Critical Frequency Of Ionospheric F2 Layer Research Articles

Statistical Analysis of the Critical Frequency foF2 Dependence
 on Various Solar Activity Indices

A description of the ionospheric F2-layer critical frequency foF2 dependence on solar activity byvarious indices (proxies) has been analyzed. The results of the vertical ionospheric sounding at the Juliusruhstation during 2 winter months (January and February), 2 equinox months (March and October), and a summermonth (June) are considered. Five solar proxies: Ly-α, MgII, Rz, F30, and F10.7 have been analyzed.The changes in foF2 are compared to the corresponding changes in 1957–1980. The determination coefficientR2 according to the Fisher F-test is used as a measure of the quality of the description of the foF2 dependenceon SA by each of the proxies. It has been found that a well-pronounced diurnal variation in the R2 valueis observed in winter months: this is higher at the near-noon hours than at night. In other words, all indicesdescribe the foF2 behavior better in the daytime than at night. A well-pronounced diurnal variation in R2 isalso observed for four proxies in the equinoctial months, whereas that variation is pronounced much less forRz: a strong scatter of the R2 values is seen. A diurnal variation in the R2 is absent completely in June: jumpsfrom one hour to another are observed. This analysis allows us to conclude that the most reliable SA proxiesfor description of the foF2 dependence on SA at all LT hours are MgII, F30, and Ly-α.

Modeling and Forecasting Ionospheric foF2 Variation in the Low Latitude Region during Low and High Solar Activity Years

Prediction of ionospheric parameters, such as ionospheric F2 layer critical frequency (foF2) at low latitude regions is of significant interest in understanding ionospheric variation effects on high-frequency communication and global navigation satellite system. Currently, deep learning algorithms have made a striking accomplishment in capturing ionospheric variability. In this paper, we use the state-of-the-art hybrid neural network combined with a quantile mechanism to predict foF2 parameter variations under low and high solar activity years (solar cycle-24) and space weather events. The hybrid neural network is composed of a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM), in which CNN and BiLSTM networks extracted spatial and temporal features of ionospheric variation, respectively. The proposed method was trained and tested on 5 years (2009–2014) of ionospheric foF2 observation data from Advanced Digital Ionosonde located in Brisbane, Australia (27°53′S, 152°92′E). It is evident from the results that the proposed model performs better than International Reference Ionosphere 2016 (IRI-2016), long short-term memory (LSTM), and BiLSTM ionospheric prediction models. The proposed model extensively captured the variation in ionospheric foF2 feature, and better predicted it under two significant space weather events (29 September 2011 and 22 July 2012).

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.

Joint Ionosonde Studies of F2 Layer Critical Frequency Variations in the Ionosphere Over Kharkiv and Tromsø During Fall Equinox in Quiet and Disturbed Conditions

According to the results of joint ionosonde studies of variations in the ionospheric F2 layer critical frequency over Kharkiv and Tromsø during low solar activity for fall equinox on September 22 – 24, 2020, the features of foF2 variations in middle and low latitudes were investigated for magnetically quiet and magnetically disturbed conditions. On the magnetically quiet day of September 22, 2020, the foF2 values over Kharkiv were found to exceed the foF2 values over Tromsø for the entire time interval of joint observations 02:45 - 16:45 UT. Both over Tromsø and over Kharkiv, a rapid increase in foF2 to its local maximum value was observed after the sunrise. Quasi-periodic variations in foF2 were revealed at high latitudes, which had lower amplitude compared to variations in foF2 over Kharkiv. Over both measuring sites, a pre-sunset local maximum in foF2 was observed. During magnetically disturbed conditions over Tromsø and Kharkiv, quasi-periodic fluctuations in foF2 were observed after the sunrise. Oscillations over Tromsø had lower amplitude than over Kharkiv, and were almost completely suppressed after the onset of a strong magnetic disturbance at high latitudes on September 23, 2020. The foF2 values over Tromsø exceeded its values over Kharkiv in a time interval of 10:45 – 12:15 UT. Comparison of the time variation of foF2 over Tromso on a magnetically quiet day, September 22, 2020, and on a magnetically disturbed day, September 23, 2020, showed that the foF2 value for September 23, 2020 from 10:15 to 15:00 UT exceeded the foF2 values for the same period on September 22, 2020. Comparison of the temporal variations in foF2 over Kharkiv on a magnetically quiet day, September 22, 2020, and on a magnetically disturbed day, September 24, 2020, showed that the foF2 value for September 24, 2020 exceeded its value for September 22, 2020 from 03:00 to 04:45 UT and from 07:00 to 13:00 UT. Magnetic disturbances were found to cause a rapid increase in foF2 values both over Kharkiv and Tromsø, which exceeded foF2 values under magnetically quiet conditions, and also led to a significant increase in the relative amplitudes of traveling ionospheric disturbances over Kharkiv.

