Issue 
J. Space Weather Space Clim.
Volume 5, 2015



Article Number  A24  
Number of page(s)  8  
DOI  https://doi.org/10.1051/swsc/2015022  
Published online  17 July 2015 
Research Article
Regional estimation of geomagnetically induced currents based on the local magnetic or electric field
^{1}
Finnish Meteorological Institute, Erik Palménin aukio 1, 00560
Helsinki, Finland
^{2}
Swedish Institute of Space Physics, Scheelevägen 17, 22370
Lund, Sweden
^{*} Corresponding author: ari.viljanen@fmi.fi
Received:
8
April
2015
Accepted:
15
June
2015
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 30min 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 30min maximum as a regional activity measure (GIC_{30}).
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 GIC_{30} depend linearly on dH/dt_{30}, so there is no saturation with increasing geomagnetic activity contrary to often used activity indices.
Key words: Geomagnetically induced currents
© A. Viljanen et al., Published by EDP Sciences 2015
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
1. Introduction
Modelling of geomagnetically induced currents in power grids or pipelines for past events is in principle a straightforward task. The input data consist of geomagnetic recordings, ground conductivity models and the DC model of the power grid or pipeline system (e.g. Viljanen et al. 2012). The same holds true for future events if a prediction of the magnetic field can be given as a time series. However, despite recent advanced steps in the modelling of the nearEarth space (e.g. Pulkkinen et al. 2010a, 2013), a sufficient accuracy is obviously hard to reach. For the time being, a more feasible approach is to try to quantify the expected level of GIC such as its likely peak value with error limits during a future time window.
Several previous studies have demonstrated a close relationship between the time derivative of the horizontal geomagnetic field vector (dH/dt) and GIC at a nearby location (Viljanen 1997, 1998; Viljanen et al. 2001; Hejda & Bochnicek 2005; Thomson et al. 2005; Trichtchenko et al. 2007; Ngwira et al. 2008). Under certain conditions of the ground conductivity, GIC is more closely related to the magnetic variation field itself (Watari et al. 2009; Pulkkinen et al. 2010b). However, as follows from the basic modelling method (Lehtinen & Pirjola 1985; Viljanen et al. 2012; Boteler 2014; Boteler & Pirjola 2014), GIC is definitely linked to the horizontal geoelectric field (E). The electric field is in turn related to temporal variations of the magnetic field as stated by Faraday’s law of induction.
Trichtchenko & Boteler (2004) considered the correlation between GIC and 1h or 3h geomagnetic activity indices, and between GIC and the 1h or 3h peak values of the time derivative of the magnetic field at a nearby observatory (a component of the field, denoted by dB/dt). They found that dB/dt gives the highest correlation. They also remarked that “it is much easier to predict the envelope of the GIC variations rather than the detailed GIC variations themselves”. This difficulty is quite obviously related to the fact that “it is usually easier to predict a quantity (magnetic field variation) than its time derivative with a first principlesbased model” (Tóth et al. 2014).
Gleisner & Lundstedt (2001) considered 5min averages when predicting the local magnetic field from solar wind data. Wintoft et al. (2005) presented a method to predict the 10min rootmeansquare (RMS) of the time derivatives of the horizontal magnetic field components. Wintoft (2005) also also considered the relation between the 10min standard deviation of measured GIC and the RMS magnetic field.
A more general hypothesis investigated in this paper is that the level of GIC activity in a regional power grid could be quantified with respect to dH/dt or E determined at a single site. In this study, the starting point is the 30min maximum of dH/dt at a single location as considered by Wintoft et al. (2015). Similarly to us, Weigel et al. (2003) studied 30min sequences when relating solar wind quantities to dB/dt, but they used 30min averages instead of the maximum value. Tóth et al. (2014) considered the maximum dH/dt in 20min intervals when quantifying the success of predictions. Instead of directly comparing the maximum of the measured and forecasted dH/dt, they studied whether it will exceed a threshold value in a 20min interval at a given magnetometer station.
Wintoft et al. (2015) showed that dH/dt has a high correlation with E concerning 30min maximum values. We will generalize this result by showing that such a local measure is useful in quantifying GIC activity in a large surrounding power grid.
