The enhanced variation of the magnetic field during severe to extreme geomagnetic storms induces a large geoelectric field in the subsurface. Grounded infrastructure can be susceptible to geomagnetically induced currents (GICs) during these events. Modeling the effect in real‐time and forecasting the magnitude of GICs are important for allowing operators of critical infrastructure to make informed decisions on potential impacts. As part of the UK‐funded Space Weather Innovation, Measurement, Modeling and Risk (SWIMMR) program, we implemented nine research‐level models into operational codes capable of running consistently and robustly to produce estimates of GICs in the Great Britain high voltage power transmission network, the high pressure gas pipeline network and the railway network. To improve magnetic coverage and geoelectric field modeling accuracy, three new variometer sites were installed in the UK and a 3 year campaign of magnetotelluric measurements at 53 sites was undertaken. The models rely on real‐time ground observatory data and solar wind data from satellites at the L1 Lagrange point. A mixture of empirical machine learning and numerical magnetohydrodynamic models are used for forecasting. In addition to nowcast capabilities, contextual information on the likelihood of substorms, sudden commencements and large rates of change of the magnetic field were developed. The final nowcast and forecast codes were implemented in a cloud‐based environment using modern software tools and practices. We describe the process to move from research to operations (R2O).

Increasing representation of people with disabilities in science and engineering will require systemic changes to the culture around support and accommodations. Equitable interview practices can help foster such changes. We, an interdisciplinary group of disabled and nondisabled early-career scientists who care deeply about making science more accessible to all, present a framework of suggestions based on Universal Design principles for improving the accessibility and equitability of interviews for people with disabilities and other underrepresented groups. We discuss potential challenges that may arise when implementing these suggestions and provide questions to guide discussions about addressing them.

Understanding of Earth’s geomagnetic environment is critical to mitigating the space weather impacts caused by disruptive geoelectric fields in power lines and other conductors on Earth’s surface. These impacts are the result of a chain of processes driven by the solar wind and linking Earth’s magnetosphere, ionosphere, thermosphere and Earth’s surface. Tremendous progress has been made over the last two decades in understanding the solar wind driving mechanisms, the coupling mechanisms connecting the magnetically controlled regions of near-Earth space, and the impacts of these collective processes on human technologies on Earth’s surface. Studies of solar wind drivers have been focused on understanding the responses of the geomagnetic environment to spatial and temporal variations in the solar wind associated with Coronal Mass Ejections, Corotating Interaction Regions, Interplanetary Shocks, High-Speed Streams, and other interplanetary magnetic field structures. Increasingly sophisticated numerical models are able to simulate the magnetospheric response to the solar wind forcing associated with these structures. Magnetosphere-ionosphere-thermosphere coupling remains a great challenge, although new observations and sophisticated models that can assimilate disparate data sets have improved the ability to specify the electrodynamic properties of the high latitude ionosphere. The temporal and spatial resolution needed to predict the electric fields, conductivities, and currents in the ionosphere is driving the need for further advances. These parameters are intricately tied to auroral phenomena—energy deposition due to Joule heating and precipitating particles, motions of the auroral boundary, and ion outflow. A new view of these auroral processes is emerging that focuses on small-scale structures in the magnetosphere and their ionospheric effects, which may include the rapid variations in current associated with geomagnetically induced currents and the resulting perturbations to geoelectric fields on Earth’s surface. Improvements in model development have paralleled the advancements in understanding, yielding coupled models that better replicate the spatial and temporal scales needed to simulate the interconnected domains. Many realizations of such multi-component systems are under development, each with its own limitations and advantages. Challenges remain in the ability of models to quantify uncertainties introduced by propagation of solar wind parameters, to account for numerical effects in model codes, and to handle the special conditions occurring during extreme events. The impacts to technical systems on the ground are highly sensitive to the local electric properties of Earth’s surface, as well as to the specific technology at risk. Current research is focused on understanding the characteristics of geomagnetic disturbances that are important for geomagnetically induced currents, the development of earth conductivity models, the calculation of geoelectric fields, and the modeling of induced currents in the different affected systems. Assessing and mitigating the risks to technical systems requires quantitative knowledge of the range of values to be expected under all possible geomagnetic and technical conditions. Considering the progress that has been made in studying the chain of events leading to hazardous geomagnetic disturbances, the path forward will require concerted efforts to reveal missing physics, improve modeling capabilities, and deploy new observational assets. New understanding should be targeted to accurately quantify solar wind driving, magnetosphere-ionosphere-thermosphere coupling, and the impacts on specific technologies. The research, modeling, and observations highlighted here provide a framework for constructing a plan by which the international science community can comprehensively address the growing threat to human technologies caused by geomagnetic disturbances.

