Abstract To address the complex spatiotemporal characteristics of ionospheric total electron content (TEC), a bidirectional convolutional long short‐term memory network with a dynamic cross‐attention mechanism for spatiotemporal feature weighting (CASF‐BiConvLSTM) is proposed and further combined with the sliding interquartile range (SIQR) method, referred to as CASF‐SIQR, for pre‐earthquake TEC anomaly detection. TEC data from the Center for Orbit Determination in Europe (CODE) are divided into training (1999–2014), validation (2015), and testing (2016–2018) sets. Compared with the BiConvLSTM baseline, the proposed CASF‐BiConvLSTM achieves consistent improvements across annual, seasonal, and latitudinal variations, with an average root mean square error reduction of about 21% during 2016–2018. For TEC anomaly detection, the CASF‐SIQR framework achieves significantly lower false rates during solar and geomagnetic quiet periods, underscoring the importance of accurate TEC prediction in effectively suppressing false anomalies. During the Jiuzhaigou and Luding earthquakes, more TEC anomalies were identified than with the traditional SIQR method, with detection rates improved by 22.23% and 13.29%, respectively. During the pre‐earthquake periods, the detection rates reach 40.83% and 32.78%, representing improvements of 23.61% and 13.73% over SIQR. During the strong geomagnetic storm on 26 August 2018, the validation reveals that the proposed framework can utilize the spatiotemporal characteristics of detected TEC anomalies to distinguish geomagnetic storm disturbances from pre‐earthquake anomalies. These results demonstrate the framework's greater sensitivity and effectiveness across geophysical conditions, offering valuable insights for TEC modeling, prediction, and detection of potential earthquake‐related anomalies.
Abstract This paper presents a major update to the PyIRI model, a fully vectorized Python implementation of the International Reference Ionosphere (IRI). The update reformulates the IRI core coefficients using spherical harmonics in Quasi‐Dipole (QD) latitude and Magnetic Local Time coordinates, replacing the legacy geographic‐coordinate‐based basis functions of the CCIR (Consultative Committee on International Radio) and URSI (International Union of Radio Science) models of the foF2 critical plasma frequency parameter. The resulting coefficients remain valid over decadal timescales, with re‐derivation recommended every 5 years to account for magnetic pole drift. This new formulation removes the dependence on the modified dip angle, enabling a more transparent and physically interpretable representation of ionospheric climatology. Diurnal variability is expressed through real Fourier series, with the optimal number of spatial and temporal coefficients determined via reconstruction error analysis. The coefficients are stored in standardized NetCDF format and validated against IRI‐2020. Artificial small‐scale oscillations present in the original IRI climatology are substantially reduced in the new formulation, leading to an ionosphere more consistent with the SAMI physics‐based model. The updated PyIRI model also includes previously unavailable IRI parameters, such as the SHU‐2015 and AMTB2013 options for hmF2, and the B0 and B1 F2‐layer thickness and shape parameters. Spherical harmonic coefficients are extracted identically across the foF2 CCIR, foF2 URSI, hmF2 SHU‐2015, hmF2 AMTB2013, M(3000)F2, and foEs parameters.
Abstract Geomagnetic disturbances (GMDs) are rapid changes in the magnetic field of the Earth that may drive geomagnetically induced currents (GICs), known to cause damage to infrastructure such as power grids and pipelines. Globally, GIC measurements are often hard to obtain; therefore, GMDs are used as a proxy for GICs. GMDs have been previously shown to occur on different timescales which are related to different formation mechanisms such as magnetic storms, substorm activities and sudden commencements. 1 min cadence ground‐magnetometer data has been available for decades and are the basis of many studies showing the frequency and intensity of GMDs. In recent years one‐second cadence data have become available from a network of magnetometers through the SuperMAG portal. We take the 1‐s data, averaged 10 s data, and compare the number of GMDs identified with the number found from the 1‐minute data to test the GMDs related to the different timescales. We find that 1‐s data consistently record higher amplitude GMDs as well as recording overall more GMDs, thus showing the importance of 1‐s cadence data. This is most important for GMDs between 00 and 03 magnetic local time and at low geomagnetic latitudes (>50°) as these are the locations where difference between the number of GMD detections between the 1‐s data and the 1‐min data is the largest. However, the 1‐s data have many artefacts due to unreliable data points in them leading a 10‐s median being more reliable at correctly detecting GMDs.
