Search Results (299)

This study investigates subauroral phenomena during the main phase of the 10 May 2024 geomagnetic storm using a combination of ground-based observations from the WD IZMIRAN observatory (magnetometer, ionosonde, and all-sky imager) and Global Self-consistent Model of the Thermosphere, Ionosphere, Protonosphere (GSM TIP) simulations. During 18:00–20:00 UT, we identified the simultaneous occurrence of ionospheric signatures of Polarization Jets (PJ)/Sub-Auroral Ion Drifts (SAID) and Strong Thermal Emission Velocity Enhancement (STEVE) over Kaliningrad, consistent with previously reported PJ/SAID identification from DMSP drift velocity measurements. This identification is supported by: (1) characteristic purple emissions (clearly visible in all three channels) moving rapidly westward; (2) U-shaped structures in ionogram sequences; (3) the reproduction of supersonic westward plasma drifts within a narrow latitudinal band by the first-principles model; and (4) observed and simulated significant Ne depletion. The estimated ion drift velocity from all-sky imaging (assuming an emission altitude of 200 km) is consistent with GSM TIP simulations, which predicted PJ/SAID velocities of ~750 m/s driven by a latitudinally narrow (~3°) but longitudinally extended (>50°) poleward electric field (40 mV/m). Simulations reveal that this PJ/SAID phenomenon causes a reversal of the zonal thermospheric wind at 250 km and induces Ne disturbances across the 200–700 km altitude range. The electron temperature enhancement (up to 1500 K) exhibits a “falling drop” shape, peaking at 350 km, while ion heating exceeds 150 K. The neutral temperature shows a dual response: frictional heating at 120–160 km and localized cooling at 175–250 km due to drop in electron density. Additionally, an increase in atomic oxygen concentration was predicted within the 90–200 km range across the PJ/SAID longitudinal sector. Full article

There is increasing interest in space domain awareness (SDA), motivating the use of non-traditional sensors for space surveillance. One such sensor is the Buckland Park Stratospheric–Tropospheric (BPST) very high frequency (VHF) radar, which has demonstrated an ability to detect over 2000 resident space objects (RSO) daily. A by-product of the RSO observations is the measurement of ionospheric group retardation, which can be used to estimate the total electron content (TEC) between the ground and the satellite altitude. This paper describes the use of BPST radar observations of Starlink satellites to measure vertical TEC (vTEC) from the ground to 490 km and from the ground to 560 km. The variation in BPST radar vTEC is demonstrated for both geomagnetically quiet and storm periods. The results are combined with global ionospheric TEC maps to calculate the ratio of the ionospheric to plasmaspheric (or LEO to GPS) vTEC. This allows investigation of the diurnal and annual variation in the LEO to GPS vTEC for the radar location at a temporal resolution unavailable to LEO satellite-based measurements. The results indicate that the RMS uncertainty of the BPST radar vTEC estimates is 0.41 TEC units (TECU), comparing favorably with the ≈2 TECU RMS uncertainty typically measured by GNSS receivers. The technique described in this paper may be applied to any ST or boundary layer (BL) radar without the need for hardware changes. Full article

The reliability of high-frequency (HF) frequency selection technology relies on the prediction accuracy of the Maximum Usable Frequency of the ionospheric F2 layer (MUF-F2). To improve its short-term prediction performance, a novel hybrid deep learning prediction model is proposed, which achieves accurate modeling of the complex spatiotemporal variation patterns of MUF-F2 by integrating a feature enhancement mechanism, a dual-branch feature extraction structure, and a bidirectional temporal dependency capture network. The hybrid prediction model integrates the Channel Attention mechanism (CA), Dual-Branch Convolutional Neural Network (DCNN), and Bidirectional Long Short-Term Memory network (BiLSTM). The model is trained and validated using MUF-F2 data from 5 communication links over China during geomagnetically quiet periods and 4 during geomagnetic storm periods, with the difference in the number of links attributed to experimental constraints and the disruptive effects of geomagnetic storms. Its performance is evaluated via multiple metrics, and a comparative analysis is conducted with commonly used prediction models such as the Long Short-Term Memory (LSTM) network. Experimental results show that during geomagnetically quiet periods, the proposed model achieves lower prediction errors (Root Mean Square Error (RMSE) < 1.1 MHz, Mean Absolute Percentage Error (MAPE) < 3.8%) and a higher goodness of fit (coefficient of determination (R²) > 0.94), with the average error reduction across all links ranging 8 from 6.2% to 46.9% compared with the baseline model. Under geomagnetic storm disturbance conditions, the model still maintains robust prediction performance, with R² > 0.89 for all communication links, as well as RMSE < 0.6 MHz, Mean Absolute Error (MAE) < 0.4 MHz, and MAPE < 3.3%. The study demonstrates that the proposed CA-DCNN-BiLSTM model exhibits excellent prediction accuracy and anti-interference capability under different geomagnetic activity conditions, which can effectively improve the short-term prediction accuracy of MUF-F2 and provide more reliable technical support for HF communication frequency decision-making. Full article

