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).

AbstractExtreme 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.

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 (GICs) are an impact of space weather that can occur during periods of enhanced geomagnetic activity. GICs can enter into electrical power grids through earthed conductors, potentially causing network collapse through voltage instability or damaging transformers. It would be beneficial for power grid operators to have a forecast of GICs that could inform decision making on mitigating action. Long lead‐time GIC forecasting requires magnetospheric models as drivers of geoelectric field models. However, estimation of the geoelectric field is sensitive to high‐frequency geomagnetic field variations, which operational global magneto‐hydrodynamic models do not fully capture. Furthermore, an assessment of GIC forecast uncertainty would require a large ensemble of magnetospheric runs, which is computationally expensive. One solution that is widely used in climate science is “downscaling,” wherein sub‐grid variations are added to model outputs on a statistical basis. We present proof‐of‐concept results for a method that temporally downscales low‐resolution magnetic field data on a 1‐hr timescale to 1‐min resolution, with the hope of improving subsequent geoelectric field magnitude estimates. An analog ensemble (AnEn) approach is used to select similar hourly averages in a historical data set, from which we separate the high‐resolution perturbations to add to the hourly average values. We find that AnEn outperforms the benchmark linear‐interpolation approach in its ability to accurately drive an impacts model, suggesting GIC forecasting would be improved. We evaluated the ability of AnEn to predict extreme events using the FSS, HSS, cost/loss analysis and BSS, finding that AnEn outperforms the “do‐nothing” approach.

Probabilistic Hazard Assessment (PHA) provides an appropriate methodology for assessing space weather hazards and their impact on technology. PHA is widely used in geosciences to determine the probability of exceedance of critical thresholds, caused by one or more hazard sources. PHA has proved useful with limited historical data to estimate the likelihood of specific impacts. PHA has also driven the development of empirical and physical models, or ensembles of models, to replace measured data. Here we aim to highlight the PHA method to the space weather community and provide an example of how it could be used. In terms of space weather impact, the critical hazard thresholds might include the Geomagnetically Induced Current in a specific high voltage power transformer neutral, or the local pipe-to-soil potential in a particular metal pipe. We illustrate PHA in the space weather context by applying it to a twelve-year dataset of Earth-directed solar Coronal Mass Ejections (CME), which we relate to the probability that the global three-hourly geomagnetic activity index Kp exceeds specific thresholds. We call this a “Probabilistic Geomagnetic Hazard Assessment”, or PGHA. This provides a simple but concrete example of the method. We find that the cumulative probability of Kp > 6−, > 7−, > 8− and Kp = 9o is 0.359, 0.227, 0.090, 0.011, respectively, following observation of an Earth-directed CME, summed over all CME launch speeds and solar source locations. According to the historical Kp distribution, this represents an order of magnitude increase in the a priori probability of exceeding these thresholds. For the lower Kp thresholds, the results are somewhat distorted by our exclusion of coronal hole high-speed stream effects. The PHGA also reveals useful probabilistic associations between solar source location and subsequent maximum Kp for operational forecasters.

Significant geoelectric fields are produced by the interaction of rapidly varying magnetic fields with the conductive Earth, particularly during intense geomagnetic activity. Though usually harmless, large or sustained geoelectric fields can damage grounded infrastructure such as high-voltage transformers and pipelines via geomagnetically induced currents (GICs). A key aspect of understanding the effects of space weather on grounded infrastructure is through the spatial and temporal variation of the geoelectric field. Globally, there are few long-term monitoring sites of the geoelectric field, so in 2012 measurements of the horizontal surface field were started at Lerwick, Eskdalemuir and Hartland observatories in the UK. Between 2012 and 2020, the maximum value of the geoelectric field observed was around 1 V/km in Lerwick, 0.5 V/km in Eskdalemuir and 0.1 V/km in Hartland during the March 2015 storm. These long-term observations also allow comparisons with models of the geoelectric field to be made. We use the measurements to compute magnetotelluric impedance transfer functions at each observatory for periods from 20 to 30,000 s. These are then used to predict the geoelectric field at the observatory sites during selected storm times that match the recorded fields very well (correlation around 0.9). We also compute geoelectric field values from a thin-sheet model of Britain, accounting for the diverse geological and bathymetric island setting. We find the thin-sheet model captures the peak and phase of the band-passed geoelectric field reasonably well, with linear correlation of around 0.4 in general. From these two modelling approaches, we generate geoelectric field values for historic storms (March 1989 and October 2003) and find the estimates of past peak geoelectric fields of up to 1.75 V/km in Eskdalemuir. However, evidence from high voltage transformer GIC measurements during these storms suggests these estimates are likely to represent an underestimate of the true value.

