Ionospheric Critical Frequency Research Articles

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.

Assessment of IRI2020 model accuracy in predicting ionospheric parameters: Insights from multiple ionosonde stations

This research assesses the performance of the IRI2020 model in forecasting the ionospheric critical frequency of the F2 layer (foF2) and Maximum Usable Frequency (MUF) of the ionosphere from 2020 to 2023 across multiple ionosonde stations. The primary objective is to analyse the characteristics of foF2 and MUF across various longitudinal sectors during the solar minimum to ascending phase of solar cycle 25 and to evaluate the IRI2020 model’s performance in replicating observed data. Additionally, the study explores the influence of various space weather parameters on the variability of these ionospheric parameters. The findings reveal that the IRI2020 model overestimates the daily mean values of both foF2 and MUF(3000)F2 across the stations over the three years. While exhibiting good performance in capturing the pattern of hourly predictions, the model tends to overestimate foF2 more than MUF(3000)F2, with better predictions observed for MUF(3000)F2. However, hourly estimations display a mix of underestimation and overestimation, predominantly overestimating values. Correlations ranged from 0.58 to 0.92 for foF2 and 0.72 to 0.91 for MUF(3000)F2, showing variability across stations. As solar activity increases, seasonal variations become more evident, especially in stations located in the northern hemisphere.In contrast, stations in the southern hemisphere exhibit less pronounced seasonal variations, suggesting a notable hemispheric asymmetry. Although the IRI2020 better captured the general trend of foF2 and MUF(3000)F2 in our investigation, the percentage deviation for MUF(3000)F2 ranges from 1 % to 15.1 %, which can result in a significant range reduction for practical applications like HF communications. Stations closer to the geomagnetic equatorial line exhibit stronger seasonal asymmetry and higher deviations. Some key space weather parameters, including sunspot number R, F10.7 and R/F10.7 ratio, were found to significantly influence ionospheric variability, with sunspot number R and R/F10.7 showing a stronger correlation with foF2 and MUF(3000)F2. These suggest that the variability of the solar active region parameters primarily controls ionospheric foF2 and MUF(3000)F2.

PyIRI: Whole‐Globe Approach to the International Reference Ionosphere Modeling Implemented in Python

AbstractThe International Reference Ionosphere (IRI) model is widely used in the ionospheric community and considered the gold standard for empirical ionospheric models. The development of this model was initiated in the late 1960s using the FORTRAN language; for its programming approach, the model outputs were calculated separately for each given geographic location and time stamp. The Consultative Committee on International Radio (CCIR) and International Union of Radio Science (URSI) coefficients provide the skeleton of the IRI model, as they define the global distribution of the maximum useable ionospheric frequency foF2 and the propagation factor M(3,000)F2. At the U.S. Naval Research Laboratory, a novel Python tool was developed that enables global runs of the IRI model with significantly lower computational overhead. This was made possible through the Python rebuild of the core IRI component (which calculates ionospheric critical frequency using the CCIR or URSI coefficients), taking advantage of NumPy matrix multiplication instead of using cyclic addition. This paper explains in detail this new approach and introduces all components of the PyIRI package.

Study of the ionospheric precursors associated with M w ≥6.0EQ from Ionosonde Stations and GIM TEC

To study the coupling mechanism between lithosphere, atmosphere and ionosphere, the ionosonde based earthquake (EQ) anomalies provide clear indications for the future events. The Lithospheric-Atmosphere-Ionospheric coupling (LAIC) provides information about pre and post seismic precursors over the seismogenic zone. This study presents the variations in ionospheric critical frequency (foF2) of the F2 layer and Total Electron Content (TEC) from Global Ionospheric Maps (GIMs) associated with the two Mw ≥ 6.0 EQs at Pakistan-Iran border (here-in-after, Saravan EQ) and Afghanistan. The ionosonde stations data located at Islamabad and Sonmiani provided significant foF2 variation for the April 16, 2013 (Mw7.7) and June 22, 2022 (Mw6.0) EQs in Saravan and Afghanistan, respectively. Furthermore, the foF2 values showed significant variations before the main shocks of both the events. Moreover, the TEC maps over the epicenter supported the variations of ionosonde data with maximum variations beyond the bound of the statistical method as anomalous positive variations before the two events. Moreover, prominent variations occur before both the EQs in ionosonde and TEC data but the Afghanistan event showed more pronounced variations than Saravan EQ. Additionally, it has been observed that more prominent variation occurs in both the stations within 5–10 days before the main shocks to aid another development in LAIC hypothesis.