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).

Simultaneous ionosonde investigations of the ionospheric F2 layer critical frequency and peak height at both ends of the geomagnetic tube

Based on the results of simultaneous ionosonde observations during low solar and weak magnetic activities, a coupling was found between diurnal and quasi-periodic variations in ionospheric parameters over magnetically conjugated regions, where the Ukrainian Antarctic Station (UAS) and Millstone Hill Observatory are located. A significant impact of the summer hemisphere on the nighttime variations of the F2 layer critical frequency foF2 in the magnetically conjugated region in the winter hemisphere was found. The most characteristic manifestation of this impact is the control of foF2 variations over the UAS not by the local sunset (sunrise), but by the sunset (sunrise) over Millstone Hill. It was found that the sunset over Millstone Hill leads to an increase in foF2 over the UAS, while the sunrise leads to a decrease in foF2 with a subsequent sharp increase. Both phenomena are associated with changes in the photoelectron flux from the northern hemisphere, corresponding changes in the electron temperature in the ionosphere above the UAS and the effect of these changes on the compression or rarefaction of the ionospheric plasma and changes in the plasmaspheric fluxes of H + ions. It was shown that the transition from nighttime to daytime conditions over both observation points was characterized by a significant decrease in the F2 layer peak height, and the difference in the values of this ionospheric parameter over Millstone Hill and UAS at night is due to seasonal differences in the thermospheric circulation and the difference in the behavior of the ionospheric parameters in the Northern and Southern hemispheres. Manifestations of atmospheric gravity waves, caused by the passage of local sunrise terminators, as traveling ionospheric disturbances with periods of about 90 and 75 – 120 mins over Millstone Hill and UAS, respectively, were found. These waves were most likely generated in the region located between the ionospheric F1 and F2 layers, where the sharp gradients in the electron and ion densities occur during changes in the intensity of solar radiation. It is confirmed that wave disturbances in atmospheric and ionospheric parameters can be transferred between magnetically conjugated regions by slow magnetohydrodynamic waves generated both at the heights of the ionospheric dynamo region due to the modulation of atmospheric and ionospheric parameters by atmospheric waves and the occurrence of external currents, and at the top of the plasmaspheric tube, where sharp plasma compression and heating or rarefaction and cooling occur during the passage of the solar terminator. Keywords: the ionosphere, F2 region, ionosonde measurements, geomagnetic field tube, magnetoconjugate region coupling, atmospheric gravity waves, traveling ionospheric disturbances, generation of slow magnetohydrodynamic waves

Variations of ionospheric F2 layer critical frequency in Malaysia during different solar phases

Variations of ionospheric F2 layer critical frequency in Malaysia during different solar phases

The Comparison of Responses to Geomagnetic Activity Changes of foF2 Predicted by IRI with Observations at Magnetic Conjugate Points for Middle and High Latitudes

In this study, the response to geomagnetic storms of the ionospheric F2 layer critical frequency (foF2) was investigated at the magnetic conjugate points. The hourly foF2 data observed at the magnetic conjugate points of middle and high latitudes for the geomagnetic stormy days around both of 1976 and foF2 data received from IRI-2016 Model for same points were used. The foF2 data observed in magnetic conjugate points and received from IRI Model were examined using the superposed epoch analysis method and the results obtained were compared. From the results of analysis, it was observed that the foF2 data observed at magnetic conjugate points and received from IRI Model simultaneously respond to geomagnetic activity changes for both the middle latitudes and the high latitudes.

A Combination Prediction Model of Long-Term Ionospheric foF2 Based on Entropy Weight Method.

It is critically meaningful to accurately predict the ionospheric F2 layer critical frequency (foF2), which greatly limits the efficiency of communications, radar, and navigation systems. This paper introduced the entropy weight method to develop the combination prediction model (CPM) for long-term foF2 at Darwin (12.4° S, 131.5° E) in Australia. The weight coefficient of each individual model in the CPM is determined by using the entropy weight method after completing the simulation of the individual model in the calibration period. We analyzed two sets of data to validate the method used in this study: One set is from 2000 and 2009, which are included in the calibration period (1998–2016), and the other set is outside the calibration cycle (from 1997 and 2017). To examine the performance, the root mean square error (RMSE) of the observed monthly median foF2 value, the proposed CPM, the Union Radio Scientifique Internationale (URSI), and the International Radio Consultative Committee (CCIR) are compared. The yearly RMSE average values calculated from CPM were less than those calculated from URSI and CCIR in 1997, 2000, 2009, and 2017. In 2000 and 2009, the average percentage improvement between CPM and URSI is 9.01%, and the average percentage improvement between CPM and CCIR is 13.04%. Beyond the calibration period, the average percentage improvement between CPM and URSI is 13.2%, and the average percentage improvement between CPM and CCIR is 12.6%. The prediction results demonstrated that the proposed CPM has higher precision of prediction and stability than that of the URSI and CCIR, both within the calibration period and outside the calibration period.

