Abstract. In this paper, we analyse in detail two famous space weather events; a railway problem on 13–14 July 1982 and a power blackout on 30 October 2003. Both occurred in Sweden during very intensive space weather storms and each of them a few years after the sunspot maximum. This paper provides a description of the conditions on the Sun and in the solar wind leading to the two GIC events on the ground. By applying modelling techniques introduced and developed in our previous paper, we also calculate the horizontal geoelectric field at the Earth's surface in southern Sweden during the two storms as well as GIC flowing in the southern Swedish 400 kV power grid during the event in October 2003. The results from the calculations agree with all measured data available. In the July-1982 storm, the geomagnetic field variation, ΔBx, reached values up to ~2500 nT/min and the geoelectric field reached values in the order of several volts per kilometer. In the October-2003 storm, the geomagnetic field fluctuations were smaller. However, GIC of some hundreds of amperes flowed in the power grid during the October-2003 event. Technological issues related to the railway signalling in July 1982 and to the power network equipment in October 2003 are also discussed.

[1] We here present a model for real time forecasting of the geomagnetic index Dst. The model consists of a recurrent neural network that has been optimized to be as small as possible without degrading the accuracy. It is driven solely by hourly averages of the solar wind magnetic field component B z , particle density n, and velocity V, which means that the model does not rely on observed Dst. In an evaluation based on more than 40,000 hours of solar wind and Dst data, it is shown that this model has smaller errors than other models currently in operational use. A complete description of the model is given in an appendix.

Sweden has experienced many geomagnetically induced current (GIC) events in the past, which is obviously due to the high‐latitude location of the country. The largest GIC, almost 300 A, was measured in southern Sweden in the earthing lead of a 400 kV transformer neutral during the magnetic storm on 6 April 2000. On 30 October 2003, the city of Malmö at the southern coast suffered from a power blackout caused by GIC, leaving 50,000 customers without electricity for about 20–50 min. We have developed a model that enables calculation of GIC in the southern Swedish 400 kV power grid. This work constitutes the first modeling effort of GIC in Sweden. The model is divided into two parts. The electric field is first derived using a ground conductivity model and geomagnetic recordings from nearby stations. The conductivity model is determined from a least squares fit between measured and calculated GIC. GIC are calculated using a power grid model consisting of the topology of the system and of the transformer, transmission line, and station earthing resistances as well as of the coordinates of the stations. To validate the model, we have compared measured and calculated GIC from one site. In total, 24 events in 1998 to 2000 were used. In general the agreement is satisfactory as the correct GIC order of magnitude is obtained by the model, which is usually enough for engineering applications.

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In order to accurately predict geomagnetic storms, we exploit Elman recurrent neural networks to predict the Dst index one hour in advance only from solar wind data. The input parameters are the interplanetary magnetic field z‐component Bz (GSM), the solar wind plasma number density n and the solar wind velocity V. The solar wind data and the geomagnetic index Dst are selected from observations during the period 1963 to 1987, covering 8620h and containing 97 storms and 10 quiet periods. These data are grouped into three data sets; a training set 4877h, a validation set 1978h and a test set 1765h. It is found that different strengths of the geomagnetic storms are accurately predicted, and so are all phases of the storms. As an average for the out‐of‐sample performance, the correlation coefficient between the predicted and the observed Dst is 0.91. The predicted average relative variance is 0.17, i.e. 83 percent of the observed Dst variance is predictable by the solar wind. The predicted root‐mean‐square error is 16 nT.