Surface waves on Earth's magnetopause have a controlling effect upon global magnetospheric dynamics. Since spacecraft provide sparse in situ observation points, remote sensing these modes using ground-based instruments in the polar regions is desirable. However, many open conceptual questions on the expected signatures remain. Therefore, we provide predictions of key qualitative features expected in auroral, ionospheric, and ground magnetic observations through both magnetohydrodynamic theory and a global coupled magnetosphere-ionosphere simulation of a magnetopause surface eigenmode. These show monochromatic oscillatory field-aligned currents (FACs), due to both the surface mode and its non-resonant Alfven coupling, are present throughout the magnetosphere. The currents peak in amplitude at the equatorward edge of the magnetopause boundary layer, not the open-closed boundary as previously thought. They also exhibit slow poleward phase motion rather than being purely evanescent. We suggest the upward FAC perturbations may result in periodic auroral brightenings. In the ionosphere, convection vortices circulate the poleward moving FAC structures. Finally, surface mode signals are predicted in the ground magnetic field, with ionospheric Hall currents rotating perturbations by approximately (but not exactly) 90 degrees compared to the magnetosphere. Thus typical dayside magnetopause surface modes should be strongest in the East-West ground magnetic field component. Overall, all ground-based signatures of the magnetopause surface mode are predicted to have the same frequency across L-shells, amplitudes that maximize near the magnetopause's equatorward edge, and larger latitudinal scales than for field line resonance. Implications in terms of ionospheric Joule heating and geomagnetically induced currents are discussed.

Space weather and its impact on infrastructure presents a clear risk in the modern era, as evidenced by the adverse effects of geomagnetically induced currents (GICs) in power networks. To model GICs, ground-based geomagnetic field (B-field) measurements are critical and need to be available in the region of interest. A challenge globally lies in the sparse distribution of magnetometer arrays, which are seldom located near critical power network nodes. Interpolation of the geomagnetic field (B-field) is often needed, with the spherical elementary current system (SECS) approach developed for high-latitude regions favoured. We adapt this interpolation scheme to include low-cost variometers to interpolate dB/dt directly and increase interpolation accuracy. A further adaptation to the scheme is to physically represent the mid-latitude context where most power networks and pipelines lie. The driving current systems in these regions differ from their high-latitude counterparts. Using a physics-consistent mid-latitude version of SECS, we show why previous implementations in Southern Africa are incorrect but still result in useful interpolation. The scope of these adaptations not only has direct application to research in general, but also to utilities, where effective low-cost instrumentation can be used to improve GIC modelling accuracy.

Abstract Space weather represents a severe threat to ground‐based infrastructure, satellites and communications. Accurately forecasting when such threats are likely (e.g., when we may see large induced currents) will help to mitigate the societal and financial costs. In recent years computational models have been created that can forecast hazardous intervals, however they generally use post‐processed “science” solar wind data from upstream of the Earth. In this work we investigate the quality and continuity of the data that are available in Near‐Real‐Time (NRT) from the Advanced Composition Explorer and Deep Space Climate Observatory (DSCOVR) spacecraft. In general, the data available in NRT corresponds well with post‐processed data, however there are three main areas of concern: greater short‐term variability in the NRT data, occasional anomalous values and frequent data gaps. Some space weather models are able to compensate for these issues if they are also present in the data used to fit (or train) the model, while others will require extra checks to be implemented in order to produce high quality forecasts. We find that the DSCOVR NRT data are generally more continuous, though they have been available for small fraction of a solar cycle and therefore DSCOVR has experienced a limited range of solar wind conditions. We find that short gaps are the most common, and are most frequently found in the plasma data. To maximize forecast availability we suggest the implementation of limited interpolation if possible, for example, for gaps of 5 min or less, which could increase the fraction of valid input data considerably.

