Ionospheric responses over the Antarctic region to intense space weather events: Plasma convection vs. auroral precipitation

The FY-4B satellite, launched in June 2021 as China’s new-generation geostationary meteorological satellite, carries three identical High-Energy Particle Detectors (HEPDs) that enable multi-directional, wide-spectral measurements of energetic electrons. The three units are mounted in the zenith (−Z), flight (+X with a +Y offset of 30°), and anti-flight (−X with a −Y offset of 30°) directions, allowing simultaneous observations from nine look directions over a field of view close to 180° in the 0.4–4 MeV energy range (eight energy channels). This paper systematically presents the design principles of the HEPD electron detector, the ground calibration scheme, and preliminary in-orbit validation results. The probe employs a multi-layer silicon semiconductor telescope technique to achieve high-precision measurements of electron energy spectra, fluxes, and directional anisotropy in the 0.4–4 MeV range. Ground synchrotron calibration shows that the energy resolution is better than 16% for energies above 1 MeV, and the angular resolution is about 20°, providing a solid basis for subsequent quantitative inversion. During in-orbit operation, HEPD remains stable under both quiet conditions and strong geomagnetic storms: the measured electron fluxes, differential energy spectra, and directional distributions show good agreement with GOES-16 observations in the same energy bands during quiet periods and for the first time provide from geostationary orbit pitch-angle-resolved images of the minute-scale evolution of electron enhancement events. These results demonstrate that HEPD is capable of long-term monitoring of the geostationary radiation environment and can supply high-quality, continuous, and reliable data to support studies of radiation-belt particle dynamics, data assimilation in space weather models, and radiation warnings for satellites in orbit.

Abstract Unmanned Aerial Vehicles (UAVs) are becoming integral to the emerging low‐altitude economy, operating primarily below 3,000 m for applications such as logistics, inspection, precision agriculture, and urban air mobility. The safe and reliable operation of UAVs depends critically on communication, navigation, and surveillance (CNS) systems, which provide command and control, precise positioning, and situational awareness. Although UAV hardware is largely protected from direct radiation hazards by the Earth's atmosphere and magnetosphere, space weather can still indirectly disrupt operations by affecting CNS performance. Solar flares, geomagnetic storms, and ionospheric irregularities can degrade communication links, induce navigation errors, and compromise surveillance reliability, posing risks to flight safety and operational continuity. This commentary highlights these vulnerabilities, reviews mechanisms through which space weather impacts UAV systems, and emphasizes the importance of integrating space weather awareness into UAV traffic management and system design to support a resilient low‐altitude economy.

Abstract The wave number (WN) structures of temperature from TIMED/SABER and electron density from COSMIC‐2 GIS data are extracted for the period 2020–2021 within ±45° latitudes. For the first time, a new version of the Climatological Tidal Model of the Thermosphere (CTMT.v2) is used to analyze the vertical‐temporal‐latitudinal tidal structures of temperature and density. CTMT.v2 uses solar flux dependent Hough Mode Extensions (HMEs), includes a more extensive collection of TIMED Doppler Interferometer (TIDI) data, compiles SABER V2.08, updates ion drag and dissipation, and provides tidal components for individual years. The vertical profile of CTMT.v2 tides from below are examined to investigate evolutions, variations, coupling, and impacts on structures of the atmospheric and ionospheric variables in the ionosphere, thermosphere, and mesosphere (ITM) regions. The main results are summarized as follows: (a) the antisymmetric structures of the WN1 move closer to the equator when the equatorial westward‐propagating semidiurnal components are strong above 140 km, (b) the antisymmetric component of the WN2 structure in the northern hemisphere is stronger than that in the southern hemisphere at ionospheric heights (above 105 km), (c) the WN3 structure shows intermittent equatorial symmetric structures at 105 km, but cannot form a clear hemispheric antisymmetric structure at other altitudes, (d) the stronger the WN4 structures in the E‐region, the well‐separated the crests of equatorial ionization anomaly (EIA) in the F‐region. This study highlights the need for more space‐based observations in the ∼100–400 km region and the development of models to advance the understanding of interconnections between terrestrial and space weather processes across different spatial and temporal scales.

