Ionosphere Monitoring with Remote Sensing Vol II

1. Introduction

Unveiling the physical properties of the Earth’s ionosphere is crucial for the comprehension of the dynamic processes that occur within it across various spatial and temporal scales. This understanding is essential for comprehending numerous phenomena associated with space weather. The ionosphere, composed of ions and electrons, responds to the initiation, intensification, and evolution of magnetic and electric fields, potentially altering its physical properties and energy balance. These changes can significantly affect the propagation properties of electromagnetic signals passing through the ionosphere.

Thanks to a wealth of high-quality data, these ionospheric features can be thoroughly investigated across different scales using remote sensing and in situ instruments such as ionosondes, radars, satellites, and Global Navigation Satellite Systems (GNSS) receivers.

In this context, the Special Issue “Ionosphere Monitoring with Remote Sensing Vol. II” seeks to advance our understanding of the ionosphere by utilizing data from various facilities and established ionospheric models. In more detail, this Special Issue is primarily focused on: (1) the development or improvement of tools for ionospheric tomography reconstruction [1], signal propagation [2], the prediction of the critical frequency of the F2 layer [3,4], and the automatic identification of Spread-F (SF) on ionograms [5], and (2) the study of ionospheric features, anomalies, and irregularities associated with seasonal, geomagnetic and solar activity variations, solar eclipses, and volcanic eruptions [4,6,7,8,9,10,11,12].

2. Overview of Contributions and Future Perspectives

The following Special Issue contains 13 original research papers describing results obtained using different tools, data, models, and analysis techniques and is aimed at characterizing the properties of the ionosphere that are relevant, for instance, for signal transmission and space weather studies.

Significant attention has been paid to the development or improvement of algorithms and tools to increase our knowledge of the ionosphere. Wen et al. [1] proposed a truncated mapping singular value decomposition (TMSVD) method to improve the accuracy and computational efficiency of global navigation satellite system (GNSS)-based ionospheric tomography reconstruction, which is crucial for reconstructing the three-dimensional ionospheric electron density (IED) [13]. Ill-posedness of GNSS-based computerized ionospheric tomography represents a limitation in reconstructing the IED distribution and has been approached by the development of algorithms. Among them, it has been demonstrated that TMSVD methods represent a significant improvement and they have been successfully applied to reconstruct IED images based on actual GNSS observations. The new algorithm by Wen et al. [1] was used to reconstruct ionospheric structure, diurnal variations, and semiannual anomalies using GNSS observations in Huan province, China. Moreover, the method captured the ionospheric structure during an ionospheric storm, resulting in improvement with respect to the International Reference Ionosphere (IRI) model [14].

The path of radio waves propagating through the ionospheric medium can be retrieved by means of the ionospheric ray tracing technique [15]. This technique numerically integrates Hamilton’s equations describing the propagation of electromagnetic waves in the high frequency (HF) band, namely between 3 and 30 MHz. The numerical solution provides the path of an HF radio wave propagating from a given transmitting point (and with a given azimuth of the ray) to an unknown receiving point on the Earth’s surface. Pietrella et al. [2] upgraded the applicative software tool IONORT (IONOspheric Ray Tracing) developed at the Istituto Nazionale di Geofisica e Vulcanologia by Azzarone et al. [16]. The tool uses the electron density model provided by the IRI [17]; however, its use also provides the possibility to select any other model. The original Fortran 77 source code was rewritten in Fortran 90 and thoroughly tested. However, the most important novelties concern the extension from regional to global scales (such that a user can apply the tool anywhere on Earth) and the development of a new graphical user interface.

A crucial parameter in radio wave propagation is the critical frequency of the ionospheric F2 layer, i.e., foF2, which represents the maximum frequency of a signal reflected by the ionospheric F2 layer in long-distance radio communication. This parameter changes due to the different conditions of the ionospheric layer induced, for instance, by space weather events, and may affect the signal propagation and communication quality [18]. This is the reason as to why great efforts have been made to study foF2, with the ultimate aim of forecasting the variation of this parameter following geoeffective space weather events and thus being able to pursue a series of actions to mitigate possible failures, for example, of high-frequency communication and GNSS. In the last few decades, the development of artificial intelligence technology has given a boost to the emergence of new ionospheric prediction models based on artificial neural networks (ANN), statistical machine learning (ML), and deep learning (DL).

