On 25 August 2018 the interplanetary counterpart of the 20 August 2018 coronal mass ejection (CME) hit Earth, giving rise to a strong G3 geomagnetic storm. We present a description of the whole sequence of events from the Sun to the ground as well as a detailed analysis of the observed effects on Earth's environment by using a multi-instrumental approach. We studied the ICME (interplanetary-CME) propagation in interplanetary space up to the analysis of its effects in the magnetosphere, ionosphere and at ground level. To accomplish this task, we used ground- and space-collected data, including data from CSES (China Seismo-Electric Satellite), launched on 11 February 2018. We found a direct connection between the ICME impact point on the magnetopause and the pattern of Earth's auroral electrojets. Using the Tsyganenko TS04 model prevision, we were able to correctly identify the principal magnetospheric current system activating during the different phases of the geomagnetic storm. Moreover, we analysed the space weather effects associated with the 25 August 2018 solar event in terms of the evaluation of geomagnetically induced currents (GICs) and identification of possible GPS (Global Positioning System) losses of lock. We found that, despite the strong geomagnetic storm, no loss of lock had been detected. On the contrary, the GIC hazard was found to be potentially more dangerous than other past, more powerful solar events, such as the 2015 St Patrick's Day geomagnetic storm, especially at latitudes higher than 60^∘ in the European sector.

Geomagnetically induced currents (GICs), occurring as a result of space weather events, represent a hazard for the secure and safe operation of electrical power grids and oil/gas pipelines. The most exposed countries are those at high latitudes where, in the past, the occurrence of intense GICs has seriously damaged part of their power networks. However, very powerful space weather events have resulted in intense GICs also at middle and low latitudes. The GIC index is a proxy of the geoelectric field, and it can be estimated straightforwardly from magnetic observatory data. In this work, the GIC index is computed to investigate the possible impact of space weather events on the Italian territory. We first calculate the GIC index using data from the magnetic observatories of Castello Tesino, Duronia, and Lampedusa, together covering the whole Italian latitudinal extension, and show its behavior during the 2015 St. Patrick's day storm. Then, we consider measurements from the two longest running Italian magnetic observatories, that is, Castello Tesino and L'Aquila, and estimate the GIC index over more than 20 years of observations. A preliminary characterization of the general risk to which the Italian power grid network is exposed is given. Results show that during periods of high magnetic activity, potentially detrimental GICs could flow through the power network, especially at the highest Italian latitudes that are characterized by a low conductivity lithosphere.

Forecasting geomagnetic indices represents a key point to develop warning systems for the mitigation of possible effects of severe geomagnetic storms on critical ground infrastructures. Here we focus on SYM‐H index, a proxy of the axially symmetric magnetic field disturbance at low and middle latitudes on the Earth's surface. To forecast SYM‐H, we built two artificial neural network (ANN) models and trained both of them on two different sets of input parameters including interplanetary magnetic field components and magnitude and differing for the presence or not of previous SYM‐H values. These ANN models differ in architecture being based on two conceptually different neural networks: the long short‐term memory (LSTM) and the convolutional neural network (CNN). Both networks are trained, validated, and tested on a total of 42 geomagnetic storms among the most intense that occurred between 1998 and 2018. Performance comparison of the two ANN models shows that (1) both are able to well forecast SYM‐H index 1 h in advance, with an accuracy of more than 95% in terms of the coefficient of determination R²; (2) the model based on LSTM is slightly more accurate than that based on CNN when including SYM‐H index at previous steps among the inputs; and (3) the model based on CNN has interesting potentialities being more accurate than that based on LSTM when not including SYM‐H index among the inputs. Predictions made including SYM‐H index among the inputs provide a root mean squared error on average 42% lower than that of predictions made without SYM‐H.

Preliminary Swarm Langmuir probe measurements recorded during March 2015, a period of time including the St. Patrick storm, are considered. Specifically, six time periods are identified: two quiet periods before the onset of the storm, two periods including the main phase of the storm, and two periods during the recovery phase of the storm. Swarm electron density values are then compared with the corresponding output given by the International Reference Ionosphere (IRI) model, according to its three different options for modelling the topside ionosphere. Since the Swarm electron density measurements are still undergoing a thorough validation, a comparison with IRI in terms of absolute values would have not been appropriate. Hence, the similarity of trends embedded in the Swarm and IRI time series is investigated in terms of Pearson correlation coefficient. The analysis shows that the electron density representations made by Swarm and IRI are different for both quiet and disturbed periods, independently of the chosen topside model option. Main differences between trends modelled by IRI and those observed by Swarm emerge, especially at equatorial latitudes, and at northern high latitudes, during the main and recovery phases of the storm. Moreover, very low values of electron density, even lower than 2 × 104 cm−3, were simultaneously recorded in the evening sector by Swarm satellites at equatorial latitudes during quiet periods, and at magnetic latitudes of about ±60° during disturbed periods. The obtained results are an example of the capability of Swarm data to generate an additional valuable dataset to properly model the topside ionosphere.