Abstract The term ‘space weather’ indicates the physical conditions at the Sun and in the space environment between the Sun and Earth, that can influence the operation of spaceborne and ground systems and affect human activities and health. Scientific research on the space weather is therefore important to forecast the potential impact of perturbations driven by the Sun activity on biological and technological systems. This work discusses a learning module aiming at introducing high school pupils to the characteristics of the Sun, the relationships with the Earth and the impact that phenomena of solar origin have on our planet. The module consists of experimental observations and of class lectures, both aiming at coupling the curricular teaching at school with actual research topics.

Abstract Space weather could have a profound impact on Earth’s climate. Compared to the present-day Sun, the young Sun would have been more magnetically active and should have experienced more frequent extreme space weather events, in the form of coronal mass ejections and solar energetic particles, which steadily bombarded Earth’s upper atmosphere. These particles enhanced atmospheric chemistry, potentially resulting in large amounts of kinetically produced greenhouse gases, such as CO, H 2 , N 2 O, and HCN. In this work, to explore whether the Sun–Earth interaction could address the faint young Sun paradox (FYSP), we use a chain of three models: (i) an Earth-like planet surface temperature model, (ii) a radiative-convective model (EOS), and (iii) a thermochemical and photochemical kinetic model. We also apply this modeling pipeline to the present-day Earth atmosphere to assess the potential impact of a prolonged period of intense solar activity. Our main results can be summarized as follows. First, we find that, for an Archean Earth-like atmosphere of 90% N 2 , 10% CO 2, and trace amounts of either CH 4 or H 2 , the surface temperature increase due to space weather-produced greenhouse species is no larger than 0.3 K, which makes this solution to the FYSP unviable. Second, we find that the contribution of nitrogen species (N 2 O and HCN) to this temperature increase is negligible and that the main greenhouse contributors are instead CO and H 2 . Third, on present-day conditions, the cumulative effects of a prolonged period of intense solar activity would decrease the surface temperature by ∼3 K.

Abstract The impact of severe space weather events driven by interplanetary coronal mass ejections (ICMEs) on the space environment of planets remains an active area of research. The intense space weather event of 2024 October 10–11 was driven by an isolated ICME characterized by well-defined sheath and magnetic cloud structures, which offered a unique opportunity to investigate their sequential impacts on the terrestrial space environment. In situ plasma and magnetic field measurements from NASA’s ACE, Wind, and ISRO’s Aditya-L1 spacecraft are utilized to identify the structure of this ICME. Concurrently, data from GOES-16 and -18, along with observations from the Magnetospheric Multiscale mission, are employed to analyze the magnetosphere’s distinct response to the various components of the ICME. Intense magnetic field compression, driven by enhanced solar wind pressure in the ICME sheath, exposed geostationary satellites positioned in the morning and noon local time sectors to interplanetary space as the magnetopause boundary moved Earthward, reaching below 5 R E . Most of the intensification of the ring current took place within the transit of the ICME sheath, indicative of the superior role of the sheath plasma. During the passage of the turbulent ICME-sheath, a severe intensification of field-aligned currents (FACs), equatorward expansion, and multiple bands of FACs were noted within the Earth’s magnetosphere–ionosphere system, unlike in the magnetic cloud phase. These observations underscore the critical role of the ICME sheath in driving extreme space weather effects at Earth. Further, we contrast this storm’s drivers and its geoeffectiveness with those of the 2024 May 10 superstorm—the most extreme geomagnetic event at Earth in two decades.