Influence of geomagnetic disturbances on scintillations of GLONASS and GPS signals as observed on the Kola Peninsula

We have compared effects of geomagnetic disturbances during magnetic storms of various types (CME and CIR) and during an isolated substorm on scintillations of GLONASS and GPS signals, using a Septentrio PolaRx5 receiver installed in Apatity (Murmansk Region, Russia). We analyze observational data for 2021. The magnetic storms of November 3–4, 2021 and October 11–12, 2021 are examined in detail. The November 3–4, 2021 magnetic storm was one of the most powerful in recent years. The analysis shows that the scintillation phase index reaches its highest values during nighttime and evening substorms (σϕ≈1.5–1.8), accompanied by a negative bay in the magnetic field. During magnetic storms, positive bays in the magnetic field, associated with an increase in the eastward electrojet, lead, however, to quite comparable values of the phase scintillation index.
 An increase in phase scintillations during nighttime and evening disturbances correlates with an increase in the intensity of ULF waves (Pi3/Pc5 pulsations) and with the appearance of aurora arcs. This confirms the important role of ULF waves in forming the auroral arc and in developing ionospheric irregularities. The predominance of the green line in the spectrum of auroras indicates the contribution of disturbances in the ionospheric E layer to the scintillation increase. Pulsating auroras, associated with ionospheric disturbances in the D layer, do not lead to a noticeable increase in phase scintillations. Analysis of ionospheric critical frequencies according to ionosonde data from the Lovozero Hydrometeorological Station indicates the contribution of the sporadic Es layer of the ionosphere to jumps in phase scintillations.
 The difference between phase scintillation values on GLONASS and GPS satellites during individual disturbances can be as great as 1.5 times, which may be due to different orbits of the satellites. At the same time, the level of GLONASS/GPS scintillations at the L2 frequency is higher than at the L1 frequency. We did not find an increase in the amplitude index of scintillations during the events considered.

Влияние геомагнитных возмущений на сцинтилляции сигналов ГЛОНАСС и GPS спутников по данным наблюдений на Кольском полуострове

We have compared effects of geomagnetic disturbances during magnetic storms of various types (CME and CIR) and during an isolated substorm on scintillations of GLONASS and GPS signals, using a Septentrio PolaRx5 receiver installed in Apatity (Murmansk Region, Russia). We analyze observational data for 2021. The magnetic storms of November 3–4, 2021 and October 11–12, 2021 are examined in detail. The November 3–4, 2021 magnetic storm was one of the most powerful in recent years. The analysis shows that the scintillation phase index reaches its highest values during nighttime and evening substorms (σϕ≈1.5–1.8), accompanied by a negative bay in the magnetic field. During magnetic storms, positive bays in the magnetic field, associated with an increase in the eastward electrojet, lead, however, to quite comparable values of the phase scintillation index.
 An increase in phase scintillations during nighttime and evening disturbances correlates with an increase in the intensity of ULF waves (Pi3/Pc5 pulsations) and with the appearance of aurora arcs. This confirms the important role of ULF waves in forming the auroral arc and in developing ionospheric irregularities. The predominance of the green line in the spectrum of auroras indicates the contribution of disturbances in the ionospheric E layer to the scintillation increase. Pulsating auroras, associated with ionospheric disturbances in the D layer, do not lead to a noticeable increase in phase scintillations. Analysis of ionospheric critical frequencies according to ionosonde data from the Lovozero Hydrometeorological Station indicates the contribution of the sporadic Es layer of the ionosphere to jumps in phase scintillations.
 The difference between phase scintillation values on GLONASS and GPS satellites during individual disturbances can be as great as 1.5 times, which may be due to different orbits of the satellites. At the same time, the level of GLONASS/GPS scintillations at the L2 frequency is higher than at the L1 frequency. We did not find an increase in the amplitude index of scintillations during the events considered.

A Reconstruction Method for Ionospheric foF2 Spatial Mapping over Australia

To improve the accuracy of predicting the ionospheric critical frequency of the F2 layer (foF2), a reconstruction method for the spatial map of the ionospheric foF2 based on modified geomagnetic dip coordinates is proposed. Based on the strong correlation between the ionospheric foF2 and geomagnetic coordinates, the variation function of ionospheric distance is built. In the end, the spatial map of the ionospheric foF2 is predicted by solving the Kriging equation. The results show that the regional characteristics of the ionospheric foF2 analyzed by the proposed method are consistent with the observations. Compared with the reconstructed value of foF2 using traditional geographic coordinates, the root-mean-square error (RMSE) in high solar activity years decreased by 0.43 MHz, and the relative RMSE decreased by 5.48%; The RMSE decreased by 0.35 MHz during low solar activity which is 5.99% lower to relative RMSE. The research results provide support for high-frequency communication frequency selection.

