parameters, solar flux ($F_{10.7}$), geomagnetic activity index ($Kp$), and key ionospheric parameters (NmF2 and hmF2). To support the modeling framework, two sub-models are first constructed to estimate NmF2 and hmF2 when direct measurements are unavailable. The full model is trained using COSMIC-1 data and evaluated against independent datasets, including COSMIC-1, GRACE, and incoherent scatter radar (ISR). The results show that the proposed sub-models reduce relative errors by 4.5% for hmF2 and 11.0% for NmF2 compared with IRI-2016. For the full topside $N_e$ modeling, the proposed approach achieves improvements of 35%, 36%, and 53% relative to IRI-2016 when evaluated against COSMIC-1, GRACE, and ISR datasets, respectively. A systematic analysis of input variables further indicates that both physical drivers and ionospheric structural parameters play essential roles in determining model performance. The new model incorporated with NmF2 and hmF2 sub-models still achieves a 16% improvement over IRI-2016 based on ISR data. In addition to statistical improvements, the model reproduces key ionospheric features, including the equatorial ionization anomaly (EIA) and the midlatitude summer nighttime anomaly (MSNA), under different solar activity conditions. These results demonstrate that the proposed model captures not only the statistical variability but also the underlying physical behavior of the topside ionosphere. Full article

This study investigates the ionospheric response in the Southern and Northern Hemispheres over the period from 25 December 2024 to 7 January 2025. A major geomagnetic storm occurred on 1 January 2025, following the consecutive solar wind eruptions on 29–31 December 2024 and 1 January 2025. Global geomagnetic activity monitoring data showed that the Kp index surged to 8+, indicating the occurrence of this major geomagnetic storm. By analyzing the ionosonde, GNSS-TEC, and satellite in situ detection data from Learmonth, Australia (−21.8° N, 114.1° E), as well as Wake Island (19.29° N, 166.65° E), we found that the ionospheric anomalies in the two regions exhibited different patterns. The ionospheric parameters in Learmonth changed much more severely than those in Wake Island in the Pacific region. Relative to normal conditions, the disturbed ionosphere over Learmonth during 1–3 January 2025 exhibited a strong negative storm phase: f_oF₂ decreased by 31.4%, TEC dropped by 27.17%, and M3000F₂ declined by 41.2%, while h_mF₂ increased by 5.2%. This work provides an analysis of the differences in the ionosphere between the Northern and Southern Hemispheres affected by geomagnetic storms in late 2024. These findings highlight the need to incorporate hemispheric asymmetry into ionospheric dynamics models. Full article

The ionosphere is the ionized region of the atmosphere, extending roughly from 60 km to 1000 km in altitude. During flares, the near-Earth space is subjected to high-energy X-ray and EUV (extreme ultraviolet radiation) radiation, which also impacts the ionosphere. The changes in the ionospheric parameters measured by ionosondes, namely the f_min (minimum frequency) and foF2 (F2-layer ordinary-mode critical frequency) values, were examined during solar flares that occurred in geomagnetically quiet conditions (Dst (Disturbance Storm Time index) > −40 nT, Kp (planetary K-index) < 4). The necessary data were obtained by manually evaluating ionograms recorded by the Czech DPS4D ionosonde at Pruhonice (PQ052). The degree of variation was compared to quiet reference days, allowing for the determination of the deviations in the required values (df_min, dfoF2). The time series of the deviations were investigated. Furthermore, the relationship between the deviations and a “geoeffectiveness” parameter of the solar flare was also examined. The X-ray flux, the solar zenith angle of the station at the time of the event, and the position of the flare on the solar disk were also taken into account for the determination of the “geoeffectiveness” parameter. A positive correlation was observed between df_min and the geoeffectiveness parameter of the flare, which was more significant than the correlation between the dfoF2 and the geoeffectiveness parameter. Full article

