Space Weather

In this work, we present a metastable helium lidar system for the measurements of metastable helium He(23S) density in the thermosphere and lower exosphere. This lidar system consists of a high power 1,083 nm pulsed laser, a 1 m aperture laser beam expander, six 1 m aperture receiving telescopes and a superconducting nanowire single-photon detector (SNSPD). This system realizes metastable helium density detection up to 1,000 km. Daily rapid variation of metastable helium density within several hours was measured with a height resolution of 50 km for the first time. It demonstrates the capability of ground-based lidar for continuous height-resolved detection of the atmospheric metastable helium in the height range of 200–1,000 km. This is a promising tool to help study the coupling of neutral and ionized atmosphere in this height range and further providing observation basis for space weather prediction.

Wave particle interactions are very important to understand the intricate evolution of the Earth's radiation belt electrons. Kinetic simulations, in terms of solving the Fokker-Planck equation based on the quasilinear theory, are usually used to simulate the radiation belt electron dynamic evolution. However, the global wave and plasma density distributions adopted in the kinetic simulations are very difficult to be directly obtained by satellites. Here we present a new model, by integrating the machine learning technique and kinetic simulations, to analyze the spatiotemporal evolution of radiation belt electrons scattered by lower band chorus (LBC). Compared to the observations, our integrated model produces effectively the global distribution of plasmapause location, plasma density, and LBC intensity, and assesses quantitatively the scattering effect driven by LBC waves at different magnetic local times (MLT), L-shell (the Mcllwain L-parameter), and time. Incorporating the effect of radiation electron drift, we further use the 2-D Fokker-Planck equation to simulate the variations of electron phase space density in different MLT sectors at a fixed L, and find that the integrated model replicates reasonably the multi-MeV electron acceleration at L = 4.5 during the period from the main phase to the early recovery phase of the storm. Our results demonstrate that such an integrated model, on basis of a combination of the machine learning technique and kinetic simulations, provides valuable means for improved understanding of the global dynamic evolution of the Earth's radiation belt electrons.

Interplanetary (IP) shocks are perturbations observed in the solar wind. IP shocks correlate well with solar activity, being more numerous during times of high sunspot numbers. Earth-bound IP shocks cause many space weather effects that are promptly observed in geospace and on the ground. Such effects can pose considerable threats to human assets in space and on the ground, including satellites in the upper atmosphere and power infrastructure. Thus, it is of great interest to the space weather community to (a) keep an accurate catalog of shocks observed near Earth, and (b) be able to forecast shock occurrence as a function of the solar cycle (SC). In this work, we use a supervised machine learning regression model to predict the number of shocks expected in SC25 using three previously published sunspot predictions for the same cycle. We predict shock counts to be around 275 ± 10, which is ∼47% higher than the shock occurrence in SC24 (187 ± 8), but still smaller than the shock occurrence in SC23 (343 ± 12). With the perspective of having more IP shocks on the horizon for SC25, we briefly discuss many opportunities in space weather research for the remainder years of SC25. The next decade or so will bring unprecedented opportunities for research and forecasting effects in the solar wind, magnetosphere, ionosphere, and on the ground. As a result, we predict SC25 will offer excellent opportunities for shock occurrences and data availability for conducting space weather research and forecasting.

No abstract is available for this article.

The Equatorial Electrojet (EEJ) is one of the important near-earth space weather phenomena which exhibits significant diurnal, seasonal and solar activity variations. This paper investigates the EEJ variations at diurnal, seasonal and solar cycle time scales from the Indian sector and portrays a new empirical EEJ field model developed using the observations spanning over nearly two solar cycles. The Method of Naturally Orthogonal Components (MNOC), also known as Principal Component Analysis (PCA), was employed to extract the dominant patterns of principal diurnal, semi-diurnal, and ter-diurnal components contributing to the EEJ variation. The amplitudes of these diurnal, semi-diurnal, and ter-diurnal components in EEJ are found to vary significantly with the season and solar activity. The seasonal and solar activity dependencies of these principal components are modeled using suitable bimodal distribution functions. Finally, the empirical model for EEJ field was built by combining the principal components with their corresponding modeled amplitudes. This model accurately reproduces the diurnal, seasonal and solar activity variations of EEJ. The modeled monthly mean variations of EEJ field at ground exhibit excellent correlation of 0.96 with the observations with the root mean square error <5 nT. It also successfully captures the seasonal and solar activity variations of Counter Electrojet (CEJ). Finally, this model named “Indian Equatorial Electrojet (IEEJ) Model” is made publicly available for interested scientific users (https://iigm.res.in/system/files/IEEJ_model.html).