2. Data and methods
Because we consider the 30min maximum of dH/dt, we cannot directly determine the electric field from such a single value. However, we can use previously measured magnetic field time series from which we can derive an empirical relationship with the computed electric field at the given location.
We applied the EURISGIC ground conductivity models (Ádám et al. 2012; Viljanen et al. 2014) to calculate 1min values of the electric fields in 1996–2008 at selected points in Europe using the method by Viljanen et al. (2012). The resulting time series were partitioned into 30min sequences (00:0000:29 UT, 00:3000:59, …), and the maximum dH/dt and E for each of them were determined. We did not directly use the measured value of dH/dt, but we always interpolated it from all available recordings using the method of spherical elementary current systems (SECS) by Amm & Viljanen (1999). The SECS method uses as input observed variations of the ground magnetic field and provides as output an equivalent ionospheric current system that reproduces the observations. From the equivalent currents, we can calculate the ground field at any point. This ensures a uniform time series without gaps at all sites even if the measurements from a site were not available for all time steps. Although the magnetic field is then not precisely equal to the measured one, the interpolated value gives a reasonably accurate estimate (Pulkkinen et al. 2003; McLay & Beggan 2010).
Calculated GIC values are based on the simplified power grid models introduced by Viljanen et al. (2012). We will consider the following cases: Nordic (Finland, Sweden, Norway), Baltic (Estonia, Latvia, Lithuania), British isles (UK, Ireland), Continent (continental Europe from Portugal in the west to Romania in the east; from Italy in the south to Denmark in the north). The measure used for GIC activity is the average current per node in the given grid model. So it represents a regional behaviour in contrast to the pointwise value of dH/dt or E.
3. Results
3.1. Relation between the electric field and time derivative of the magnetic field
Figure 1 shows the sum of GIC in the Nordic highvoltage power grid, and the electric and magnetic fields at the Uppsala (UPS) geomagnetic observatory during the large magnetic storm on 30 October 2003. The 1min time series demonstrates that high GIC levels are clearly related to dH/dt or to the modelled E. Even a better correlation becomes evident when considering 30min maximum values also plotted in the figure. We note that UPS is located at about (60 N, 17 E), whereas the Nordic grid model covers the latitude range of about 55–70 N and longitude range of about 3–30 E. Consequently, a large part of the grid lies at several hundreds of kilometres away from UPS. Despite this, there is a pronounced relationship between the local quantities dH/dt and E to the regional sum of GIC. Later in this paper we will also consider the grid in the continental Europe, whose size is even much larger, and still find a similar relationship.
Fig. 1. (a) Sum of GIC divided by the number of nodes in the Nordic power grid at 18–24 UT on 30 October 2003 as 1min values, (b) horizontal electric field at UPS, (c) time derivative of the horizontal magnetic field (dH/dt) at UPS. 30min maximum values are plotted as blue asterisks at the midpoints of each 30min period of 18:00–18:29, 18:30–18:59, etc. 
Figure 2 shows the relation between the 30min maximum of E and dH/dt at four observatories in October 2003 (Wintoft et al. 2015). We note that the maximum E is not necessarily simultaneous with the maximum dH/dt. However, there is a clear linear dependence at all sites with high correlation coefficients. So we can simply express the maximum electric field as(1)where p is an empirical coefficient specific for each site, and max_{30} refers to 30min sequences. The coefficient depends on the local (1D) ground conductivity model. If we use a different model then we must recalculate p too. In the following text, we will use the symbols E_{30} = max_{30} (E) and dH/dt_{30} = max_{30} (dH/dt).
Fig. 2. Maximum of the modelled 1min horizontal electric field versus the maximum of dH/dt for 30min sequences 00:00–00:29, 00:30–00:59, … UT in October 2003. (a) ABK, (b) UPS, (c) BFE, (d) FUR. C is the linear correlation coefficient, p is the slope of the fitted linear curve in (mV/km)/(nT/min). Figure adopted from Wintoft et al. (2015). 