Nonlinear dependence study of ionospheric F2 layer critical frequency with respect to the solar activity indices using the mutual information method

This paper analyzes the dependence of the ionospheric F2 layer critical frequency, foF2, on two widely used solar activity indices (12-month running mean of the sunspot number (R12) and the solar radio flux of 10.7 cm wavelength (F10.712)) by using mutual information method at the ionospheric station of Darwin (geographic 12.4°S, 131.5°E) in Australia. The results show that the foF2 have different responses to R12 and F10.712 in different solar activity years. In the high and low solar activity years, the foF2 is more dependent on R12 rather than F10.712. In the moderate solar activity years, the foF2 is more dependent on F10.712 rather than R12. We further verify the effectiveness of the mutual information on the selection of the solar activity index by developing the prediction models of the foF2. Additionally, the results are compared against the International Reference Ionosphere (IRI) model. The data of training period used in the model is from 1998 to 2015, except 2000, 2009 and 2013. Data for 2000, 2009 and 2013 are used to validate the prediction accuracy, for high, low and moderate solar activity years, respectively. The results demonstrate that the favorable prediction results can be obtained when the solar activity index with large mutual information is used as the input variable of the foF2 prediction model. The proposed method not only can quantitatively analyze the nonlinear relationship between the foF2 and solar activity indices but also provides an effective way of selecting the solar activity indices for the foF2 prediction.

Ionospheric foF2 disturbance forecast using neural network improved by a genetic algorithm

A single station short-term ionospheric disturbance forecasting model has been developed with a genetic algorithm-based neural network (GA-NN). The genetic algorithm is used to optimize the initial weights of the neural network to avoid the local minimum during NN training. Using this model, the single station predictions of the ionospheric F2 layer critical plasma frequency, foF2, with the time-scale 1–24 h in advance in the China region have been investigated. Input parameters of the forecasting GA-NN model include the Beijing time (GMT+8), season information, solar zenith angle, day number, solar activity, geomagnetic activity, neutral winds, geographic coordinates and previous values of foF2. The training dataset in this model are obtained from the ionosonde stations in China. The data coverage is from 1990 to 2004 (more than one solar cycle) except 1995 and 2000. The data of ionospheric disturbances in 1995 (solar minimum) and 2000 (solar maximum) are used as the validation dataset. The prediction results at the different stations show that 1 h ahead prediction is more accurate than predictions of 3, 6, 12 and 24 h ahead. Comparisons between the observed and predicted values of foF2 in the low and middle latitudes during the year of solar minimum (1995) and solar maximum (2000) indicate that, the prediction accuracy at middle latitudes are generally better than that at low latitudes. The prediction root-mean-square error (RMSE) in the low solar activity is smaller than that in the high solar activity. The ionospheric disturbances prediction results manifest that the model works well even when the observed values of foF2 are far away from the monthly median value and the ionospheric storm lasts for 18 h.

A Prediction Model of Ionospheric foF2 Based on Extreme Learning Machine

AbstractThe highly nonlinear variation of the ionospheric F2 layer critical frequency (foF2) greatly limits the efficiency of communications, radar, and navigation systems that employ high‐frequency radio waves. This paper proposes an effective method to predict the foF2 using the extreme learning machine (ELM). Compared with the previous neural network model based on feedforward algorithm, the ELM model offers the advantages of faster training speed and less manual intervention. The ELM model is trained with the daily hourly values of foF2 at Darwin (12.4°S, 131.5°E) in Australia. The training data are selected from 1995 to 2012, except 1997 and 2000, which includes all periods of quiet and disturbed geomagnetic conditions. The foF2 data to verify model performance are selected in 1997, 2000, and 2013, which are low, high, and moderate solar activity years, respectively. The prediction results have shown that the proposed ELM model can achieve faster training process while maintaining the similar accuracy compared with BPNN. In addition, the proposed ELM model is compared with the International Reference Ionosphere model prediction. The ELM model predicts the foF2 values more accurately than the International Reference Ionosphere model in low (1997), moderate (2013), and high (2000) solar activity years, as clearly seen on the yearly root‐mean‐square error. As far as the author's knowledge, this is the first time that the ELM model is applied to predict foF2.

