Space Weather

Syndicate content Wiley: Space Weather: Table of Contents
Table of Contents for Space Weather. List of articles from both the latest and EarlyView issues.
Updated: 15 hours 34 min ago

Validating a UK Geomagnetically Induced Current Model Using Differential Magnetometer Measurements

Fri, 02/23/2024 - 15:37
Abstract

Extreme space weather can damage ground-based infrastructure such as power lines, railways and gas pipelines through geomagnetically induced currents (GICs). Modeling GICs requires knowledge about the source magnetic field and the electrical conductivity structure of the Earth to calculate ground electric fields during enhanced geomagnetic activity. The electric field, in combination with detailed information about the power grid topology, enable the modeling of GICs in high-voltage (HV) power lines. Directly monitoring GICs in substations is possible with a Hall probe, but scarcely realized in the UK. Therefore we deployed the differential magnetometer method (DMM) to measure GICs at 12 sites in the UK power grid. The DMM includes the installation of two fluxgate magnetometers, one directly under a power line affected by GICs, and one as a remote site. The difference in recordings of the magnetic field at each instrument yields an estimate of the GICs in the respective power line segment via the Biot-Savart law. We collected data across the UK in 2018–2022, monitoring HV line segments where previous research indicated high GIC risk. We recorded magnetometer data during several smaller storms that allow detailed analysis of our GIC model. For the ground electric field computations we used recent magnetotelluric (MT) measurements recorded close to the DMM sites. Our results show that there is strong agreement in both amplitude and signal shape between measured and modeled line and substation GICs when using our HV model and the realistic electric field estimates derived from MT data.

Unsupervised Anomaly Detection With Variational Autoencoders Applied to Full‐Disk Solar Images

Thu, 02/22/2024 - 15:40
Abstract

Deep learning is successful in many fields due to its ability to learn strong feature representations without the need for hand-crafted features, resulting in models with high representational power. However, many of these models are based on supervised learning and therefore depend on the availability of large annotated data sets. These are often difficult to obtain because they require human input. A common challenge for researchers in space weather is the sparsity of annotations in many of the available data sets, which are either unlabeled or have ambiguous labels. To alleviate the data bottleneck of loosely annotated data sets, unsupervised deep learning has become an important strategy, with anomaly detection being one of the most prominent applications. Unsupervised models have been successfully applied in various domains, such as medical imaging or video surveillance, to distinguish normal from abnormal data. In this work, we investigate how a purely unsupervised approach can be used to detect and extract solar phenomena in extreme ultraviolet images from the NASA SDO spacecraft. We show how a model based on variational autoencoders can be used to detect out-of-distribution samples and to localize regions of interest for solar activity. By using an unsupervised approach, we hope to contribute to space weather monitoring tools and further improve the understanding of space weather drivers.

Climatology of Dayside E‐Region Zonal Neutral Wind Shears From ICON‐MIGHTI Observations

Wed, 02/21/2024 - 13:05
Abstract

Large vertical shears in the E-region neutral zonal winds can lead to ion convergences and contribute to plasma irregularities, but climatological studies of vertical shears of horizontal winds in a global scale are lacking due to the limitations of data coverage. The Ionospheric Connection Explorer (ICON) Michelson Interferometer for Global High-resolution Thermospheric Imaging (MIGHTI) has provided neutral wind observations with an unprecedented spatial coverage. In this study, the climatology of dayside E-region neutral wind shears has been examined using 2-years’ data (2020–2021). Specifically, the study focuses on large wind shears with a magnitude larger than 20 m/s/km, since large wind shears are more likely to cause significant perturbation in the ionosphere-thermosphere (I-T) system. The results show that the probability of occurrence of large shears is strongly dependent on the altitude, with the vertical profile varying with shear direction, latitude, season, and local time. In general, below 110 km altitude, large negative shears of the eastward wind are most likely to happen during summer at 8–10 LT in 25°N–40°N latitudes, showing a high probability across nearly all longitudes. Meanwhile, large positive shears tend to occur in 10°S–10°N latitudes, with peak probabilities exhibiting roughly consistent longitudinal structures across 8–10 LT in all seasons. The discrepancies between positive and negative large shear distributions underlie different global tidal influences. The large-shear occurrence probabilities above 110 km are generally small, except in latitudes above 25°N during the winter for positive shears.

