Space Weather

Volcanic eruptions provide broad spectral forcing to the atmosphere and understanding the primary mechanisms that are relevant to explain the variety in waveform characteristics in the Ionosphere-Thermosphere (IT) is still an important open question for the community. In this study, Global Navigation Satellite System (GNSS) Total Electron Content (TEC) data are analyzed and compared to simulations performed by the Global Ionosphere-Thermosphere Model with Local Mesh Refinement (GITM-R) for the first phase of the 2015 Calbuco eruption that occurred on 22 April. A simplified source representation and spectral acoustic-gravity wave (AGW) propagation model are used to specify the perturbation at the lower boundary of GITM-R at 100 km altitude. Two assumptions on the propagation structure, Direct Spherical (DS) and Ground Coupled (GC), are compared to the GNSS data and these modeling specifications show good agreement with different aspects of the observations for some waveform characteristics. Most notably, GITM-R is able to reproduce the relative wave amplitude of AGWs as a function of radial distance from the vent, showing acoustic dominant forcing in the near field (<500 km) and gravity dominant forcing in the far-field (>500 km). The estimated apparent phase speeds from GITM-R simulations are consistent with observations with ∼10% difference from observation for both acoustic wave packets and a trailing gravity mode. The relevance of the simplifications made in the lower atmosphere to the simulated IT response is then discussed.

We propose a novel deep learning framework, named SYMHnet, which employs a graph neural network and a bidirectional long short-term memory network to cooperatively learn patterns from solar wind and interplanetary magnetic field parameters for short-term forecasts of the SYM-H index based on 1- and 5-min resolution data. SYMHnet takes, as input, the time series of the parameters' values provided by NASA's Space Science Data Coordinated Archive and predicts, as output, the SYM-H index value at time point t + w hours for a given time point t where w is 1 or 2. By incorporating Bayesian inference into the learning framework, SYMHnet can quantify both aleatoric (data) uncertainty and epistemic (model) uncertainty when predicting future SYM-H indices. Experimental results show that SYMHnet works well at quiet time and storm time, for both 1- and 5-min resolution data. The results also show that SYMHnet generally performs better than related machine learning methods. For example, SYMHnet achieves a forecast skill score (FSS) of 0.343 compared to the FSS of 0.074 of a recent gradient boosting machine (GBM) method when predicting SYM-H indices (1 hr in advance) in a large storm (SYM-H = −393 nT) using 5-min resolution data. When predicting the SYM-H indices (2 hr in advance) in the large storm, SYMHnet achieves an FSS of 0.553 compared to the FSS of 0.087 of the GBM method. In addition, SYMHnet can provide results for both data and model uncertainty quantification, whereas the related methods cannot.

This study focuses on utilizing the increasing availability of satellite trajectory data from global navigation satellite system-enabled low-Earth orbiting satellites and their precision orbit determination (POD) solutions to expand and refine thermospheric model validation capabilities. The research introduces an updated interface for the GEODYN-II POD software, leveraging high-precision space geodetic POD to investigate satellite drag and assess density models. This work presents a case study to examine five models (NRLMSIS2.0, DTM2020, JB2008, TIEGCM, and CTIPe) using precise science orbit (PSO) solutions of the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2). The PSO is used as tracking measurements to construct orbit fits, enabling an evaluation according to each model's ability to redetermine the orbit. Relative in-track deviations, quantified by in-track residuals and root-mean-square errors (RMSe), are treated as proxies for model densities that differ from an unknown true density. The study investigates assumptions related to the treatment of the drag coefficient and leverages them to eliminate bias and effectively scale model density. Assessment results and interpretations are dictated by the timescale at which the scaling occurs. DTM2020 requires the least scaling (∼−7%) to achieve orbit fits closely matching the PSO within an in-track RMSe of 7 m when scaled over 2 weeks and 2 m when scaled daily. The remaining models require substantial scaling of the mean density offset (∼30 − 75%) to construct orbit fits that meet the aforementioned RMSe criteria. All models exhibit slight over or under-sensitivity to geomagnetic activity according to trends in their 24-hr scaling factors.

