Anna Belehaki

Large-Scale Traveling Ionospheric Disturbances (LSTIDs) are wave-like perturbations of the ambient ionospheric electron density that can have adverse effects on radio wave propagation within or across the ionosphere. Forecast and early identification of LSTIDs is essential information that helps mitigate their impact on radio signal reception. To this end, we develop a new short-term machine learning model to forecast the occurrence of LSTIDs and apply it to specific locations in Europe. The model is trained on time series of LSTID drivers and characteristics of observed LSTIDs. The selection of the input data rests on the assumption that the intensity of the auroral electrojets is regulated by the Lorentz force. The resulting Joule heating generates Atmospheric Gravity Waves (AGWs) in the lower thermosphere and LSTIDs in the ionosphere. The Total Electron Content (TEC) gradients and the intensity of the auroral electrojets are representative drivers of LSTIDs. The characteristics of observed LSTIDs are obtained using the HF Interferometry method (HF-INT) on a network of European Digisonde stations. The method searches for coherent oscillations in the Maximum Usable Frequency for a 3000 km radio path via reflection from the F2 layer (MUF(3000)F2) and finds bounds on time intervals during which such activity occurs in a specific region. HF-INT provides the Spectral Energy Contribution (SEC), which is the contribution of the LSTIDs to the total variability of a given MUF(3000)F2 time series. The LSTID drivers and observed characteristics are leveraged to forecast LSTID occurrences using an advanced Machine Learning method, the Temporal Fusion Transformer (TFT) model. The performance of the TFT model is compared with traditional models, such as the k-Nearest Neighbor classifier (k-NN) and the Feed-forward Neural Networks (FNNs). We consider two distinct scenarios: values of SEC greater than 50% indicating moderate and strong LSTID activity, and values of SEC greater than 70% indicating strong LSTID activity. The performance of the classifiers is assessed through the F1-score metric, which takes values between 0 and 1 (the higher its value, the better the classifier’s performance). The forecasting accuracy of TFT decreases from 0.9 to approximately 0.6 as the forecasting horizon increases from 5 min to 2 h ahead. The FNNs have the next best performance, and k-NN has inferior performance. The qualitative analysis of the TFT results provides evidence of a stronger dependence of the performance of the models on historical time series of SEC at the Digisonde locations that are closer to the auroral oval, and much weaker dependence at the lower latitude Digisonde locations. This might be the result of the decreasing LSTID amplitude as it propagates equatorward and may highlight the dissipative nature of the ionospheric medium. Our work demonstrates that a successful forecast of LSTID occurrence requires careful data pre-processing and proper consideration of the drivers, of the propagation pattern, and of other phenomena, such as the damping effect and the interhemispheric propagation.

Large-Scale Travelling Ionospheric Disturbances (LSTIDs) are wave-like ionospheric fluctuations, generally triggered by geomagnetic storms, which play a critical role in space weather dynamics. In this work, we present a machine learning model able to forecast the occurrence of LSTIDs over the European continent up to three hours in advance. The model is based on CatBoost, a gradient boosting framework. It is trained on a human-validated LSTID catalogue with the various physical drivers, including ionogram information, geomagnetic, and solar activity indices. There are three forecasting modes depending on the demanded scenarios with varying relative costs of false positives and false negatives. It is crucial to make the model predictions explainable, so that the output contribution of each physical factor input is visualised through the game-theoretic SHapley Additive exPlanation (SHAP) formalism. The validation procedure consists of a global-level evaluation and interpretation step, firstly, followed by an event-level validation against independent detection methods, which highlights the model’s predictive robustness and suggests its potential for real-time space weather forecasting. Depending on the operating mode, we report an improvement ranging from +72% to +93% over the performance of a rule-based benchmark. Our study concludes with a comprehensive analysis of future research directions and actions to be taken towards full operability. We discuss probabilistic forecasting approaches from a cost-sensitive learning perspective, along with performance-centric model monitoring. Finally, through the lens of the conformal prediction framework, we further comment on the uncertainty quantification for end-user risk management and mitigation.

Tropospheric jet-front systems and intense convection are known to be potential sources of atmospheric gravity waves (AGWs) which can propagate upward. When AGWs reach the height of the coupled thermosphere-ionosphere system they interact with the ionised medium and cause wave-like oscillations known as travelling ionospheric disturbances (TIDs). The main purpose of the present study is to investigate medium-scale TID (MSTID) activity during the passage of a jet-front system using different observational techniques, namely: vertical ionospheric soundings at seven European ionosonde stations; Continuous Doppler Sounding System (CDSS) in Czech Republic; oblique Digisonde-to-Digisonde (D2D) sounding; and Total Electron Content (TEC) measurements provided by ground-based Global Navigation Satellite System (GNSS) receivers co-located with the ionosondes. A strong jet-front system accompanied by intense convection passed through continental Europe between 25 and 29 August 2023. The severe convection and the unusually persistent meridional jet stream caused a strong vertical wind shear across the entire troposphere which was favourable to the generation of atmospheric gravity waves. During the event, the geomagnetic activity can be considered as calm. The most intense MSTID activity was observed in ionograms, in the single station detrended TEC (dTEC) measurements, and also in the CDSS Doppler shift records during the daytime (~ 06 - 13 UT) on 27 August, when the jet stream reached its highest velocities (~ 200 km/h) over continental Europe. Signatures of MSTIDs were also observed during other analysed time periods of the event. Based on the ionospheric and meteorological observations, the location and time of these enhanced TID activities coincided with local thunderstorms and a Mesoscale Convective System (MCS) event. According to the records, the dominant periods of the disturbances varied between 15 and 70 minutes. When summarising, our multi-instrumental observations confirmed that jet-front systems accompanied by severe tropospheric convection could be significant sources of AGWs which reach the ionosphere and trigger MSTIDs.