This study investigates how solar and geomagnetic driver selection affects 24 h-ahead global ionospheric vertical total electron content (VTEC) prediction under different geomagnetic conditions. A four-step feature selection strategy involving importance evaluation, redundancy elimination, physical interpretability prioritization, and performance validation was developed to identify five key drivers from candidate solar and geomagnetic factors. Using global ionospheric maps provided by the Center for Orbit Determination in Europe (CODE) from 2014 to 2018, a non-overlapping 90-day temporal block scheme was adopted to reduce the risk of temporal information leakage. Six ablation experiments were conducted to compare the predictive performance of different driver combinations. The results show that the full-factor configuration selected by the proposed strategy achieved the most favorable overall performance among the tested combinations, although the global-average improvement relative to the baseline remained modest. The optimal driver combination varied with geomagnetic disturbance level, and the contribution of external drivers showed clear latitudinal dependence. In addition, the full-factor configuration yielded a more balanced global error distribution and was associated with slower error accumulation over the 24 h horizon. These findings suggest that physically guided driver selection is useful for constructing more physically meaningful driver combinations and for improving long-horizon prediction stability within a unified ConvLSTM-based framework. Full article

The aim of this study was to assess the impact of variations in the 3-hourly geomagnetic activity level during the period of the lowest solar and geomagnetic activity on the psychological state of patients who underwent coronary artery bypass grafting or valve surgery. The study was performed in Kaunas, Lithuania, during 2008–2012. The psychological state of 233 patients was assessed using the Symptom Checklist-90-Revised instrument (SCL-90-R) at 1.5 months, 1 year, and 2 years after the surgery (N = 531). During days of a negative difference between k-index sums at 18:00–00:00 h and 06:00–12:00, all SCL scores were statistically significantly higher. A low k- sum during 18:00–00:00 on the previous day was associated with an increase in anxiety, anger–hostility, phobic anxiety, paranoid ideation, and psychoticism. The combination of these conditions was associated with higher values of the SCL scores. These effects were observed at 1.5 and 12 months after the surgery. During the period lasting from 18:00 on the previous day to 12:00 on the day of the test, variations in k-indices that were not in line with the general trend of changes in the k-index were associated with a poorer psychological state in patients after open-heart surgery. Full article

This study investigates the super intense geomagnetic storm of 10–11 May 2024, during which the Dst index reached −412 nT, marking the most severe event of the last two decades. An artificial neural network (ANN) model was developed to estimate the geomagnetic storm indices Dst, Kp, and ap using hourly solar wind parameters (Bz, E, P, N, and V) obtained from the OMNI database. The model successfully reproduced the rapid and nonlinear variations observed during the main phase of the storm. The correlation coefficients (R) between observed and estimated values were 99.5%, 98.8%, and 99.1% for Dst, Kp, and ap, respectively. The corresponding mean square error (RMSE) values were 5.9 nT for Dst, 4.2 for Kp, and 2.1 nT for ap. Despite the extreme geomagnetic disturbance conditions, the ANN architecture maintained high estimative stability and accuracy, particularly during the sharp Dst decrease associated with southward Bz excursions. These results demonstrate that ANN-based approaches can effectively model the nonlinear dynamics of superstorms and provide a reliable complementary tool for forecasting extreme geomagnetic events. Full article

Sporadic E (Es) layers exhibit strong intermittency and highly skewed intensity distributions, exerting significant impacts on high-frequency communication and navigation systems and posing challenges for data-driven prediction. Conventional single-stage regression models are often dominated by abundant non-event samples and therefore tend to underestimate Es intensity during occurrence periods. To address this issue, this study proposes a unified two-stage neural network framework that decouples the prediction of Es occurrence probability from the estimation of Es intensity. The model is trained using multi-station ionosonde observations, incorporating cyclic representations of seasonal and local time variations together with solar and geomagnetic indices and station-aware encoding to enable unified learning across multiple stations. Results show that the proposed two-stage framework achieves event-only MAE values of 0.53–0.76 MHz and RMSE values of approximately 1.0–1.4 MHz at most mid- and low-latitude stations, with larger errors at the high-latitude Casey station (MAE ≈ 1.45 MHz and RMSE ≈ 2.31 MHz). The consistently bounded MRE values (≈0.18–0.23) observed across multiple stations demonstrate that the framework effectively mitigates severe data imbalance and suppresses spurious high-intensity estimates under non-Es conditions. Full article