Thermospheric density is one of the main sources of uncertainty in the estimation of satellites' position and velocity in low-Earth orbit. This has negative consequences in several space domains, including space traffic management, collision avoidance, re-entry predictions, orbital lifetime analysis, and space object cataloging. In this paper, we investigate the prediction accuracy of empirical density models (e.g., NRLMSISE-00 and JB-08) against black-box machine learning (ML) models trained on precise orbit determination-derived thermospheric density data (from CHAMP, GOCE, GRACE, SWARM-A/B satellites). We show that by using the same inputs, the ML models we designed are capable of consistently improving the predictions with respect to state-of-the-art empirical models by reducing the mean absolute percentage error (MAPE) in the thermospheric density estimation from the range of 40%–60% to approximately 20%. As a result of this work, we introduce Karman: an open-source Python software package developed during this study. Karman provides functionalities to ingest and preprocess thermospheric density, solar irradiance, and geomagnetic input data for ML readiness. Additionally, it facilitates developing and training ML models on the aforementioned data and benchmarking their performance at different altitudes, geographic locations, times, and solar activity conditions. Through this contribution, we offer the scientific community a comprehensive tool for comparing and enhancing thermospheric density models using ML techniques.

This study presents a data-driven approach to quantify uncertainties in the ionosphere-thermosphere (IT) system due to varying solar wind parameters (drivers) during quiet conditions (Kp < 4) and fixed solar radiation and lower atmospheric conditions representative of 16 March 2013. Ensemble simulations of the coupled Whole Atmosphere Model with Ionosphere Plasmasphere Electrodynamics (WAM-IPE) driven by synthetic solar wind drivers generated through a multi-channel variational autoencoder (MCVAE) model are obtained. Applying the polynomial chaos expansion (PCE) technique, it is possible to estimate the means and variances of the QoIs as well as the sensitivities of the QoIs with regard to the drivers. Our results highlight unique features of the IT system's uncertainty: (a) the uncertainty of the IT system is larger during nighttime; (b) the spatial distributions of the uncertainty for electron density and zonal drift at fixed local times present 4 peaks in the evening sector, which are associated with the low-density regions of longitude structure of electron density; (c) the uncertainty of the equatorial electron density is highly correlated with the uncertainty of the zonal drift, especially in the evening sector, while it is weakly correlated with the vertical drift. A variance-based global sensitivity analysis suggests that the IMF Bz plays a dominant role in the uncertainty of electron density. A further discussion shows that the uncertainty of the IT system is determined by the magnitudes and universal time variations of solar wind drivers. Its temporal and spatial distribution can be modulated by the average state of the IT system.

This study has developed a new TEC-based ionospheric data assimilation system for 3-D regional ionospheric imaging over the South American sector (TIDAS-SA) (45°S–15°N, 35°–85°W, and 100–800 km). The TIDAS-SA data assimilation system utilizes a hybrid Ensemble-Variational approach to incorporate a diverse set of ionospheric data sources, including dense ground-based Global Navigation Satellite System (GNSS) line-of-sight Total Electron Content (TEC) data, radio occultation data from the Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2), and altimeter TEC data from the JASON-3 satellite. TIDAS-SA can produce a reanalyzed three-dimensional (3-D) electron density spatial variation with a high time cadence, yielding spatial-temporal resolution of 1° (latitude) × 1° (longitude) × 20 km (altitude) × 5 min. This allows us to reconstruct and study the 3-D ionospheric morphology with multi-scale structures. The performance of the data assimilation system is validated against independent ionosonde and in situ measurements through an experiment for a strong geomagnetic storm event on 03–04 November 2021. The results demonstrate that TIDAS-SA can provide detailed and altitude-resolved information that accurately characterizes the storm-time ionospheric disturbances in vertical and horizontal domains over the equatorial and low-latitude regions of South America.