Using Solar Orbiter as an Upstream Solar Wind Monitor for Real Time Space Weather Predictions

Tue, 02/20/2024 - 15:58
Abstract

Coronal mass ejections (CMEs) can create significant disruption to human activities and systems on Earth, much of which can be mitigated with prior warning of the upstream solar wind conditions. However, it is currently extremely challenging to accurately predict the arrival time and internal structure of a CME from coronagraph images alone. In this study, we take advantage of a rare opportunity to use Solar Orbiter, at 0.5 au upstream of Earth, as an upstream solar wind monitor. In combination with low-latency images from STEREO-A, we successfully predicted the arrival time of two CME events before they reached Earth. Measurements at Solar Orbiter were used to constrain an ensemble of simulation runs from the ELEvoHI model, reducing the uncertainty in arrival time from 10.4 to 2.5 hr in the first case study. There was also an excellent agreement in the B z profile between Solar Orbiter and Wind spacecraft for the second case study, despite being separated by 0.5 au and 10° longitude. The opportunity to use Solar Orbiter as an upstream solar wind monitor will repeat once a year, which should further help assess the efficacy upstream in-situ measurements in real time space weather forecasting.

The Impact of Non‐Equilibrium Plasma Distributions on Solar Wind Measurements by Vigil's Plasma Analyser

Tue, 02/20/2024 - 08:02
Abstract

In order to protect society from space weather impacts, we must monitor space weather and obtain early warnings for extreme events if possible. For this purpose, the European Space Agency is currently preparing to launch the Vigil mission toward the end of this decade as a space-weather monitor at the fifth Lagrange point of the Sun–Earth system. Vigil will carry, amongst other instruments, the Plasma Analyser (PLA) to provide quasi-continuous measurements of solar wind ions. We model the performance of the PLA instrument, considering typical solar wind plasma conditions, to compare the expected observations of PLA with the assumed input conditions of the solar wind. We evaluate the instrument performance under realistic, non-equilibrium plasma conditions, accounting for temperature anisotropies, proton beams, and the contributions from α-particles. We examine the accuracy of the instrument's performance over a range of input solar wind moments. We identify sources of potential errors due to non-equilibrium plasma conditions and link these to instrument characteristics such as its angular and energy resolution and its field of view. We demonstrate the limitations of the instrument and potential improvements such as applying ground-based fitting techniques to obtain more accurate measurements of the solar wind even under non-equilibrium plasma conditions. The use of ground processing of plasma moments instead of on-board processing is crucial for the extraction of reliable measurements.

Correcting Projection Effects in CMEs Using GCS‐Based Large Statistics of Multi‐Viewpoint Observations

Tue, 02/20/2024 - 07:49
Abstract

This study addresses the limitations of single-viewpoint observations of Coronal Mass Ejections (CMEs) by presenting results from a 3D catalog of 360 CMEs during solar cycle 24, fitted using the Graduated Cylindrical Shell (GCS) model. The data set combines 326 previously analyzed CMEs and 34 newly examined events, categorized by their source regions into active region (AR) eruptions, active prominence (AP) eruptions, and prominence eruptions (PE). Estimates of errors are made using a bootstrapping approach. The findings highlight that the average 3D speed of CMEs is ∼1.3 times greater than the 2D speed. PE CMEs tend to be slow, with an average speed of 432 km s−1. AR and AP speeds are higher, at 723 and 813 km s−1, respectively, with the latter having fewer slow CMEs. The distinctive behavior of AP CMEs is attributed to factors like overlying magnetic field distribution or geometric complexities leading to less accurate GCS fits. A linear fit of projected speed to width gives a gradient of ∼2 km s−1 deg−1, which increases to 5 km s−1 deg−1 when the GCS-fitted ‘true’ parameters are used. Notably, AR CMEs exhibit a high gradient of 7 km s−1 deg−1, while AP CMEs show a gradient of 4 km s−1 deg−1. PE CMEs, however, lack a significant speed-width relationship. We show that fitting multi-viewpoint CME images to a geometrical model such as GCS is important to study the statistical properties of CMEs, and can lead to a deeper insight into CME behavior that is essential for improving future space weather forecasting.

