foF2 Data Research Articles

First Results for the Influence of Two Geomagnetic Storms during 10-12 May 2024 On Mid-latitude Ionosphere

The main idea of this study is to analyze the ionospheric anomalies in selected points related to the extreme geomagnetic storm which occurred during the period 10–12 May 2024. In the present research, data of the critical frequency of the F2 region (foF2) from the vertical sounding of ionospheric station Rome (Italy) and foF2 data for the territory of Sofia (Bulgaria) obtained by empirical modelling were used. The selection of Rome ionospheric station is related to the fact that this ionosonde is located at the geographic latitude coinciding with the latitude of Sofia, which suggests similar characteristics of the ionosphere. In order to evaluate the type of response (positive or negative), the relative deviation of the values was used, taking into account the influence of the local time for the individual points (1 hour). The results of the of the ionospheric response analysis show a well expressed negative response, more significant at Rome station. Another interesting result related to the inertia of the ionosphere is expressed in the observed response delay depending on the onset of the geomagnetic storm. From the comparison of the measured data and those obtained through the empirical model it can be seen that for the purposes of ionospheric forecasting, the model is able to describe the behaviour of the ionosphere with sufficient accuracy. The ionospheric response is supported by an additional study of the behaviour of the Total Electron Content (TEC), which response is similar to that of foF2.

A hybrid deep learning-based short-term forecast model for ionospheric foF2 in East Asia region

The short-term forecast of the critical frequency of the ionospheric F2 layer (foF2) is particularly challenging due to its nonlinear and non-stationary characteristics. To improve the short-term forecast accuracy of foF2, we propose a short-term hybrid deep learning model that integrates the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and long short-term memory (LSTM) algorithms. The proposed model leverages the ICEEMDAN decomposition algorithm to expand foF2 time series data into multi-dimensional space and utilizes the LSTM algorithm to preserve long-term data information, capturing detailed changes in foF2 parameters and obtaining internal information about foF2. The 2014 and 2017 foF2 data from four observation stations located in Mohe, Beijing, Jeju, and Kokubunji in the mid-latitudes of East Asia were utilized for modeling and 1-hour forecast analysis. The forecast values of the proposed model align closely with the measured data, exhibiting minimal fluctuation in forecast error and effectively tracking the changing trend of foF2. The average root mean square error (RMSE) across the four stations in 2014 is 0.40 MHz, with a relative RMSE (RRMSE) of 7.11 % and a coefficient of determination (R2) of 0.98. The average RMSE in 2017 is 0.43 MHz, RRMSE is 7.68 %, and R2 is 0.92. The proposed model reveals significant improvements in the forecast accuracy of the comparison model compared with the CCIR, URSI, NN, and LSTM models. Especially compared with the CCIR and URSI models in the International Reference Ionosphere (IRI), demonstrating superior real-time tracking capability for foF2 and effectively addressing the challenge of poor short-term forecast accuracy observed in the IRI model.

Modeling and Forecasting Ionospheric foF2 Variation Based on CNN-BiLSTM-TPA during Low- and High-Solar Activity Years

The transmission of high-frequency signals over long distances depends on the ionosphere’s reflective properties, with the selection of operating frequencies being closely tied to variations in the ionosphere. The accurate prediction of ionospheric critical frequency foF2 and other parameters in low latitudes is of great significance for understanding ionospheric changes in high-frequency communications. Currently, deep learning algorithms demonstrate significant advantages in capturing characteristics of the ionosphere. In this paper, a state-of-the-art hybrid neural network is utilized in conjunction with a temporal pattern attention mechanism for predicting variations in the foF2 parameter during high- and low-solar activity years. Convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM), which is capable of extracting spatiotemporal features of ionospheric variations, are incorporated into a hybrid neural network. The foF2 data used for training and testing come from three observatories in Brisbane (27°53′S, 152°92′E), Darwin (12°45′S, 130°95′E) and Townsville (19°63′S, 146°85′E) in 2000, 2008, 2009 and 2014 (the peak or trough years of solar activity in solar cycles 23 and 24), using the advanced Australian Digital Ionospheric Sounder. The results show that the proposed model accurately captures the changes in ionospheric foF2 characteristics and outperforms International Reference Ionosphere 2020 (IRI-2020) and BiLSTM ionospheric prediction models.