Submarine cables have experienced problems during extreme geomagnetic disturbances because of geomagnetically induced voltages adding or subtracting from the power feed to the repeaters. This is still a concern for modern fiber-optic cables because they contain a copper conductor to carry power to the repeaters. This paper provides a new examination of geomagnetic induction in submarine cables and makes calculations of the voltages experienced by the TAT-8 trans-Atlantic submarine cable during the March 1989 magnetic storm. It is shown that the cable itself experiences an induced electromotive force (emf) and that induction in the ocean also leads to changes of potential of the land at each end of the cable. The process for calculating the electric fields induced in the sea and in the cable from knowledge of the seawater depth and conductivity and subsea conductivity is explained. The cable route is divided into 9 sections and the seafloor electric field is calculated for each section. These are combined to give the total induced emf in the cable. In addition, induction in the seawater and leakage of induced currents through the underlying resistive layers are modeled using a transmission line model of the ocean and underlying layers to determine the change in Earth potentials at the cable ends. The induced emf in the cable and the end potentials are then combined to give the total voltage change experienced by the cable power feed equipment. This gives results very close to those recorded on the TAT-8 cable in March 1989.

In this study, we present ionospheric observations of field-aligned currents from AMPERE and the ESA Swarm A satellite, in conjunction with high-resolution thermospheric density measurements from accelerometers on board Swarm C and GRACE-FO, for the third and 4 February 2022 geomagnetic storms that led to the loss of 38 Starlink internet satellites. We study the global storm time response of the thermospheric density enhancements, including their decay and latitudinal distribution. We find that the thermospheric density enhances globally in response to high-latitude energy input from the magnetosphere-solar wind system and takes at least a full day to recover to pre-storm density levels. We also find that the greatest density perturbations occur at polar latitudes consistent with the magnetosphere-ionosphere dayside cusp, and that there appeared to be a saturation of the thermospheric density during the geomagnetic storm on the fourth. Our results highlight the critical importance of high-latitude ionospheric observations when diagnosing potentially hazardous conditions for low-Earth-orbit satellites.

Geoelectric fields are generated at the Earth's surface and can lead to the induction of hazardous geomagnetically induced currents (GIC) in infrastructure like power grids, railways and pipelines during geomagnetic storms. Magnitude and orientation of the geoelectric fields, in relation to the infrastructure, are key features needed to determine the intensity of GIC. Here, we developed the first geoelectric hazard map for the island of Ireland, with the aim of providing detailed information that can help stakeholders mitigate the impact of GICs. The hazard map was developed by modeling and mapping the geoelectric field across Ireland for 28 years (1991–2018) using magnetic field data with magnetotelluric transfer functions. The approach for developing the hazard map calculates the probability of exceeding a hazardous geoelectric field threshold (500 mV/km) during large geomagnetic storms, taking directionality and amplitude into account. We found hazardous geoelectric fields to be mostly localized in areas in the west, south-west and northern coast. We observed that the geoelectric field have a stronger dominant orientation than the orientation of the geomagnetic field, often constraining the hazardous geoelectric field in particular directions only. We demonstrate a seasonal/diurnal effect is present in the geoelectric field time series. The impact of galvanic distortion was also assessed, and we demonstrate that there is a significant difference in terms of amplitude and direction between both models.