Ionospheric F2-Layer critical frequency retrieved from COSMIC radio occultation: A statistical comparison with measurements from a meridional ionosonde chain over Southeast Asia

In this paper, we compared the F2-Layer critical frequency (foF2) derived from FORMOSAT-3/COSMIC radio occultation (RO) and ionosondes at Chiang Mai, Chumphon and Kototabang during the years 2008–2015 to evaluate the performance of COSMIC RO over Southeast Asia region. The results show that the time development of foF2 values derived from COSMIC RO generally agrees well with those from ionosonde measurements. However, the differences between the foF2 derived from COSMIC RO and that derived from ionosonde observations display latitudinal dependence. COSMIC RO tends to underestimate foF2 at Chiang Mai and Kototabang, which is near to the north EIA crest and the south one, respectively, while a little overestimate foF2 at Chumphon, which is close to the geomagnetic equator. COSMIC RO agrees best with ionosonde at Chumphon and worst at Chiang Mai. At each ionosonde station, the quality of COSMIC RO data degrades with the increase of solar activity. In addition, at the station Chiang Mai and Kototabang, COSMIC RO performs better in summer than in equinox and winter. Furthermore, the differences in foF2 derived from COSMIC RO and that from ionosonde measurements vary with local time, i.e., the differences in foF2 are generally smaller at night and larger in noontime when equatorial ionization anomaly (EIA) is well developed.

Nonlinear dependence of ionospheric F2 layer critical frequency on solar activity in southern latitudes during solar cycle 23

Latitudinal, seasonal, and diurnal variations in the nonlinear response of F2 layer critical frequency (foF2) with respect to 10.7 cm solar flux data (F10.7) in southern stations during the 23rd solar cycle (1996–2008) are investigated. A two-segmented linear fit method is used in both ascending and descending phases of the cycle, thus revealing the nonlinear response of foF2 with F10.7. The rate of change in foF2 with respect to F10.7 may decrease (saturation) or increase (amplification) at high solar activity epochs. Furthermore, the foF2 values for the same F10.7 value differ between the ascending and descending phases of the cycle (hysteresis). The linear temporal increment of foF2 with F10.7 at low solar activity epochs is more evident in low-latitude stations. A low-latitude station, Vanimo, shows clear evidence of saturation in all seasons. In high-latitude stations, the effect is weak in the midnight to morning hours, particularly during the descending phase of the cycle. The amplification effect is dominant only at high latitudes mainly during early morning hours of March Equinox. Negative hysteresis effect is observed to be stronger at lower latitudes, but positive effects are seen to be stronger at higher latitudes.

Relationship between ionospheric F2-layer critical frequency, F10.7, and F10.7P around African EIA trough

Improved ionospheric modeling requires a better understanding of the relationship between ionospheric parameters and their influencing solar and geomagnetic sources. Published reports of the validation of the International Reference Ionosphere (IRI) for quiet-time revealed either underestimation or overestimation at a greater magnitude during high solar fluxes, especially at low latitude. With daily foF2 data from Ouagadougou (geor. 12.4°N, 1.5°W) covering a solar cycle, we have presented preliminary results from the analysis of solar dependence of six different classifications of the data: (i) daily values, (ii) monthly mean, (iii) daily quiet values (with Ap⩽20), (iv) monthly-quiet-mean values, (v) monthly median, and (vi) monthly-quiet-median values. All six classifications show good nonlinear relationship with both F10.7 and F10.7P, however, the differences between the dependence of classes (i) and (iii) of foF2 on the two solar indices is more substantial than those of classes (ii), (iv), (v), and (vi). Of all the six classes, the monthly averages are best related to both solar activity indices. Further analysis shows that magnetic disturbances are non-influential in the variations of the monthly mean of both solar activity indices; this makes both good indices for quiet-time modeling. Likewise, F10.7 and F10.7P are indistinguishable for long-term modeling around the African EIA trough region. While monthly median values may be best for mid-latitude region, either the mean/median values could be used for low-latitude region. However, it could be worthwhile to examine the distribution of the data from the station under consideration.