Abstract. Satellite operations and other space assets are gravely impaired by space weather disturbances, but existing forecasting systems often lack accuracy and integrated multi-objective prediction functionality. This paper suggests a unified AI-based multi-objective system for space weather prediction to enhance protection of satellites from solar-terrestrial disturbances. Five prediction tasks are addressed by the architecture: 1. solar active region classification; 2. solar flare prediction; 3. prediction of coronal mass ejection travel time; 4. Kp-index-based prediction of geomagnetic storms; and 5. satellite danger level classification. Multi-source inputs such as video sequences, heliophysical observations, magnetograms, and solar images were applied in developing and evaluating expert machine learning and deep learning models. The CNN+LSTM model for predicting flares had 0.67 accuracy and 0.47 F1-score, with good recall for quiet-class events and poor recall for flare events because of class imbalance. The system achieved 0.97 accuracy and 0.96 F1-score for active region classification. LightGBM performed better than XGBoost for CME trip time, with an R² of 0.86 and RMSE of 1.52 hours. XGBoost outperformed LightGBM in the prediction of Kp index (R² = 0.82, RMSE = 0.63), while LightGBM showed lower performance (R² = 0.67). The XGBoost classifier delivered strong multi-class performance with 0.999 accuracy and 0.87 F1-score for satellite risk level classification. The models demonstrated stability and robustness through satisfactory generalization over Solar Cycles 23 and 24. The five models are integrated for real-time application with a Gradio-based interface. Focus will be on enhancing flare detection and addressing class imbalance.

Abstract Magnetic reconnection in a flare current sheet is widely believed to be the main energy release process powering solar flares and coronal mass ejections (CMEs). Modeling this process and determining the channels for the energy release, mass motions, and heating has long been a major goal in space science. We present results from a two-fluid magnetohydrodynamic simulation of an eruptive flare/CME using a newly developed version of the Space Weather Modeling Framework that incorporates two major advances in numerical capability. First, we use the STatistical InjecTion of Condensed Helicity formalism for the energy buildup, so that we start with a potential-field minimum-energy state and slowly form a sheared filament channel over a polarity inversion line as is observed on the Sun. Second, we use a new formulation of the plasma energetics that is explicitly energy conserving while calculating separate electron and ion temperatures and separate parallel and perpendicular pressures, as desired. For this first simulation with our new model, we opted for the nonadiabatic heating to go solely into the protons and for an isotropic pressure. We discuss the resulting energetics of the reconnection and, in particular, the plasma heating in the reconnecting current sheets, mass acceleration, and shock formation. We also discuss the implications of our results for flare/CME observations and for understanding solar eruptions in general.

This study presents a comprehensive analysis of pre- and co-seismic ionospheric disturbances associated with the 2023 Ms6.2 Jishishan earthquake by leveraging the unique observational strengths of BDS, particularly its high-orbit satellites. A multi-parameter space weather index was employed to effectively isolate seismogenic signals from geomagnetic disturbances, confirming that the main shock occurred during geomagnetically quiet conditions. Statistical analysis of 41 historical earthquakes (Mw ≥ 5.5) reveals that 47.2% were associated with detectable Total Electron Content (TEC) anomalies. An inverse correlation between earthquake magnitude and anomaly detectability within a 31-day window suggests prolonged precursor durations for larger events may produce longer-duration precursory signals, which challenge conventional detection methods. The synergistic capabilities of BDS Geostationary Earth Orbit (GEO) and Inclined Geosynchronous Orbit (IGSO) satellites were demonstrated: GEO satellites provide unprecedented temporal stability for continuous TEC monitoring, while IGSO satellites enable high-resolution spatial mapping of Co-seismic Ionospheric Disturbances (CIDs). The detected CIDs propagated at velocities below 1.6 km/s, consistent with acoustic gravity wave (AGW) mechanisms. A case study during a geomagnetically active period further reveals modulated CID propagation characteristics, indicating potential coupling between seismic forcing and space weather. Our findings validate BDS as a powerful and precise tool for ionospheric seismology and provide critical insights into Lithosphere–Atmosphere–Ionosphere Coupling (LAIC) dynamics.