These models may perform better than traditional models [19,20,21]. Bi et al. [20] proposed a high-performance hybrid neural network with a quantile mechanism to model and forecast foF2 variations at low latitudes following space weather events during solar cycle 24. In particular, the model takes advantage of foF2 data together with the solar radio flux at 10.7 cm (F10.7), the sunspot number, the vertical component of the interplanetary magnetic field, and the geomagnetic indices ap and Dst from 2009 to 2014. They found that the proposed model performs better than other previous models such as IRI during both high and low solar activity levels and geomagnetic storm periods. Wang et al. [4] improved the prediction accuracy of foF2 by using an ML-based dynamic prediction method that takes into account the variation of foF2 with the solar cycle. The model uses dynamic training due to dynamic data updating and performs better than the last updated IRI model and the Asia Regional foF2 model.

Ionospheric plasma irregularities manifest in many ways. One of the most important is SF, which causes scintillation and degrades GNSS, communication, and radar signals. An easy and effective way to observe SF and study its diurnal and seasonal changes is through the use of ionosondes. However, identifying SF in ionogram traces is not always easy. Automatic scaling of ionograms has been extensively used for several decades [22,23,24]. Recently, in the machine learning era, Lan et al. [25] demonstrated that convolutional neural networks (CNNs) represent a promising method for SF automatic identification. Rao et al. [26] presented an auto-detection technique to identify Sporadic E and SF signatures, reaching a detection efficiency of up to 96.7%. Fen et al. [5] proposed a statistical analysis of SF in East Asia by using a Bayesian classifier. Their automatic identification algorithm reached an accuracy of up to 97% and was applied to five stations to expand the research of SF in that area.

Ionospheric anomalies, which include annual, semiannual, winter, equatorial, and mid-latitude summer nighttime anomalies, among others, are ionospheric variations mainly related to plasma dynamics and changes in the composition of the atmosphere [27]. Since its discovery [28], the winter anomaly has been extensively studied over the last century. It consists of daytime F2-layer peak electron density (NmF2) greater in winter than in summer at middle latitudes. The basic physical mechanism that is responsible for this anomaly is the change in the ratio O/N2 [29] resulting from global circulation in the thermosphere [30]. Conversely, the annual anomaly [31] consists of NmF2 from both hemispheres being greater during December than during June. Wang et al. [4] assessed the contribution of the winter anomaly to the annual anomaly based on regression analysis over 10 years of total electron content (TEC) and F10.7 data during solar cycle 24 conditioned to geomagnetic quietness. Among the other remarkable results, they found that TEC in the winter hemisphere is the main factor that determines the annual anomaly during high solar activity. Specifically, it contributes more than 50% to the annual anomaly when F10.7 exceeds 90 solar flux units (SFU), and up to 65% when F10.7 exceeds 130 SFU.

Ionospheric anomalies have also been studied in conjunction with natural phenomena such as earthquakes, volcanic eruptions, and nuclear blasts [32,33,34,35,36]. For example, Feng et al. [7] studied ionospheric anomalies before the submarine volcanic eruption of Hunga Tonga–Hunga Ha’apai in January 2022. The authors used GNSS data, global ionospheric maps, and radio occultation data to detect negative TEC anomalies starting from ten days before the eruption near its epicenter. Moreover, a few days earlier, the equatorial ionization anomaly (EIA) double peak showed relevant shifts and decreased until disappearing and merging in a single peak. The authors were able to rule out the effects of solar and geomagnetic activity, together with the lower atmospheric forcing.