On equatorial spread F occurrence: A multi-dimensional quantitative assessment

The present study investigates the role of gravity wave induced seed perturbations in the occurrence of Equatorial Spread F (ESF) under the influence of the post sunset background conditions modulated by prevailing electrodynamics and neutral wind. Ionospheric foF2 data sets over geomagnetic equatorial station Trivandrum (8.5°N, 77°E and magnetic dip 0.68°N-corresponding to the period of study) corresponding to vernal and autumnal equinoctial periods encompassing high, low and moderate solar activity years, are used for the study. Meridional wind data is obtained either from ESA’s sun-synchronous satellite GOCE (Gravity field and steady-state Ocean Circulation Explorer) or derived using ionosonde h’F (base height of ionosphere at 2.5 MHz) data from Trivandrum (TVM- 8.5°N, 77°E and magnetic dip 0.68°N) and Sriharikota (SHAR−13.7°N, 80.2°E and magnetic dip 6.9°N-for period of study). This particular study is carried out for geomagnetically quiet days of Vernal Equinox (VE) and Autumnal Equinox (AE) seasons, which are most favoured for ESF occurrence over Indian longitudes. Considering thermospheric wind, ion-neutral collisions, and electric field effects in association with gravity wave seed, threshold curve is generated, which clearly demarcates ESF and NSF (Non spread F) days. Previous studies have addressed ESF variability in electrodynamical domain alone (wherein the layer is above a threshold level). The present study, for the first time, succeeds in demarcating ESF and NSF days by incorporating effects of electric field, neutral wind, collisional RT instability term, and gravity wave seed perturbations simultaneously irrespective of threshold height.

Validation of the IRI-2020 model for the topside-plasmasphere using GNSS TEC measurements

The 2020 update of the International Reference Ionosphere (IRI) model introduces a new topside option, COR2, for the formulation of its electron density profile, as well as two plasmaspheric extension options based on the GCPM model of Gallagher et al. (2000) and the IMAGE/RPI model of Ozhogin et al. (2012). We validate the COR2-Gallagher and COR2-Ozhogin topside-plasmasphere options of IRI-2020 using GNSS VTEC measurements and compare them to the NeQuick model used as the default option in IRI-2016. VTEC data from a total of 21 GNSS ground-based receivers are compared to predictions of the IRI model, ranging from low (< 30°) to middle (30°-60°) magnetic latitudes, during high (year 2014; F10.7∼140 sfu) and low (year 2019; F10.7∼70 sfu) solar activity. Peak plasma frequency (foF2) data from co-located digisondes are ingested into the model to ensure the relevance of the comparison. The three topside-plasmasphere options perform equally well at mid-latitudes (RMSE∼3 TECU). At low latitudes, the NeQuick option (RMSE∼9 TECU) performs better than both the COR2-Gallagher and COR2-Ozhogin options (RMSE > 10 TECU). For all options, the modeled VTEC is consistently overestimated during the autumn/winter daytime at mid-latitudes regardless of solar activity, and throughout the day at low latitudes during low solar activity.

Simultaneous monitoring of the limited area ionosphere with the use of GPS and ionosonde

The importance of this study lies in its contribution to the field of ionospheric science, particularly in the monitoring and prediction of ionospheric parameters. This study focuses on comparing two techniques for spatio-temporal kriging of critical ionospheric parameters, namely the F2 layer critical frequency (foF2) and Total Electron Content (TEC), under both quiet and disturbed ionospheric conditions. The first technique, Ordinary Kriging (OK), relies solely on foF2 data, while the second technique, Kriging with External Drift (KED), incorporates the dynamic relationship between foF2 and TEC data to improve the interpolation process. This study demonstrates how KED, which utilizes dynamic secondary information, offers advantages over OK. By simultaneously monitoring the ionosphere from a single ionosonde station and collecting data from visible GPS satellites, KED enhances the accuracy of spatial structural analysis, enabling more precise estimations of ionospheric characteristics across interpolation grid nodes. The research highlights that the non-uniform distribution of ionosonde stations, especially in regions with limited foF2 data, can benefit from KED's incorporation of TEC data. Additionally, the study reveals the potential for improving TEC maps by assimilating foF2 data located at the periphery of the observation area. Furthermore, the paper discusses the determination of semivariogram structures for both foF2 and TEC data, emphasizing the spatial correlations between data points. These findings contribute to a better understanding of ionospheric behavior and the potential for enhanced ionospheric modeling.

