Alfonsi, Lucilla

On 30 October 2020 at 11:51 UT, a magnitude 7.0 earthquake occurred in the Dodecanese sea (37.84°N, 26.81°E, 10 km depth) and generated a tsunami with an observed run-up of more than 1 m on the Turkish coasts. Both the earthquake and the tsunami produced acoustic and gravity waves that propagated upward, triggering co-seismic and co-tsunamic ionospheric disturbances. This paper presents a multi-instrumental study of the ionospheric impact of the earthquake and related tsunami based on ionosonde data, ground-based Global Navigation Satellite Systems (GNSS) data and data from DORIS beacons received by Jason3 in the Mediterranean region. Our study focuses on the Total Electron Content to describe the propagation of co-seismic and co-tsunami ionospheric disturbances (CSID, CTID), possibly related to gravity waves triggered by the earthquake and tsunami. We use simultaneous vertical ionosonde soundings to study the interactions between the upper and lower atmosphere, highlighting the detection of acoustic waves generated by the seismic Rayleigh waves reaching the ionosonde locations and propagating vertically up to the ionosphere. The results of this study provide a detailed picture of the Lithosphere-Atmosphere–Ionosphere coupling in the scarcely investigated Mediterranean region and for a relatively weak earthquake.

Statistical models of the variability of plasma in the topside ionosphere based on the Swarm data have been developed in the “Swarm Variability of Ionospheric Plasma” (Swarm-VIP) project within the European Space Agency’s Swarm+4D-Ionosphere framework. The models can predict the electron density, its gradients for three horizontal spatial scales – 20, 50 and 100 km – along the North-South direction and the level of the density fluctuations. Despite being developed by leveraging on Swarm data, the models provide predictions that are independent of these data, having a global coverage, fed by various parameters and proxies of the helio-geophysical conditions. Those features make the Swarm-VIP models useful for various purposes, which include the possible support for already available ionospheric models and proxy of the effect of ionospheric irregularities of the medium scales that affect the signals emitted by Global Navigation Satellite Systems (GNSS). The formulation, optimisation and validation of the Swarm-VIP models are reported in Paper 1 (Wood et al. 2024. J Space Weather Space Clim. in press). This paper describes the performance assessment of the models, by addressing their capability to reproduce the known climatological variability of the modelled quantities, and the ionospheric weather as depicted by ground-based GNSS, as a proxy for the ionospheric effect on GNSS signals. Additionally, we demonstrate that, under certain conditions, the model can better reproduce the ionospheric variability than a physics-based model, namely the Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIE-GCM).

Amplitude and phase scintillation indexes (S4 and SigmaPhi) provided by Ionospheric Scintillation Monitoring (ISM) receivers are the most used GNSS-based indicators of the signal fluctuations induced by the presence of ionospheric irregularities. These indexes are available only from ISM receivers which are not as abundant as other types of professional GNSS receivers, resulting in limited geographic distribution. This makes the scintillation indexes measurements rare and sparse compared to other types of ionospheric measurements available from GNSS receivers. Total Electron Content (TEC), on the other hand, is an ionospheric parameter available from a wide range of multi-frequency GNSS receivers. Many efforts have worked on establishing scintillation indicators based on TEC, and geodetic receivers in general, introducing various metrics, including the Rate of TEC change (ROT) and ROT Index (ROTI). However, a possible relationship between TEC and its variation, and the corresponding scintillation index that an Ionospheric Scintillation Monitor (ISM) receiver would estimate is not trivial. In principle, TEC can be retrieved from carrier phase measurements of the GNSS receiver, as . We investigate how to estimate SigmaPhi from time series of TEC and ROT measurements from an ISM in Ny-Ålesund (Svalbard) using Machine Learning (ML). To evaluate its usability to estimate SigmaPhi from geodetic receivers, the model is tested using TEC data provided by a quasi-co-located geodetic receiver belonging to the International GNSS Service (IGS) network. It is shown that the model performance when TEC from the IGS receiver is used gives comparable results to the model performance when TEC from the ISM receiver is utilised. The model's ability to infer the exact value of the scintillation index is bound to Mean Square Error (MSE) = 0.1 radians^2 when SigmaPhi < 0. 8 radians. For SigmaPhi > 0. 8 radians the MSE reaches 0.18 and 0.45 radians^2 in operative testing using ISM and IGS measurements, respectively. However, the model’s ability to detect phase scintillation from IGS TEC measurements is comparable to expert visual inspection. Such a model has potential in alerting against phase fluctuations resulting in enhanced SigmaPhi, especially in locations where ISM receivers are not available, but other types of dual-frequency GNSS receivers are present.