This study used Global Navigation Satellite System (GNSS) observations from the China Crustal Movement Observation Network (CMONOC) and the Kunming Continuously Operating Reference Station (KMCORS) network to investigate ionospheric response characteristics over China during the geomagnetic storm of 4–6 November 2023, and to assess their impacts on CORS-based real-time kinematic (RTK) positioning performance in the low-latitude Kunming region. A quantitative assessment was conducted by integrating regional two-dimensional dTEC (%) maps over China, BeiDou Navigation Satellite System (BDS) Geostationary Earth Orbit (GEO) total electron content (TEC), the rate of TEC index (ROTI), and RTK positioning solutions to evaluate ionospheric disturbances, irregularity activity, and associated degradation in positioning performance. Results indicate that, during geomagnetic storms, ionospheric responses over China exhibit pronounced phase-dependent and latitudinal variations. During the second geomagnetic storm on 5–6 November, positive responses were dominant at mid-to-high latitudes, whereas alternating positive and negative responses were observed at low latitudes. During the recovery phase, the Kunming region successively experienced a positive ionospheric storm lasting approximately 10 h, followed by a negative ionospheric storm lasting about 7 h, with relative TEC variations reaching a maximum of approximately 90%. The GEO TEC time series was consistent with the temporal evolution of the two-dimensional dTEC (%), while ROTI increased markedly during the disturbance enhancement period (21:00 UT on 5 November to 07:00 UT on 6 November 2023). During periods of enhanced ionospheric response and irregularities, RTK positioning performance was observed to deteriorate markedly. The fixed-solution rate at medium-to-long baseline stations decreased from nearly 100% to close to 0%, accompanied by an increase in vertical positioning errors to approximately 20 cm, whereas short-baseline stations were only minimally affected. These results indicate that ionospheric disturbances during geomagnetic storms exert a pronounced impact on CORS-based RTK positioning services in the Kunming region, with the magnitude of this impact being closely related to baseline length. Full article

The exceptionally strong geomagnetic storm of 10–11 May 2024 injected new energetic protons and electrons into the terrestrial radiation belts, creating extraordinary conditions to study the loss mechanisms scattering these particles into the atmosphere after the storm. For the first time, four electron belts were observed during several weeks. We show that this structure was due to electron loss, highly dependent on specific positions. Using the proton and electron fluxes measured by the Energetic Particle Telescope, EPT, on board PROBA-V, we determine the lifetimes of these populations depending on their energy ranges and positions. We show that the lifetimes are much longer for protons than for electrons, which enables us to determine their time variations independently. For electrons, the wave–particle loss mechanisms depend on the background ionosphere–plasmasphere density. The lifetimes determined after the May 2024 and 10 October 2024 big events are compared with average ones to understand their unusual specificity for the formation of four and three belts, respectively. For the injected protons of 9.5 to 13 MeV, the lifetime is minimum at L~1.9, where the fluxes are maximum, showing a lifetime depending on the flux intensity. Loss is due to pitch angle diffusion and collisions with electrons and nuclei in the ambient plasma and neutral atmosphere. At the outer edge of the proton belt, the flux is depleted at all energies after the geomagnetic perturbation, and we determine that the progressive time of refilling after the storm generally reaches more than 40 days. There is an excellent discrimination between the different populations of energetic electrons (0.5–8 MeV) and the injected protons (9.5–13 MeV) that are still observed several months after the event. Such results contribute to advancing understanding of the interactions between the terrestrial atmosphere and space radiation. 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

Space weather monitoring depends critically on passive sensor systems that detect and measure natural solar and geospace emissions without transmitting radio frequency energy. These include riometers, solar radio monitors, interplanetary scintillation detectors, GNSS-based ionospheric sensors, and broadband solar spectrographs that enable the provision of critical data required to forecast geomagnetic storms, protect critical infrastructures, and support aviation services, satellite operations, and defense services. However, with the increasing proliferation of radiocommunication technologies such as 5G/6G networks, dense HF/VHF/UHF deployments, and large constellations of low-Earth-orbit (LEO) satellites, the interference threat to these exceptionally sensitive receivers has grown. Most of these operate near the thermal noise floor and thus require strict protection criteria to ensure continuity of data. This review and perspective article provides a cross-disciplinary synthesis of scientific requirements, documented RFI case studies, and ongoing regulatory developments related to spectrum protection for passive space weather sensors. It systematically integrates perspectives on physical, technical, and regulatory aspects that are typically addressed separately in the literature. The article reviews the operating principles of major sensor classes and analyzes documented RFI cases affecting GNSS, riometers, CALLISTO, BINGO, and systems impacted by LEO satellite emissions, drawing from existing reports and regulatory submissions. Building on this evidence base, the work comparatively evaluates regulatory methods under consideration for WRC-27 shows that hybrid approaches combining primary allocations in core observation bands with secondary status and coordination procedures in adjacent bands offer the most viable path forward. This synthesis contextualizes and analyzes how technical protection criteria can be integrated with existing and evolving regulatory instruments to inform spectrum governance. The study concludes that without coordinated international spectrum management incorporating explicit protection thresholds and registration procedures, the long-term viability of space weather monitoring infrastructure faces significant risk in an increasingly congested radio frequency environment. Full article