Validation of the European Ionosonde Service nowcasting foF2 maps over the eastern Mediterranean

The Cyprus ionosonde (Digisonde) has been operating since 2009. Therefore it has been monitoring ionospheric conditions over the eastern Mediterranean region for more than 14 years. It is the only ionosonde operating in the area and it does not contribute to any existing European database and subsequently has not been utilised in the frames of any Space Situational Awareness (SSA) program. In the frames of the European Space Agency (ESA) activity “Potential Enhancements in Ionospheric Monitoring under SSA (PEIMSSA)” a quantitative validation of the the European Ionosonde Service (EIS) was undertaken. This validation is based on 8 years of manually scaled F-layer critical frequency (foF2) data that was compared with the corresponding interpolated values from EIS nowcasting foF2 maps over Europe. The aim of the study was to quantify EIS accuracy over the eastern Mediterranean with respect to manually scaled foF2 data from the Cyprus Digisonde. The findings of this study suggest that there are systematic underestimation-overestimation features that appear in all years considered. The most notable feature is an overestimation of EIS over Cyprus in the interval 00:00–03:00 UT, from October to March and an underestimation from April to August. An underestimation is also systematically observed in the interval, 14:00–16:00 UT from April to December and 22:00–23:00 UT in September to October. This overestimation-underestimation pattern is evident throughout all years under investigation with a higher mean % difference discrepancy in low than in high solar activity years. During disturbed days we observe, for all years, EIS slightly overestimating Cyprus foF2 values.

Mid-latitude ionospheric disturbances during the major Sudden Stratospheric Warming event of 2018 observed by sub-ionospheric VLF/LF signals

This study examines the impact of a major sudden stratospheric warming (SSW) event in February 2018 on the ionosphere of the mid-to–high latitudes in the Northern Hemisphere. The SSW event commenced on February 11 and persisted for about 18 days. The polar stratospheric temperature at 10 hPa increased by about 50 K during February 17 (daytime) and February 18 (nighttime). To detect lower ionospheric variations, VLF/LF signals from four transmitters received by three receivers in the European sector and one receiver in the eastern USA are analyzed along the five propagation paths. The analysis of the diurnal variations in the European sector indicates a significant fluctuation in signal amplitude exceeding ∼5 dB during nighttime on the most disturbed day (February 13). The 1-h averaged nighttime amplitude also showed a maximum deviation of more than ±2σ on February 13. Notably, this occurrence took place two days after the onset of the SSW event at the European receiver locations. No delay in the occurrence of the maximum anomaly was observed in the westward propagation path in the eastern USA. We also analyzed the critical frequency of the F2 layer (foF2) of the ionosphere during February 2018, recorded by Lowell digisonde stations located near the VLF/LF receivers. The foF2 data shows a significant maximum anomaly of ∼1 MHz during nighttime on February 13 at the European stations, coinciding with the SSW period. This anomaly is consistent with the observed VLF/LF anomaly and aligns with the observed temperature variations. However, the deviations in foF2 at the USA stations are not as prominent as those observed at the European stations.

Seasonal Variation of the F2-Layer Critical Frequency and Comparison with the IRI-2016 Model during Two Extremes of Solar Activity Phase of Solar Cycle 22