Figure 3 shows the monthly values of the coefficient p of Eq. (1). Its variation from month to month in 1996–2008 is rather small, so it is reasonable to use a single value for each site, for which we select the average of the monthly values. Wintoft et al. (2015) provide the table of the coefficients at several European observatories, so we do not repeat them here.
Fig. 3. Monthly mean values of the coefficient p of Eq. (1) in 1996–2008. (a) ABK, (b) UPS, (c) BFE, (d) FUR. The number of points (227,952) is the total number of 30min sequences in 1996–2008. 
3.2. Regional GIC activity
As shown above, E_{30} is highly correlated with dH/dt_{30}, so the latter gives a good proxy for GIC too. The next step is to derive a quantitative dependence of GIC on the electric field in 30min sequences. As the GIC measure, we use the sum of calculated currents through the neutral leads at substations similarly to Viljanen et al. (2012, 2014), and consider its 30min maximum GIC_{30} = max_{30} (sum(GIC)/N), where N is the number of substations. It is then compared to the value of dH/dt_{30} or E_{30} at a single location.
An example of a monthly result is shown in Figure 4. There is a high linear correlation between dH/dt_{30} or E_{30} and GIC_{30}. We have denoted the largest 1% of values separately in the figure by the red colour. In an ideal case, the set of the largest values of dH/dt_{30} or E_{30} would be equal to the corresponding set of GIC_{30}. However, there is some scatter in the tail of the distribution: for a given large dH/dt_{30} or E_{30}, GIC_{30} can have a fairly large range. A similar feature is visible in E_{30} when plotted against dH/dt_{30} (Fig. 2).
Fig. 4. 30min maximum of the sum of GIC divided by the number of nodes in the Nordic highvoltage power grid in October 2003 as a function of dH/dt_{30} (a) and E_{30} (b) at UPS. Red crosses indicate the 15 largest values of GIC_{30}. Triangles indicate the 15 largest values of dH/dt_{30} or E_{30}. The blue curve is a fitted straight line. 
Next, we repeated the calculations for the 13year period of 1996–2008. Figure 5 shows histograms of GIC_{30} values that correspond to dH/dt_{30} at a given range. We note that even if dH/dt_{30} belongs to the largest percentile (top 1% of all values), GIC_{30} can still be at a relatively low level (down to about 2% of the maximum in 1996–2008). Only if dH/dt_{30} belongs to the largest permille (top 0.1% values), GIC_{30} nearly always reaches values equal to or larger than about 10% of the maximum in 1996–2008. We can also state that dH/dt_{30} must belong to the largest permille before the regional GIC is likely to reach values close to the maximum in 1996–2008.
Fig. 5. Histograms of the 30min maximum of the GIC sum divided by the number of nodes in the Nordic highvoltage power grid for different ranges of dH/dt_{30} at UPS. For example, the upper left plot shows the distribution when dH/dt_{30} varies between 6 and 23 nT/min which correspond to the percentile limits of 90% and 99%, respectively. The total number of 30min values in 1996–2008 is 227,952. The largest value of GIC_{30} is 38.9 A. 
An alternative way to present the results is shown in Figure 6. Instead of expressing the magnitude of GIC in amperes, we use percentile bins in the horizontal axis. An advantage is that this is not fixed to absolute values of GIC, but to a relative scale. In other words, given the percentile range of dH/dt_{30}, we can assess the distribution of GIC_{30} with respect to the maximum within the period under study. In years 1996–2008, such reference events are 15 July 2000 and 29–30 October 2003 (Viljanen et al. 2014).
Fig. 6. Histograms of the 30min maximum of the GIC sum divided by the number of nodes in the Nordic highvoltage power grid for different ranges of dH/dt_{30} at UPS. Percentile bins are used in the horizontal axis. The range of GIC_{30} in amperes is also given in each panel. For example, the upper left plot shows the distribution when dH/dt_{30} varies between 0.10 and 0.38 nT/s which correspond to the percentile limits of 90% and 99%, respectively. Compare also to Figure 5. 