This study develops a semi-empirical framework for Dst index forecasting by integrating the PPMLR-MHD model of Earth's magnetosphere with four established empirical models (Burton, UCB, AK2, and Wang). The framework partitions the physics: global magnetopause and tail currents are simulated by MHD, while the ring current contribution is provided empirically. Performance was evaluated using Prediction Efficiency (PE) and Correlation Coefficient (CC) across 18 storms categorized as moderate (-50 > Dst >= -100 nT), intense (-100 > Dst >= -200 nT), and super (Dst < -200 nT). Key results show that all hybrid models improve correlation compared to their standalone empirical counterparts, but the optimal model for amplitude accuracy is intensity-dependent. The "MHD + Burton" model excels for moderate storms (avg. PE = 0.71, CC = 0.93). Conversely, for superstorms, only the "MHD + Wang" model is reliable (avg. PE = 0.86, CC = 0.95), while "MHD + Burton" fails. The superior performance of "MHD + Wang" under extreme conditions is attributed to its physics-based, solar wind dynamic pressure-dependent decay time. This work validates a practical, intensity-adaptive hybrid approach that combines physical fidelity with computational efficiency for improved operational Dst forecasting. Plain Language Summary Predicting the Dst index, a key measure of geomagnetic storm strength, is vital for protecting technology and infrastructure from space weather. We introduce a new forecasting method that combines a computer model simulating Earth's magnetic environment (an MHD model) with different statistical models of the ring current-a major storm driver. Testing on recent storms shows that no single combination works best for all events. For common, moderate storms, pairing the MHD model with the simpler Burton model yields the most accurate forecasts. However, for rare but extreme superstorms, the MHD model must be paired with the more complex Wang model to avoid severe overpredictions and produce reliable forecasts. This is because the Wang model better represents how the ring current behaves under extreme solar wind conditions. Our findings provide forecasters with a clear, adaptive strategy: use the MHD + Burton model for typical conditions and switch to MHD + Wang when extreme solar activity is expected. This practical approach balances detailed physics with speed, offering a promising tool for real-time warnings of severe space weather.
Thermospheric mass density governs aerodynamic drag in low Earth orbit and is a primary source of uncertainty in orbit prediction and conjunction assessment, particularly during geomagnetic disturbances. We present AETHER- (Accelerometer-driven Estimation of THERmospheric density-A Physics-Informed Probabilistic Prediction Platform), a machine-learning-based global thermospheric density forecasting model that provides multi-step forecasts up to 6 hr ahead using a 3-hr input window, with predictive uncertainty estimates. AETHER- formulates thermospheric density forecasting as a sequence-to-sequence regression task conditioned on recent space weather evolution and a user-specified sequence of future times and locations. To enhance physical consistency and generalization, AETHER- incorporates JB2008 and NRLMSISE-00 density estimates evaluated at future locations, along with solar, geomagnetic, and solar-wind drivers. The network employs dual recurrent encoders and an evidential Normal-Gamma output head to jointly estimate forecast mean and uncertainty. The model is evaluated using independent satellite test cases spanning quiet, moderate, and extreme geomagnetic conditions. During quiet periods, AETHER- achieves high forecast skill . Under moderate activity, strong skill is retained , with reduced physical-domain errors than empirical baseline models. During extreme storm conditions, deterministic forecast skill degrades as expected yet remains robust (-0.90). Predictive uncertainty remains well calibrated across all regimes. These results establish AETHER- as a practical, low-latency, uncertainty-aware capability for thermospheric density forecasting that supports orbit prediction, drag-risk assessment, and operational decision-making over its validated altitude range of approximately 300-520 km, with highest confidence in the data-rich 400-520 km region.