Abstract: Introduction: The variation of the ionosphere is mostly studied using the critical frequency of the F2-layer (foF2) whose values can also be predicted by an ionospheric model. The widely used model for predicting ionospheric parameters is the International Reference Ionosphere (IRI). Aim: This paper aims to demonstrate how well the current International Reference Ionosphere (IRI-2016) model performs in predicting the critical frequency of the F2-layer (foF2) over two equatorial stations during two extremes of solar activity phase of solar cycle 22.. Methods: The hourly foF2 experimental data collected during the Maximum Phase of Solar Activity MPSA year (1989) and Minimum Phase of Solar Activity MnPSA year (1986) at Ougadougou (Geomagnetic Latitude 0.59 oN, Geomagnetic Longitude 71.46 oE) in the African longitudinal sector and Manila (Geomagnetic Latitude 3.4 oN, Geomagnetic Longitude 191.1 oE) in the Asia longitudinal sector as well as the predicted foF2 data by the IRI-2016 model were used in this study. Sunspot data from Zurich was utilized as a measure of solar activity phase. The foF2 data were grouped into four seasons before analysis began. Comparing the seasonal means of the experimental foF2 data and the IRI-2016 modeled foF2, it was possible to determine how closely the model matched the experimental data at the different seasons and longitudinal sectors Results: The results showed that the IRI-2016 model overestimate and underestimate the observed foF2 at different periods of the day during the equinox and solstice seasons. Observation showed that the highest positive and negative percentage deviations were observed mostly during the post-midnight hours. Observation also showed that Seasonal mean values of the IRI-2016 model of both options showed remarkable improvement at this two stations since their values have little difference from the observed foF2 values. Conclusion: The discrepancy (underestimation and overestimation) in the IRI-2016 model is found larger during MPSA year than during MnPSA year. The URSI option performs better than the CCIR option since its predicted values are much closer to the observed values. Both options of the model perform better in the Asian longitudinal sector than the African longitudinal sector.

Performance of the IRI-2016 and IRI-Plas 2020 considering Mg II as EUV solar proxy

To assess upper atmosphere parameters, current models use different variables to account for solar activity levels. Studies that evaluate the most recent solar cycles have shown that when dealing with the ionospheric F2 layer, and in particular with foF2, the Mg II index is a better choice than other solar activity proxies to account for solar EUV variation. The International Reference Ionosphere, version 2016 (IRI-2016), a model widely used by the scientific community, uses the Ionosonde Global (IG) index to assess foF2. A most recent and expanded version, IRI-Plas, allows selecting a solar activity proxy among 7 options including IG and Mg II. In the present work, the performance of IRI considering Mg II to assess foF2 is analyzed, comparing three modeling cases: IRI-2016 with its default solar activity index (IG), IRI-2016 with Mg II instead of IG, and IRI-Plas choosing Mg II as the solar proxy option. This model comparison was evaluated in terms of their agreement with measured foF2 data from four stations located in Japan (Northern Hemisphere), and four in Australia (Southern Hemisphere). Monthly medians for the 24 h, along two periods of three years were considered: 2000–2002 and 2008–2010, which correspond to epochs of maximum and minimum solar activity levels, respectively. Based on goodness-of-fit statistics, our results show that in the case of IRI-2016, the Mg II implementation does not reveal a significant improvement for foF2 assessment, and the IRI-Plas performance with the Mg II option is only slightly better than the other two options. Dispersion diagrams between measured and modeled foF2 also evince a similar performance between the three models. All models show the same over- and underestimation patterns, except in the nighttime case during the minimum activity level period for both Australian and Japanese stations where IRI-Plas outperforms the other two model options. The comparable performances of the three options might be attributed to a stronger variation in the evaluated periods which could be related to seasonal changes rather than to solar EUV proxies. Future studies considering time series over several years, covering at least a complete solar activity cycle, are required to determine stronger model performance differences.

Wavelet coherence analysis foF2 over Boulder station during different geomagnetic activity

&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; A geomagnetic storm is a major disturbance of Earth’s magnetosphere that occurs when there is a very efficient exchange of energy from the solar wind into the space environment surrounding Earth. During solar and geomagnetic activities, critical frequency of F2 layer (foF2) varies in a great extent. The focus of the current study is on variations in foF2, the critical frequency of the F2 layer, during the intense geomagnetic storm of solar cycle 24 and quiet storm of solar cycle 25. The foF2 data of mid latitude station BC840 (Boulder) in the American region have been used to study geomagnetic and solar wind properties linked with foF2. Time series analysis and Wavelet Coherence analysis have been used to establish the relationship between those parameters. We have found considerable effect of solar and geomagnetic storm disturbances on foF2 during the geomagnetic storm time. The foF2 is enhanced prior to the storm and depletes during recovery phase with no significant changes observed during weak storm. The enhancement or depletion in foF2 may be used as precursor of geomagnetic storms. With long trend analysis and larger database we can make a catalog which can be used to predict the geomagnetic storm and deepen our understanding in impacts of storms on communication signals due to energy injected in ionosphere.&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D;