Table 1 collects results for a selected set of observatories corresponding to different parts of European highvoltage power grids. An expected result is that GIC_{30} is highest in North Europe and decreases rapidly towards the south (Viljanen et al. 2014). The selection of an observatory to characterise GIC activity does not seem to be very sensitive. For example, both NUR (Nurmijärvi, Finland) and UPS (Uppsala, Sweden) yield nearly equal results for GIC_{30} in the Nordic grid. However, the observatory must not obviously be very far from the grid of interest. We tested this by using AQU (L’Aquila, Italy) to characterise GIC_{30} in the Nordic grid more than 1000 km north of AQU. As the last rows in Table 1 show, estimates for GIC levels are then more scattered, although the difference from the result by NUR or UPS is not as large as could have been intuitively expected. This observation may give rise to further analysis of scale lengths of geomagnetic variations.
Characteristics of GIC_{30} as a function of dH/dt_{30} at selected geomagnetic observatories. Columns (90–99% etc.) refer to a set of sorted dH/dt_{30} values. For example, the first column is for dH/dt_{30} belonging to the largest 10% of all values excluding the top 1%. For each observatory, we consider a specific grid model indicated below the observatory code. The three rows for each observatory contain the following information: range of dH/dt_{30}, mean and standard deviation of GIC_{30}, range of GIC_{30}. The insert table gives corrected geomagnetic coordinates (CGM) calculated for the year 2002 (http://omniweb.gsfc.nasa.gov/vitmo/cgm_vitmo.html).
4. Conclusions
Previous studies have indicated a high correlation between dH/dt and GIC at single substations of power grids. Our results show that the 30min maximum of the time derivative of the horizontal magnetic field (dH/dt_{30}) at a single point yields a proxy for GIC activity in a larger region as measured by the average current per node in given grid. 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 a surrounding area. Longterm statistics of dH/dt_{30} is often available, so it is possible to quantify how rare GIC events can be expected given a forecasted dH/dt_{30}.
The use of geomagnetic activity indices such as K, A_{k}, A_{p} in the past to estimate GIC activity (e.g. Campbell 1978; Boteler et al. 1982; Lundby et al. 1985) has restrictions due to the fixed range of index values (Kappenman 2005). This is problematic concerning the largest geomagnetic events that are also the most probable ones to cause immediate major effects on power grids. As shown in this paper, the electric field (E_{30}) and GIC_{30} are nearly linearly related to dH/dt_{30}, so there is no saturation with increasing geomagnetic activity.
We should note the scatter related to rare large events. A corresponding feature was noted, for example, by Pulkkinen et al. (2012) who studied 100year scenarios of GIC, and considered the distribution of the modelled electric field and its extrapolation to 1in100 year value in their Figure 1. There is a notable uncertainty range in the estimated extreme value. Thomson et al. (2011) faced the same feature when deriving 100 and 200year extrapolations to the magnetic variation field and its time derivative. However, our Table 1 gives a guideline with error limits for estimating expected GIC magnitudes at a given level of geomagnetic activity as defined by dH/dt_{30}.
We have also demonstrated that a local geomagnetic recording can be used to estimate GIC levels in a wider region than just close to an observatory. This is a useful result especially if there are only few magnetometer stations in the area of a power grid, which restricts the full accurate modelling of GIC.
Acknowledgments
The research leading to these results received funding from the European Community’s Seventh Framework Programme (FP7/20072013) under Grant Agreement No. 260330 (EURISGIC). We thank the institutes maintaining geomagnetic observatories in Europe. The editor thanks Cleiton da Silva Barbosa and an anonymous referee for their assistance in evaluating this paper.