A completely new work of its kind is that of Valdes Abreu et al. [8], who studied the ionospheric effects of two solar eclipses observed in South America and Antarctica during periods of disturbed geomagnetic activity and in different seasons. The rationale behind this interesting work is that solar eclipses provide a great opportunity to investigate the response of the ionosphere to sharp transitions from light to dark to light. During solar eclipses, the sudden decrease in photoionization and photoelectron heating results in the loss of plasma with a remarkable decrease in electron density. As a consequence, TEC fluctuates, and its variations may influence regions outside the moon’s shadow due to a number of processes. While at high latitudes ionospheric effects induced by solar eclipses are mainly driven by magnetosphere–ionosphere coupling [37,38,39], at low latitudes, such effects are due to processes involving the EIA [40]. Valdes Abreu et al. [8] compared the effects of both eclipses on TEC over South America and Antarctica in different seasons by using data from 390 GNSS stations, the European Space Agency’s Swarm A satellite, and the Defense Meteorological Satellite Program DMSP F18 satellite. They also investigated the effects of these eclipses on the EIA. They found that although the forcing was due to solar and geomagnetic activity, there were evident plasma depletions during the penumbra, with differential vertical TEC (DVTEC) values lower than 40%. Moreover, different values of DVTEC in the crests of the EIA were found during the two different eclipse periods.

Particular interest lies in the study of ionospheric plasma dynamics at mid and high latitudes, where physical processes are strongly influenced by coupling with the magnetosphere and solar wind. In fact, at high latitudes, the ionosphere quickly responds to solicitations of external origin and exhibits distinctive features [41]. In light of this, research aimed at ionospheric modeling in recent decades has benefited from the ever-increasing number and quality of available data, with the aim of reproducing the observed varying spatiotemporal structures, which depend not only on latitude, longitude, and season but also on solar and geomagnetic activity and solar wind conditions (primarily the physical properties of the plasma and interplanetary magnetic field). Such models are also crucial for identifying the various physical mechanisms at work. To achieve this, it is of primary importance to identify the scales at which these mechanisms operate. Lovati et al. [9] applied the Multivariate Empirical Mode Decomposition (MEMD) technique to electron density data acquired by the Swarm A satellite to detect the spatial scales impacting high-latitude ionospheric plasma dynamics. The modes extracted via MEMD contribute to different weights in the original data. The authors identified four modes that represent (i) noise, (ii) a constant frequency such as the satellite’s orbit, (iii) seasonality, and (iv) geomagnetic activity dependence. Such modes are able to discern some specific processes, e.g., the ionospheric circulation typical of the polar cap and phenomena driven by sunlit, recombination, and neutral wind. Lovati et al. [9] de facto advanced our understanding of the spatiotemporal features in electron density, which future models should take into account for a better prediction of future ionospheric conditions.

The increase in ionization in the upper atmosphere leads to ionospheric disturbances driven, for example, by bursts of UV and X-ray radiation from the Sun. During solar flares, such bursts result in increased ionization velocity and the subsequent increase in electron density in the ionosphere. The variation in electron concentration may give rise to the absorption of HF radio waves, especially in the lower ionosphere [42,43,44]. However, strong solar flares may also impact the upper ionospheric layers [45,46]. Sergeeva et al. [10] studied the effect of two solar flares in 2022 at low latitudes, in Mexico, on both the topside and the lower ionosphere. They confirmed that the X-ray solar radiation increased electron density in the lower ionosphere more than in the upper layers. Moreover, the fainter flare (an M9.6 flare) resulted in irregularities at medium scales; in comparison, the stronger flare (an X1.3 flare) caused medium- and small-scale irregularities. The results of other studies [10] highlight the fact that the narrowing of the frequency operation range and signal amplitude decrease in ionograms exhibited a few minutes delay longer for the fainter flare than for the stronger one; the recovery of the ionosphere began before the flare ended; and the flare effects lasted longer for the stronger flare. The ionospheric response to flares depends more on the active region position on the Sun and the flare class than the zenith angle.