The 12 November 2025 G4 geomagnetic storm—the third most intense of solar cycle 25—was triggered by a complex shock-ICME (interplanetary coronal mass ejection) structure as a result of three ICMEs and driven shocks that arrived on 11–12 November. The main enhancement in the interplanetary magnetic field occurred in the sheath region behind the shock driven by the second ICME. The Dst index reached −217 nT (the SYM-H index reached −254 nT) and the maximum Kp index was 9-. To comprehensively analyze the causes of the storm and its complex effects on near-Earth space, we used a multi-instrumental data set, involving data from satellite missions (ACE, SDO, PROBA2), GNSS networks, ionosondes, optical instruments, high-frequency radars (SuperDARN-like), and cosmic ray monitors. The auroral oval expanded equatorward (down to ~35° N in America). We recorded a super equatorial plasma bubble that almost reached the auroral oval boundary. The equatorial anomaly crests intensified, exceeding 175 TECU, and shifted poleward (8–10°). At mid-latitudes, the F2 layer critical frequency exhibited a strong negative disturbance (−50%) during the main phase, followed by an unusually prolonged and intense positive phase (+100%). GPS Precise Point Positioning errors increased to 2–3 m at high latitudes and in regions affected by the equatorial bubble. The event also featured a Forbush decrease and ground-level enhancement (GLE 77 according to the database hosted by the University of Oulu) associated with the X5.1 solar flare. The results underscore the complex chain of processes from solar storm to geomagnetic and ionospheric responses, highlighting the risks to satellite-based navigation and communication systems. Full article

This study investigates whether the moderate geomagnetic storm of 14 November 2012 was associated with measurable variability in selected power-quality parameters of the Polish National Power System, utilising anonymised, standardised hourly transmission data alongside solar-wind and geomagnetic drivers. Cross-correlation analysis reveals location-dependent, time-lagged couplings, with the strongest correlation, r = 0.74, between a current-harmonic component and the Dst index at a lag of −8 h. The most pronounced anticorrelation, with r = −0.66, occurs between current harmonics and the Ap index at lags of −9 to −11 h during a storm interval that reached $Ds{t}_{min}=-108$ nT. Principal Component Analysis and Hierarchical Agglomerative Clustering distinguish internally driven grid variability from externally driven storm-time signatures, demonstrating that seven principal components capture $89.54\%$ and $86.47\%$ of the variance at the two most responsive locations. These findings indicate that moderate storms can coincide with detectable changes in power-transfer and harmonic-related parameters at specific substations, supporting the need for multi-event studies and physics-based geoelectric or geomagnetically induced current (GIC) modelling to assess operational significance. Overall, this analysis demonstrates that space weather may contribute to observable variability in the Polish power grid. However, further research incorporating additional geomagnetic events, seasonal variability, and geophysical modelling is necessary to fully assess operational impacts and inform potential mitigation strategies. The findings highlight the importance of continued monitoring and interdisciplinary analysis to support long-term resilience planning. Full article

This study presents U-Net deep learning of total electron content (TEC) obtained from Global Ionosphere Maps (GIMs) to forecast ionospheric TEC over the African 0–40° N latitude sector during geomagnetic storms which have occurred between 2011 and 2024. Before being utilized in the deep learning procedure, the GIM-TEC data were improved by assimilating ground-based vertical TEC (VTEC) observations from available Global Navigation Satellite System (GNSS) receiver stations. The U-Net one-hour-ahead prediction of TEC was examined during the intense geomagnetic storm of May 2024. Additionally, the model’s accuracy and reliability were evaluated through quantitative comparison with established climatological models, including IRI-2020 and AfriTEC storm time models. The results indicate that the integration of data assimilation with the deep learning framework yields TEC estimates that closely agree with observations, achieving a RMSE of approximately 5 TECU. On the other hand, the IRI-2020 model exhibits substantially larger errors, with RMSE ~10–17 TECU, while the AfriTEC model shows the poorest performance, with RMSE reaching approximately 15–22 TECU. Further, the U-Net was validated using two equatorial and mid-latitude GNSS stations whose data were excluded from the assimilation process, achieving RMSE values of 4.44 and 6.75 TECU and correlation coefficients of 0.93 and 0.97, confirming the model forecasting capability for reproducing ionospheric TEC variability. These results establish the model as a precise, robust tool for TEC prediction in regions with sparse GPS coverage that is crucial for ionospheric monitoring and space weather applications. Full article