Analysis of the Ionospheric Response to Sudden Stratospheric Warming and Geomagnetic Forcing over Europe during February and March 2023

A study of the behavior of the main characteristics of the ionosphere over Europe during the 26–28 February 2023 ionospheric storm was carried out in this present work. The additional influence of sudden stratospheric warming on the ionosphere was considered. The behavior of the critical frequency of the ionosphere foF2 (characterizing the maximum electron density), the peak height of the F2-layer (hmF2), and Total Electron Content (TEC) were investigated through their relative deviations from the quiet conditions. The behavior of the TEC over Europe showed the geographic latitudinal dependence of the response. The variability in the ionospheric critical frequency was represented by the data of 10 ionospheric stations for vertical sounding located in two groups: (i) near the prime meridian and (ii) near the 25° E meridian. Some differences were found in the response compared to the TEC response, which was explained by the different responses of the top maximum region and bottom maximum region. The peak height of the F2 layer varied strongly during the storm, which was due to the forced drift of ionospheric plasma induced by additional electric fields. The present detailed analysis of the ionospheric response shows that the considered storm exhibited characteristic features inherent in the winter season but with some manifestations of reactions in equinox conditions.

Variation of ionosonde [formula omitted] and its comparison with IRI-2016 & a local model over Pakistan longitude sector during solar cycle 22

The study presents the variation of ionospheric critical frequency of E-layer (foE), its comparison with International Reference Ionosphere (IRI-2016) over Pakistan and suggests an artificial neural network (ANN) based algorithm to predict noontime daily hourly values. The ionosonde measurements of Karachi (Geog. Coord: 24.95°N, 67.13°E, dip latitude = 17.0°N), Multan (30.18°N, 71.48°E, 21.8°N) and Islamabad (33.75°N, 73.13°E, 25.2°N) have been used for the purpose. The observed foE values have been analysed during the high, moderate and low solar activity years 1989, 1992 and 1996, respectively. Results show a strong correlation between foE and solar activity which was expected because the E-layer is essentially solar controlled. It is found that foE peaks around noon with higher values during summer. The annual relative percentage deviation between data and IRI-2016 modelled values remains less than 6% for all locations and years under study. The ANN based algorithm predicts daily noontime foE values with a goodness of fit of 78%, compared to the IRI-2016 prediction, which has a goodness of fit of 71%. It is noted that the performance of IRI-2016 in predicting monthly hourly median values of foE improves with decreasing solar activity. The study confirms the data authenticity of foE over Pakistan and suggests an ANN based algorithm in predicting noontime values with no immediate updates to IRI-2016 for this Asian longitude sector.

How deterministic is the Earth ionosphere's response to solar activity?

This contribution is aimed at an analysis of the dynamics of free-electron density fluctuations in the ionospheric critical plasma frequency f0F2 by using some tools from the theory of nonlinear dynamical systems. The results suggest the existence of low-dimensional attractors that point to a characterization of the free electron density fluctuations in the f0F2 as a deterministic chaotic system. The study carried out focused on the response of the ionosphere to solar activity as a function of the ascending and descending phases of the solar cycle.

The geoeffectiveness of TIE-GCM simulations of ionospheric critical frequency foF2 at the equatorial station of Thiruvananthapuram in the Indian sector

An extensive intercomparison of ionospheric foF2 observations and NCAR Thermosphere-Ionosphere ElectrodynamicsGeneral Circulation Model(TIE-GCM)simulations has been carried out for the dip equatorial location of Thiruvananthapuram. Ionosonde measurements for geomagnetically quiet days of 2002, 2006 and 2008, representing solar maximum, solar minimum and deep solar minimum conditions have been used for the analysis.In general TIE-GCM simulations reproduced the temporal and seasonal characteristics of foF2 over Thiruvananthapuram reasonably well for all the three solar activity conditions. Seasonally the difference between the measured and the simulated foF2 tended to be higher during winter (maximum of 25%). Additionally, it is found that TIE-GCM is not reproducing the reduction in the foF2 values in the noon hours i.e. the bite out, which is very prominent in the foF2 observations predominantly during 2002. A detailed analysis revealed that, there is good agreement between the modeled and measured values for the whole observation period, with an R value of 0.81. From the comparison it is clear that the model underestimates the observations in general but for the periods when bite out is prominent, the model gives an over estimation.The comprehensive comparisons during different solar activity conditions have shown that the difference between modeled and measured ionospheric peak densities lies in the range of.10 to −25%. This study brings out the efficacy of the model in simulating the temporal seasonal and solar cycle variability of ionospheric foF2 over the equatorial Indian region.