Radiation from space in the form of galactic cosmic rays (GCRs) generates a persistent background of ionizing radiation in Earth's atmosphere. The dose rate of ionizing radiation due to GCRs increases from sea level to aviation altitudes. The Nowcast of Aerospace Ionizing RAdiation System (NAIRAS) model is a state-of-the-art model for predicting radiation dose rates at aviation altitudes and is used to limit doses to aircrew and passengers. However, dosimetry data from the Automated Radiation Measurements for Aerospace Safety (ARMAS) system flown on commercial aircraft have revealed dose rates at aviation altitudes greater than predicted by NAIRAS. One theory, supported by correlation analyses, posits that these so-called excess dose rates are caused by relativistic electron precipitation (REP) driven by hiss waves in the inner magnetosphere. In this work, we use a validated Monte Carlo model of particle transport through the atmosphere in combination with GCR measurements from low Earth orbit (LEO) to attempt to explain the ARMAS dose rate measurements with GCRs alone. We find that our predicted GCR dose rates are in statistical agreement with the ARMAS data, but still underestimate the dose rates for some events. We then simulate REP using electron measurements from LEO and find that REP can only explain up to about of the difference between GCR dose rates and ARMAS data in the most extreme cases. With support from previous literature, we conclude that REP is unlikely to be the source of the discrepancies between GCR dose rate predictions and ARMAS measurements.
Abstract Modern submarine communication cables, though fiber‐optic in nature, remain vulnerable to space weather hazards due to their internal conductive cables used for powering repeaters. During geomagnetic storms, variations in the geomagnetic field induce geoelectric fields that drive geomagnetically induced voltages along these cables. This study validates the Submarine Cable Upset By Auroral Streams (SCUBAS) model framework by analyzing the induced voltages on two transatlantic submarine cables—TAT‐1 and TAT‐8—during the 11 February 1958 and 13 March 1989 superstorms, respectively. SCUBAS models the cable as a segmented conductor placed atop oceanic and subsea conductivity structures and calculates voltages from both cable‐parallel electric fields and coastal Earth potential at cable terminals. Model outputs are compared against digitized observations from historical literature. SCUBAS successfully captures both large‐scale voltage patterns and event‐specific dynamics, reproducing peak voltages within a median percentage error of <30%. High skill scores (>0.85) further confirm the reliability of the model across events and spatial locations. The results highlight that both subsea induction and coastal Earth potential shifts contribute significantly to geomagnetically induced voltages, emphasizing the need for comprehensive modeling in space weather risk assessments. This validation positions SCUBAS as a robust tool for evaluating the vulnerability of submarine cables to geomagnetic disturbances, with relevance for space weather forecasting, improving infrastructure resilience, and future mitigation strategies.
Abstract Substorms produce rapid fluctuations in Global Navigation Satellite System (GNSS) signals, leading to degraded positioning performance at high latitudes. This study investigates the correlation of auroral structures on Global Positioning System (GPS) Precise Point Positioning (PPP) error during three substorm events with different strengths, showing that stronger substorms tend to produce larger position errors. Using a combination of GPS and auroral optical data from nearly 100 ground stations in Alaska, we identify spatial and temporal relations between phase fluctuations, total electron content enhancements, and PPP error. The most severe PPP error, reaching 50 m and losing position solution, occurred during the substorm expansion phase and was co‐located with discrete auroral arcs including the poleward boundary of the substorm bulge and auroral streamers. Moderate phase fluctuations and PPP error were also observed over diffuse aurora as a widespread feature of the auroral oval that has traditionally not been considered a significant contributor to scintillation. The vertical component of the position errors, which has practical significance for aviation applications, was larger than the horizontal position errors. These results indicate that high‐latitude GNSS degradation is related to substorm strength and active auroral forms, including both discrete and diffuse auroras. The results highlight the importance of considering various types of auroral forms in future mitigation strategies.
