We investigate the role of auroral particle precipitation in small-scale (below hundreds of meters) plasma structuring in the auroral ionosphere over the Arctic. To the scope, we together analyse data recorded by an Ionospheric Scintillation Monitor Receiver (ISMR) of Global Navigation Satellite System (GNSS) signals and by an All-Sky Camera located in Longyearbyen, Svalbard (Norway). We leverage on the raw GNSS samples provided at 50 Hz by the ISMR to evaluate amplitude and phase scintillation indices at 1 s time resolution and the Ionosphere-Free Linear Combination at 20 ms time resolution. The simultaneous use of the 1 s GNSS-based scintillation indices allows identifying the scale size of the irregularities involved in plasma structuring in the range of small (up to few hundreds of meters) and medium-scale size ranges (up to few kilometers) for GNSS frequencies and observational geometry. Additionally, they allow identifying the diffractive and refractive nature of the found fluctuations on the recorded GNSS signals. Six strong auroral events and their effects on plasma structuring are studied. Plasma structuring down to scales of hundreds of meters are seen when strong gradients in auroral emissions at 557.7 nm cross the line of sight between the GNSS satellite and receiver. Local magnetic field measurements confirm small-scale structuring processes coinciding with intensification of ionospheric currents. Since 557.7 nm emissions primarily originate from the ionospheric E-region, plasma instabilities from particle precipitation at E-region altitudes are considered to be responsible for the signatures of small-scale plasma structuring highlighted in the GNSS scintillation data.

In this work, we investigate various types of ionospheric disturbances observed over Europe after the earthquake in Turkey on 6 February 2023.By combining observations from Doppler sounding systems, ionosondes, and GNSS receivers, we are able to discern different types of disturbances, propagating with different velocities and through different mechanisms. We can detect the co-seismic disturbances produced in the ionosphere close to the epicenter, as well the ionospheric signatures of acoustic waves propagating as a consequence of propagating seismic waves.

The aim of this work is to present a global ionospheric prediction model based on deep learning (DL) to forecast Total Electron Content 24 hours in advance under different space weather conditions. Three different DL techniques have been compared to select the most suitable for the purpose of an operational service: Long Short Term Memory (LSTM), Gated Recurrent Units (GRU) and Convolutional Neural Networks (CNN). The modeling approach inherits and extends what has been proposed by Cesaroni and co-authors (2020). We use TEC on 18 selected grid points of Global Ionospheric Maps (GIMs) as the target parameter and Kp index as the external input. We use a dataset from 2005-2016 for training and testing, we also analyze case studies from 2017 under different geomagnetic conditions. Results show that CNN models have better predictive capabilities than the other two DL models, even under geomagnetically disturbed conditions. Considering the first 24 hours of forecasting, CNN exhibits errors between 0.5 and 2 TECu, while LSTM and GRU errors can reach 3 TECu. We also show how all the proposed models outperform the two naive models: the so-called “frozen ionosphere” and a 27 days averaged model. Moreover, we implemented the models using incremental training to update them as new data arrives and thus the trained model is able to adapt to rapid changes within the previous 24 hs to the forecasting. Thus, the proposed model can be implemented in an operative manner for Space Weather applications and services.

The 15 January 2022 eruption of the Hunga volcano provides a unique opportunity to study the reaction of the ionosphere to large explosive events. In particular, this event allows us to study the global propagation of travelling ionospheric disturbances using various instruments. We focus on the detection of the ionospheric disturbances caused by this eruption over Europe, where dense networks of both ionosondes and GNSS receivers are available. This event took place on the day of a geomagnetic storm. We show how data from different instruments and from different observatories can be combined to clearly distinguish the TIDs produced by the eruption from those caused by concurrent geomagnetic activity. By comparing observations obtained from multiple types of instruments, we also show that TIDs produced by various mechanisms are present simultaneously, with different types of waves affecting different physical quantities.

The Istituto Nazionale di Geofisica e Vulcanologia (INGV) has a long tradition in collecting scientific data to support upper atmosphere physics research. In addition to the historical equipment no longer operative, an ever-growing number of permanent observatories at high, low, and middle latitudes are part of the INGV network dedicated to the ionospheric and Space Weather monitoring. The management of the data produced by such a dynamic infrastructure required the development of an IT system capable to fulfill several requirements. Among them, the capability to manage and provide access to the continuous flow of information produced by the remote instruments and, at the same time, guarantee the preservation and availability of the historical series, a valuable legacy of this scientific field. To meet these needs, the SWIT-eSWua system was developed and has recently came into operation. The SWIT (Space Weather Information Technology) database management system can store a huge amount of spatially and temporally distributed data, standardizing the observations performed by different instruments and making them available in near real-time. The system is based on open-source software and containers-based virtualization, an architecture that potentially could be deployed in other research facilities to realize a distributed ionospheric monitoring network. The eSWua (electronic Space Weather upper atmosphere) access layer includes several services that allow to share these data with the scientific community. The web-platform (www.eswua.ingv.it) allows to explore, analyse and download all the different kind of historical and real-time data collected by SWIT at multiple levels of elaboration. A dedicated RESTful web-service, a registry for the metadata, the implementation of open data policy and persistent identifiers are just some of the other components which are being integrated into this layer. This work will provide a global view of the SWIT-eSWua architecture and describe the best practices implemented toward the long-term preservation of these data and the realization of a FAIR ecosystem.