Novel approach to geomagnetically induced current forecasts based on remote solar observations

Pulkkinen, A.; Taktakishvili, A. L.; Odstrčil, D.; Jacobs, W. E.

doi:10.1029/2008sw000447

Cited by 10 publications

(14 citation statements)

References 24 publications

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“…The solar wind convective electric field defined here as E sw = − v · B z , where v is the bulk solar wind speed and B z is the z component of the solar wind magnetic field B in GSM coordinates, is an important parameter quantifying the strength of the solar wind driving of the magnetospheric activity. Pulkkinen et al [2008, 2009] used convective electric field predictions generated by WSA‐ENLIL cone model approach to couple CME and solar wind bulk plasma and magnetic field parameters to geomagnetic field fluctuations on the surface of the Earth. They estimated geomagnetically induced current (GIC) levels at high‐latitude locations.…”

Section: Estimation Of the Convective Electric Field Induced By The Cmementioning

confidence: 99%

Modeling of coronal mass ejections that caused particularly large geomagnetic storms using ENLIL heliosphere cone model

et al. 2011

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[1] In our previous paper we reported the results of modeling of 14 selected well-observed strong halo coronal mass ejection (CME) events using the WSA-ENLIL cone model combination. Cone model input parameters were obtained from white light coronagraph images of the CME events using the analytical method developed by Xie et al. (2004). This work verified that coronagraph input gives reasonably good results for the CME arrival time prediction. In contrast to Taktakishvili et al. (2009), where we started the analysis by looking for clear CME signatures in the data and then proceeded to model the interplanetary consequences at 1 AU, in the present paper we start by generating a list of observed geomagnetic storm events and then work our way back to remote solar observations and carry out the corresponding CME modeling. The approach used in this study is addressing space weather forecasting and operational needs. We analyzed 36 particularly strong geomagnetic storms, then tried to associate them with particular CMEs using SOHO/LASCO catalogue, and finally modeled these CMEs using WSA-ENLIL cone model. Recently, Pulkkinen et al. (2010) developed a novel method for automatic determination of cone model parameters. We employed both analytical and automatic methods to determine cone model input parameters. We examined the CME arrival times and magnitude of impact at 1 AU for both techniques. The results of the simulations are compared with the ACE satellite observations. This comparison demonstrated that WSA-ENLIL model combination with coronagraph input gives reasonably good results for the CME arrival times for this set of "geoeffective" CME events as well.Citation: Taktakishvili, A., A. Pulkkinen, P. MacNeice, M. Kuznetsova, M. Hesse, and D. Odstrcil (2011), Modeling of coronal mass ejections that caused particularly large geomagnetic storms using ENLIL heliosphere cone model, Space Weather, 9, S06002,

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Section: Estimation Of the Convective Electric Field Induced By The Cmementioning

confidence: 99%

Modeling of coronal mass ejections that caused particularly large geomagnetic storms using ENLIL heliosphere cone model

et al. 2011

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“…It was found by Pulkkinen et al (2009) that Level 1 forecasts are able to give reasonably accurate predictions for the start time of the GIC events; for the analyzed storm events the mean error is about 5 h. However, the approach systematically underestimated the length of the events, the mean error is about 17 h. Further, if a ''hit'' is defined as a prediction for which the observed maximum value falls within the predicted 68% probability GIC range, the forecast ratios (for all levels of GIC) for the two stations were 11/3 and 7/7, respectively. Although the result may sound modest, it is argued that considering the notable leap taken in the new approach in comparison to earlier efforts, already the fact that the predicted GIC magnitudes are somewhat comparable to the observed GIC should be considered as a satisfactory and promising result.…”

Section: Level 1 Forecastsmentioning

confidence: 95%

“…The probabilistic coupling is established by methods developed in Pulkkinen et al (2008) and by using the local ground conductivities and system parameters derived by methods developed in Pulkkinen et al (2007b). The details of the Level 1 forecasting approach are given in Pulkkinen et al (2009). It should be noted that, in principle, heliospheric MHD model output could be used as an input to a magnetospheric MHD model that would then provide fully first-principlesbased estimates of GIC also in Level 1 forecasts.…”