Ionospheric total electron content (TEC) is a key indicator of the space environment. Geophysical forcing from above and below drives its spatial and temporal variations. A full understanding of physical and chemical principles, available and well-representable driving inputs, and capable computational power are required for physical models to reproduce simulations that agree with observations, which may be challenging at times. Recently, data-driven approaches, such as deep learning, have therefore surged as means for TEC prediction. Owing to the fact that the geophysical world possesses a sequential nature in time and space, Transformer architectures are proposed and evaluated for sequence-to-sequence TEC predictions in this study. We discuss the impacts of time lengths of choice during the training process and analyze what the neural network has learned regarding the data sets. Our results suggest that 12-layer, 128-hidden-unit Transformer architectures sufficiently provide multi-step global TEC predictions for 48 hr with an overall root-mean-square error (RMSE) of ∼1.8 TECU. The hourly variation of RMSE increases from 0.6 TECU to about 2.0 TECU during the prediction time frame.

On 3 February 2022, 38 satellites launched by SpaceX re-entered the atmosphere and were subsequently destroyed. An investigation found that a minor geomagnetic storm occurred on 3–4 February 2022 led to a neutral density enhancement and large atmospheric drag. To better understand the responses of the thermosphere to geomagnetic storms, the method proposed by Li et al. (2023, https://doi.org/10.1029/2022ja030988) was employed to extract exospheric temperature (Tex) from ionosonde electron density profiles (∼150–200 km) in Beijing (geolocation: 39.56°N; 116.2°E; geomagnetic location: 30.16°N; 172.08°W) station. The retrieved Tex was plugged into the NRLMSISE-00 model to calculate the corresponding neutral density. Derived results showed a ∼2%–7% enhancement in Tex and a ∼15%–38% enhancement in neutral density at 430 km. The relative deviation in neutral density on the satellites’ orbital trajectory ranges from ∼10% (210 km) to ∼35% (500 km) on 3 February, and from ∼13% (210 km) to ∼60% (500 km) on 4 February. Furthermore, the neutral density reproduced the variations observed by the SWARM-C satellite fairly well both on quiet and disturbed days. These results suggest that even a minor geomagnetic storm can cause significant changes in neutral temperature and neutral density at middle latitudes. Additionally, the application of our inversion method, combined with the global, long-term and real-time ionospheric observations from ionosondes, provides an opportunity to improve the capability of thermosphere forecasting and nowcasting.

Surface charging is one of the most common causes of spacecraft anomalies. When and to what potential the spacecraft is charged are two important questions in space weather. Here, for a Chinese geosynchronous navigation satellite, we infer the extreme negative surface charging potentials from the ion differential fluxes measured by a low-energy ion spectrometer. Without the solar eclipse effect away from the midnight, the charging potentials are found to have a negative limit which is determined by the maximum SuperMAG electrojet index in the preceding 2 hr. Such an empirical relation can be reasonably explained by the dependence of 1–50 keV electron fluxes on substorm strength. Similar relations may also exist for other inner magnetospheric spacecraft in the non-eclipse region, which would be useful for spacecraft engineering and space weather alerts.

Space weather can have major impacts on electrical infrastructure. Multiple instances of transformer damage have been attributed to geomagnetic storms in recent decades, for example, the Hydro Quebec incident of 1989 and the November 2001 storm in New Zealand. While many studies exist on the impacts of geomagnetic storms on power transformers in New Zealand, no studies exist that employ Dissolved Gas Analysis (DGA) techniques to relate geomagnetic storms to transformer gassing. A relationship has been reported between geomagnetic activity and DGA for South Africa, while none was found in a recent study in Great Britain. This paper attempts to examine this research question by examining dissolved gas data across eight power transformers in different substations in New Zealand from 2016 to 2019. Case studies were conducted which analyzed the DGA readings of each transformer alongside horizontal magnetic field component rate of change measurements at Eyrewell across six geomagnetic storms. These case studies were then augmented with an analysis of the entire data set where magnetic field measurements were compared with individual gas rates to establish a correlation between gas production and geomagnetic activity. Analysis of the results of this study concluded that no link had been found between the production of combustible gasses in a transformer and geomagnetic activity during the observation period. However, we note our dissolved gas analysis was largely in a geomagnetically quieter period, which may limit our analysis. The production of combustible gasses is not correlated to geomagnetic storms for the time period and transformers analyzed.