Relationships Between foF2 and Various Solar Activity Proxies

AbstractTo study ionospheric climate, to model the ionosphere (e.g., the International Reference Ionosphere—IRI) and to investigate its long‐term changes and trends, solar activity proxies/indices have been used, because long and homogeneous data series of solar ionizing flux are not available. To identify the optimum solar activity proxies, we use yearly average foF2 data of 11 ionospheric stations from middle and low/equatorial latitudes of four continents over 1976–2014 and six solar activity proxies, F10.7, sunspot numbers, F30, Mg II, He II, and solar H Lyman‐α flux. Mg II and F30 are found to be the best solar proxies for variability of foF2 at middle latitudes, not the usually used F10.7 or sunspot numbers. At equatorial latitudes the situation seems to be different with likely He II as the optimum solar proxy but all low/equatorial results are very preliminary. Solar activity describes 99% of the total variance of yearly foF2 and the foF2 dependence on solar proxies is highly linear at middle latitudes. The dependence of foF2 on F10.7 and sunspot numbers is significantly steeper in 1996–2014 than in 1976–1995, whereas for F30 both intervals provide the same dependence. We recommend for investigating the midlatitude yearly values of foF2 the solar proxy F30 followed by Mg II as the second one, not traditional F10.7 or sunspot numbers.

Comparative Analysis of Global and Regional Ionospheric Responses during Two Geomagnetic Storms on 3 and 4 February 2022

The present work examines the spatial and temporal distribution of positive and negative TEC anomalies on the global and regional scale. To study the local response of the ionosphere, foF2 data from ground ionosonde stations and TEC data from Madrigal and CODE databases have been used. The relative deviation, which also determines the type of TEC response during geomagnetic storms on 3 and 4 February 2022, is considered. In the present study, the regions of positive and negative TEC anomalies and their evolution during storms are examined in detail. As a result of the study, estimates of the following were obtained: (i) the location of the sectors of the polar regions, in where the particle precipitation from the solar wind is observed, (ii) the mid-latitude regions, in which the mechanism of influence of the O/N2 ratio dominates, and (iii) the region around the equator, in which the influence of the electric field dominates. An attempt was made to determine which mechanism of influence of geomagnetic storms on the ionospheric electron density is dominant in different regions. The following main mechanisms are considered: (a) the additional ionization from the particles’ precipitation, (b) the change of the ratio of atomic oxygen (O) to molecular nitrogen (N2) due to the heating of the neutral air, and (c) the influence on the equatorial ionospheric anomaly.

A Regional Ionospheric Storm Forecasting Method Using a Deep Learning Algorithm: LSTM

AbstractAn ionospheric storm forecasting method was proposed using a deep learning algorithm, LSTM (long short‐term memory). We used the perturbation index to denote the level of an ionospheric storm, deduced from foF2 data, and helped to remove most of the local time and seasonal variations in the ionosphere. In constructing the model, a number of correlated factors were used as inputs, including the properties of coronal mass ejections, solar flare bursts, interplanetary conditions, and geomagnetic and ionospheric states, and the output was whether an ionospheric storm occurred locally in the next 24 hr. Data sets from 2007 to 2014 were used to train the model, and those from 2015 to 2016 were used for validation. The results showed that the model behaved well in most events. The mean precision rate, recall rate, accuracy, and F1 score of the model were 71.7%, 59.7%, 92.7%, and 65.0% in northern China and 78.9%, 56.3%, 96.3% and 65.0% in southern China, respectively. The LSTM forecasting model performed better than other models such as persistence, multiple‐layer perceptron and support vector machine models. Case studies also showed good performance during geomagnetic storms of different strengths. We believe that this model can be beneficial for functional ionospheric storm operation.