A significant increase in the number of anthropogenic objects in Earth orbit has necessitated the development of satellite conjunction assessment and collision avoidance capabilities for new spacecraft. Neutral mass density variability in the thermosphere, driven by enhanced geomagnetic activity and solar EUV absorption, is a major source of satellite propagation error. This work investigates the impacts of space weather driver forecasting uncertainty on satellite drag and collision avoidance maneuver decision-making. Since most operational space weather driver forecasts do not offer an uncertainty assessment, the satellite operator community is left to make dangerous assumptions about the trustworthiness of the forecast models they use to perform satellite state propagation. Climatological persistence-based forecast models are developed for F10.7 and Kp. These models accurately capture the heteroscedastic and, at times, highly non-Gaussian uncertainty distribution on forecasts of the drivers of interest. A set of realistic satellite conjunction scenarios is simulated to demonstrate the contributions of space weather driver forecast uncertainty on the probability of collision and maneuver decisions. Improved driver forecasts, especially forecasts of F10.7, are demonstrated to be very useful for enabling durable maneuver decisions with additional lead time (up to 24 hr for the period examined), though the improvement depends on the specific conjunction scenario of interest.

The space weather effects at the Earth's magnetosphere are mostly driven by the solar wind that carries the interplanetary magnetic field (IMF). In this paper, we use 2 years of data in the solar wind from lunar orbiting ARTEMIS and MMS spacecraft upstream of the Earth's bow shock to study the structure of the IMF. We determine the lag times of IMF structures and their dependence on spacecraft positions by conducting an information theory analysis and comparing it with two traditional analysis methods: cross-correlation (CC) analysis and minimum variance of magnetic field analysis (MVAB). For the events with long time intervals (i.e., >4 hr) and with small-spatial separation between the MMS and ARTEMIS along the y GSM -direction (i.e., <40R e , where R e is the Earth's radius), the lag times based on the CC and the mutual information (MI) analyses statistically agree with each other, with p-values of 1.675 × 10−7 and 4.833 × 10−9, with the confidence of 95%. Both the results based on MI and CC have a large deviation from the results from MVAB. For some of the events, such a deviation could be improved by taking the fast mode speed into account; however, p-tests showed that they were not statistically significant to the 95% confidence level.

Geoelectric fields produced by time-varying magnetic fields during geomagnetic storms can result in potentially damaging geomagnetically induced currents (GICs) in long conductors at the Earth's surface. GICs can pose a significant risk to the integrity of grounded electrical infrastructure, particularly high-voltage transformers. In this study, an inferred GIC is calculated using an augmented differential magnetometer measurement (DMM) technique on a 500 kV transmission line in central Alberta and is validated using a proximal transformer neutral-to-ground (TNG) current measurement by AltaLink L.P. using a Hall probe at a transformer substation. This research outlines a custom-built and innovative DMM design by which both DMM sensors deployed around a power line measure the background geomagnetic disturbance (GMD) field and the magnetic field generated locally by the GIC. We show how this modified approach provides two independent estimates for GIC derived using only ΔB y or ΔB z , the magnetic field components perpendicular to the line carrying GIC. Results for a geomagnetic storm on 12 Oct 2021 show contemporaneous peaks in the TNG current and the DMM-inferred GIC. The two data sets have similar waveforms and are within the same order of magnitude. The background GMD is reconstructed using DMM and shows excellent correlation to the measured GMD at the permanent Canadian Array for Real-time Investigations of Magnetic Activity magnetic station at Ministik Lake, approximately 48.5 km away. Based on the results presented here, we verify the added utility value of DMM for temporary deployments for assessing GIC risk in electrical power grids.