The Swarm satellite mission has been used for numerous studies of the ionosphere. Here we use a global product, based on electron density measurements from Swarm that characterises ionospheric variability. The IPIR (Ionospheric Plasma IRregularities product) provides characteristics of plasma irregularities in terms of their amplitudes, gradients and spatial scales and assigns them to geomagnetic regions. Ionospheric irregularities and fluctuations are often the cause of errors in position, navigation, and timing (PNT) based on the Global Navigation Satellite Systems (GNSS), in which signals pass through the ionosphere. The IPIR dataset also provides an indication, in the form of a numerical value index (IPIR index), of the severity of irregularities affecting the integrity of trans-ionospheric radio signals and hence, the accuracy of GNSS positioning. We analysed datasets from Swarm A and ground-based scintillation receivers. Time intervals (when Swarm A passes over the field of view of the ground-based GPS receiver) are compared to ground-based scintillation data, collecting an azimuthal selection of the GNSS data relevant to the Swarm satellite overpass. We provide validations of the IPIR product against the ground-based measurements from 23 ground-based receivers, focusing on GPS TEC and scintillation data in low-latitude, auroral and polar regions, and in different longitudinal sectors. We have determined the median, mean, maximum and standard deviation of the parameter values for both datasets and each conjunction point. We found a weak correlation of the intensity of both phase and amplitude scintillation with the IPIR index.

We investigate the response of the topside ionosphere, auroral and polar sectors, to the forcing of the geospace during September 2017. Specifically, we aim at characterizing such a response in terms of the involved spatial scales and of their intensification during the different auroral and polar cap activity conditions experienced in the selected month, that is characterized by severe geomagnetic storm conditions. For our purposes, we leverage on and compare various in situ plasma density data products provided by the Swarm constellation of the European Space Agency (ESA). The spatio-temporal variability of the involved scales in the plasma density observation is featured through the application of the Fast Iterative Filtering (FIF) signal decomposition technique and, for the first time in the ionospheric field, of a FIF-derived dynamical spectrum called ‘‘IMFogram”. The instantaneous time-frequency representation provided through the IMFogram illustrates the time development of the multi-scale processes with spatial and temporal resolutions higher than those obtained with traditional signal processing techniques. To demonstrate this, the IMFogram is tested against Fast Fourier and Continuous Wavelet Transforms. With our fine characterization, we highlight how scale cascading and intensification processes in the plasma density observations follow the ionospheric currents activity, as depicted through the auroral activity and polar cap indices, and through the field-aligned currents data product provided by Swarm.

ESA's Swarm constellation entered in a “overfly” configuration in the period between September and October 2021, when the longitudinal distance between the lower pair and the upper satellite was at its minimum since the launch of the spacecrafts. In addition, the local time of the nighttime tracks was favorable to detect and study the morphology of post-sunset equatorial plasma bubbles (EPBs). In this study, we focus on the Swarm overfly occurring between 00:41 UT and 00:59 UT on 30 September 2021, which covered one of the most densely instrumented regions for the study of the ionospheric irregularities embedded in the EPBs: the South American sector. By exploiting the use of ground-based receivers of Global Navigation Satellite System (GNSS) signals in combination with the Swarm plasma density measurements, we study the irregularities in the EPB formed at ∼60°W and investigate the different scales of the irregularities and the cascading processes along the magnetic flux tubes. We also highlight how diffusion along the magnetic field lines occurs simultaneously with the plasma uplift, contributing then to the correct interpretation of the EPB evolution and decay process. The precious overfly conditions also allow the introduction of ionosphere-related quantities, evaluated across the tracks at satellite altitudes enlarging the possibilities given by the same quantities already available along the tracks. Such opportunity envisages the possibility to proxy the impact of EPBs on GNSS signals with Low-Earth Orbit satellite data provided by future missions specifically dedicated to the characterization of the near-Earth environment and ionospheric studies.