References
 Ádám, A., E. Prácser, and V. Wesztergom. Estimation of the electric resistivity distribution (EURHOM) in the European lithosphere in the frame of the EURISGIC WP2 project. Acta Geod. Geoph. Hung., 47, 377–387, 2012, DOI: 10.1556/AGeod.47.2012.4.1. [CrossRef] [Google Scholar]
 Amm, O., and A. Viljanen. Ionospheric disturbance magnetic field continuation from the ground to the ionosphere using spherical elementary current systems. Earth Planets Space, 51, 431–440, 1999, DOI: 10.1186/BF03352247. [Google Scholar]
 Boteler, D. Methodology for simulation of geomagnetically induced currents in power systems. J. Space Weather Space Clim., 4, A21, 2014, DOI: 10.1051/swsc/2014018. [CrossRef] [EDP Sciences] [Google Scholar]
 Boteler, D.H., and R.J. Pirjola. Comparison of methods for modelling geomagnetically induced currents. Ann. Geophys., 32, 1177–1187, 2014, DOI: 10.5194/angeo3211772014. [CrossRef] [Google Scholar]
 Boteler, D.H., T. Watanabe, R.M. Shier, and R.E. Horita. Characteristics of geomagnetically induced currents in the B.C. Hydro 500 kV system. IEEE T. Power Appar. Syst., PAS101, 1447–1456, 1982, DOI: 10.1109/TPAS.1982.317192. [CrossRef] [Google Scholar]
 Campbell, W.H. Induction of auroral zone electric currents within the alaska pipeline. Pure Appl. Geophys., 116, 1143–1172, 1978, DOI: 10.1007/BF00874677. [CrossRef] [Google Scholar]
 Gleisner, H., and H. Lundstedt. A neural networkbased local model for prediction of geomagnetic disturbances. J. Geophys. Res., 106 (A5), 8425–8433, 2001, DOI: 10.1029/2000JA900142. [CrossRef] [Google Scholar]
 Hejda, P., and J. Bochnicek. Geomagnetically induced pipetosoil voltages in the Czech oil pipelines during October–November 2003. Ann. Geophys., 23, 3089–3093, 2005, DOI: 10.5194/angeo2330892005. [CrossRef] [Google Scholar]
 Kappenman, J.G. An overview of the impulsive geomagnetic field disturbances and power grid impacts associated with the violent SunEarth connection events of 29–31 October 2003 and a comparative evaluation with other contemporary storms. Space Weather, 3, S08C01, 2005, DOI: 10.1029/2004SW000128. [Google Scholar]
 Lehtinen, M., and R. Pirjola. Currents produced in earthed conductor networks by geomagneticallyinduced electric fields. Ann. Geophys., 3, 479–484, 1985. [Google Scholar]
 Lundby, S., B.E. Chapel, D.H. Boteler, T. Watanabe, and R.E. Horita. Occurrence frequency of geomagnetically induced currents: a case study on a B.C. Hydro 500 kV power line. J. Geomag. Geoelectr., 37, 1097–1114, 1985, DOI: 10.5636/jgg.37.1097. [CrossRef] [Google Scholar]
 McLay, S.A., and C.D. Beggan. Ann. Geophys., 28, 1795–1805, 2010, DOI: 10.5194/angeo2817952010. [CrossRef] [Google Scholar]
 Ngwira, C.M., A. Pulkkinen, L.A. McKinnell, and P.J. Cilliers. Improved modeling of geomagnetically induced currents in the South African power network. Space Weather, 6, S11004, 2008, DOI: 10.1029/2008SW000408. [CrossRef] [Google Scholar]
 Pulkkinen, A., O. Amm, A. Viljanen, and BEAR Working Group. Ionospheric equivalent current distributions determined with the method of spherical elementary current systems. J. Geophys. Res., 108 (A2), 1053, 2003, DOI: 10.1029/2001JA005085. [CrossRef] [Google Scholar]
 Pulkkinen, A., M. Hesse, S. Habib, L. Van der Zel, B. Damsky, F. Policelli, D. Fugate, W. Jacobs, and E. Creamer. Solar shield: forecasting and mitigating space weather effects on highvoltage power transmission systems. Nat. Hazards, 53, 333–345, 2010a, DOI: 10.1007/s110690099432x. [CrossRef] [Google Scholar]
 Pulkkinen, A., R. Kataoka, S. Watari, and M. Ichiki. Modeling geomagnetically induced currents in Hokkaido, Japan. Adv. Space Res., 46, 1087–1093, 2010b, DOI: 10.1016/j.asr.2010.05.024. [CrossRef] [Google Scholar]
 Pulkkinen, A., E. Bernabeu, J. Eichner, C. Beggan, and A.W.P. Thomson. Generation of 100year geomagnetically induced current scenarios. Space Weather, 10, S04003, 2012, DOI: 10.1029/2011SW000750. [CrossRef] [Google Scholar]