We combine wavelet analysis and data fusion to investigate geomagnetically induced currents (GICs) on the Mäntsälä pipeline and the associated horizontal geomagnetic field, BH, variations during the late main phase of the 17 March 2013 geomagnetic storm. The wavelet analysis decomposes the GIC and BH signals at increasing “scales” to show distinct multi-minute spectral features around the GIC spikes. Four GIC spikes >10 A occurred while the pipeline was in the dusk sector—the first sine-wave-like spike at ∼16 UT was “compound.” It was followed by three “self-similar” spikes 2 hr later. The contemporaneous multi-resolution observations from ground-(magnetometer, SuperMAG, SuperDARN), and space-based (AMPERE, Two Wide-Angle Imaging Neutral-atom Spectrometers) platforms capture multi-scale activity to reveal two magnetospheric modes causing the spikes. The GIC at ∼16 UT occurred in two parts with the negative spike associated with a transient sub-auroral eastward electrojet that closed a developing partial ring current loop, whereas the positive spike developed with the arrival of the associated mesoscale flow-channel in the auroral zone. The three spikes between 18 and 19 UT were due to bursty bulk flows (BBFs). We attribute all spikes to flow-channel injections (substorms) of varying scales. We use previously published MHD simulations of the event to substantiate our conclusions, given the dearth of timely in-situ satellite observations. Our results show that multi-scale magnetosphere-ionosphere activity that drives GICs can be understood using multi-resolution analysis. This new framework of combining wavelet analysis with multi-platform observations opens a research avenue for GIC investigations and other space weather impacts.

We comprehensively analyzed geomagnetic perturbations using ground magnetic records from over 400 stations spanning four solar cycles, from 1976 to 2023. We assess the perturbations in the three magnetic components separately. Our study covers low, middle, and high magnetic latitudes in the northern magnetic hemisphere, with the primary objective of quantifying extreme values and evaluating their variability on magnetic latitude, local time, and solar cycle phases “minimum, ascending, maximum, and declining.” Our findings reveal spatial patterns to be less discernible as perturbations intensify, with distinct responses at middle and high latitudes. The extreme values, defined as percentiles 0 and 100, were observed to be localized and randomly distributed in local time, especially in the east magnetic component. Additionally, we observed dusk-dawn asymmetries in the magnitude of perturbations related to the auroral electrojets, indicating complex interactions between the magnetosphere and ionosphere. Furthermore, the results reveal a preference for the most significant extreme values to occur in the declining phase of the solar cycle. These insights deepen our understanding of geomagnetic perturbations and their variability, contributing to space weather forecasting and mitigation strategies.

Additional ionospheric information is essential for mitigating errors in single-frequency (SF) Global Navigation Satellite Systems (GNSS) positioning. The increasing number of low-cost dual-frequency (DF) receiver users faces limitations in tracking DF observables compared to traditional geodetic receivers. Consequently, ionospheric correction algorithms (ICAs) are also essential for low-cost DF devices in hybrid-frequency positioning. To evaluate the performance of commonly used ICAs during solar cycle 25, our study presents a global statistical investigation of the contribution of five broadcast ionospheric models (BIMs) and the International GNSS Service (IGS) combined real-time global ionospheric maps (IRTG) to the positioning domain, covering both quiet and perturbed ionospheric conditions. The BIMs investigated include the GPS Klobuchar (GPSK), Galileo NequickG (NEQG), NTCM-GlAzpar (NTCMG), BDS-2 Klobuchar (BDSK), and BeiDou Global Ionospheric delay correction Model (BDGIM). Experimental results from standard point positioning indicate that IRTG demonstrates superior overall accuracy compared to all BIMs, with a mean 3D root mean squared (RMS) of 2.76 m during perturbed period. Specifically, GPSK, NTCMG, NEQG, BDGIM, and BDSK exhibit RMS values of 2.03, 1.67, 1.72, 1.62, and 2.36 m during quiet conditions, and 4.02, 3.17, 2.86, 3.14, and 4.44 m during perturbed conditions, respectively. Among the BIMs, NEQG demonstrates superior performance at middle and high latitudes but exhibits lower accuracy than NTCMG and BDGIM at low latitudes during daytime under quiet conditions. BDGIM performs slightly better than NTCMG at low latitudes but slightly worse at high latitudes. BDSK shows notable improvement for high- and mid-latitude regions since 3 June 2020.

Coronal mass ejections (CMEs) drive space weather effects at Earth and the heliosphere. Predicting their arrival is a major part of space weather forecasting. In 2013, the Community Coordinated Modeling Center started collecting predictions from the community, developing an Arrival Time Scoreboard (ATSB). Riley et al. (2018, https://doi.org/10.1029/2018sw001962) analyzed the first 5 years of the ATSB, finding a bias of a few hours and uncertainty of order 15 hr. These metrics have been routinely quoted since 2018, but have not been updated despite continued predictions. We revise analysis of the ATSB using a sample 3.5 times the size of that in the original study. We find generally the same overall metrics, a bias of −2.5 hr, mean absolute error of 13.2 hr, and standard deviation of 17.4 hr, with only a slight improvement comparing between the previously-used and new sets. The most well-established, frequently-submitted model results tend to outperform those from seldomly-contributed models. These “best” models show a slight improvement over the 11 year span, with more scatter between the models during early times and a convergence toward the same error metrics in recent years. We find little evidence of any correlations between the arrival time errors and any other properties. The one noticeable exception is a tendency for late predictions for short transit times and vice versa. We propose that any model-driven systematic errors may be washed out by the uncertainties in CME reconstructions in characterization of the background solar wind, and suggest that improving these may be the key to better predictions.