Investigation of the relationship between f0f2 and the TEC using single station neural network models

The relationship between the ionospheric critical plasma frequencies (f0F2) and GNSS-TEC (Global Navigation Satellite System –Total Electron Content) measurements was investigated using an artificial neural network method. About 20 pairs of ionosonde-GNSS receiver stations from 2000 to 2016 were used. Results from this work indicate that the relationship between f0F2 and TEC is mostly affected by the seasons, followed by the level of solar activity, and then the local time. Geomagnetic activity was the least significant of the factors investigated. The relationship between f0F2 and TEC was also shown to exhibit spatial variation; the variation is less conspicuous for closely located stations. Single station models predicted the f0F2 more accurately at their particular localities and clearly overestimated values of the f0F2 ionosonde observations when used at different localities. This finding indicates that model predictions are better (in terms of reduced prediction errors) for the stations for which they are developed than for a different station. Our result visibly point out that models developed for a particular station cannot be effectively applied in another station located farther apart in space. The new approach described in this study represents an important contribution in space weather prediction.

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’t express the signature of the observed vertical drift E×B. But they express an intense counter electrojet at the place of mean intensity or high electrojet.

Investigating the Compatibility of IRI and ASAPS Models in Predicting the foF2 Ionospheric Parameter over the Mid Latitude Region

In this research, an investigation for the compatibility of the IRI-2016 and ASAPS international models was conducted to evaluate their accuracy in predicting the ionospheric critical frequency parameter (foF2) for the years 2009 and 2014 that represent the minimum and maximum years of solar cycle 24. The calculations of the monthly average foF2 values were performed for three different selected stations distributed over the mid-latitude region. These stations are Athens - Greece (23.7o E, 37.9 o N), El Arenosillo - Spain (-6.78 o E, 37.09 o N), and Je Ju - South Korea (124.53 o E, 33.6 o N). The calculated values using the two tested models were compared with the observed foF2 datasets for each of the three selected locations. The results showed that the two tested models gave good and close results for all selected stations compared to the observed data for the studied period of time. At the minimum solar cycle 24, the ASAPS model showed in general better values than the IRI-2016 model at Athens, El Arenosillo and Je Ju stations for all tested methods. At maximum solar cycle 24, the IRI-2016 model showed higher and closer values to the observed data at Athens and El Arenosillo stations, while the ASAPS model showed better values at Je Ju station.

The Impact of Coronal Mass Ejections on the Seasonal Variation of the Ionospheric Critical Frequency f0F2

We investigated the relations between the monthly average values of the critical frequency (f0F2) and the physical properties of the coronal mass ejections (CMEs), then we examined the seasonal variation of f0F2 values as an impact of the several CMEs properties. Given that, f0F2 were detected by PRJ18 (Puerto Rico) ionosonde station during the period 1996–2013. We found that the monthly average values of f0F2 are varying coherently with the sunspot number (SSN). A similar trend was found for f0F2 with the CMEs parameters such as the CME energy (linear correlation coefficient R = 0.73), width (R = 0.6) and the speed (R = 0.6). The arrived CMEs cause a plasma injection into the ionosphere, in turn, increasing the electron density, and consequently, f0F2 values. This happens in the high latitudes followed by the middle and lower latitudes. By examining the seasonal variation of f0F2, we found that the higher correlation between f0F2 and CMEs parameters occurs in the summer, then the equinoxes (spring and autumn), followed by the winter. However, the faster CMEs affect the ionosphere more efficiently in the spring more than in the summer, then the winter and the autumn seasons.