No abstract is available for this article.

Global ionospheric total electron content (TEC) maps exhibit TEC intensifications and depletions of various sizes and shapes. Characterizing key features on TEC maps and understanding their dynamic coupling with external drivers can significantly benefit space weather forecasting. However, comprehensive analysis of ionospheric structuring over decades of TEC maps is currently lacking due to large data volume. We develop feature extraction software based on image processing techniques to extract TEC intensification regions, that is, contiguous regions with sufficiently elevated TEC values than surrounding areas, from global TEC maps. Applying the software to the Jet Propulsion Laboratory Global Ionospheric Map data, we generate a TEC intensification data set for years 2003–2022 and carry out a statistical study on the number and strength of TEC intensifications. We find that the majority of the TEC maps (about 86%) are characterized with one or two intensification(s), while the rest of the TEC maps have three or more intensifications. Both the number and strength of TEC intensifications exhibit semi-annual variation that peaks near equinoxes and dips near solstices, as well as an annual asymmetry with larger values around December solstice compared to June solstice. The number and strength of intensifications increase with enhanced solar extreme-violet irradiance. The strength of intensifications also increases with elevated geomagnetic activity, but the number of intensifications does not. In addition, the number of intensifications is not correlated with the strength of intensifications.

An intelligent high-definition and short-term prediction of ionograms with/without Spread-F for the observation at Hainan (19.5°N, 109.1°E, magnetic 11°N) is presented in this paper, which comprises a spatio-temporal ConvGRU network and a super-resolution EDSR network. Our prediction is based on spatio-temporal features in the ionogram graph only. There are 469,227 ionograms classified into 5 categories, that is, frequency/range/mix/strong range/no Spread F, over a solar cycle (14 years) labeled manually by the research group, and we process these ionograms into two data sets for training the two networks mentioned above. A series of comprehensive experiments have been designed and conducted to determine the optimal super-parameters. Our method inputs 8 consecutive authentic ionograms (lasting 2 hr) and generates the next 2 figures (next 30 min). Remarkably, all predicted figures achieve a high accuracy rate of over 94% in predicting the occurrence of Spread-F.

Low Earth Orbit satellites offer extensive data of the radiation belt region, but utilizing these observations is challenging due to potential contamination and difficulty of intercalibration with spacecraft measurements at Highly Elliptic Orbit that can observe all equatorial pitch-angles. This study introduces a new intercalibration method for satellite measurements of energetic electrons in the radiation belts using a Data assimilation (DA) approach. We demonstrate our technique by intercalibrating the electron flux measurements of the National Oceanic and Atmospheric Administration (NOAA) Polar-orbiting Operational Environmental Satellites (POES) NOAA-15,-16,-17,-18,-19, and MetOp-02 against Van Allen Probes observations from October 2012 to September 2013. We use a reanalysis of the radiation belts obtained by assimilating Van Allen Probes and Geostationary Operational Environmental Satellites observations into 3-D Versatile Electron Radiation Belt (VERB-3D) code simulations via a standard Kalman filter. We compare the reanalysis to the POES data set and estimate the flux ratios at each time, location, and energy. From these ratios, we derive energy and L* dependent recalibration coefficients. To validate our results, we analyze on-orbit conjunctions between POES and Van Allen Probes. The conjunction recalibration coefficients and the data-assimilative estimated coefficients show strong agreement, indicating that the differences between POES and Van Allen Probes observations remain within a factor of two. Additionally, the use of DA allows for improved statistics, as the possible comparisons are increased 10-fold. Data-assimilative intercalibration of satellite observations is an efficient approach that enables intercalibration of large data sets using short periods of data.