Starting from 29 January 2022, a series of solar eruptions triggered a moderate geomagnetic storm on 3 February 2022, followed subsequently by another. Despite the typically minimal impact of unintense storms on space technology, 38 out of the 49 Starlink satellites underwent orbital decay, re-entering Earth's atmosphere. These satellite losses were attributed to enhanced atmospheric drag conditions. This study employs numerical simulations, utilizing our test particle simulation code, to investigate the dynamics of the inner radiation belt during the two magnetic storms. Our analysis reveals an increase in proton density and fluxes during the transition from the recovery phase of the first storm to the initial phase of the second, primarily driven by intense solar wind dynamic pressure. Additionally, we assess Single Event Upset (SEU) rates, which exhibit a 50% increase in comparison to initial quiet conditions.

Sporadic E (Es) layers are plasma irregularities significantly affecting radio communication and navigation systems. And, their dominant formation mechanism at mid-latitudes, known as the wind shear theory, suggests that they serve as indicators of the atmosphere-ionosphere coupling processes in the mesosphere and lower thermosphere region. On 15 January 2022, the Hunga Tonga-Hunga Ha'api submarine volcanic eruption provided a unique opportunity to investigate the Es layer responses to lower atmospheric perturbations. Using the FORMOSAT-7/COSMIC-2 radio occultation and ground-based ionosonde observations, this study reveals the spatial-temporal behaviors of the Es layers after the Hunga volcanic eruption. The results show that significant Es layer perturbations occurred over the northwest of the epicenter ∼4 hr after the eruption and lasted for approximately ∼22 hr. We also calculated the geographical distribution of the vertical ion convergence (VIC) using neutral winds obtained from the Michelson Interferometer for Global High-resolution Thermospheric Imaging on the Ionospheric Connection Explorer (ICON) satellite. A comparison of the geographical distribution of positive VIC and Es layer perturbations shows a good agreement, which indicates that the enhanced Es layers are caused by strong VIC associated with the atmospheric perturbations due to the eruption. This study presents observational evidence for coupling between the Es layer and lower atmospheric perturbations, which can be helpful for understanding the occasionality and variability of Es layer occurrence.

Monitoring and investigation of the solar-terrestrial space environment is a huge challenge for humans in space age. To this end, China has established the Ground-based Space Environment Monitoring Network, namely Chinese Meridian Project (CMP). The project comprises three major systems: the Space Environment Monitoring System, Data and Communication System, and Scientific Application System. The Space Environment Monitoring System adopts a well-designed monitoring architecture, known as “One Chain, Three Networks, and Four Focuses,” to achieve stereoscopic and comprehensive monitoring of the entire solar-terrestrial space. The “One-Chain” component utilizes optical, radio, interplanetary scintillation, cosmic ray instruments to cover the causal chain of space weather disturbances from the solar surface to near-Earth space. For the ionosphere, middle and upper atmosphere, and magnetic field, instruments are deployed along longitudes of 120° and 100°E, and latitudes of 30° and 40°N, forming the “Three Networks.” Furthermore, more powerful monitoring facilities or large-scale instruments have been deployed in four key regions: the high-latitude polar region, mid-latitude region in northern China, low-latitude region at Hainan Island, and the Tibet region. These four regions are crucial for disturbances propagation and evolution, or possess unique geographical and topographical characteristics. The Data and Communication System and Scientific Application System are designed for data collecting, processing, storage, mining, and providing user service based on data acquired by the Space Environment Monitoring System. The data obtained by CMP will be shared with the global scientific community, facilitating enhanced collaboration on space weather and space physics research.