Abstract Severe space weather was identified as a risk to the UK in 2010 as part of a wider review of natural hazards triggered by the societal disruption caused by the eruption of the Eyjafjallajökull volcano in April of that year. To support further risk assessment by government officials, and at their request, we developed a set of reasonable worst‐case scenarios and first published them as a technical report in 2012 (current version published in 2020). Each scenario focused on a space weather environment that could disrupt a particular national infrastructure such as electric power or satellites, thus, enabling officials to explore the resilience of that infrastructure against severe space weather through discussions with relevant experts from other parts of government and with the operators of that infrastructure. This approach also encouraged us to focus on the environmental features that are key to generating adverse impacts. In this paper, we outline the scientific evidence that we have used to develop these scenarios, and the refinements made to them as new evidence emerged. We show how these scenarios are also considered as an ensemble so that government officials can prepare for a severe space weather event, during which many or all of the different scenarios will materialize. Finally, we note that this ensemble also needs to include insights into how public behavior will play out during a severe space weather event and hence the importance of providing robust, evidence‐based information on space weather and its adverse impacts.

Measurements from six longitudinally separated magnetic observatories, all located close to the 53° mid-latitude contour, are analysed. We focus on the large geomagnetic disturbance that occurred during 7 and 8 September 2017. Combined with available geomagnetically induced current (GIC) data from two substations, each located near to a magnetic observatory, we investigate the magnetospheric drivers of the largest events. We analyse solar wind parameters combined with auroral electrojet indices to investigate the driving mechanisms. Six magnetic field disturbance events were observed at mid-latitudes with dH/dt > 60 nT/min. Co-located GIC measurements identified transformer currents >15 A during three of the events. The initial event was caused by a solar wind pressure pulse causing largest effects on the dayside, consistent with the rapid compression of the dayside geomagnetic field. Four of the events were caused by substorms. Variations in the Magnetic Local Time of the maximum effect of each substorm-driven event were apparent, with magnetic midnight, morning-side, and dusk-side events all occurring. The six events occurred over a period of almost 24 h, during which the solar wind remained elevated at >700 km s−1, indicating an extended time scale for potential GIC problems in electrical power networks following a sudden storm commencement. This work demonstrates the challenge of understanding the causes of ground-level magnetic field changes (and hence GIC magnitudes) for the global power industry. It also demonstrates the importance of magnetic local time and differing inner magnetospheric processes when considering the global hazard posed by GIC to power grids.

Neil Rogers, Juliane Hübert, Gemma Richardson and Alan Thomson report from an RAS meeting that considered how to understand the potential hazard to our technology and infrastructure on the ground from extreme solar activity

Neil Rogers, Juliane Hubert, Gemma Richardson, and Alan Thomson report from a RAS meeting in March that considered the potential for extreme solar activity to damage ground infrastructure.

Abstract Geomagnetically induced currents (GICs) during a space weather event have previously caused transformer damage in New Zealand. During the 2015 St. Patrick's Day Storm, Transpower NZ Ltd has reliable GIC measurements at 23 different transformers across New Zealand's South Island. These observed GICs show large variability, spatially and within a substation. We compare these GICs with those calculated from a modeled geolectric field using a network model of the transmission network with industry‐provided line, earthing, and transformer resistances. We calculate the modeled geoelectric field from the spectra of magnetic field variations interpolated from measurements during this storm and ground conductance using a thin‐sheet model. Modeled and observed GIC spectra are similar, and coherence exceeds the 95% confidence threshold, for most valid frequencies at 18 of the 23 transformers. Sensitivity analysis shows that modeled GICs are most sensitive to variation in magnetic field input, followed by the variation in land conductivity. The assumption that transmission lines follow straight lines or getting the network resistances exactly right is less significant. Comparing modeled and measured GIC time series highlights that this modeling approach is useful for reconstructing the timing, duration, and relative magnitude of GIC peaks during sudden commencement and substorms. However, the model significantly underestimates the magnitude of these peaks, even for a transformer with good spectral match. This is because of the limited range of frequencies for which the thin‐sheet model is valid and severely limits the usefulness of this modeling approach for accurate prediction of peak GICs.