This study investigates the temporal and latitudinal variability of the ionosphere over the Indian longitude region during the intense geomagnetic storm from 1 to 3 June 2025, using GNSS receiver observations and magnetometer recordings, along with space-based measurements from in situ Swarm satellite, COSMIC-2 radio occultation, GUVI/TIMED-derived O/N₂ ratios, and model-derived electric fields. This particular event is relatively new and is characterized by the bifurcated variation with two distinct main phases separated by a short-lived recovery phase. The results revealed distinct features associated with the geomagnetic storm, including positive and negative ionospheric phases, thermospheric compositional changes, and the latitudinal propagation of disturbances. On 1 June, the observed strong positive ionospheric storm was driven by Prompt Penetration Electric Fields (PPEFs) and equatorward neutral winds, which triggered the upliftment of F-region plasma to higher altitudes through the enhanced equatorial fountain effect, leading to an unusually long-lasting Total Electron Content (TEC) enhancement from day to night. The analysis also revealed the distinct latitudinal behaviour, exhibiting the clear poleward extension of the Equatorial Ionization Anomaly (EIA) crest and significant TEC enhancements (~150–200% of the quiet day values) from low to mid latitudes as compared to the equatorial location through an efficient plasma redistribution. Conversely, pronounced negative ionospheric storm effect at almost all latitudinal locations on 2 June confirms complex and unusual storm-time dynamics, with inhibited upward plasma drifts due to the presence of Disturbance Dynamo Electric Fields (DDEFs), while the thermospheric O/N₂ ratio caused an extensive decrease in electron density over the Indian region. Minor negative storm noticed on 3 June coincides with the storm recovery period, reflecting prolonged disturbance dynamo effects and gradual recovery in thermospheric conditions. Overall, the current study highlights the strong sensitivity of the regional ionosphere to prevailing coupled electrodynamic-thermospheric forcing during the June 2025 geomagnetic storm that has not yet been reported for this event over the Indian longitude sector. Moreover, the findings from this study underscore peculiar storm-time behaviour of summer solstice ionosphere over the Indian longitude sector, driven by complex coupled processes which could be incorporated into ionospheric models and forecasting frameworks. 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

As Akasofu noted, no two geomagnetic storms are identical, yet the storm that occurred between 12 and 14 November 2025 stands out as an exceptional phenomenon. Its impact was evident across multiple layers of the ionosphere and numerous parameters, making it essential to conduct a comprehensive multi-parameter analysis of this event. Such an analysis relied upon data from the four LAERT topside sounders mounted aboard the recently launched Ionosfera-M satellites. Global ionospheric dynamics were thoroughlyexamined during the storm period, particularly focusing on the polar and auroral zones, along with the equatorial anomaly region. Notable features included sharp electron density gradients, widespread F-layer disturbances, and the formation of giant plasma bubbles. These elements collectively contributed to the dynamic picture of the ionospheric storm captured through multi-parameter measurements by the LAERT sounders. Full article

This study examines the spatiotemporal evolution of midlatitude ionospheric disturbances during the intense geomagnetic storm on 10–11 October 2024, focusing on the North American and European sectors. It utilizes multi-instrument datasets from ground-based observations, including Global Navigation Satellite System (GNSS) receivers and ionosondes, supplemented by the measurements from the Swarm, DMSP and GUVI/TIMED satellites. The results reveal significant longitudinal and latitudinal variations in regional ionospheric responses, specifically related to Storm Enhanced Density (SED) and the midlatitude trough. Key findings include: (a) During the main phase of the storm, the North American midlatitude ionosphere exhibited a pronounced longitudinal contrast: a positive SED-driven phase in the west versus a negative trough-dominated phase in the east. In the early recovery phase, the western sector transitioned to a trough-induced negative phase, while the eastern sector showed a positive phase related to auroral particle precipitation during substorms. (b) The North American SED featured a strong northwest-extending plume with a westward shift velocity of 200–300 m/s at 45°N, and a sharp density gradient of 60–65 TECU on its northeastern side, in contrast to the trough. (c) The European sector displayed a “sandwich-like” latitudinal pattern, with “positive–negative–positive” variations during the storm. (d) The European sector’s storm-time trough expanded rapidly equatorward, reaching a minimum of ~35° magnetic latitude (MLAT), while broadening latitudinally to a width of 18–20°. These density gradient structures, along with the longitudinal/latitudinal differences, highlight the dynamic processes occurring in the magnetosphere–ionosphere–thermosphere system during intense storms and contribute to the understanding of storm-response mechanisms across different sectors. 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