The application of deep learning in high-precision ionospheric parameter prediction has become one of the focus in space weather research. In this study, an improved model called Mixed Convolutional Neural Networks (CNN)—Bi-Long Short Term Memory is proposed for predicting future ionospheric Total Electron Content (TEC). The model is trained using the longest available (25 years) Global Ionospheric Maps-TEC and evaluated the accuracy of ionospheric storm predictions. The results indicate that using historical TEC in the solar-geographical reference frame as input driving data achieves higher prediction accuracy compared to that in the geocentric coordinate system. Additionally, by comparing different input parameters, it is found that incorporating the Kp, ap, and Dst indices as inputs to the model effectively improves its accuracy, especially in long-term forecasting where R2 increased by 3.49% and Root Mean Square Error decreased by 13.48%. Compared with BiLSTM-Deep Neural Networks (DNN) and CNN-BiLSTM, the Mixed CNN-BiLSTM model has the highest prediction accuracy. It suggests that the utilization of CNN modules for processing spatial information, along with the incorporation of DNN modules to incorporate geomagnetic indices for result correction. Moreover, in short-term predictions, the model accurately forecasts the evolution process of ionospheric storms. When extending the predicted length, although there are cases of prediction errors, the model still captures the entire process of ionospheric storms. Furthermore, the predicted results are significantly influenced by longitude, magnetic latitude, and local time.

Magnetotelluric (MT) impedances from 62 sites in southern South Island of Aotearoa New Zealand have been used to model geomagnetically induced currents (GIC) in four transformers during two solar storms. Induced electric fields during the storms are calculated from the MT impedances using the magnetic fields measured at the Eyrewell (EYR) geomagnetic observatory, approximately 200 km north of the study area. Calculated GIC during the sudden storm commencements (SSC) give a generally good match to GIC measured by the network operator, Transpower New Zealand. Long period GIC (periods longer than about 10,000 s) are less well modeled. Calculations based on thin-sheet modeling, which has restrictions on the shortest period of variation which can be modeled, perform less well for the GIC associated with SSC, but are equally good, if not better, at modeling longer period GIC. Consistent underestimation of large GIC at one transformer (HWBT4) near Dunedin are likely to be the result of uncertainty in the assumed values of line, transformer, and earthing resistances. The assumption of a spatially uniform magnetic field across the study area, which is implied by use of the magnetic field measured at EYR as a basis for calculation, may also lead to incorrect calculation of GIC. For one storm use of magnetic field data from a magnetometer within the study area leads to much improved modeling of the observed GIC. This study compares modeled and measured GIC using specifically measured MT impedance data.

The high-precision prediction of total ionospheric electron content (TEC) is of great significance for improving the accuracy of global navigation satellite systems. There are two problems with the current prediction of TEC: (a) The existing TEC prediction models mainly based on stacked structure, which has insufficient predictive ability when the network has fewer layers, and loss of fine-grained features when there are more layers, resulting in a decrease in predictive performance; (b) The existing research on ionospheric TEC prediction mainly focuses on building deep learning prediction models, while there is little research on optimizing the hyper-parameters of TEC prediction models. Optimization can help find a better quasi-optimal hyperparameter combination and improve the performance of the model. This paper proposed an automatic deep learning framework for global TEC map prediction, named MAOOA-Residual-Attitude-BiConvLSTM. This framework includes a TEC prediction model, Residual-Attention-BiConvLSTM, which can simultaneously consider both coarse-grained and fine-grained spatiotemporal features. It also includes an optimization algorithm, MAOOA, for optimizing the hyper-parameters of the model. We conducted comparative experiments between our framework and C1PG, ConvLSTM, ConvGRU, and ED-ConvLSTM during high solar activity years, low solar activity years, and a magnetic storm event. The results indicate that in all cases, the framework proposed in this paper outperforms the comparative models.

ANCHOR is a novel assimilative model developed at the U.S. Naval Research Laboratory, which was designed for rapid assimilative runs. ANCHOR uses recently developed PyIRI model for the background and for the formation of the background covariance matrix. It only takes a few minutes for ANCHOR to complete the data assimilation (DA) for one day, including data pre-processing and model set up. ANCHOR extracts ionospheric parameters from radio occultation (RO) and ionosonde data using PyIRI formalism and assimilates them as point measurements into maps of the background parameters using a Kalman Filter approach. This paper introduces the ANCHOR algorithm, discusses its coordinate system and background, explains the background covariance formation, discusses the extraction of the ionospheric parameters from the data and the assimilation process, and, finally, shows the results of the observing system simulation experiment with synthetic data simulated using the SAMI3 model. ANCHOR reduces the root mean square errors in the analysis by more than a half for all of the ionospheric parameters in comparison to the background. Finally, this paper discusses advantages and limitations of the parametrized ionospheric DA, highlighting the avenues for its future improvement.

No abstract is available for this article.