Abstract Techniques developed in the past few years enable the derivation of multiscale ion convection and particle precipitation patterns from high‐resolution ground‐based observations, and it has been shown in previous studies that such multiscale geomagnetic forcing can contribute significantly to ionospheric and thermospheric disturbances. In this work, the global ionosphere–thermosphere model (GITM) is utilized to simulate the 27 March 2014 substorm event. Simulations are driven by both the original and spatially smoothed multiscale Super Dual Auroral Radar Network electric potential patterns, and the differences between the two sets of simulations are used to evaluate the effects of mesoscale (<500 km) ion convection. GITM‐simulated neutral winds are compared with scanning Doppler imagers (SDI) wind measurements at Toolik Lake (68.6°N, 149.6°W). Neutral wind variations have been further separated into large‐scale (>500 km) and mesoscale (<500 km) structures. Data‐model comparisons show that, while GITM captures large‐scale wind variations reasonably well, it underestimates the magnitudes of mesoscale winds. Both mesoscale ion convection and precipitation are found to substantially enhance the simulated mesoscale neutral winds. Quantitative analyses at a fixed location and over two vortex regions reveal that F‐region neutral wind variations (at ∼270 km) typically lag mesoscale ion drift enhancements by a few to ∼30 min. The maximum magnitude of mesoscale ion drifts is consistently ∼6 times larger than that of the associated neutral wind changes.
Abstract The Ionospheric Monitoring Beacon network in Iran provides continuous, multi‐frequency high‐frequency (HF) transmissions using WSPRNET modulation, enabling global reception and near–real‐time monitoring of ionospheric propagation conditions. In this study, we analyzed HF signals transmitted from Shiraz, Tabriz, Mashhad, and Birjand across several frequencies spanning 7–18 MHz. Reception reports from worldwide WSPRNET stations were used to characterize frequency‐dependent diurnal behavior, spatial coverage, and temporal variability under geomagnetically quiet conditions and during the ionospheric disturbances associated with the intense geomagnetic storm of 12 November 2025. The observations revealed systematic dependencies of HF propagation on transmission frequency, transmitter location, and storm‐time ionospheric electron density modifications. Lower frequencies exhibited delayed, absorption‐dominated responses consistent with enhanced D‐ and lower F‐region ionization, whereas higher frequencies showed rapid enhancements or disruptions associated with F‐region restructuring and electron density redistribution. Transmitter‐dependent differences reflected variations in latitude, background ionospheric structure, and propagation geometry across Iran. To interpret these results, three‐dimensional ray‐tracing simulations were conducted using physics‐based ionospheric models constrained by GNSS‐derived total electron content. Modified electron density distributions incorporating storm‐time tilts and gradients reproduced the observed multi‐frequency propagation anomalies, including changes in hop structure and geographic reach. These results demonstrated that distributed, multi‐frequency HF beacon networks effectively constrain regional ionospheric structure and its response to geomagnetic forcing, supporting space weather monitoring and HF communication applications.
Abstract Geomagnetic storms represent a space weather hazard to power transmission networks due to the effects of induced geo‐electric fields within the conducting surface of the Earth. These drive electric currents in power transmission lines which can flow to ground through the neutral‐ground connections of transformers. Geomagnetically induced currents (GIC) can negatively impact the operation of high voltage transformers through asymmetric half‐cycle transformer core saturation. In this study GIC measurements, derived effective current, and reactive power responses (Q) of the single‐phase bank autotransformer, T4, operating at 220 kV in the Halfway Bush substation, Dunedin, New Zealand have been analyzed over the period 2013–2017. During 8 elevated GIC events linear enhancements of reactive power consumption (Qcon, MVAr) occurred, even with comparatively low levels of GIC (i.e., <7 A). This is consistent with transformer core saturation where there is little tolerance or “headroom” to GIC in the design for single‐phase bank transformers. Reactive power measurements show high variability. The removal of the non‐GIC variability is difficult and introduces uncertainty into the identification of GIC‐driven responses. In this analysis we consider two techniques to isolate GIC‐induced reactive power responses. We find that transformer T4 exhibited a reactive power response of ∼0.115 MVAr/A. The results are a factor of 2–3 larger than MVAr changes in a nearby three‐phase, three‐limb autotransformer, aligning with previous research. The results are consistent with, but less extreme than modeling studies found for transformer units operating at significantly higher voltages.