The growing reliance on satellites highlights the need to understand how space weather quantitatively impacts the reliability and efficiency of power subsystems. While it is well established that space weather disturbances can trigger anomalies in satellite operations, most existing studies lack integrated, data-driven approaches capable of capturing the complex, nonlinear interactions between space weather parameters and satellite health telemetry. This study addresses this gap by introducing a novel four-stage data driven workflow to examine the relationship between key space weather indicators (proton flux, AL index, galactic cosmic rays (GCR), and solar wind density) and the NN1_Voltage and TBS1_Current and temperature of the EgyptSat-1 satellite power subsystem (T1BS, T3BS). The workflow includes: (1) data preprocessing; (2) To handle the high dimensionality and complexity of the data, a two-stage non-linear feature selection approach was employed. In the first stage, an unsupervised Restricted Boltzmann Machine (RBM) was applied to extract a compact and structurally stable feature subset. This was followed by a supervised mutual information (MI) validation step to ensure maximum predictive relevance to the satellite target parameters (T1BS and T3BS). (3) six machine learning models namely: Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, Random Forest Regressor, Adaptive Boost, Gradient Boosting, and Voting Regressor, to capture dynamic system behaviours; and (4) anomaly detection and validation by correlating prediction residuals with space weather disturbances, using STL decomposition and Z-score for GCR and P10 anomaly detection, and coincidence rate analysis to assess temporal alignment. The Random Forest (RF) model exhibited strong predictive performance. For NN1_Voltage, the mean squared error (MSE) was 0.00147 (95% CI: 0.00120-0.00184), the root mean squared error (RMSE) was 0.038 (95% CI: 0.0346-0.0428), the mean absolute error (MAE) was 0.028 (95% CI: 0.026-0.031), and the mean absolute percentage error (MAPE) was 0.09% (95% CI: 0.08-0.10%). For TBS1_Current, RF achieved an MSE of 0.0405 (95% CI: 0.0319-0.0489), RMSE of 0.201 (95% CI: 0.179-0.221), MAE of 0.153 (95% CI: 0.136-0.169), and MAPE of 2.4% (95% CI: 2.1-2.6%). Furthermore, analysis of detected anomalies revealed temporal coincidence rates of 31% with GCR disturbances and 27% with P10 proton events. Statistical validation using chi-squared and Fisher's exact tests yielded significant p-values (e.g., p = 2.83 × 10⁻³ for GCR; p = 5.63 × 10⁻⁷ for P10), suggesting a potential relationship worth further investigation. This analysis is particularly relevant for assessing unexplained satellite failures such as the loss of EgyptSat-1 and contributes to improved resilience and monitoring strategies for future missions. While the proposed workflow shows strong predictive performance, its validation is currently limited to a single satellite dataset, highlighting the need for broader cross-mission testing. This study not only enhances our understanding of space weather impacts on satellite power systems but also demonstrates the potential of machine learning in improving anomaly detection and resilience of satellites operating in challenging space environments.

Abstract. Earth's magnetic field, a dynamic shield influenced by internal and external forces, holds critical insights into space weather forecasting and the planet's core dynamics. The Choutuppal (CPL) and Hyderabad (HYB) magnetic observatories in India are pioneering this field by delivering high-resolution geomagnetic data to INTERMAGNET with unprecedented speed and precision. Utilizing a novel, low-cost protocol, CPL transmits 1 s resolution data and HYB provides 1 min data, both achieving a latency of less than 300 s, making CPL one of the first Indian observatories to send 1 s real-time data to GIN (Geomagnetic Information Node). This rapid data transmission enhances global collaboration in space weather prediction, safeguarding critical infrastructure like satellites and power grids from solar storms. To further elevate data utility, we developed Python-based software for real-time visualization and quality control at both observatories. This tool generates plots, performs initial quality checks, and computes first differences at 1 s and 1 min intervals, with a latency under 300 s. By enabling daily evaluation of data quality, the software facilitates the identification of anomalies and noise, supporting the preparation of quasi-definitive data essential for geomagnetic research. Our Python server and web applications are designed with the future in mind, integrating artificial intelligence (AI) and machine learning (ML) capabilities. These advancements at CPL and HYB are set to transform the processing, forecasting, and visualization of geomagnetic data. By improving both the accuracy and accessibility of these data, we aim to revolutionize geomagnetic research, making it more precise, accessible, and actionable.