This paper presents a multi-parameter global statistical model of extreme horizontal geomagnetic field fluctuations (dBH/dt), which are a useful input to models assessing the risk of geomagnetically induced currents in ground infrastructure. Generalised Pareto (GP) distributions were fitted to 1-min measurements of |dBH/dt| from 125 magnetometers (with an average of 28 years of data per site) and return levels (RL) predicted for return periods (RP) between 5 and 500 years. Analytical functions characterise the profiles of maximum-likelihood GP model parameters and the derived RLs as a function of corrected geomagnetic latitude, λ. A sharp peak in both the GP shape parameter and the RLs is observed at |λ| = 53° in both hemispheres, indicating a sharp equatorward limit of the auroral electrojet region. RLs also increase strongly in the dayside region poleward of the polar cusp (|λ| > 75°) for RPs > 100 years. We describe how the GP model may be further refined by modelling the probability of occurrences of |dBH/dt| exceeding the 99.97th percentile as a function of month, magnetic local time, and the direction of the field fluctuation, dBH, and demonstrate that these patterns of occurrence align closely to known patterns of auroral substorm onsets, ULF Pc5 wave activity, and (storm) sudden commencement impacts. Changes in the occurrence probability profiles with the interplanetary magnetic field (IMF) orientation reveal further details of the nature of the ionospheric currents driving extreme |dBH/dt| fluctuations, such as the changing location of the polar cusp and seasonal variations explained by the Russell-McPherron effect.

Space weather poses a hazard to grounded electrical infrastructure such as high voltage (HV) transformers, through the induction of geomagnetically induced currents (GICs). Modeling GICs requires knowledge of the source magnetic field and the Earth's electrical conductivity structure, in order to calculate the geoelectric fields generated during magnetic storms, as well as knowledge of the topology of the HV network. Direct measurement of GICs at the ground neutral in substations is possible with a Hall effect probe, but such data are not widely available. To validate our HV network model, we use the differential magnetometer method (DMM) to measure GICs in the 400 kV grid of Great Britain. We present DMM measurements for the 26 August 2018 storm at a site in eastern Scotland with up to 20 A recorded. The line GIC correlates well with Hall probe measurements at a local transformer, though they differ in amplitude by an order of magnitude (a maximum of similar to 2 A). We deployed a long-period magnetotelluric (MT) instrument to derive the local impedance tensor which can be used to predict the geoelectric field from the recorded magnetic field. Using the MT-derived electric field estimates, we model GICs within the network, accounting for the difference in magnitude between the DMM-measured line currents and earth currents at the local substation. We find that the measured line and earth GICs match the expected GICs from our network model, confirming that detailed knowledge of the complex network topology and its resistance parameters is essential for accurately computing GICs.

We introduce the inductive effects of polar cap motions towards and way from the Sun into magnetospheric electrodynamics and show how this explains observed Universal Time variations in hemispheric geomagnetic indices. The large (and growing) hemispheric asymmetry in the offsets of the geomagnetic (dip or eccentric dipole) poles from Earth’s rotational axis means that the effect is not cancelled out in global indices. By adding this effect to that of the Russell-McPherron effect on solar wind-magnetosphere coupling, that of ionospheric conductivities, and that of the solar wind dynamic pressure and dipole tilt on the near-Earth tail lobe field and cross-tail current sheet, we are able to model the persistent “equinoctial” time-of-day/time-of-year pattern (with additional net Universal time variations) observed in the an, as and am geomagnetic indices since 1959. We discuss the implications for the longitudinal dependence of the effects of extreme space weather events