Abstract Prediction efficiency (PE) is a skill score that compares the data‐model metric of mean square error against the variance of the observations (i.e., using the average of the observed values as the “reference model” in the general skill score formula). This has proven to be highly useful for analysis using “continuous metrics”—those data‐model comparison techniques that employ the exact values of the observations and model results—especially when comparing the new model's performance against an existing model. Another major grouping of data‐model comparisons is “event detection analysis” in which all observational and numerical values are converted into a binary yes‐no categorization of being in or out of “event state.” There is, however, no equivalent skill score to PE within the event detection toolkit. This study proposes two such options, one based on the proportion correct metric and another based on the critical success index metric. Like PE, these new skill scores use the observations as the reference model, which provides complete independence of the reference model from the new model. It is demonstrated, reevaluating two space weather data‐model comparison studies, that these skill scores provide context for model evaluation that is unique to other existing metrics and valuable for the assessment, especially with respect to comparing the new model's performance against an existing model. Reference models in existing skill scores are based in part on the new model's performance against the data, which leads to ambiguous interpretation for intermodel comparison usage.
Abstract This study presents a detailed case study of the ionospheric impacts of moderate (G2) and severe (G4) geomagnetic storms over the Latin American sector, with particular emphasis on the formation and suppression of equatorial plasma bubbles (EPBs). The moderate storm of 7–8 October, 2024, presented a rare case of EPB suppression despite favorable conditions, including a pronounced pre‐reversal enhancement (PRE), with vertical plasma drifts exceeding 40 m/s. Post‐midnight EPBs were observed exclusively in the western side of the Latin American sector. The observed suppressions may be associated with the influence of meridional winds and the presence of an Es layer near the onset time of EPBs. In contrast, the severe storm of 10–11 October, 2024, triggered super and long‐lasting EPB across both eastern and western sectors. The enhancement of EPBs during this event is attributed to the combined effects of an undershielding prompt penetration electric field (PPEF) and disturbance dynamo electric field (DDEF). Moreover, the storm's main phase was marked by a pronounced expansion of the Equatorial Ionization Anomaly (EIA) and a significant increase in total electron content (TEC), followed by a decrease during the recovery phase. These findings reinforce the importance of regional and local factors in ionospheric storm‐time responses and suggest the need for further statistical studies to improve EPB occurrence forecasts.
Abstract Ground‐based Global Navigation Satellite System (GNSS) receivers provide primary total electron content (TEC) measurements used to monitor ionospheric variability, yet their sparse distribution over oceanic and equatorial regions limits global coverage and model accuracy. A far‐ultraviolet spectrograph aboard the Global‐scale Observations of the Limb and Disk (GOLD) mission provides continuous observations of the OI 135.6 nm emission radiance, which can be used to infer TEC across these undersampled regions. We present a framework for filtering and calibrating TEC from GOLD observations using the retrieval technique developed by Qin et al. (2023, https://doi.org/10.1029/2023ea003222) to produce an enhanced, bias‐corrected data set (gTEC) that can be combined with well‐validated ground‐based TEC sources, such as the Madrigal TEC. After filtering GOLD observations to remove solar and auroral contamination, we apply an adaptive quantile‐mapping calibration method that aligns the cumulative distribution functions of GOLD and Madrigal TEC. The resulting gTEC achieves a mean absolute error of ∼1–2 TECU relative to Madrigal, reproducing ∼53% of its natural variability within one standard deviation. Daily averaged spatial coverage improves by ∼11.62 ± 3.65% for frames with corresponding GOLD data, especially over the equatorial Atlantic, where existing databases lack sufficient observations. The integration of gTEC into the Video Imputation with SoftImpute, Temporal smoothing and Auxiliary data (VISTA) ionospheric TEC algorithm yields considerable improvement in the reproduction of the Equatorial Ionization Anomaly (EIA) structure and asymmetry, as well as mesoscale Equatorial Plasma Bubble (EPB) features. These results demonstrate that gTEC can substantially enhance global ionospheric specification and modeling and improve data assimilation capabilities.
