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

Wintoft, Peter; Wik, Magnus; Lundstedt, H.; Eliasson, L.

doi:10.5194/angeo-23-3095-2005

Cited by 16 publications

(11 citation statements)

References 33 publications

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“…A number of models predicting GIC‐related proxies based on the temporal derivatives of the horizontal geomagnetic field components ( H X and H Y ) have been developed [ Weigel et al , ; Wintoft et al , ; Lotz and Cilliers , ]. These are all neural network‐based models driven by inputs based on solar wind (SW) plasma and magnetic field parameters measured by spacecraft upstream of the bow shock.…”

Section: Introductionmentioning

confidence: 99%

Regression‐based forecast model of induced geoelectric field

2017

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The electric field induced due to solar activity is the driver of geomagnetically induced currents (GIC) in grounded conductor networks such as power grids. We present a regression‐based (i.e., empirical) model for quantitatively predicting the upper envelope of induced E field components, using near‐Earth measurements of solar wind plasma and magnetic field parameters as input arguments to the model at three midlatitude locations. Model parameters and the set of input arguments used are determined by an iterative regression process that relies on a large data set comprising selected events from 2000 to 2015. Testing on out‐of‐sample events over two years (2013 and 2015) yields correlation of between 0.68 and 0.75 when predicting the northward horizontal component of induced E field and 0.45–0.61 for the eastward component at the three stations. The forecast lead time is extended by using predicted SW parameters (from the WSA‐Enlil models) as model input. As proof of concept we predict GIC based on measured and predicted input parameters and compare that with measured GIC at a South African substation for the two St. Patrick's day events (March 2013 and 2015).

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Section: Introductionmentioning

confidence: 99%

Regression‐based forecast model of induced geoelectric field

2017

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“…The magnetic field of the Earth is known to respond to pressure pulses in the solar wind, as well as southward turning of the interplanetary magnetic field (IMF). Many authors have used neural networks [ Lotz and Cilliers , ; Wintoft et al , ], superposed epoch analysis [ Hutchinson et al , ; Zhang and Moldwin , ], other methods such as linear prediction filtering [ Lam , ], and solar wind air mass analysis [ McPherron and Siscoe , ] to forecast the impact on the geomagnetic field. The disturbance storm time index ( Dst ) [ Sugiura , ] is commonly used to indicate the strength of a geomagnetic storm; hence, much work has gone into forecasting Dst from the solar wind parameters [ O'Brien and McPherron , ; Temerin and Li , ] and IMF [ Pallocchia et al , ].…”

Section: Introductionmentioning

confidence: 99%

Analysis of geomagnetic hourly ranges

Danskin

Lotz

2015

Space Weather

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In an attempt to develop better forecasts of geomagnetic activity, hourly ranges of geomagnetic data are analyzed with a focus on how the data are distributed. A lognormal distribution is found to be able to characterize the magnetic data for all observatories up to moderate disturbances with each distribution controlled by the mean of the logarithm of the hourly range. In the subauroral zone, the distribution deviates from the lognormal, which is interpreted as motion of the auroral electrojet toward the equator. For most observatories, a substantial deviation from the lognormal distribution was noted at the higher values and is best modeled with a power law extrapolation, which gives estimates of the extreme values that may occur at observatories which contribute to the disturbance storm time (Dst) index and in Canada.

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“…While Pulkkinen et al [2007] investigated the capability of the global model of reproducing the GIC‐related ionosphere current and magnetic field fluctuations through one single geomagnetic event on 24–29 October 2003, and Weigel et al [2003] and Wintoft et al [2005] studied the coupling of solar wind parameters to the ground magnetic field perturbations and their time derivatives through empirical mapping or neural network models that are driven by solar wind data, this study examines more events and use the University of Michigan's MHD code, investigating the performance of the code on predicting the ground‐based magnetic perturbations and their time derivatives, utilizing over 150 magnetometers. In this simulation, magnetic events with dynamic IMF conditions are chosen and the model is driven utilizing measured upstream solar wind conditions instead of approximating the system using a single steady‐state simulation as in the work of Ridley et al [2001].…”

Section: Introductionmentioning

confidence: 99%

Validation of the space weather modeling framework using ground‐based magnetometers

Ridley

2008

Space Weather

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[1] Geomagnetically induced currents (GICs) can disrupt power grid operations, causing significant interference for many people; therefore, predictions of ground-based magnetic perturbations and their time derivatives are quite important. This study quantifies how well the University of Michigan's Global MHD code predicts approximately 150 ground-based magnetometer traces for a number of storm-time intervals. It is found that in order to accurately represent the magnetic perturbation, Biot-Savart integrals over the entire hemisphere are needed, especially when calculating the vertical component. The 4 May 1998 storm is examined in detail. The code compares well with some stations, quantified by a normalized root mean squared error and cross correlation, while missing even the general trends for other stations. When multiple magnetometer station perturbations are averaged together, the model does an adequate job in the north and vertical components but reverses the trends in the eastward component. The code does significantly better when comparing an AL-like index but does not have as strong a variation as the actual data. Comparison of dB/dt in a wide window rather than simultaneously shows better model performance in capturing events but worse in yielding false alarms. It is further found that the MHD code models the magnetic perturbations better in the summer hemisphere than in the winter hemisphere.Citation: Yu, Y., and A. J. Ridley (2008), Validation of the space weather modeling framework using ground-based magnetometers, Space Weather, 6, S05002,

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

Cited by 16 publications

References 33 publications

Regression‐based forecast model of induced geoelectric field

Regression‐based forecast model of induced geoelectric field

Analysis of geomagnetic hourly ranges

Validation of the space weather modeling framework using ground‐based magnetometers

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