An Improved Stochastic Model for the Geodetic GNSS Receivers Under Ionospheric Scintillation at Low Latitudes

Sun, 02/18/2024 - 16:59
Abstract

The receiver tracking error stochastic (RTES) model can improve GNSS precise point positioning (PPP) performance under ionospheric scintillation. However, it relies on scintillation products derived from ionospheric scintillation monitoring receivers (ISMRs), which means the RTES model cannot be used for abundant geodetic GNSS receivers. In this study, we propose an improved RTES, referred to as Impr_RTES model, to mitigate scintillation effects on geodetic GNSS receivers at low latitudes, where severe scintillation frequently occurs. In the Impr_RTES model, the tracking error variances at the output of code delay locked loop are calculated by using the index S 4c , and these of phase locked loop are modeled by using the rate of total electron content index (ROTI) and S 4c . Both S 4c and ROTI can be derived from geodetic GNSS receivers. The performance of the Impr_RTES model is validated by using the data sets from ISMR and geodetic receivers, respectively. Using one month of GPS data collected at HNLW station installed with ISMR in Hainan of China from 1 to 28 February in 2023, statistical results indicate that the PPP solution based on Impr_RTES model can improve the positioning accuracy by approximately 22.6%, 23.8%, and 30.2% in the east, north, and up directions, respectively, over the elevation angle stochastic (EAS) model. Meanwhile, the positioning performance of Impr_RTES PPP is comparable to that of RTES PPP. For the GPS data from geodetic receivers, experimental results suggest that compared with EAS, the Impr_RTES model can obviously mitigate scintillation effects on PPP.

Ionospheric Disturbances Generated by the 2015 Calbuco Eruption: Comparison of GITM‐R Simulations and GNSS Observations

Sat, 02/17/2024 - 10:53
Abstract

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.

Prediction of the SYM‐H Index Using a Bayesian Deep Learning Method With Uncertainty Quantification

Thu, 02/15/2024 - 06:13
Abstract

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.

Assessing Thermospheric Neutral Density Models Using GEODYN's Precision Orbit Determination

Mon, 02/05/2024 - 10:20
Abstract

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.

Improving Thermospheric Density Predictions in Low‐Earth Orbit With Machine Learning

Sat, 02/03/2024 - 14:43
Abstract

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.

Quantifying Uncertainties in the Quiet‐Time Ionosphere‐Thermosphere Using WAM‐IPE

Sat, 02/03/2024 - 12:08
Abstract

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.

3‐D Ionospheric Imaging Over the South American Region With a New TEC‐Based Ionospheric Data Assimilation System (TIDAS‐SA)

Fri, 02/02/2024 - 05:20
Abstract

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.

An Examination of Geomagnetic Induction in Submarine Cables

Fri, 02/02/2024 - 05:09
Abstract

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.

The 2022 Starlink Geomagnetic Storms: Global Thermospheric Response to a High‐Latitude Ionospheric Driver

Fri, 02/02/2024 - 04:44
Abstract

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.

Mapping Geoelectric Field Hazards in Ireland

Thu, 02/01/2024 - 14:49
Abstract

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.

Forecasting of Global Ionosphere Maps With Multi‐Day Lead Time Using Transformer‐Based Neural Networks

Wed, 01/31/2024 - 07:13
Abstract

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.

The Daytime Variations of Thermospheric Temperature and Neutral Density Over Beijing During Minor Geomagnetic Storm on 3–4 February 2022

Wed, 01/31/2024 - 06:43
Abstract

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.

A Substorm‐Dependent Negative Limit of Non‐Eclipse Surface Charging of a Chinese Geosynchronous Satellite

Tue, 01/30/2024 - 10:24
Abstract

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.

Assessment of Space Weather Impacts on New Zealand Power Transformers Using Dissolved Gas Analysis

Tue, 01/30/2024 - 09:58
Abstract

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.

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