Space Weather Effects Observed in the Northern Hemisphere during November 2021 Geomagnetic Storm: The Impacts on Plasmasphere, Ionosphere and Thermosphere Systems

On 3 November 2021, an interplanetary coronal mass ejection impacted the Earth’s magnetosphere leading to a relevant geomagnetic storm (Kp = 8-), the most intense event that occurred so far during the rising phase of solar cycle 25. This work presents the state of the solar wind before and during the geomagnetic storm, as well as the response of the plasmasphere–ionosphere–thermosphere system in the European sector. To investigate the longitudinal differences, the ionosphere–thermosphere response of the American sector was also analyzed. The plasmasphere dynamics was investigated through field line resonances detected at the European quasi-Meridional Magnetometer Array, while the ionosphere was investigated through the combined use of ionospheric parameters (mainly the critical frequency of the F2 layer, foF2) from ionosondes and Total Electron Content (TEC) obtained from Global Navigation Satellite System receivers at four locations in the European sector, and at three locations in the American one. An original method was used to retrieve aeronomic parameters from observed electron concentration in the ionospheric F region. During the analyzed interval, the plasmasphere, originally in a state of saturation, was eroded up to two Earth’s radii, and only partially recovered after the main phase of the storm. The possible formation of a drainage plume is also observed. We observed variations in the ionospheric parameters with negative and positive phase and reported longitudinal and latitudinal dependence of storm features in the European sector. The relative behavior between foF2 and TEC data is also discussed in order to speculate about the possible role of the topside ionosphere and plasmasphere response at the investigated European site. The American sector analysis revealed negative storm signatures in electron concentration at the F2 region. Neutral composition and temperature changes are shown to be the main reason for the observed decrease of electron concentration in the American sector.

Long-Term Changes in Ionospheric Climate in Terms of foF2

There is not only space weather; there is also space climate. Space climate includes the ionospheric climate, which is affected by long-term trends in the ionosphere. One of the most important ionospheric parameters is the critical frequency of the ionospheric F2 layer, foF2, which corresponds to the maximum ionospheric electron density, NmF2. Observational data series of foF2 have been collected at some stations for as long as over 60 years and continents are relatively well covered by a network of ionosondes, instruments that measure, among others, foF2. Trends in foF2 are relatively weak. The main global driver of long-term trends in foF2 is the increasing concentration of greenhouse gases, namely CO2, in the atmosphere. The impact of the other important trend driver, the secular change in the Earth’s main magnetic field, is very regional, being positive in some regions, negative in others, and neither in the rest. There are various sources of uncertainty in foF2 trends. One is the inhomogeneity of long foF2 data series. The main driver of year-to-year changes in foF2 is the quasi-eleven-year solar cycle. The removal of its effect is another source of uncertainty. Different methods might provide somewhat different strengths among trends in foF2. All this is briefly reviewed in the paper.

FoF2 Seasonal Prediction With IRI-2016 Over Quiet Activity at Dakar Station

This study deals with comparison between Dakar station ionospheric F2 layer critical frequency (foF2) data and both subroutines (CCIR and URSI) of IRI-2016 model predictions. Dakar station is located near the crest of the African Equatorial Ionization Anomaly (EIA) region. Comparisons are made for very quiet activity during the four seasons (spring, summer, autumn and winter) over both solar cycles 21 and 22. The quietest days per season are determined by taking the five days with the lowest aa. The relative standard deviation of modeled foF2 values is used to assess the quality of IRI model prediction. Model predictions are suitable with observed data by day than by night. The accuracy is better during spring season and poor during winter season. During all seasons, both model subroutines don&amp;rsquo;t express the signature of the observed vertical drift E&amp;times;B. But they express an intense counter electrojet at the place of mean intensity or high electrojet.

