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.

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.

Signal monitoring and recording station architectures based on software-defined radio (SDR) have been proposed and implemented since several years. However, the large amount of data to be transferred, stored, and managed when high sampling frequency and high quantization depth are required, poses a limit to the performance, mostly because of the data losses during the data transfer between the front-end and the storage unit. To overcome these limitations, thus allowing a reliable, high-fidelity recording of the signals as required by some applications, a novel architecture named SMART (Signal Monitoring, Analysis and Recording Tool) based on the implementation of Docker containers directly on a Network Attached Storage (NAS) unit is presented. Such paradigms allow for a fully open-source system being more affordable and flexible than previous prototypes. The proposed architecture reduces computational complexity, increases efficiency, and provides a compact, cost-effective system that is easy to move and deploy. As a case study, this architecture is implemented to monitor RFIs on Global Navigation Satellite System (GNSS) L1/E1 and L5/E5 bands. The sample results show the benefits of a stable, long-term capture at a high sampling frequency to characterize the RFIs spectral signature effectively.

Radio Frequency (RF) signals transmitted by Global Navigation Satellite Systems (GNSS) are exploited as signals of opportunity in many scientific activities, ranging from sensing waterways and humidity of the terrain to the monitoring of the ionosphere. The latter can be pursued by processing the GNSS signals through dedicated ground-based monitoring equipment, such as the GNSS Ionospheric Scintillation and Total Electron Content Monitoring (GISTM) receivers. Nonetheless, GNSS signals are susceptible to intentional or unintentional RF interferences (RFIs), which may alter the calculation of the scintillation indices, thus compromising the quality of the scientific data and the reliability of the derived space weather monitoring products. Upon the observation of anomalous scintillation indices computed by a GISTM receiver in the Mediterranean area, the study presents the results of the analysis and characterization of a deliberate, unclassified interferer acting on the L1/E1 GNSS signal bands, observed and captured through an experimental, software defined radio setup. The paper also highlights the adverse impacts of the interferer on the amplitude scintillation indices employed in scientific investigations, and presents a methodology to discriminate among regular and corrupted scintillation data. To support further investigations, a dataset of baseband signals samples affected by the RFI is available at IEEE DataPort.

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 National Antarctic Data Center (NADC) is the ICT infrastructure designed to gather, handle, publish and provide access to the large amount of scientific data collected by several projects in the framework of the Italian Antarctic National Research Program (PRNA). Aim of the infrastructure is to provide a single integrated system that allows the final users to easily access and share data wherever they are stored. The architecture is based on a System-of-Systems (SoS) concept: a set of systems (functional nodes) interconnected together with each other by means of mediation and adaptation services running on a central infrastructure (common node). The common node is managed by the five Organizations (CNR, INGV, ENEA, OGS, MNA) that contribute to the NADC and is devoted to a regular harvesting of the metadata. Each functional node consists of an existing metadata and data management system implemented by each Organization. Istituto Nazionale di Geofisica e Vulcanologia (INGV) hosts one of those functional nodes and it is managing, among others, data/metadata produced by the permanent geomagnetic and ionospheric observatories installed in Antarctica since 1985. The functional nodes are interconnected and federated together by means of interfaces and standard data/metadata models. This distributed architecture allows to interconnect heterogeneous systems and digital infrastructures in a flexible, scalable and sustainable way. This paper describes the general infrastructure and, as an example of functional node, the contribution of the data management related to the Antarctic Ionospheric and Geomagnetic Observatories managed by INGV at Mario Zucchelli station (74°41′42″S, 164°06′50.4″E) and Concordia base (75°05′59.91″S, 123°19′57.38″E).

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.

Science is undergoing very rapid changes due to the larger number of people having the opportunity to do science and also with the development of new and revolutionary techniques (e.g. machine learning). The new concepts discovered to end up not gaining the deserved prominence and are quickly discarded. We live in a world where great discoveries have already been made and small new ideas need to be cultivated and developed to rise to their deserved significance. An interesting image can be a start point to attract scientists to know more about some subject, but they are considered a time-consuming effort and difficult to be done. The worries with the creation of the images are not limited to beauty, but accurate drawing is fundamental to science. Illustrated scientific posters made by dedicated design are attractive but are often connected to companies and associated with the sale of a product. Although the idea presented in simple posters can lead to the imagination of new structures or relationships, one image of this idea could more easily explain the concept and attract people, encouraging a scientific debate. A list of commercial programs can be cited as Linkscape, Adobe packages (CorelDRAW, illustrator, etc.), Microsoft packages (Paint, PowerPoint, etc.) or even Matlab functions and this work will present images produced for a better understanding of ionospheric sciences. It is common knowledge that human memory is mostly visual. The digital images can not only be used in paper posters but also in digital posters, projections, websites, etc. The idea is to motivate ionospheric scientists to draw their discoveries. The images, and the scientific work itself, begin in a simple way and gain complexity with the advancement of the scientific discoveries and could be used for the author as a personal way to instigate a continuation of the study. Complex systems studies like the ionospheric sciences are often composed of multidisciplinary groups and are important to quickly explain the meaning of some concept. To investigate the perception of the scientist about the importance of images in explaining scientific concepts a questionnaire was done to collect information about the importance of scientific images in different fields of investigation.