Long-term trends in ionospheric electron density have been investigated using vertical sounding data from 14 ionosonde stations spanning European, Asian, and American longitude sectors. The study examines the relationship between the ionospheric F2 layer peak electron density (NmF2) and height (hmF2) at noontime and midnight with the solar 30 cm radio flux index (F30) over periods up to 71 years. Over two solar cycles until 1984-1988, 11-year running means of both ionospheric parameters are found to be proportional to corresponding F30 data, with low root mean square variabilities around noon of +/- 0.6% and +/- 0.1%, respectively. Due to the low variability the linear models as a function of F30 serve as reference for subsequent observations. Results reveal a significant reduction of NmF2 day- and nighttime values of about 19.6% and 10.8%, respectively, in 2022, which corresponds to about 5.6% and 2.8% per decade. In parallel, hmF2 day and night reductions in 2022 are about 5.7% and 4.9%, respectively, which corresponds to about 1.8% and 1.6% per decade. The observed long-term decrease in NmF2 exhibits a close association with the temperature anomaly (TA) at Earth's surface, leading to the adoption of the term 'electron density anomaly' (EDA). Similarly, changes in peak height (hmF2) are referred to as the Peak Height Anomaly (PHA). Time-shifted EDA and PHA data, offset by 3-6 years, demonstrate a strong correlation with TA, suggesting a common physical mechanism that is linked to the increasing concentration of greenhouse gases in the atmosphere.
Space hurricanes are distinct space weather phenomena that occur during extremely quiet geomagnetic conditions, and exhibit hurricane-like cyclonic auroral bright spot structures. This phenomenon can induce severe space weather effects, including radio communication disruptions, navigation and positioning errors, and over-the-horizon radar detection impairments. Previous identification relied mainly on manual inspection of space hurricanes, which is inefficient, subjective, and lacks tools for high-precision automatic recognition. To address these challenges, we developed a deep learning model by incorporating attention mechanisms and multi-scale feature extraction, trained on more than a decade of satellite-based extreme ultraviolet (EUV) image data. Through systematic hyperparameter optimization and adaptive learning rate scheduling, the model achieves high-precision automatic identification and pixel-level localization of space hurricanes, reaching an accuracy of 97.90% on a challenging global data set. We also developed an end-to-end detection and localization system with visual interactive capabilities. This research provides crucial support for modeling of polar space weather and lays a foundation for developing space environment risk warning and adaptive regulation systems.