Section: Level 1 Forecastsmentioning

confidence: 99%

“…Instead, Level 1 GIC forecasts were evaluated by means of 14 CME events (for details, see Pulkkinen et al 2009;Taktakishvili et al 2009). The observed CMEs were used to drive the cone model and ENLIL and the output were used to determine the start and the end times of the disturbances at the Earth and to compute the probabilities of maximum GIC for two nodes of the North American power transmission system.…”

Section: Level 1 Forecastsmentioning

confidence: 99%

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Solar shield: forecasting and mitigating space weather effects on high-voltage power transmission systems

et al. 2009

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In this paper, central elements of the Solar Shield project, launched to design and establish an experimental system capable of forecasting the space weather effects on high-voltage power transmission system, are described. It will be shown how Sun-Earth system data and models hosted at the Community Coordinated Modeling Center (CCMC) are used to generate two-level magnetohydrodynamics-based forecasts providing 1-2 day and 30-60 min lead-times. The Electric Power Research Institute (EPRI) represents the end-user, the power transmission industry, in the project. EPRI integrates the forecast products to an online display tool providing information about space weather conditions to the member power utilities. EPRI also evaluates the economic impacts of severe storms on power transmission systems. The economic analysis will quantify the economic value of the generated forecasting system. The first version of the two-level forecasting system is currently running in real-time at CCMC. An initial analysis of the system's capabilities has been completed, and further analysis is being carried out to optimize the performance of the system. Although the initial results are encouraging, definite conclusions about system's performance can be given only after more extensive analysis, and implementation of an automatic evaluation process using forecasted and observed geomagnetically induced currents from different nodes of the North American power transmission system. The final output of the Solar Shield will be a recommendation for an optimal forecasting system that may be transitioned into space weather operations.A. Pulkkinen (

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“…The cone model has been the subject of a number of recent studies (e.g., Zhao, Plunkett, and Liu, 2002;Xie, Ofman, and Lawrence, 2004;Xue, Wang, and Dou, 2005; and the concept has been used to derive initial parameters for transients launched into a heliospheric magnetohydrodynamic (MHD) code (Odstrcil, Riley, and Zhao, 2004). The combination of the cone and heliospheric MHD modeling has been found to be of potentially significant value from the space weather perspective Pulkkinen et al, 2009). Although the cone model clearly is only a rough approximate way to characterize the three-dimensional structure of CMEs (for a more complex characterization of CMEs, see, e.g., Thernisien, Howard, and Vourlidas, 2006), the mathematical simplicity of the model is, however, one of the central requirements for the success of the automated processing described below.…”

Section: Introductionmentioning

confidence: 99%

Automatic Determination of the Conic Coronal Mass Ejection Model Parameters

2009

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Characterization of the three-dimensional structure of solar transients using incomplete plane of sky data is a difficult problem whose solutions have potential for societal benefit in terms of space weather applications. In this paper transients are characterized in three dimensions by means of conic coronal mass ejection (CME) approximation. A novel method for the automatic determination of cone model parameters from observed halo CMEs is introduced. The method uses both standard image processing techniques to extract the CME mass from white-light coronagraph images and a novel inversion routine providing the final cone parameters. A bootstrap technique is used to provide model parameter distributions. When combined with heliospheric modeling, the cone model parameter distributions will provide direct means for ensemble predictions of transient propagation in the heliosphere.An initial validation of the automatic method is carried by comparison to manually determined cone model parameters. It is shown using 14 halo CME events that there is reasonable agreement, especially between the heliocentric locations of the cones derived with the two methods. It is argued that both the heliocentric locations and the opening half-angles of the automatically determined cones may be more realistic than those obtained from the manual analysis.

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Novel approach to geomagnetically induced current forecasts based on remote solar observations

Cited by 10 publications

References 24 publications

Modeling of coronal mass ejections that caused particularly large geomagnetic storms using ENLIL heliosphere cone model

Modeling of coronal mass ejections that caused particularly large geomagnetic storms using ENLIL heliosphere cone model

Solar shield: forecasting and mitigating space weather effects on high-voltage power transmission systems

Automatic Determination of the Conic Coronal Mass Ejection Model Parameters

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