Space weather as a geohazard to modern technological infrastructure has come to the forefront of electromagnetic research in the past years. Geomagnetically induced currents (GICs) are generated by the rapidly changing magnetic fields during geomagnetic storms and sub-storms and the resulting induced electric fields into the ground. GICs can pose great risk to e.g. transformers in HV power grids and their monitoring and modelling is an ongoing effort in many higher and mid-latitude countries. Modelling of GICs in HV power grids requires knowledge about the magnetic field variations, the induced electric field via a conductivity model or through the magnetotelluric (MT) impedance tensor, and a detailed representation of the grid topology. In the UK we have traditionally used a thin-sheet model for the calculation of electric fields during storm times due to very limited availability of MT data, but also as a fast and computational cost-effective approach. Using the Differential Magnetometer Method (DMM) in several locations of the grid has enabled us to indirectly measure GICs and validate them against the model. Here we present a case study from a location in Scotland, where we incorporate the different approaches and data sets that combine to a comprehensive analysis of GICs in this subset of the UK power grid.

Aims: This paper presents a H2020 project aimed at developing an advanced space weather forecasting tool, combining the MagnetoHydroDynamic (MHD) solar wind and coronal mass ejection (CME) evolution modelling with solar energetic particle (SEP) transport and acceleration model(s). The EUHFORIA 2.0 project will address the geoeffectiveness of impacts and mitigation to avoid (part of the) damage, including that of extreme events, related to solar eruptions, solar wind streams, and SEPs, with particular emphasis on its application to forecast geomagnetically induced currents (GICs) and radiation on geospace.Methods: We will apply innovative methods and state-of-the-art numerical techniques to extend the recent heliospheric solar wind and CME propagation model EUHFORIA with two integrated key facilities that are crucial for improving its predictive power and reliability, namely (1) data-driven flux-rope CME models, and (2) physics-based, self-consistent SEP models for the acceleration and transport of particles along and across the magnetic field lines. This involves the novel coupling of advanced space weather models. In addition, after validating the upgraded EUHFORIA/SEP model, it will be coupled to existing models for GICs and atmospheric radiation transport models. This will result in a reliable prediction tool for radiation hazards from SEP events, affecting astronauts, passengers and crew in high-flying aircraft, and the impact of space weather events on power grid infrastructure, telecommunication, and navigation satellites. Finally, this innovative tool will be integrated into both the Virtual Space Weather Modeling Centre (VSWMC, ESA) and the space weather forecasting procedures at the ESA SSCC in Ukkel (Belgium), so that it will be available to the space weather community and effectively used for improved predictions and forecasts of the evolution of CME magnetic structures and their impact on Earth.Results: The results of the first six months of the EU H2020 project are presented here. These concern alternative coronal models, the application of adaptive mesh refinement techniques in the heliospheric part of EUHFORIA, alternative flux-rope CME models, evaluation of data-assimilation based on Karman filtering for the solar wind modelling, and a feasibility study of the integration of SEP models.

Space weather phenomena have been studied in detail in the peer-reviewed scientific literature. However, there has arguably been scant analysis of the potential socioeconomic impacts of space weather, despite a growing gray literature from different national studies, of varying degrees of methodological rigor. In this analysis, we therefore provide a general framework for assessing the potential socioeconomic impacts of critical infrastructure failure resulting from geomagnetic disturbances, applying it to the British high-voltage electricity transmission network. Socioeconomic analysis of this threat has hitherto failed to address the general geophysical risk, asset vulnerability, and the network structure of critical infrastructure systems. We overcome this by using a three-part method that includes (i) estimating the probability of intense magnetospheric substorms, (ii) exploring the vulnerability of electricity transmission assets to geomagnetically induced currents, and (iii) testing the socioeconomic impacts under different levels of space weather forecasting. This has required a multidisciplinary approach, providing a step toward the standardization of space weather risk assessment. We find that for a Carrington-sized 1-in-100-year event with no space weather forecasting capability, the gross domestic product loss to the United Kingdom could be as high as £15.9 billion, with this figure dropping to £2.9 billion based on current forecasting capability. However, with existing satellites nearing the end of their life, current forecasting capability will decrease in coming years. Therefore, if no further investment takes place, critical infrastructure will become more vulnerable to space weather. Additional investment could provide enhanced forecasting, reducing the economic loss for a Carrington-sized 1-in-100-year event to £0.9 billion.