Geomagnetically induced currents (GICs) are a significant space weather hazard. Their accurate assessment requires estimating the geoelectric field, which depends on the Earth's subsurface electrical conductivity. While long-period magnetotelluric (MT) surveys are ideal, archives of legacy broadband MT data, originally acquired for geological exploration, remain underused. We evaluate the feasibility of repurposing such data to reconstruct the geoelectric field during geomagnetic storms. Analyzing the two most intense storms of 2024 (May and October) using data from a broadband MT station in southern Italy, we combined the local MT impedance tensor with 1 Hz magnetic field data from the nearby Duronia geomagnetic observatory. Our analysis focused on the 2-8,000 s period band, critical for GIC assessment. To validate the modeled electric fields, we compared them with corresponding frequency components extracted from direct measurements using empirical mode decomposition. The modeled and observed fields show good agreement, confirmed by mutual information analysis, indicating statistically significant dependence, especially during storm main phases. We further show that reconstruction capability over the available impedance period range is storm-dependent: for intense events, a tensor limited to similar to 1,000 s may still provide a useful first-order estimate, whereas for moderate storms the loss of long-period content has a larger impact on accuracy. This demonstrates that legacy broadband MT data can provide quantitative estimates of geoelectric field components relevant to GIC hazard assessment, provided recordings are sufficiently long. Existing MT archives with extended acquisition times can represent a practical and cost-effective resource for preliminary space weather hazard screening and historical studies.
The Solar TErrestrial RElations Observatory (STEREO) mission has laid a foundation for advancing real-time space weather forecasting by enabling the evaluation of heliospheric imager (HI) data for predicting coronal mass ejection (CME) arrivals at Earth. This study employs the ELEvoHI model to assess how incorporating STEREO/HI data from the Lagrange 5 (L5) perspective can enhance prediction accuracy for CME arrival times and speeds. Our investigation, preparing for the upcoming ESA Vigil mission, explores whether the progressive incorporation of HI data in real-time enhances the forecasting accuracy. The role of human tracking variability is evaluated by comparing predictions based on observations by three different scientists, highlighting the influence of manual biases on forecasting outcomes. Furthermore, the study examines the efficacy of deriving CME propagation directions using HI-specific methods versus coronagraph-based techniques, emphasizing the trade-offs in prediction accuracy. Our results demonstrate the potential of HI data to significantly improve operational space weather forecasting when integrated with other observational platforms, especially when HI data from beyond 35 elongation are used. These findings pave the way for optimizing real-time prediction methodologies, providing valuable groundwork for the forthcoming Vigil mission and enhancing preparedness for CME-driven space weather events.
We report new measurements of high-energy protons ejected by the sun during the May 2024 Ganon superstorm. Our observations were made by the Neutron Spectrometer (NS) on the Psyche spacecraft, which was located at a solar distance of approximately 2 AU and a Sun-Earth-Probe separation angle of 75 degrees. This unique vantage point can provide new insights into the nature of this exceptional space weather event. Our observations validated predictions from the WSA-ENLIL heliospheric model, which forecasted that the Psyche spacecraft would be magnetically-connected to the Coronal Mass Ejection (CME) on May 10th-11th, and that the Psyche spacecraft would pass through the periphery of the proton-rich region driven by the superstorm's CME on May 13th. This agreement both validates the large-scale CME propagation predicted by ENLIL and underscores the value of observations from a variety of geometries. The Psyche spacecraft will continue to make unique observations of solar particle events throughout its interplanetary cruise to asteroid 16 Psyche. We believe that these observations will be a valuable resource for refining particle-transport models and improving forecasts of solar energetic proton arrival times and intensities.
The study of chemical releases into the ionosphere is crucial for understanding ionospheric dynamics and managing space environmental effects. In this work, we investigate the impact of rocket exhaust emissions, particularly CO2 and H2O, on electron density in the ionosphere. To accurately capture the Te-dependent chemical and transport processes, we develop a TIE-GCM-based deep neural network (DNN) inversion model that infers diffusion and reaction coefficients directly from reconstructed electron density. Using this framework, continuous rocket exhaust releases are modeled as a series of point sources along the launch trajectory, enabling realistic simulations of the spatial and temporal evolution of H2O and CO2 diffusion and the resulting electron density depletion. Our results demonstrate that rocket-released chemicals induce pronounced "electron holes" in the F-layer, with the DNN inversion model effectively capturing the temperature-sensitive chemical responses that are often underestimated by empirical models. This approach not only enhances the predictive accuracy of ionospheric electron density variations but also provides a robust framework for studying coupled chemical-dynamical processes in the near-Earth space environment.