 Pulkkinen, A., L. Rastätter, M. Kuznetsova, H. Singer, C. Balch, et al. Communitywide validation of geospace model ground magnetic field perturbation predictions to support model transition to operations. Space Weather, 11, 369–385, 2013, DOI: 10.1002/swe.20056. [CrossRef] [Google Scholar]
 Thomson, A.W.P., A.J. McKay, E. Clarke, and S.J. Reay. Surface electric fields and geomagnetically induced currents in the Scottish Power grid during the 30 October 2003 geomagnetic storm. Space Weather, 3, S11002, 2005, DOI: 10.1029/2005SW000156. [CrossRef] [Google Scholar]
 Thomson, A., E. Dawson, and S. Reay. Quantifying Extreme Behaviour in Geomagnetic Activity. Space Weather, 9, S10001, 2011, DOI: 10.1029/2011SW000696. [CrossRef] [Google Scholar]
 Tóth, G., X. Meng, T.I. Gombosi, and L. Rastätter. Predicting the time derivative of local magnetic perturbations. J. Geophys. Res. [Space Phys.], 119, 310–321, 2014, DOI: 10.1002/2013JA019456. [CrossRef] [Google Scholar]
 Trichtchenko, L., and D.H. Boteler. Modeling geomagnetically induced currents using geomagnetic indices and data. IEEE T. Plasma Sci., 32, 1459–1467, 2004, DOI: 10.1109/TPS.2004.830993. [CrossRef] [Google Scholar]
 Trichtchenko, L., A. Zhukov, R. van der Linden, S.M. Stankov, N. Jakowski, et al. November 2004 space weather events: Realtime observations and forecasts. Space Weather, 5, S06001, 2007, DOI: 10.1029/2006SW000281. [CrossRef] [Google Scholar]
 Viljanen, A. The relation between geomagnetic variations and their time derivatives and implications for estimation of induction risks. Geophys. Res. Lett., 24, 631–634, 1997, DOI: 10.1029/97GL00538. [CrossRef] [Google Scholar]
 Viljanen, A. Relation of geomagnetically induced currents and local geomagnetic variations. IEEE Trans. Power Delivery, 13, 1285–1290, 1998, DOI: 10.1109/61.714497. [CrossRef] [Google Scholar]
 Viljanen, A., H. Nevanlinna, K. Pajunpää, and A. Pulkkinen. Time derivative of the horizontal geomagnetic field as an activity indicator. Ann. Geophys., 19, 1107–1118, 2001, DOI: 10.5194/angeo1911072001. [CrossRef] [Google Scholar]
 Viljanen, A., R. Pirjola, M. Wik, A. Adam, E. Pracser, Ya. Sakharov, and Yu. Katkalov. Continental scale modelling of geomagnetically induced currents. J. Space Weather Space Clim., 2, A17, 2012, DOI: 10.1051/swsc/2012017. [CrossRef] [EDP Sciences] [Google Scholar]
 Viljanen, A., R. Pirjola, E. Pracser, J. Katkalov, and M. Wik. Geomagnetically induced currents in Europe. Modelled occurrence in a continentwide power grid. J. Space Weather Space Clim., 4, A09, 2014, DOI: 10.1051/swsc/2014006. [CrossRef] [EDP Sciences] [Google Scholar]
 Watari, S., M. Kunitake, K. Kitamura, T. Hori, T. Kikuchi, et al. Measurements of geomagnetically induced current in a power grid in Hokkaido, Japan. Space Weather, 7, S03002, 2009, DOI: 10.1029/2008SW000417. [Google Scholar]
 Weigel, R.S., A.J. Klimas, and D. Vassiliadis. Solar wind coupling to and predictability of ground magnetic fields and their time derivatives. J. Geophys. Res., 108 (A7), 1298, 2003, DOI: 10.1029/2002JA009627. [CrossRef] [Google Scholar]
 Wintoft, P. Study of the solar wind coupling to the time difference horizontal geomagnetic field. Ann. Geophys., 23, 1949–1957, 2005, DOI: 10.5194/angeo2319492005. [CrossRef] [Google Scholar]
 Wintoft, P., M. Wik, H. Lundstedt, and L. Eliasson. Predictions of local ground geomagnetic field fluctuations during the 7–10 November 2004 events studied with solar wind driven models. Ann. Geophys., 23, 3095–3101, 2005, DOI: 10.5194/angeo2330952005. [CrossRef] [Google Scholar]
 Wintoft, P., M. Wik, and A. Viljanen. Solar wind driven empirical forecast models of the time derivative of the ground magnetic field. J. Space Weather Space Clim., 5, A7, 2015, DOI: 10.1051/swsc/2015008. [CrossRef] [EDP Sciences] [Google Scholar]
Cite this article as: Viljanen A, Wintoft P & Wik M. Regional estimation of geomagnetically induced currents based on the local magnetic or electric field. J. Space Weather Space Clim., 5, A24, 2015, DOI: 10.1051/swsc/2015022.