It is well known that the ionosphere is a highly dynamic medium, and the electron density can vary significantly within a very short period of time at a given location. One of the reasons for the irregular variations are ionospheric signatures of Atmospheric Gravity Waves (AGWs) – Travelling Ionospheric Disturbances (TIDs). TIDs are one of the major and frequent wave-like perturbations of the ionospheric plasma. Medium scale TIDs (MSTIDs) have been described as perturbations characterized by a wavelength, period and phase speed of 50–500 km, 12–60 min and 50–400 m/s, respectively. The wave-like effects of the MSTIDs are one of the main obstacles for accurate interpolation of ionospheric corrections in a medium scale reference of the Global Positioning System (GPS) networks as the ionospheric delay is almost proportional to Total Electron Content (TEC) along the signal path and inversely proportional to the frequency squared. MSTIDs are a common phenomenon from high to low latitudes.The main objective of the T-FORS project is the development of new validated models able to issue forecasts and alerts for TIDs several hours ahead, exploiting a broad range of observations of the solar corona, the interplanetary medium, the magnetosphere, the ionosphere and lower atmosphere. This paper presents our results on the analysis of dynamic events that trigger MSTIDs. The events identified for the purposes of the analysis were such as geomagnetic disturbances, deep tropospheric convections, earthquakes and volcano eruptions. Based on the T-FORS methodologies all available data (detrended TEC, Continuous Doppler Sounding System -CDSS measurements, Digisonde vertical and oblique soundings and gradients of the electron density, reference meteorological and seismic data) were used for this analysis. The MSTIDs occurrence was recorded and analysed and propagation pattern for these events were extracted and compared with the results of the climatological model. This comparison will support the definition of alerts criteria when the detected disturbances exceed the climatology. Physical conditions that triggered enhanced MSTID activity were also analysed to compile an inventory of parameters that can be considered as early indicators of enhanced MSTIDs.

This study demonstrates a rich complexity of the time–frequency ionospheric signal spectrum, dependent on the measurement type and platform. Different phenomena contributing to satellite-derived and ground-derived geophysical data that only selected signal bands can be potentially sensitive to seismicity over time, and they are applicable in lithosphere–atmosphere–ionosphere coupling (LAIC) studies. In this study, satellite-derived and ground-derived ionospheric observations are filtered by a Fourier-based band-pass filter, and an experimental selection of potentially sensitive frequency bands has been carried out. This work focuses on band-pass filtered ionospheric observations and seismic activity in the region of the Aegean Sea over a two-year time period (2020–2021), with particular focus on the entire system of tectonic plate junctions, which are suspected to be a potential source of ionospheric disturbances distributed over hundreds of kilometers. The temporal evolution of seismicity power in the Aegean region is represented by the record of earthquakes characterized by M ≥ 4.5, used for the estimation of cumulative seismic energy. The ionospheric response to LAIC is explored in three data types: short inspections of in situ electron density (Ne) over a tectonic plate boundary by Swarm satellites, stationary determination of three Ne density profile parameters by the Athens Digisonde station AT138 (maximum frequency of the F2 layer: foF2; maximum frequency of the sporadic E layer: foEs; and frequency spread: ff), and stationary measure of vertical total electron content (VTEC) interpolated from a UPC-IonSAT Quarter-of-an-hour time resolution Rapid Global ionospheric map (UQRG) near Athens. The spectrograms are made with the use of short-term Fourier transform (STFT). These frequency bands in the spectrograms, which show a notable coincidence with seismicity, are filtered out and compared to cumulative seismic energy in the Aegean Sea, to the geomagnetic Dst index, to sunspot number (SN), and to the solar radio flux (F10.7). In the case of Swarm, STFT allows for precise removal of long-wavelength Ne signals related to specific latitudes. The application of STFT to time series of ionospheric parameters from the Digisonde station and GIM VTEC is crucial in the removal of seasonal signals and strong diurnal and semi-diurnal signal components. The time series formed from experimentally selected wavebands of different ionospheric observations reveal a moderate but notable correlation with the seismic activity, higher than with any solar radiation parameter in 8 out of 12 cases. The correlation coefficient must be treated relatively and with caution here, as we have not determined the shift between seismic and ionospheric events, as this process requires more data. However, it can be observed from the spectrograms that some weak signals from selected frequencies are candidates to be related to seismic processes.

In this paper we analyze Digisonde observations obtained in the European region to specify the effects of large-scale travelling ionospheric disturbances (LSTIDs) on the ionospheric characteristics that define the conditions in the bottomside ionosphere. While this type of disturbances affects all frequency ranges in the F region, the most pronounced effect is detected in the foF2 critical frequency, where the density is the highest. During LSTID activity, a significant uplifting of the F2 layer is observed to accompany an oscillation pattern in the foF2. Concurrent variations in the height of the peak electron density hmF2 and the corresponding scale height, Hm are also observed. These findings are used to propose a new methodology for the identification of LSTIDs, comprising a combination of different criteria. The efficiency of the proposed methodology is tested at middle latitudes during geomagnetically quiet and disturbed intervals as well as during time periods of lower atmosphere forcing affecting the ionosphere.

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 (TIDs) using various instruments. We focus on detecting 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 observatories can be combined to distinguish the TIDs produced by the eruption from those caused by concurrent geomagnetic activity. The Lamb wavefront was detected as the strongest disturbance in the ionosphere, travelling between 300 and 340 m/s, consistent with the disturbances in the lower atmosphere. 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. This illustrates the importance of analysing data from multiple independent instruments in order to obtain a full picture of an event like this one, as relying on only a single data source might result in some effects going unobserved.