Geomagnetically induced currents are induced after a complex interaction between the sun and earth’s magnetic field. GICs may cripple power systems leading to voltage collapse and a drop in power quality. While the commonly-used dc-model for GICs is valid for steady-state analyses of power systems and transformer responses, GICs must be represented with their continuously varying magnitudes and frequency components to understand the dynamic power system’s response to solar storms. In this paper, we used measured GIC data from the 2003 Halloween storm to compare several signal processing techniques. The fast fourier transform with a fixed window size, short-time fourier transform with a variable window size, wavelet transform, and particle-swarm optimization methods were used to identify the GIC frequency characteristics. To understand the effects of these GIC characteristics on voltage stability, a multi-machine network was modelled on a digital real-time simulator (OPAL-RT). The results reveal the limitations of fixed window FFT in capturing the dominant high-frequency GIC bandwidth arising during peak solar activity. Wavelet and short time fourier transform are recommended for GIC data analysis as they provide time-frequency localization. High-frequency GICs during storm sudden commencements and pulsation activity are more of a concern for voltage stability than lower frequency components. Novel results from this study suggest that limiting contingency analysis to only high magnitudes of dc/GIC does not cater for the underlining GIC dynamics. In addition to magnitude thresholds, contingency analysis should incorporate GIC characteristics such as frequency which affect voltage dynamics and thus power system stability.

Abstract Geomagnetically induced currents (GICs) are driven by the geoelectric field induced by fluctuations of Earth's magnetic field. Drivers of intense GICs are often associated with large impulsive events such as coronal mass ejections. To a lesser extent fluctuations from regular oscillations of the geomagnetic field, or geomagnetic pulsations, have also been identified as possible drivers of GICs. In this work we show that these low‐frequency pulsations are directly observed in measured GIC data from power networks. Due to the low‐pass nature of GICs, Pc5 and lower‐frequency pulsations drive significant GICs for an extended duration even at midlatitudes. Longer‐period Ps6‐type disturbances apparently not typical of midlatitudes are seen with GIC amplitudes comparable to the peak GIC at storm sudden commencement. The quasi‐ac (alternating current) nature of the sustained pulsation driving affects the power system response and cannot be properly modeled using only direct current (dc) models. A further consideration is that the often used dB/dt GIC proxy is biased to the sampling rate of the geomagnetic field measurements used. The dB/dt metric does not adequately characterize GIC activity at frequencies in the low ultralow‐frequency (ULF) range, and a frequency‐weighted proxy akin to geoelectric field should be used instead.

Typical geomagnetically induced current (GIC) modelling assumes the induced quasi-DC current at a node in the transmission network is linearly related to the local geoelectric field by a pair of network parameters. Given a limited time-series of measured geomagnetic and GIC data, an empirical method is presented that results in a statistically significant generalised ensemble of parameter estimates with the error in the estimates identified. The method is showcased for different transmission networks and geomagnetic storms and, where prior modelling exists, shows improved GIC estimation. Furthermore, modelled networks can be locally characterised and probed without any further network knowledge. Insights include network parameter variation, effective network directionality and response. Merging the network parameters and geoelectric field estimation, a transfer function is derived which offers an alternative approach to assessing transformer exposure to GICs.

Geomagnetically induced currents (GICs) in power systems are conventionally modelled as direct currents, based on their commonly described quasi-DC nature. For more representative power system dynamic response modelling, it is necessary to model GICs using low-frequency AC. After analysing spectra of geomagnetic field measurements and GICs in several power systems, preliminary results indicate that the GIC spectra depend on the magnitude and frequency spectrum of the geomagnetic disturbance, the earth conductivity and the electrical characteristics of the network. In this paper, analysis based on measured GIC power spectra suggests that the dominant frequencies to use in GIC modelling and simulation are typically below 50 mHz. The associated error introduced by only using these frequencies is also quantified. The results have implications for modelling the geoelectric fields inducing GICs, dynamic models of power system response and the need for GICs and their associated driving fields to be measured at intervals not exceeding 10 s.

