The European risk from geomagnetically induced currents (EURISGIC)

EURISGIC (www.eurisgic.eu) was the first continental-scale study of the geomagnetically induced current (GIC) hazard to Europe’s power transmission system. EURISGIC had a number of strands to it, including modelling GIC in the European system and understanding the possible extremes that the system could face. These project strands were represented by nine distinct work packages: • The construction of the first ever European power transmission grid model and an update of the existing UK model • The development of detailed conductivity models for Europe and, separately, the UK • The building of geomagnetic, GIC and related science databases • The production of a GIC risk map for Europe • The investigation of worst case scenarios and extremes in the grid models • The development of the NASA ‘Solar Shield’ magnetospheric and solar wind model for use in the European context • The enhancement of a prototype GIC and geomagnetic forecast system for Europe • The making of geomagnetic, geoelectric and GIC measurements to enhance our knowledge and validate models • The education of the public and other stakeholders through scientific papers and other materials. To assess and guide progress on the project a team of industry advisors was assembled. These advisors included senior power engineers from major electrical transmission system operators from across Europe, including National Grid in the UK. In this poster we demonstrate some of the major findings of the project. The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement no 260330

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.Plain Language SummaryOne aspect of space weather is a magnetic signature across the surface of the Earth. The creation of this signal involves nonlinear interactions of electromagnetic forces on charged particles and can therefore be difficult to predict. The perturbations that space storms and other activity causes in some observation sets, however, are fairly regular in their pattern. Some of these measurements have been compiled together into a single value, a geomagnetic index. Several such indices exist, providing a global estimate of the activity in different parts of geospace. Models have been developed to predict the time series of these indices, and various statistical methods are used to assess their performance at reproducing the original index. Existing studies of geomagnetic indices, however, use different approaches to quantify the performance of the model. This document defines a standardized set of statistical analyses as a baseline set of comparison tools that are recommended to assess geomagnetic index prediction models. It also discusses best practices, limitations, uncertainties, and caveats to consider when conducting a model assessment.Key PointsWe review existing practices for assessing geomagnetic index prediction models and recommend a “standard set” of metricsAlong with fit performance metrics that use all data‐model pairs in their formulas, event detection performance metrics are recommendedOther aspects of metrics assessment best practices, limitations, uncertainties, and geomagnetic index caveats are also discussedPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147764/1/swe20790_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147764/2/swe20790.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147764/3/swe20790-sup-0001-2018SW002067-SI.pd

Space Weather Physics, Prediction and classification of solar wind structures and geomagnetic activity using artificial neural networks.

This thesis concerns the application of artificial neural network techniques to space weather physics. The networks applied include multi-layer error-backpropagation, radial basis function, and self-organized maps. Different parts in the solar-terrestrial chain are analysed with the emphasis on developing methods for real time predictions of geomagnetic activity. The neural networks are general models which utilize learning algorithms to adjust the free parameters of the models based on data samples. The models used here rely heavily on observations of solar magnetic fields, measurements of solar wind plasma and magnetic fields, and indices of geomagnetic activity. The thesis consists of an introductory part followed by 5 papers. The introduction describes part of the solar-terrestrial physics that is relevant to the papers and includes a summary of the applied neural networks used. Papers I and II describe the application of multi-layer error-backpropagation networks to the solar wind-magnetosphere coupling, where the geomagnetic activity is described by the Dst index. It is shown that real time predictions of the Dst index can be made one hour in advance. Papers III and IV examine the possibility to predict the daily average solar wind velocity from solar magnetic field observations. The model consists of a potential field model describing the solar coronal magnetic fields and a radial basis function neural network for the mapping from the corona to the solar wind. Paper V considers the analysis of hourly average solar wind structures at 1 AU using self-organizing maps. It is found that it is possible to identify specific solar wind events on the self-organized maps that are associated to geomagnetic storms occurring several hours later

The EURISGIC database : a tool for GIC research

The goals of the EU/FP7 project EURISGIC (European Risk from Geomagnetically Induced Currents) include the production of the first ever risk map of GIC throughout Europe as well as the first European-wide real-time prototype forecast service of GIC in power systems. The research has been designed to help meet these challenges and will rely on a well organised, easy to access database containing high quality, well defined data from various sources. Four main categories of measurements have been identified as essential components of the database. These are in-situ solar wind observations, ground-based geomagnetic field, electric field data and GIC measurements. This poster describes the plans for the EURISGIC database and its current status. The four main data sets are discussed with regard to their importance for the research packages and final products. A final data archive is planned to extend beyond the current project to establish a legacy for future GIC research

Regional estimation of geomagnetically induced currents based on the local magnetic or electric field