An extreme geomagnetic storm comparable to the Carrington event in 1859 could have significant impact on modern infrastructure such as power grids. A previously published simulation by Blake et al. (2021, Space Weather, doi:10.1029/2020SW002585) reconstructed the magnetic field observations at Colaba, India, during the Carrington storm and provided estimates of magnetic field variations around the world. We use these results as an input to a first-principle modelling method to estimate the geoelectric field in Fennoscandia based on a 3-dimensional ground conductivity model. We compare the results with the Oct 2003 Halloween storm, which is one of the strongest events in the past 100 years and of which spatially dense magnetometer recordings are available in North Europe. Comparison of the maximum modelled geoelectric field values in Fennoscandia indicates that a Carrington-class storm could generate electric fields 1.4-20.4 times larger than the Halloween storm did, with the Carrington to Halloween ratio having a mean of 6.7 and standard deviation of 2.7.

The variation in the maximum usable frequency (MUF) during geomagnetic disturbances is a key parameter for high-frequency (HF) radio communications. This study investigates MUF variability and related ionospheric parameters during the first geomagnetic superstorm of solar cycle 24, on 17 March 2015 (the Saint Patrick’s Day storm). Using Digisondes at Sao Luis (equatorial) and Campo Grande (low-latitude, near the southern crest of the Equatorial Ionization Anomaly), we analyzed storm-time changes in the F region. During the main phase, two episodes of eastward Prompt Penetration Electric Fields produced rapid uplifts of the F2-layer peak height at São Luis, reaching altitudes up to 520 km, accompanied by MUF decreases of approximately 25% relative to quiet-day values. In contrast, Campo Grande exhibited a more subdued response, with MUF deviations generally remaining within 15–20% of quiet-time conditions. During the recovery phase, the likely occurrence of a westward disturbance dynamo electric field was inferred from suppression of the Pre-Reversal Enhancement and decreased F-layer heights at São Luis. Comparative analysis highlights distinct regional responses: São Luis showed strong storm-time deviations, while Campo Grande remained comparatively stable under the impacts of Equatorial Ionization Anomaly effects. These results provide quantitative evidence of localized geomagnetic storm impacts on MUF in the Brazilian sector, offering insights that may improve space weather monitoring and HF propagation forecasting.

In recent years, with the rapid expansion of low-Earth-orbit (LEO) satellite constellations, the orbital decay of LEO satellites caused by atmospheric heating from solar irradiance and geomagnetic activity has become increasingly prominent. Accurately understanding the orbital decay behavior of LEO satellites during geomagnetic storms is essential for managing orbital lifetime, orbit determination, orbit control, and collision risk assessment. This study investigates the combined effects of solar radiation intensity and geomagnetic storm intensity on LEO satellite orbital decay by analyzing 130 representative intense geomagnetic storms from 1965 to 2025. The results demonstrate that during geomagnetic storms, both solar irradiance and geomagnetic activity jointly influence orbital decay: solar irradiance primarily determines the total decay magnitude, while geomagnetic activity mainly affects short-term decay rates through transient disturbances. Furthermore, solar radiation intensity shows a stronger correlation with orbital decay than storm intensity. Therefore, effective orbit maintenance strategies for LEO satellites should emphasize the influence of solar radiation intensity in addition to geomagnetic storm intensity. Our findings provide valuable references for developing operational orbit maintenance protocols for LEO satellites during space weather events.

Abstract In this work, we propose the use of the Most Extreme Space Weather Events (MESWE) dataset, which combines 12 solar corona and solar wind parameters to predict the disturbance storm time (Dst) index 4 or 8 hr ahead by deep learning models. The dataset focuses on periods of extreme solar activity in the past 30 yr that triggered great and severe geomagnetic storms, as measured by geomagnetic indices. Using the MESWE dataset and leveraging the gated recurrent units type of recurrent neural network, we obtain a significantly reduced persistence effect compared to models trained with conventional methodologies in the Dst index forecasting community. The performance of the models is compared quantitatively by using the dynamic time warping method. In addition, we introduce a methodology to reduce the risk of the split of the validation test set that can lead to misleading performance assessments. The methodology of combining imagery and in situ data and the results presented in this work are directly aligned with the objectives of the European Space Agency Vigil mission.