Alternative Approach for Tsunami Early Warning Indicated by Gravity Wave Effects on Ionosphere

The rapid displacement of the ocean floor during large ocean earthquakes or volcanic eruptions causes the propagation of tsunami waves on the surface of the ocean, and consequently internal gravity waves (IGWs) in the atmosphere. IGWs pierce through the troposphere and into the ionospheric layer. In addition to transferring energy to the ionosphere, they cause significant variations in ionospheric parameters, so they have considerable effects on the propagation of radio waves through this dispersive medium. In this study, double-frequency measurements of the Global Positioning System (GPS) and ionosonde data were used to determine the ionospheric disturbances and irregularities in response to the tsunami induced by the 2011 Tohoku earthquake. The critical frequency of the F2 layer (foF2) data obtained from the ionosonde data also showed clear disturbances that were consistent with the GPS observations. IGWs and tsunami waves have similar propagation properties, and IGWs were detected about 25 min faster than tsunami waves in GPS ground stations at the United States west coast, located about 7900 km away from the tsunami’s epicenter. As IGWs have a high vertical propagation velocity, and propagate obliquely into the atmosphere, IGWs can also be used for tsunami early warning. To further investigate the spatial variation in ionospheric electron density (IED), ionospheric profiles from FORMOSAT-3/COSMIC (F3/C) satellites were investigated for both reference and observation periods. During the tsunami, the reduction in IED started from 200 km and continued up to 272 km altitude. The minimum observed reduction was 2.68 × 105 el/cm3, which has happened at 222 km altitude. The IED increased up to 767 km altitude continuously, such that the maximum increase was 3.77 × 105 el/cm3 at 355 km altitude.

Statistical analysis of ionospheric total electron content (TEC): long-term estimation of extreme TEC in Japan

Ionospheric total electron content (TEC) is one of the key parameters for users of radio-based systems, such as the Global Navigation Satellite System, high-frequency communication systems, and space-based remote sensing systems, since total ionospheric delay is proportional to TEC through the propagation path. It is important to know extreme TEC values in readiness for hazardous ionospheric conditions. The purpose of this study is to estimate extreme TEC values with occurrences of once per year, 10 years, and hundred years in Japan. In order to estimate the extreme values of TEC, a cumulative distribution function of daily TEC is derived using 22 years of TEC data from 1997 to 2018. The extreme values corresponding to once per year and 10 years are 90 and 110 TECU, respectively, in Tokyo, Japan. On the other hand, the 22-year data set is not sufficient to estimate the once-per-100-year value. Thus, we use the 62-year data set of manually scaled ionosonde data for the critical frequency of the F-layer (foF2) at Kokubunji in Tokyo. First, we study the relationship between TEC and foF2 for 22 years and investigate the slab thickness. Then the result is applied to the statistical distribution of foF2 data for 62 years. In this study, two methods are applied to estimate the extreme TEC value. In the first method, the distribution of slab thickness is artificially inflated to estimate extreme TEC values. In the second method, extreme slab thicknesses are applied to estimate extreme TEC values. The result shows that the once-per-100-year TEC is about 150–190 TECU at Tokyo. The value is also estimated to be 180–230 TECU in Kagoshima and 120–150 TECU in Hokkaido, in the southern and northern parts of Japan, respectively.

A Bidirectional Long Short-Term Memory-Based Ionospheric foF2 and hmF2 Models for a Single Station in the Low Latitude Region

Equatorial electrojet (EEJ) and the subsequent development of equatorial ionization anomaly (EIA) are responsible for the highly complex and nonlinear variability nature of the ionosphere. Prediction of ionospheric parameters like Ionospheric F2 layer Critical frequency (foF2) and peak height (hmF2) feature at low latitude regions is of significant interest in understanding the ionospheric weather effects on communication and navigation systems. The role of artificial intelligence-based machine learning algorithms is successful in the prediction of ionospheric variability. In this letter, a deep learning model based on Bidirectional long short-term memory (Bi-LSTM) technique is implemented for predicting foF2 and hmF2 parameters. The Bi-LSTM method was trained and tested on one-year (2015) ionospheric foF2 and hmF2 data from Canadian Advanced Digital Ionosonde (CADI) located at Hyderabad, India (17.47 °N, 78.57 °E). Bi-LSTM model captures time sequence processing features using past and present foF2 and hmF2 data samples. It is evident from the results that the Bi-LSTM model performs better than long short-term memory (LSTM), neural networks (NNs), and International Reference Ionosphere (IRI) 2016 models in predicting foF2 and hmF2 values. The performance of the Bi-LSTM model tested and found to better predict ionospheric foF2 and hmF2 features for two significant geomagnetic storms occurred in the year 2015 (March and June).