During a period of intense solar activity and highly disturbed geomagnetic conditions, a large Forbush decrease began on 10 May 2024 accompanied by a historic geomagnetic storm that lasted for four days. This extreme geomagnetic disturbance classified as G5 according to “NOAA Space Weather Scale for Geomagnetic Storms” is referred to in the literature as the Mother’s Day Storm. This resulted from multiple, at least seven, Coronal Mass Ejections (CMEs) that had been occurring since 7 May. In addition, on 11 May, a powerful X5.8 class solar flare, reaching its maximum at 01:32 UT, was followed by an abrupt increase in proton flux with energies > 100 MeV (with onset on 11 May at 01:45 UT and peaking at 02:45 UT), as recorded by GOES satellites. This resulted in a Ground Level Enhancement (GLE), identified as GLE74, occurring on 11 May 2024 during the recovery phase of the deep Forbush decrease (~15%). This Solar Energetic Particle (SEP) event consisted of both impulsive and gradual components, where the high-energy tail of the gradual component was recorded by several stations of the worldwide ground-based neutron monitor network. Approximately 15 minutes after the onset of the SEP event and 40 minutes prior to its peak, an alert was issued by the GLE Alert++ system of the Athens Neutron Monitor Station of the National and Kapodistrian University of Athens (NKUA), available as a federated product on the ESA SWE Portal under the Space Radiation Expert Service Centre. In this paper, a description of the solar activity, i.e., solar flares and CMEs, occurring during this time period is given. Moreover, recordings of cosmic ray data obtained by ground-based neutron monitors are used to perform a detailed analysis of GLE74. Finally, the response of the NKUA GLE Alert++ system to GLE74 is thoroughly presented. Full article

This study investigates the solar origins of short-term periodicities in the near-Earth solar wind and interplanetary magnetic field (IMF) using long-term observations (1995–2024) and Potential Field Source Surface modeling. We establish that the 27-day periodicity in solar wind speed and its harmonics (13.5-day and 9-day) are governed by the combined influence of polar and low-latitude coronal holes. Polar coronal holes serve as the fundamental stabilizers of the global coronal structure, while the rotation of the Sun in the presence of low-latitude coronal holes acts as the primary mechanism generating periodic fluctuations. The absence of low-latitude coronal holes diminishes or erases these periodicities. For IMF components forming the Parker spiral, the periodicity is controlled by the structure of the heliospheric current sheet (HCS). A stable 27-day period emerges under a two-sector IMF configuration (HCS average slope $\mathrm{SL}>0.4$, latitudinal extent beyond ±30°), while a stable four-sector structure ($\mathrm{SL}>0.6$, latitudinal extent beyond ±60°) superimposes a clear 13.5-day periodicity. However, periodicity weakens or disappears when the HCS is flat and equatorial, or when global structural changes and transient disturbances disrupt recurrence patterns. In contrast, $B_z^{\mathrm{GSE}}$ exhibits weak periodicity due to its transient nature, while $B_z^{\mathrm{GSM}}$ shows intermittent 27-day periodicity modulated by the Russell-McPherron effect. Consequently, geomagnetic indices (Kp, Dst, AE) display periodic behavior similar to $B_z^{\mathrm{GSM}}$, consistent with its crucial role in solar wind-magnetosphere coupling. These results quantitatively link solar surface morphology to heliospheric recurrence, clarifying the conditions under which periodicities emerge or are suppressed throughout the Sun-Earth system. Full article

PredRNN is a spatiotemporal prediction model based on ST-LSTM units, capable of simultaneously extracting spatiotemporal features from ionospheric Total Electron Content (TEC). However, its internal convolutional operations require large kernels to capture low-frequency features, which can easily lead to model over-parameterization and consequently limit its performance. Although some studies have employed wavelet transform convolution (WTConv) to improve feature extraction efficiency, the introduced noise interferes with effective feature representation. To address this, this paper proposes a denoising wavelet transform convolution (DWTConv) and constructs the DWTPred-Net model with it as the key component. To systematically validate the model’s performance, we compared it with mainstream models (C1PG, ConvLSTM, and ConvGRU) under different solar activity conditions. The results show that both $MAE$ and $RMSE$ of DWTPred-Net are greatly reduced under all test conditions. In high solar activity, DWTPred-Net reduces $RMSE$ by 13.81%, 6.19%, and 9.28% compared to the C1PG, ConvLSTM, and ConvGRU, respectively. In low solar activity, the advantage of DWTPred-Net becomes even more pronounced, with $RMSE$ reductions further increasing to 19.39%, 11.51%, and 16.10%, respectively. Furthermore, in additional tests across different latitudinal bands and during geomagnetic storm events, the model consistently demonstrates superior performance. These multi-perspective experimental results collectively indicate that DWTPred-Net possesses obvious advantages in improving TEC prediction accuracy. Full article