Abstract We develop a climatological model for the Arctic ionosphere based on more than one solar cycle of data (2010–2021) of the rate of change of the total electron content index (ROTI) maps from the International GNSS Service (IGS). The IGS ROTI maps are daily averaged in magnetic latitude and local time coordinates. To develop a climatological model, the ROTI maps are decomposed into base functions and coefficients using the empirical orthogonal function (EOF) method. The EOF method converges very quickly, and the first four EOFs reflect the majority (96%) of the total data variability. Furthermore, different EOF components can reflect different drivers of ionospheric irregularities. The first EOF reflects the averaged ROTI activity and the impact of the solar radiation and geomagnetic activity; the second EOF reflects the impact of the interplanetary magnetic field (IMF) Bz and electric field; the third and fourth EOFs reflect the dawn‐dusk asymmetry around the auroral oval and polar cap, and they can be related to the IMF By. To build an empirical model, we fit the EOF coefficients using helio‐geophysical indices from four different categories (solar activity; geomagnetic indices; IMF; the solar wind coupling function). The final EOF model is dependent on seven selected indices (F10.7P, Kp, Dst, Bt, By, Bz, and EKL). The statistical data‐model comparisons show satisfactory results with a good correlation coefficient. However, the model cannot capture the significant expansion of the dayside ROTI activity during strong geomagnetic storms. Future effort is needed to provide corrections to the model during severe storms.

We develop a climatological model for the Northern Hemisphere based on a long-term dataset (2010-2021) of the rate of change of the total electron content (TEC) index (ROTI) maps from the International GNSS Service (IGS). The IGS ROTI maps are daily averaged in magnetic latitude and local time coordinates. To develop a climatological model, the ROTI maps are decomposed into a few base functions and coefficients using the empirical orthogonal function (EOF) method. The EOF method converges very quickly, and the first four EOFs reflect the majority (96%) of the total data variability. Furthermore, different EOF components can reflect different drivers of ionospheric irregularities. The first EOF reflects the averaged ROTI activity and the impact of the solar radiation and geomagnetic activity; the 2nd EOF reflects the impact of the interplanetary magnetic field (IMF) Bz and electric field; the 3rd and 4th EOFs reflect the dawn-dusk asymmetry around the auroral oval and polar cap, and they can be related to the IMF By. To build an empirical model, we fit the EOF coefficients using helio-geophysical indices from four different categories (solar activity; geomagnetic indices; IMF; the solar wind coupling function). The final EOF model is dependent on seven selected indices (F10.7P, Kp, Dst, Bt, By, Bz and Ekl). The statistical data-model comparisons show satisfactory results with a good correlation coefficient. However, the model cannot capture the significant expansion of the dayside ROTI activity during strong geomagnetic storms. Future effort is needed to provide corrections to the model for severe storms.

Ionospheric scintillation originates from the scattering of electromagnetic waves through spatial gradients in the plasma density distribution, drifting across a given propagation direction. Ionospheric scintillation represents a disruptive manifestation of adverse space weather conditions through degradation of the reliability and continuity of satellite telecommunication and navigation systems and services (e.g., European Geostationary Navigation Overlay Service, EGNOS). The purpose of the experiment presented here was to determine the contribution of auroral ionization structures to GPS scintillation. European Incoherent Scatter (EISCAT) measurements were obtained along the same line of sight of a given GPS satellite observed from Tromso and followed by means of the EISCAT UHF radar to causally identify plasma structures that give rise to scintillation on the co-aligned GPS radio link. Large-scale structures associated with the poleward edge of the ionospheric trough, with auroral arcs in the nightside auroral oval and with particle precipitation at the onset of a substorm were indeed identified as responsible for enhanced phase scintillation at L band. For the first time it was observed that the observed large-scale structures did not cascade into smaller-scale structures, leading to enhanced phase scintillation without amplitude scintillation. More measurements and theory are necessary to understand the mechanism responsible for the inhibition of large-scale to small-scale energy cascade and to reproduce the observations. This aspect is fundamental to model the scattering of radio waves propagating through these ionization structures. New insights from this experiment allow a better characterization of the impact that space weather can have on satellite telecommunications and navigation services.

Ionospheric tomography is a powerful technique for studying and monitoring the upper atmosphere and its dynamics. This is extremely relevant to attempts made to reduce vulner‐ abilities of Global Navigation Satellite System (GNSS) signal propagation due to the presence of ionospheric charged particles. The concentration of the latter can be measured by consid‐ ering time and phase delays of GNSS signals. By integrating the measured density values, it is possible to calculate the Total Electron Content (TEC) along a specific signal path. Iono‐ spheric tomography uses GNSS TEC observations in order to compose three dimensional reconstructions of the ionosphere through an operation called inversion. In contrast to other applications (e.g. medical or industrial), ionospheric tomography cannot rely on a designed scanning instrument. Satellites and receivers do not entirely surround the ionosphere, therefore providing an incomplete scan-geometry. Furthermore, GPS ground-receivers are distributed unevenly on the earth’s surface which translates to poor data coverage, resulting in a lack of necessary information. MIDAS (Multi-Instrument Data Analysis System) [1, 2] is an ionospheric tomography software package developed at the University of Bath by the INVERT group. In order to overcome the limitations due to poor data coverage, MIDAS is assisted by external information. To date, empirical models are utilized to support the inversion in MIDAS algorithms, especially in relation to the missing vertical information. The idea behind this project is to implement a purely physics-based ionospheric model into MIDAS; ANIMo was built for this intent, which may be applied in different modes. ANIMo can simply be used to substitute erroneous reconstructions or to aid the inversion by adding

The ionosphere is an ionized medium which affects the electromagnetic signal that travels through it. The state of the ionosphere is studied in terms of Total Electron Content (TEC). This is a measurable observation, that in our case, is obtained from an estimation of the delay of the signal transmitted from a satellite and received at a ground station [1,2]. Computerized Ionospheric Tomography (CIT) uses a collection of observations to estimate the state of the ionosphere. CIT is also denoted as an inverse problem.