Ionospheric effects of solar flares and their associated particle ejections in March 2012

Flares of March 4–9, 2012 were accompanied by an intensification of solar electromagnetic and corpuscular radiations and five coronal mass ejections. Bursts of X-rays and increased solar cosmic ray fluxes caused an increase in ionospheric absorption manifesting itself in data from vertical sounding stations as enhancements of the lowest frequency of reflections up to 4–6MHz at the daytime and as the disappearance of reflections in the ionograms of high latitude stations. Interplanetary coronal mass ejections (ICME) generated March 7–8 moderate and March 8–11 intense magnetic storms accompanied by ionospheric disturbances. At the peaks of both magnetic storms there were abrupt afternoon–evening decreases in the ionospheric F2-layer critical frequency (foF2). During the March 7–8 storm, the foF2 decrease concurred with the reversal of the interplanetary magnetic field azimuthal component (IMF By) which initiated restructuring of magnetospheric convection; during the March 8–11 storm, with the abrupt weakening of the interplanetary magnetic field southward component (IMF Bz) which triggered a substorm.

Unusual Enhancement of Ionospheric F2 Layer Critical Frequency Before the 23 August 2011 Virginia (USA) Earthquake

Unusual Enhancement of Ionospheric F2 Layer Critical Frequency Before the 23 August 2011 Virginia (USA) Earthquake

Empirical modeling of ionospheric F2 layer critical frequency over Wakkanai under geomagnetic quiet and disturbed conditions

The hourly values of the ionospheric F2 layer critical frequency, foF2, recorded at Wakkanai ionosonde station (45.4°N, 141.7°E) have been collected to construct a middle-latitude single-station model for forecasting foF2 under geomagnetic quiet and disturbed conditions. The module for the geomagnetic quiet conditions incorporates local time, seasonal, and solar variability of climatological foF2 and its upper and lower quartiles. It is the first attempt to predict the upper and lower quartiles of foF2 to account for the notable day-to-day variability in ionospheric foF2. The validation statistically verifies that the model captures the climatological variations of foF2 with higher accuracy than IRI does. The storm-time module is built to capture the geomagnetic storm induced relative deviations of foF2 from the quiet time references. In the geomagnetically disturbed module, the storm-induced deviations are described by diurnal and semidiurnal waves, which are modulated by a modified magnetic activity index, the Kf index, reflecting the delayed responses of foF2 to geomagnetic activity forcing. The coefficients of the model in each month are determined by fitting the model formula to the observation in a least-squares way. We provide two options for the geomagnetic disturbed module, including or not including Kalman filter algorithm. The Kalman filter algorithm is introduced to optimize these coefficients in real time. Our results demonstrate that the introduction of the Kalman filter algorithm in the storm time module is promising for improving the accuracy of predication. In addition, comparisons indicate that the IRI model prediction of the F2 layer can be improved to provide better performances over this region.

Diurnal variations of the total electron content under quiet helio-geomagnetic conditions

The morphology of averaged diurnal variations of total electron content (TEC) under quiet helio-geomagnetic conditions for all latitudinal bands and various longitudes has been studied using Global Ionospheric Maps (GIMs) datasets. The diurnal TEC variation maximum is generally registered at 14–15 LT. The maximum is 38±5, 14±2, 10±2 TECU (TECU is generally accepted TEC unit) at the equatorial, middle and high latitudes. The nighttime TEC minimum is within 5–7 TECU regardless of a season, latitude and longitude. At the equatorial latitudes TEC exhibits the most significant daily/season variations and the asymmetry of its behavior in the hemispheres near the equinox. Abnormal diurnal TEC variations (evening maximum, near-noon minimum) are observed at middle and high latitudes in summer due to atmospheric wind effects. The comparison of the averaged diurnal TEC variations with the behavior of the ionospheric F2-layer critical frequency indicated that GIMs describe daily/annual TEC variations reasonably well.

Variations in the fractal dimension of fluctuations of the ionospheric F2 layer critical frequency

The 40-year period of observations of short-term variations (with characteristic times of up to 1–2 days) in the critical frequency of the ionospheric F2 layer (foF2) is analyzed. The continuous (with a step of 1 h) series of fluctuations (F) of the foF2 critical frequency (with eliminated daily variations) has been calculated using the hourly variations in foF2 at Moscow stations. The fractal dimension (FRH) of the fluctuations, characterizing short-term variations in foF2, has been determined and analyzed on a 30-day interval, using the Higuchi method. It has been established that FRH estimates substantially change in time. The 11-year cycle, which is in antiphase with the solar cycle, and the total annual and semiannual variations, similar to the variations observed in the normalized critical frequency of the E region and in the electron density of the D region, are clearly defined in these changes. Thus, the parameters of fast variations in the ionospheric F2 layer are affected by the phase of the 11-year solar cycle and by the position of the Earth in the orbit or seasonal variations in the atmosphere.