Abstract Global Navigation Satellite Systems (GNSS) radio occultation (RO) is a satellite remote sensing technique that uses GNSS measurements collected on low-Earth orbiting (LEO) satellites to profile the Earth's neutral atmosphere and ionosphere with high vertical resolution and global coverage. A theoretical study has shown that high-rate estimation of GNSS satellite clock offsets is critical to the retrieval of bending angles in GNSS RO processing, particularly for GLONASS. Inspired by this study, we have generated high-rate GNSS satellite clock products at 2-second intervals using the GINAN GNSS software, based on globally-distributed ground GNSS stations. Then we perform RO bending angle retrievals for the COSMIC-2 mission using one week of our clock products. The fundamental noise of GLONASS RO has dropped by 34% when using 2-second clock products compared to 30-second clock products. Furthermore, the uncertainty of the bending angle estimation in GPS+GLONASS RO has reached < 1×10^(-6) radian, an ultra-precise level that allows the RO technique to monitor weather and space weather with a higher sensitivity.

Abstract As space weather has significantly matured as a field, the Space Weather journal is implementing two major changes: a new scope and a new Editor‐in‐Chief (EiC). As part of the revised scope, the novelty of submitted manuscripts is now a major quality to be assessed by the editors and reviewers. The goal of the journal remains primarily to advance our understanding of fundamental space phenomena that have direct impact on technology, to improve the forecasting of such phenomena, and to provide space environment climatology models. This new scope is the last major change implemented by the departing EiC who will be succeeded in January 2026 by Dr. Steven K. Morley. The new EiC will now oversee a large editorial board and a journal with over 400 submissions in 2025.

Abstract The ionospheric plasma parameters derived from the Langmuir Probe (LP) measurements aboard Swarm satellites (2014-2024) provide critical insights supporting Thailand’s upcoming 2026 SSACube satellite mission. This study investigates the long-term variability of ionospheric electron density ( N e ), electron temperature ( T e ), and spacecraft potential ( V s ) to establish design specifications for space weather instrumentation. All analyses utilized quarterly averaged data to emphasize long-term ionospheric responses across solar cycle phases. Strong correlations between solar activity (quantified by F10.7 index) and plasma parameters were observed, with N e exhibiting correlation coefficients of 0.95 across all satellites. Solar cycle phase analysis revealed significant variations, with N e values nearly tripling from solar minimum (75,000 to 90,000 cm −3 ) to the ascending phase (207,000 to 255,000 cm –3 ). The V s maintained consistent negative values with stronger negative potentials during solar minimum. Altitude-dependent analysis using data from Swarm-B’s higher orbit compared to Swarm-A and C showed decreasing N e with increasing altitude during descending and minimum phases, with this dependency diminishing during the ascending phase. The observed ranges of N e (6×10 4 to 3×10 6 cm –3 ), T e (2×10³ to 3×10 3 K), and V s (–3 to –1.5 V) serve as key benchmarks for SSACube’s instrument design. These findings provide a solid basis for ionospheric analysis and support Thailand’s space weather and Space Situational Awareness (SSA) capabilities.

Abstract Standard visualizations of Extreme Ultraviolet (EUV) solar imagery often fail to convey the full complexity of the Sun’s corona, especially in faint off-limb regions. This can leave the misleading impression of the Sun as a bright ball in a dark void, rather than revealing it as the dynamic, structured source of the solar wind and space weather. A variety of enhancement algorithms have been developed to address this challenge, each with its own strengths and tradeoffs. We introduce the Radial Histogram Equalizing Filter (RHEF), a novel hybrid technique that optimizes contrast in high dynamic range solar images. By combining the spatial awareness of radial graded filters with the perceptual benefits of histogram equalization, RHEF reveals faint coronal structures and works out of the box—without requiring careful parameter tuning or prior dataset characterization. RHEF operates independently on each frame, and it enhances on-disk and off-limb features uniformly across the field of view. For additional control, we also present the Upsilon redistribution function—a symmetrized cousin of gamma correction—as an optional post-processing step that provides intuitive programmatic tonal compression. We benchmark RHEF against established methods and offer guidance on filter selection across various applications, with examples from multiple solar instruments provided in an appendix. Implemented and available in both Python and IDL, RHEF enables immediate improvements in solar coronal visualization.