As reliance on power networks has increased over the last century, the risk of damage from geomagnetically induced currents (GICs) has become a concern to utilities. The current state of the art in GIC modelling requires significant geophysical modelling and a theoretically derived network response, but has limited empirical validation. In this work, we introduce a probabilistic engineering step between the measured geomagnetic field and GICs, without needing data about the power system topology or the ground conductivity profiles. The resulting empirical ensembles are used to analyse the TVA network (southeastern USA) in terms of peak and cumulative exposure to 5 moderate to intense geomagnetic storms. Multiple nodes are ranked according to susceptibility and the measured response of the total TVA network is further calibrated to existing extreme value models. The probabilistic engineering step presented can complement present approaches, being particularly useful for risk assessment of existing transformers and power systems.

It has long been known that large grounded conducting networks on the surface of Earth are affected by solar activity and geomagnetic storms. Power networks are such extensive grounded conductors and are susceptible to geomagnetically induced currents (GICs). GICs at any specific node in a power network are assumed to be linearly related to the horizontal vector components of an induced plane-wave geoelectric field by a pair of network parameters. These network parameters are not easily measured in the network, but may be estimated empirically. In this work, we present a new approach of using an ensemble of network parameters estimates. The ensembles include a huge number of parameter pair estimates calculated from simultaneously solving pairs of time instances of the governing GIC equation. Each individual estimate is not the true state of the system, but a possible state. Taking the ensemble as a whole though gives the most probable parameter estimate. The most probable parameter estimate for both network parameters, as defined by their respective ensembles, is used directly in the modelling of GICs. The ensembles themselves however allow for further analysis into the nature of GICs. An improvement is seen when comparing the out-of-sample performance of the ensemble estimates with previous GIC modelling in the South African power network during the Halloween Storm of 2003. For the first time, it is shown that errors in the GIC modelling chain are absorbed into the network parameter estimates. Using a range of estimates from the ensemble, a GIC prediction band is produced. This band corresponds to an error estimate for predicted GIC. Furthermore, it has been explicitly shown that estimated network parameters vary with GIC magnitude during an event. This behaviour is then used to refine the parameter estimation further and allow for real time dynamic network parameter estimation.

One of the best tools we have in geophysical modelling is the ability to interpolate the horizontal geomagnetic field at the surface of the Earth. This is especially useful in regions, such as southern Africa, where there is a sparse array of absolute magnetometers available for geomagnetic field measurements. In terms of geomagnetic field interpolation, the spherical elementary current systems (SECS) spatial interpolation scheme has shown to be very successful, and the planar approximation of this method adequate for modelling at mid-latitudes. The SECS interpolation scheme is physics based, making use of the Biot-Savart law and equivalent ionospheric currents to interpolate measured geomagnetic field data. As with most interpolation methods, more data points result in lower error. Therefore, we adapt the SECS method to work with variometers. These instruments measure variations in magnetic field and are more abundant in southern Africa. Merging the two resulting interpolated datasets, the initial absolute geomagnetic field interpolation can be significantly improved. This improved interpolation scheme is not only incredibly useful locally, where a sparse magnetometer array is a challenge, but can also be applied just as effectively in other cases across the globe where there are numerous magnetometers and variometers available.

The electric field induced due to solar activity is the driver of geomagnetically induced currents (GIC) in grounded conductor networks such as power grids. We present a regression‐based (i.e., empirical) model for quantitatively predicting the upper envelope of induced E field components, using near‐Earth measurements of solar wind plasma and magnetic field parameters as input arguments to the model at three midlatitude locations. Model parameters and the set of input arguments used are determined by an iterative regression process that relies on a large data set comprising selected events from 2000 to 2015. Testing on out‐of‐sample events over two years (2013 and 2015) yields correlation of between 0.68 and 0.75 when predicting the northward horizontal component of induced E field and 0.45–0.61 for the eastward component at the three stations. The forecast lead time is extended by using predicted SW parameters (from the WSA‐Enlil models) as model input. As proof of concept we predict GIC based on measured and predicted input parameters and compare that with measured GIC at a South African substation for the two St. Patrick's day events (March 2013 and 2015).