Information on the vertical distribution of plasma in the ionosphere–plasmasphere system and electron density profile can be retrieved using the slab thickness. This fundamental ionospheric parameter is also related to the plasma scale height and neutral temperature. In addition, it is an important component in ionospheric modeling and data assimilation methods. In fact, it is defined as the ratio between TEC and NmF2 and represents the equivalent depth in meters of the ionosphere given a uniform electron density of NmF2 [47,48,49]. Like other ionospheric parameters, the slab thickness has distinctive features varying with local time, latitude, longitude, season, solar, and geomagnetic activity. Due to the scarcity of ionospheric facilities, there are relatively few statistical studies of slab thickness at high latitudes. In previous works, it was found that nighttime mean values of the slab thickness are larger than in the daytime, with the highest and lowest mean values being in the daytime in summer and winter, respectively [50]. Different features have been found by other authors. Yadav and Bhawre [51] showed that the mean values of the slab thickness are greatest during the equinox and smallest during the summer. Pignalberi et al. [52] found that the largest slab thickness occurs during the nighttime and dawn hours in winter and the equinox. In addition, the pre-sunrise peak is more evident during low- and mid-solar activity periods, except for in the summer, while the post-sunset peak is evident only in winter except for the high solar activity. Feng et al. [6] investigated the variation in the slab thickness in the high latitudes of East Asia in order to improve the global empirical modeling of this parameter and study the effects of geomagnetic activity on it. They utilized TEC and ionosonde data at Yakutsk using 7 years’ worth of data from 2010 to 2017 and found a dependence of diurnal and seasonal features of slab thickness on solar and geomagnetic activity. The slab thickness is greatest during winter and smallest during the summer. Moreover, during the summer and equinox, there are smaller diurnal variations and peaks with different strengths during sunrise and post-sunset times. The geomagnetic activity affects the slab thickness, enhancing it during geomagnetic storms due to intense particle precipitation and an expanded plasma convection electric field, as highlighted by examining a case study in June 2013.

Fluctuations in electron density are responsible for variations in the refractive index of the ionosphere, causing refraction and diffraction phenomena that underlie amplitude and phase variations in electromagnetic signals (i.e., scintillation) such as GNSS signals transmitted by navigation satellites. Phase scintillation can be quantified via an index based on the ensemble standard deviation of detrended phase measurements. Phase scintillation results in cycle slips or, at worst, in the complete loss of the lock of the receiver [53,54,55]. The former consists of the temporary disruption of phase tracking that can be detected, for instance, by changes in TEC [56]. Beeck et al. [11] statistically investigated the relationship between phase scintillation and cycle slips for GPS and the Galileo constellation in Greenland and performed a simulation of the effects on these signals. The authors found a high percentage of cycle slips for the Galileo constellation, while signal outages were the most relevant impact of phase scintillation on GPS data. They concluded that there was a different response to ionospheric disturbances for the two facilities due to the GPS signal underestimating TEC during large TEC changes.

Clumps of ionized plasma of various spatial scales are known as ionospheric field-aligned irregularities (FAIs) and are recognized to be sources of disturbance for navigation, communication, and radar systems. In particular, mid-latitude FAIs in the ionospheric E-region have been extensively studied since their detection with Arecibo radar more than four decades ago [57,58,59]. Yamamoto et al. [60] found two types of FAIs in the E layer: daytime continuous echoes at 90–100 km and nighttime quasi-periodic echoes at 100–130 km. The mechanisms of generation proposed include gravity waves [61], gradient drift instability [62], Kelvin–Helmohotz instability [63], and sporadic E instability [64]. To overcome the range resolution limitations of VHF radars that make it impossible to resolve small-scale structures in a radar-illuminated volume, multifrequency radar imaging (RIM) technology has been developed and successfully used. Chen et al. [12] applied the RIM technique and found that the spatial resolution of FAIs in the E-region greatly improved, and it was possible to obtain finer structures such as QP echoes. The features observed suggest that their generation is modulated by gravity waves. This technique was also applied for the observation of multiple layers of the E-region FAIs. The authors found that the multilayered FAI features at night may be due to gradient-drift instability acting on multiple ionized layers formed by tides or waves.