Modeling and analysis of ionospheric parameters based on multicomponent model

The results of modeling and analysis of ionospheric parameters during disturbed geomagnetic field are presented. Hourly and 15-min data of the ionospheric critical frequency (foF2) at Paratunka site (Kamchatka, Russia, 53.0 N and 158.7 E) for the period 1968–2019 were applied. The modeling was based on ionospheric parameter multicomponent model developed by the authors. In order to refine model parameters, three classes of anomalies were introduced. They characterize strong, moderate and weak ionospheric disturbances. On the basis of the modeling, we detected short-period ionospheric disturbances of moderate and weak intensity, preceding magnetic storms. They characterize occurrences of oscillatory processes in the ionosphere at the background of increased solar activity. Comparisons with the International Reference Ionosphere model (IRI-2016) and the median method were made. That showed the efficiency of the proposed model. For automatic performance of the approach, computational solutions were suggested to detect and to estimate ionospheric disturbance parameters.

An Adaptive Forecasting Method for Ionospheric Critical Frequency of F2 Layer

AbstractTo achieve further improvements in quantitative predictability, a chaos‐based adaptive forecasting method for the critical frequency of the F2 layer (foF2) is proposed for the development of an ionospheric forecasting technique for one hour ahead. This method has three new characteristics. (1) It is based on Volterra filters and it has a simplified structure with easy implementation. (2) Based only on past measured data, it can forecast foF2 values without the requirement for past or forecast values of any solar and geomagnetic indices. (3) It can achieve high forecast accuracy with a small training dataset of 27 days (one solar rotation period). Diurnal, seasonal, and annual comparisons of measured and forecasted foF2 values are presented to illustrate the applicability and suitability of the proposed method. Statistical results reveal that the foF2 values calculated using the proposed model are consistent with the trend of measurements irrespective of whether geomagnetic conditions are quiet or disturbed. The average RMSE and RRMSE values were 0.86 MHz and 17.36%, respectively, when using measured data from periods of past 27 days during 2008–2015. The proposed method has potential to forecast other ionospheric characteristic parameters, and that it could achieve satisfactory regional or global 1‐hr forecasting if combined with a spatial reconstruction technique.

An algorithm for detecting intense anomalous changes in the time dependence of ionospheric parameters

The paper presents a modified multicomponent model of ionospheric parameter time series. The model describes regular variations and anomalous changes of a multi-scale structure that characterize the occurrence of ionospheric irregularities. Identification of the model components is based on a combined application of the wavelet transform and autoregressive-integrated moving average models. An algorithm for analyzing ionospheric parameters has been developed on the basis of the proposed model. The algorithm allows the intensive ionospheric anomalies characterizing the occurrence of strong ionospheric storms to be detected on-line. Results of the evaluation of the algorithm performance are presented. The evaluation is performed by the example of processing and analyzing hourly and 15-minute data on the ionospheric critical frequency (foF2) during magnetic storms in 2015 – 2017. The performed estimations showed the efficiency of the algorithm and the possibility of its application for space weather forecasting.

The impact of CMEs on the critical frequency of F2-layer ionosphere (foF2)

AbstractThe ionospheric critical frequency (foF2) from ionosonde measurements at geographic high, middle, and low latitudes are analyzed with the occurrence of coronal mass ejections (CMEs) in long term variability of the solar cycles. We observed trends of monthly maximum foF2 values and monthly averaged values of CME parameters such as speed, angular width, mass, and kinetic energy with respect to time. The impact of CMEs on foF2 is very high at high latitudes and low at low latitudes. The time series for monthly maximum foF2 and monthly-averaged CME speed are moderately correlated at high and middle latitudes.

The effect of different phases of severe geomagnetic storms on the low latitude ionospheric critical frequencies

In this paper, the effect of different phases of severe geomagnetic storms on low latitude ionospheric critical frequencies (foF2) is investigated. For this purpose, hourly ionospheric critical frequency (foF2) data measured at the low latitude ionosonde station Manila during 1981 and 1991 is examined. The investigation is carried out using superposed epoch analysis method considering the disturbance storm time index Dst ≤ −100 nT hours as event times. To examine depending on local time the effect of the phases of geomagnetic storms on foF2, this analysis was conducted for separately for the day hours, night hours, and all hours during both the main and the recovery phases of the severe geomagnetic storms and the results were compared with each other. It is observed that for both 1981 and 1991, the highest change (increase or decrease) in foF2 values occurs at the event times for all hours of day during both the main and the recovery phase of severe geomagnetic storms. Also, during the main phases of severe geomagnetic storms, the foF2 values increase at day hours, while the foF2 values decrease at night hours. However, during the recovery phases of severe geomagnetic storms, the foF2 values decrease at day hours, while the foF2 values increase at night hours. For both the day and night hours, the changes in the foF2 values during the recovery phase of severe geomagnetic storms are greater than the changes in the foF2 values during the main phase of severe geomagnetic storms.