Predicting the impacts of coronal mass ejections (CMEs) is a major focus of current space weather forecasting efforts. Typically, CME properties are reconstructed from stereoscopic coronal images and then used to forward model a CME's interplanetary evolution. Knowing the uncertainty in the coronal reconstructions is then a critical factor in determining the uncertainty of any predictions. A growing number of catalogs of coronal CME reconstructions exist, but no extensive comparison between these catalogs has yet been performed. Here we develop a Living List of Attributes Measured in Any Coronal Reconstruction (LLAMACoRe), an online collection of individual catalogs, which we intend to continually update. In this first version, we use results from 24 different catalogs with 3D reconstructions using Solar Terrestrial Relations Observatory observations between 2007 and 2014. We have collated the individual catalogs, determining which reconstructions correspond to the same events. LLAMACoRe contains 2,954 reconstructions for 1,862 CMEs. Of these, 511 CMEs contain multiple reconstructions from different catalogs. Using the best-constrained values for each CME, we find that the combined catalog reproduces the generally known solar cycle trends. We determine the typical difference we would expect between two independent reconstructions of the same event and find values of 4.0° in the latitude, 8.0° in the longitude, 24.0° in the tilt, 9.3° in the angular width, 0.1 in the shape parameter κ, 115 km/s in the velocity, and 2.5 × 1015 g in the mass. These remain the most probable values over the solar cycle, though we find more extreme outliers in the deviation toward solar maximum.

In this work, a model for calculating the galactic cosmic rays (GCRs) radiation dose rate is developed. The model is based on a GCR modulation model, which is established by Shen and Qin, and the fluence-dose conversion coefficients (FDCCs) published by the International Commission on Radiological Protection (ICRP). With the model, the radiation absorbed dose rate of GCRs near the lunar surface over long time periods is calculated and compared with the observation data from the Cosmic Ray Telescope for the Effects of Radiation and the Lunar Lander Neutron and Dosimetry. First, the energy spectrum of GCRs at 1 AU in the ecliptic, where the lunar orbit is located, is computed using the GCR modulation model. Then, using the FDCCs of ICRP 123, the absorbed dose rates of 15 human organs/tissues at the lunar orbit position are calculated to represent the general absorbed dose rate of the body (in water). Furthermore, considering the albedo radiation (excluding neutrons) and using the water-silicon conversion coefficients, the total absorbed dose rates of GCRs near the lunar surface (in silicon) are calculated, it is shown that our modeling results generally agree with the observations from spacecraft. This work is useful for future manned space exploration to the Moon or other celestial bodies in the solar system.

Current magnetohydrodynamics (MHD) models largely rely on synoptic magnetograms, such as the ones produced by the Global Oscillation Network Group (GONG). Magnetograms are currently available mostly from the front side of the Sun, which significantly reduces the accuracy of MHD modeling. Extreme Ultraviolet (EUV) images can instead be obtained from other vantage points. To investigate the potential, we explore the possibility of using EUV information from the Atmospheric Imaging Assembly (AIA) to directly generate the input for the state-of-the-art 3D MHD model European Heliospheric FORecasting Information Asset (EUHFORIA). Toward this goal, we develop a method called Transfer-Solar-GAN which combines a conditional generative adversarial network with a transfer learning approach to overcome training data set limitations. The source domain data set is constructed from multiple pairs of the central portion of co-registered AIA and Helioseismic and Magnetic Imager (HMI) line of sight (LOS) full-disk images, while the target domain is constructed from pairs of portions of AIA and GONG sine-latitude synoptic maps that we call segments. We evaluate Transfer-Solar-GAN by comparing modeled and measured solar wind velocity and magnetic field density parameters at the L 1 Lagrange point and along the Parker Solar Probe (PSP) trajectory which were determined with EUHFORIA using both empirical GONG and artificial-intelligence (AI)-synthetic synoptic magnetograms as inputs. Our results demonstrate that the Transfer-Solar-GAN model can provide the necessary information to run solar physics models by EUV information. Our proposed model is trained with only 528 paired image segments and enforces a reliable data division strategy.