Swarm is the first European Space Agency (ESA) constellation mission for Earth Observation. Three identical Swarm satellites were launched into near-polar orbits on 22 November 2013. Each satellite hosts a range of instruments, including a Langmuir probe, GPS receivers, and magnetometers, from which the ionospheric plasma can be sampled and current systems inferred. In March 2018, the CASSIOPE/e-POP mission was formally integrated into the Swarm mission through ESA’s Earthnet Third Party Mission Programme. Collectively the instruments on the Swarm satellites enable detailed studies of ionospheric plasma, together with the variability of this plasma in space and in time. This allows the driving processes to be determined and understood. The purpose of this paper is to review ionospheric results from the first seven years of the Swarm mission and to discuss scientific challenges for future work in this field.

In this study, we investigate the negative ionospheric response over the European sector during two storms that took place on 8 September 2017, primarily, by exploiting observations over ten European locations. The spatial and temporal variations of TEC, foF2 and hmF2 ionospheric characteristics are examined with the aim to explain the physical mechanisms underlying the strong negative ionospheric response. We detected very sharp electron density (in terms of foF2 and TEC) decrease during the main phases of the two storms and we attributed this phenomenon to the large displacement of the Midlatitude Ionospheric Trough (MIT). Our study also revealed that the two storms show different features caused by different processes. In addition, Large Scale Traveling Ionospheric Disturbances (LSTIDs) were observed during both storms, followed by enhanced Spread F conditions over Digisonde stations. The regional dependence of ionospheric storm effects was demonstrated, as the behavior of ionospheric effects over the northern part of Europe differed from that over the southern part.

The Antarctic and Arctic regions are Earth’s open windows to outer space. They provide unique opportunities for investigating the troposphere–thermosphere–ionosphere–plasmasphere system at high latitudes, which is not as well understood as the mid- and low-latitude regions mainly due to the paucity of experimental observations. In addition, different neutral and ionised atmospheric layers at high latitudes are much more variable compared to lower latitudes, and their variability is due to mechanisms not yet fully understood. Fortunately, in this new millennium the observing infrastructure in Antarctica and the Arctic has been growing, thus providing scientists with new opportunities to advance our knowledge on the polar atmosphere and geospace. This review shows that it is of paramount importance to perform integrated, multi-disciplinary research, making use of long-term multi-instrument observations combined with ad hoc measurement campaigns to improve our capability of investigating atmospheric dynamics in the polar regions from the troposphere up to the plasmasphere, as well as the coupling between atmospheric layers. Starting from the state of the art of understanding the polar atmosphere, our survey outlines the roadmap for enhancing scientific investigation of its physical mechanisms and dynamics through the full exploitation of the available infrastructures for radio-based environmental monitoring.

In this work we address the following question: can we use modern, cutting edge techniques conceived for the analysis of nonlinear non-stationary signals to measure scale-wise lags? To this scope, we propose a novel technique, called Intrinsic Mode Cross Correlation method, which leverages on the decomposition of nonlinear non-stationary signals by the Multivariate Fast Iterative Filtering (MvFIF) technique and the computation of a scale by scale cross correlation. We evaluate this technique on artificial signals (whose ground truth is known) and plasma density data provided by the Langmuir probes onboard the Swarm satellites. We show that this technique allows indeed to reconstruct the lag dependence on the involved spatio/temporal scales for the artificial data set (even in presence of high levels of noise), and to estimate them in a real life signal. This can pave the way to future uses of this technique in contexts in which the causation chain can be hidden in a complex, multiscale coupling of the investigated features.