The study of the preparation phase of large earthquakes is essential to understand the physical processes involved, and potentially useful also to develop a future reliable short-term warning system. Here we analyse electron density and magnetic field data measured by Swarm three-satellite constellation for 4.7 years, to look for possible in-situ ionospheric precursors of large earthquakes to study the interactions between the lithosphere and the above atmosphere and ionosphere, in what is called the Lithosphere-Atmosphere-Ionosphere Coupling (LAIC). We define these anomalies statistically in the whole space-time interval of interest and use a Worldwide Statistical Correlation (WSC) analysis through a superposed epoch approach to study the possible relation with the earthquakes. We find some clear concentrations of electron density and magnetic anomalies from more than two months to some days before the earthquake occurrences. Such anomaly clustering is, in general, statistically significant with respect to homogeneous random simulations, supporting a LAIC during the preparation phase of earthquakes. By investigating different earthquake magnitude ranges, not only do we confirm the well-known Rikitake empirical law between ionospheric anomaly precursor time and earthquake magnitude, but we also give more reliability to the seismic source origin for many of the identified anomalies.

We introduce a novel empirical model to forecast, 24 h in advance, the Total Electron Content (TEC) at global scale. The technique leverages on the Global Ionospheric Map (GIM), provided by the International GNSS Service (IGS), and applies a nonlinear autoregressive neural network with external input (NARX) to selected GIM grid points for the 24 h single-point TEC forecasting, taking into account the actual and forecasted geomagnetic conditions. To extend the forecasting at a global scale, the technique makes use of the NeQuick2 Model fed by an effective sunspot number R12 (R12eff), estimated by minimizing the root mean square error (RMSE) between NARX output and NeQuick2 applied at the same GIM grid points. The novel approach is able to reproduce the features of the ionosphere especially during disturbed periods. The performance of the forecasting approach is extensively tested under different geospatial conditions, against both TEC maps products by UPC (Universitat Politècnica de Catalunya) and independent TEC data from Jason-3 spacecraft. The testing results are very satisfactory in terms of RMSE, as it has been found to range between 3 and 5 TECu. RMSE depend on the latitude sectors, time of the day, geomagnetic conditions, and provide a statistical estimation of the accuracy of the 24-h forecasting technique even over the oceans. The validation of the forecasting during five geomagnetic storms reveals that the model performance is not deteriorated during disturbed periods. This 24-h empirical approach is currently implemented on the Ionosphere Prediction Service (IPS), a prototype platform to support different classes of GNSS users.

We analyse Swarm satellite magnetic field and electron density data one month before and one month after 12 strong earthquakes that have occurred in the first 2.5 years of Swarm satellite mission lifetime in the Mediterranean region (magnitude M6.1+) or in the rest of the world (M6.7+). The search for anomalies was limited to the area centred at each earthquake epicentre and bounded by a circle that scales with magnitude according to the Dobrovolsky’s radius. We define the magnetic and electron density anomalies statistically in terms of specific thresholds with respect to the same statistical quantity along the whole residual satellite track (|geomagnetic latitude| ≤ 50°, quiet geomagnetic conditions). Once normalized by the analysed satellite tracks, the anomalies associated to all earthquakes resemble a linear dependence with earthquake magnitude, so supporting the statistical correlation with earthquakes and excluding a relationship by chance.

The geomagnetic storm occurred on 25 August 2018, i.e. during the minimum of solar cycle 24, is currently the strongest ever probed by the first China Seismo‐Electromagnetic Satellite (CSES‐01). By integrating the in situ measurements provided by CSES‐01 (orbiting at altitude of 507 km) and by Swarm A satellite (orbiting at ca. 470 km) with ground‐based observations (ionosondes, magnetometers and Global Navigation Satellite System receivers), we investigate the ionospheric response at low‐ and mid‐ latitudes over Brazil. Specifically, we investigate the electrodynamic disturbances driven by solar wind changes, by focusing on the disturbances driving modifications of the Equatorial Electrojet (EEJ). Our proposed multi‐sensor technique analysis mainly highlights the variations in the topside and bottomside ionosphere, and the interplay between Prompt Penetrating Electric Fields and Disturbance Dynamo Electric Fields resulting in EEJ variations. Thanks to this approach and leveraging on the newly available CSES‐01 data, we complement and extend what recently investigated in the Western South American sector, by highlighting the significant longitudinal differences, which mainly come from the occurrence of a daytime counter‐EEJ during both 25 and 26 August at Brazilian longitudes and during part of 26 August only in the Peruvian sector. In addition, the increased thermospheric circulation driven by the storm has an impact on the EEJ during the recovery phase of the storm. The observations at the CSES‐01/Swarm altitudes integrated with the ground‐based observation recorded signatures of Equatorial Ionospheric Anomaly (EIA) crests formation and modification during daytime coupled with the positive ionospheric storm effects at mid‐latitude.

This work presents a contribution to the understanding of the ionospheric triggering of L-band scintillation in the region over São Paulo state in Brazil, under high solar activity. In particular, a climatological analysis of Global Navigation Satellite Systems (GNSS) data acquired in 2012 is presented to highlight the relationship between intensity and variability of the total electron content (TEC) gradients and the occurrence of ionospheric scintillation. The analysis is based on the GNSS data acquired by a dense distribution of receivers and exploits the integration of a dedicated TEC calibration technique into the Ground Based Scintillation Climatology (GBSC), previously developed at the Istituto Nazionale di Geofisica e Vulcanologia. Such integration enables representing the local ionospheric features through climatological maps of calibrated TEC and TEC gradients and of amplitude scintillation occurrence. The disentanglement of the contribution to the TEC variations due to zonal and meridional gradients conveys insight into the relation between the scintillation occurrence and the morphology of the TEC variability. The importance of the information provided by the TEC gradients variability and the role of the meridional TEC gradients in driving scintillation are critically described.

This paper presents a review on the PECASUS service, which provides advisories on enhanced space weather activity for civil aviation. The advisories are tailored according to the Standards and Recommended Practices of the International Civil Aviation Organization (ICAO). Advisories are disseminated in three impact areas: radiation levels at flight altitudes, GNSS-based navigation and positioning, and HF communication. The review, which is based on the experiences of the authors from two years of running pilot ICAO services, describes empirical models behind PECASUS products and lists ground- and space-based sensors, providing inputs for the models and 24/7 manual monitoring activities. As a concrete example of PECASUS performance, its products for a post-storm ionospheric F2-layer depression event are analyzed in more detail. As PECASUS models are particularly tailored to describe F2-layer thinning, they reproduce observations more accurately than the International Reference Ionosphere model (IRI(STORM)), but, on the other hand, it is recognized that the service performance is much affected by the coverage of its input data. Therefore, more efforts will be directed toward systematic measuring of the availability, timeliness and quality of the data provision in the next steps of the service development.