Present day challenges in understanding the geomagnetic hazard to national power grids

Power grids and pipeline networks at all latitudes are known to be at risk from the natural hazard of geomagnetically induced currents. At a recent workshop in South Africa, UK and South African scientists and engineers discussed the current understanding of this hazard, as it affects major power systems in Europe and Africa. They also summarised, to better inform the public and industry, what can be said with some certainty about the hazard and what research is yet required to develop useful tools for geomagnetic hazard mitigation

The Tsallis statistical distribution applied to geomagnetically induced currents

Geomagnetically induced currents (GICs) have been long recognized as a ground effect arising from a chain of space weather events. GICs have been measured and modeled in many countries, resulting in a considerable amount of data. Previous statistical analyses have proposed various types of distribution functions to fit long-term GICs data sets. However, these extensive statistical approaches have been only partially successful in fitting the data sets. Here we use modeled GICs data sets calculated in four countries (Brazil, South Africa, United Kingdom, and Finland) using data from solar cycle 23 to show a plausible function based on a nonextensive statistical model of the q-exponential Tsallis function. The fitted q-exponential parameter is approximately the same for all locations, and the Lilliefors test shows good agreement with the q-exponential fits. From this fit, we compute that the likely numbers of extreme GICs events over the next ten solar cycles are 1–2 for both Finland and United Kingdom, at least one for Brazil and less than one event for South Africa. Our results indicate that the nonextensive statistics are a general characteristic of GICs, suggesting that the ground current intensity has a strong temporal correlation and long-range interaction

A Study of Intense Local dB/dt Variations During Two Geomagnetic Storms

Interactions between the solar wind and the Earth's magnetosphere manifest many important space weather phenomena. In this paper, magnetosphere-ionosphere drivers of intense dB/dt produced during geomagnetic storms that occurred on 9 March 2012 and 17 March 2015 are analyzed. A multi-instrument approach combining Time History of Events and Macroscale Interactions during Substorms (THEMIS) mission space-borne and ground-based observations was adopted to examine the magnetosphere-ionosphere signatures associated with the dB/dt extremes during each storm. To complement the THEMIS measurements, ground-based magnetometer recordings and All-Sky Imager observations, equivalent ionospheric currents derived from magnetometer chains across North America and Greenland, and geosynchronous observations from the Los Alamos National Laboratory Synchronous Orbit Particle Analyzer are also examined. Our results show that the most extreme dB/dt variations are associated with marked perturbations in the THEMIS magnetospheric measurements, poleward expanding discrete aurora passing over the magnetometer sites (seen by the ground-based THEMIS All-Sky Imagers), intense Pc5 waves, rapid injection of energetic particles, and intense auroral westward currents. Substorms are considered as the major driver with a possible contribution from magnetospheric waves. The findings of this study strongly suggest that the localization of extreme dB/dt variations is most likely related to the mapping of magnetosphere currents to local ionospheric structures. ©2018. American Geophysical Union. All Rights Reserved

Model evaluation guidelines for geomagnetic index predictions

Geomagnetic indices are convenient quantities that distill the complicated physics of some region or aspect of near‐Earth space into a single parameter. Most of the best‐known indices are calculated from ground‐based magnetometer data sets, such as Dst, SYM‐H, Kp, AE, AL, and PC. Many models have been created that predict the values of these indices, often using solar wind measurements upstream from Earth as the input variables to the calculation. This document reviews the current state of models that predict geomagnetic indices and the methods used to assess their ability to reproduce the target index time series. These existing methods are synthesized into a baseline collection of metrics for benchmarking a new or updated geomagnetic index prediction model. These methods fall into two categories: (1) fit performance metrics such as root‐mean‐square error and mean absolute error that are applied to a time series comparison of model output and observations and (2) event detection performance metrics such as Heidke Skill Score and probability of detection that are derived from a contingency table that compares model and observation values exceeding (or not) a threshold value. A few examples of codes being used with this set of metrics are presented, and other aspects of metrics assessment best practices, limitations, and uncertainties are discussed, including several caveats to consider when using geomagnetic indices