Predictions of local ground geomagnetic field fluctuations during the 7-10 November 2004 events studied with solar wind driven models

The 7-10 November 2004 period contains two events for which the local ground magnetic field was severely disturbed and simultaneously, the solar wind displayed several shocks and negative <i>B<sub>z</sub></i> periods. Using empirical models the 10-min RMS and at Brorfelde (BFE, 11.67° E, 55.63° N), Denmark, are predicted. The models are recurrent neural networks with 10-min solar wind plasma and magnetic field data as inputs. The predictions show a good agreement during 7 November, up until around noon on 8 November, after which the predictions become significantly poorer. The correlations between observed and predicted log RMS is 0.77 during 7-8 November but drops to 0.38 during 9-10 November. For RMS the correlations for the two periods are 0.71 and 0.41, respectively. Studying the solar wind data for other L1-spacecraft (WIND and SOHO) it seems that the ACE data have a better agreement to the near-Earth solar wind during the first two days as compared to the last two days. Thus, the accuracy of the predictions depends on the location of the spacecraft and the solar wind flow direction. Another finding, for the events studied here, is that the and models showed a very different dependence on <i>B<sub>z</sub></i>. The model is almost independent of the solar wind magnetic field <i>B<sub>z</sub></i>, except at times when <i>B<sub>z</sub></i> is exceptionally large or when the overall activity is low. On the contrary, the model shows a strong dependence on <i>B<sub>z</sub></i> at all times

Comment on "CAWSES November 7-8, 2004, superstorm: Complex solar and interplanetary features in the post-solar maximum phase" by B. T. Tsurutani, E. Echer, F. L. Guarnieri, and J. U. Kozyra

Recently Tsurutani et al., (2008) (Paper 1) analyzed the complex interplanetary structures during 7 to 8 November, 2004 to identify their properties as well as resultant geomagnetic effects and the solar origins. Besides mentioned paper by Gopalswamy et al., (2006) the solar and interplanetary sources of geomagnetic storm on 7-10 November, 2004 have also been discussed in details in series of other papers. Some conclusions of these works essentially differ from conclusions of the Paper 1 but have not been discussed by authors of Paper 1. In this comment we would like to discuss some of these distinctions.Comment: Submitted for publication in Geophysical Research Letter

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

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

Quantifying the daily economic impact of extreme space weather due to failure in electricity transmission infrastructure

Extreme space weather due to coronal mass ejections has the potential to cause considerable disruption to the global economy by damaging the transformers required to operate electricity transmission infrastructure. However, expert opinion is split between the potential outcome being one of a temporary regional blackout and of a more prolonged event. The temporary blackout scenario proposed by some is expected to last the length of the disturbance, with normal operations resuming after a couple of days. On the other hand, others have predicted widespread equipment damage with blackout scenarios lasting months. In this paper we explore the potential costs associated with failure in the electricity transmission infrastructure in the U.S. due to extreme space weather, focusing on daily economic loss. This provides insight into the direct and indirect economic consequences of how an extreme space weather event may affect domestic production, as well as other nations, via supply chain linkages. By exploring the sensitivity of the blackout zone, we show that on average the direct economic cost incurred from disruption to electricity represents only 49% of the total potential macroeconomic cost. Therefore, if indirect supply chain costs are not considered when undertaking cost-benefit analysis of space weather forecasting and mitigation investment, the total potential macroeconomic cost is not correctly represented. The paper contributes to our understanding of the economic impact of space weather, as well as making a number of key methodological contributions relevant for future work. Further economic impact assessment of this threat must consider multiday, multiregional event

AI-ready data in space science and solar physics: problems, mitigation and action plan

In the domain of space science, numerous ground-based and space-borne data of various phenomena have been accumulating rapidly, making analysis and scientific interpretation challenging. However, recent trends in the application of artificial intelligence (AI) have been shown to be promising in the extraction of information or knowledge discovery from these extensive data sets. Coincidentally, preparing these data for use as inputs to the AI algorithms, referred to as AI-readiness, is one of the outstanding challenges in leveraging AI in space science. Preparation of AI-ready data includes, among other aspects: 1) collection (accessing and downloading) of appropriate data representing the various physical parameters associated with the phenomena under study from different repositories; 2) addressing data formats such as conversion from one format to another, data gaps, quality flags and labeling; 3) standardizing metadata and keywords in accordance with NASA archive requirements or other defined standards; 4) processing of raw data such as data normalization, detrending, and data modeling; and 5) documentation of technical aspects such as processing steps, operational assumptions, uncertainties, and instrument profiles. Making all existing data AI-ready within a decade is impractical and data from future missions and investigations exacerbates this. This reveals the urgency to set the standards and start implementing them now. This article presents our perspective on the AI-readiness of space science data and mitigation strategies including definition of AI-readiness for AI applications; prioritization of data sets, storage, and accessibility; and identifying the responsible entity (agencies, private sector, or funded individuals) to undertake the task

Using gradient boosting regression to improve ambient solar wind model predictions

Studying the ambient solar wind, a continuous pressure‐driven plasma flow emanating from our Sun, is an important component of space weather research. The ambient solar wind flows in interplanetary space determine how solar storms evolve through the heliosphere before reaching Earth, and especially during solar minimum are themselves a driver of activity in the Earth’s magnetic field. Accurately forecasting the ambient solar wind flow is therefore imperative to space weather awareness. Here we present a machine learning approach in which solutions from magnetic models of the solar corona are used to output the solar wind conditions near the Earth. The results are compared to observations and existing models in a comprehensive validation analysis, and the new model outperforms existing models in almost all measures. In addition, this approach offers a new perspective to discuss the role of different input data to ambient solar wind modeling, and what this tells us about the underlying physical processes. The final model discussed here represents an extremely fast, well‐validated and open‐source approach to the forecasting of ambient solar wind at Earth