All Tables
Characteristics of GIC_{30} as a function of dH/dt_{30} at selected geomagnetic observatories. Columns (90–99% etc.) refer to a set of sorted dH/dt_{30} values. For example, the first column is for dH/dt_{30} belonging to the largest 10% of all values excluding the top 1%. For each observatory, we consider a specific grid model indicated below the observatory code. The three rows for each observatory contain the following information: range of dH/dt_{30}, mean and standard deviation of GIC_{30}, range of GIC_{30}. The insert table gives corrected geomagnetic coordinates (CGM) calculated for the year 2002 (http://omniweb.gsfc.nasa.gov/vitmo/cgm_vitmo.html).
All Figures
Fig. 1. (a) Sum of GIC divided by the number of nodes in the Nordic power grid at 18–24 UT on 30 October 2003 as 1min values, (b) horizontal electric field at UPS, (c) time derivative of the horizontal magnetic field (dH/dt) at UPS. 30min maximum values are plotted as blue asterisks at the midpoints of each 30min period of 18:00–18:29, 18:30–18:59, etc. 

In the text 
Fig. 2. Maximum of the modelled 1min horizontal electric field versus the maximum of dH/dt for 30min sequences 00:00–00:29, 00:30–00:59, … UT in October 2003. (a) ABK, (b) UPS, (c) BFE, (d) FUR. C is the linear correlation coefficient, p is the slope of the fitted linear curve in (mV/km)/(nT/min). Figure adopted from Wintoft et al. (2015). 

In the text 
Fig. 3. Monthly mean values of the coefficient p of Eq. (1) in 1996–2008. (a) ABK, (b) UPS, (c) BFE, (d) FUR. The number of points (227,952) is the total number of 30min sequences in 1996–2008. 

In the text 
Fig. 4. 30min maximum of the sum of GIC divided by the number of nodes in the Nordic highvoltage power grid in October 2003 as a function of dH/dt_{30} (a) and E_{30} (b) at UPS. Red crosses indicate the 15 largest values of GIC_{30}. Triangles indicate the 15 largest values of dH/dt_{30} or E_{30}. The blue curve is a fitted straight line. 

In the text 
Fig. 5. Histograms of the 30min maximum of the GIC sum divided by the number of nodes in the Nordic highvoltage power grid for different ranges of dH/dt_{30} at UPS. For example, the upper left plot shows the distribution when dH/dt_{30} varies between 6 and 23 nT/min which correspond to the percentile limits of 90% and 99%, respectively. The total number of 30min values in 1996–2008 is 227,952. The largest value of GIC_{30} is 38.9 A. 

In the text 
Fig. 6. Histograms of the 30min maximum of the GIC sum divided by the number of nodes in the Nordic highvoltage power grid for different ranges of dH/dt_{30} at UPS. Percentile bins are used in the horizontal axis. The range of GIC_{30} in amperes is also given in each panel. For example, the upper left plot shows the distribution when dH/dt_{30} varies between 0.10 and 0.38 nT/s which correspond to the percentile limits of 90% and 99%, respectively. Compare also to Figure 5. 

In the text 
Current usage metrics show cumulative count of Article Views (fulltext article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 4896 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.