Previous studies have demonstrated a close relationship between the time derivative of the horizontal geomagnetic field vector (dH/dt) and geomagnetically induced currents (GIC) at a nearby location in a power grid. Similarly, a high correlation exists between GIC and the local horizontal geoelectric field (E), typically modelled from a measured magnetic field. Considering GIC forecasting, it is not feasible to assume that detailed prediction of time series will be possible. Instead, other measures summarising the activity level over a given period are preferable. In this paper, we consider the 30-min maximum of dH/dt or E as a local activity indicator (|dH/dt|30 or |E|30). Concerning GIC, we use the sum of currents through the neutral leads at substations and apply its 30-min maximum as a regional activity measure (GIC30). We show that |dH/dt|30 at a single point yields a proxy for GIC activity in a larger region. A practical consequence is that if |dH/dt|30 can be predicted at some point then it is also possible to assess the expected GIC level in the surrounding area. As is also demonstrated, |E|30 and GIC30 depend linearly on |dH/dt|30, so there is no saturation with increasing geomagnetic activity contrary to often used activity indices

Solar wind driven empirical forecast models of the time derivative of the ground magnetic field

Empirical models are developed to provide 10–30-min forecasts of the magnitude of the time derivative of local horizontal ground geomagnetic field (|dBh/dt|) over Europe. The models are driven by ACE solar wind data. A major part of the work has been devoted to the search and selection of datasets to support the model development. To simplify the problem, but at the same time capture sudden changes, 30-min maximum values of |dBh/dt| are forecast with a cadence of 1 min. Models are tested both with and without the use of ACE SWEPAM plasma data. It is shown that the models generally capture sudden increases in |dBh/dt| that are associated with sudden impulses (SI). The SI is the dominant disturbance source for geomagnetic latitudes below 50° N and with minor contribution from substorms. However, at occasions, large disturbances can be seen associated with geomagnetic pulsations. For higher latitudes longer lasting disturbances, associated with substorms, are generally also captured. It is also shown that the models using only solar wind magnetic field as input perform in most cases equally well as models with plasma data. The models have been verified using different approaches including the extremal dependence index which is suitable for rare events

Forecasting

We have developed neural network models that predict Kp from upstream solar wind data. We study the importance of various input parameters, starting with the magnetic component Bz, particle density n, and velocity V and then adding total field B and the By component. As we also notice a seasonal and UT variation in average Kp we include functions of day-of-year and UT. Finally, as Kp is a global representation of the maximum range of geomagnetic variation over 3-hour UT intervals we conclude that sudden changes in the solar wind can have a big effect on Kp, even though it is a 3-hour value. Therefore, 3-hour solar wind averages will not always appropriately represent the solar wind condition, and we introduce 3-hour maxima and minima values to some degree address this problem. We find that introducing total field B and 3-hour maxima and minima, derived from 1-minute solar wind data, have a great influence on the performance. Due to the low number of samples for high Kp values there can be considerable variation in predicted Kp for different networks with similar validation errors. We address this issue by using an ensemble of networks from which we use the median predicted Kp. The models (ensemble of networks) provide prediction lead times in the range 20–90 min given by the time it takes a solar wind structure to travel from L1 to Earth. Two models are implemented that can be run with real time data: (1) IRF-Kp-2017-h3 uses the 3-hour averages of the solar wind data and (2) IRF-Kp-2017 uses in addition to the averages, also the minima and maxima values. The IRF-Kp-2017 model has RMS error of 0.55 and linear correlation of 0.92 based on an independent test set with final Kp covering 2 years using ACE Level 2 data. The IRF-Kp-2017-h3 model has RMSE = 0.63 and correlation = 0.89. We also explore the errors when tested on another two-year period with real-time ACE data which gives RMSE = 0.59 for IRF-Kp-2017 and RMSE = 0.73 for IRF-Kp-2017-h3. The errors as function of Kp and for different years are also studied

Forecasting Kp from solar wind data: input parameter study using 3-hour averages and 3-hour range values

We have developed neural network models that predict Kp from upstream solar wind data. We study the importance of various input parameters, starting with the magnetic component Bz, particle density n, and velocity V and then adding total field B and the By component. As we also notice a seasonal and UT variation in average Kp we include functions of day-of-year and UT. Finally, as Kp is a global representation of the maximum range of geomagnetic variation over 3-hour UT intervals we conclude that sudden changes in the solar wind can have a big effect on Kp, even though it is a 3-hour value. Therefore, 3-hour solar wind averages will not always appropriately represent the solar wind condition, and we introduce 3-hour maxima and minima values to some degree address this problem. We find that introducing total field B and 3-hour maxima and minima, derived from 1-minute solar wind data, have a great influence on the performance. Due to the low number of samples for high Kp values there can be considerable variation in predicted Kp for different networks with similar validation errors. We address this issue by using an ensemble of networks from which we use the median predicted Kp. The models (ensemble of networks) provide prediction lead times in the range 20–90 min given by the time it takes a solar wind structure to travel from L1 to Earth. Two models are implemented that can be run with real time data: (1) IRF-Kp-2017-h3 uses the 3-hour averages of the solar wind data and (2) IRF-Kp-2017 uses in addition to the averages, also the minima and maxima values. The IRF-Kp-2017 model has RMS error of 0.55 and linear correlation of 0.92 based on an independent test set with final Kp covering 2 years using ACE Level 2 data. The IRF-Kp-2017-h3 model has RMSE = 0.63 and correlation = 0.89. We also explore the errors when tested on another two-year period with real-time ACE data which gives RMSE = 0.59 for IRF-Kp-2017 and RMSE = 0.73 for IRF-Kp-2017-h3. The errors as function of Kp and for different years are also studied