International Reference Ionosphere Model Research Articles

Mid- and High-Latitude Electron Temperature Dependence on Solar Activity in the Topside Ionosphere through the Swarm B Satellite Observations and the International Reference Ionosphere Model

This study focuses on the open question of the electron temperature (Te) variation with solar activity in the topside ionosphere at mid- and high latitudes. It takes advantage of in situ observations taken over a decade (2014–2023) from Langmuir probes on board the low-Earth-orbit Swarm B satellite and spanning an altitude range of 500–530 km. The study also includes a comparison with Te values modeled using the International Reference Ionosphere (IRI) model and with Millstone Hill (42.6° N. 71.5° W) incoherent scatter radar observations. The largest Te variation with solar activity was found at high latitudes in the winter season, where Te shows a marked decreasing trend with solar activity in the polar cusp and auroral regions and, more importantly, at sub-auroral latitudes in the nightside sector. Differently, in the summer season, Te increases with solar activity in the polar cusp and auroral regions, while for equinoxes, variations are smaller and less clear. Mid-latitudes generally show negligible Te variations with solar activity, which are mostly within the natural dispersion of Te observations. The comparison between measured and modeled values highlighted that future implementations of the IRI model would benefit from an improved description of the Te dependence on solar activity, especially at high latitudes.

A Simplified Method of True Height Analysis to Estimate the Real Height of Sporadic E Layers

AbstractThe sporadic E (Es) layer virtual height and the ordinary wave critical frequency are routinely measured ionogram parameters, used to characterize the Es altitude occurrence and intensity. It has become common practice to take the real height to be about equal to by assuming that signal propagation delays in the E region plasma below the layer are small and can be neglected. Although this applies for nighttime, during daytime it may overestimate significantly. The present paper relies on true height analysis theory to devise a simplified method and propose an algorithm that can estimate Es real heights reasonably well. The method relies on and ionosonde measurements and E region electron density profiles obtained from the International Reference Ionosphere model. The algorithm is applied to a typical set of Digisonde observations to compute and examine real height variations and functional dependencies. Whereas ≃ at nighttime, during daytime there are notable − differences taking values less than 10 km for most of the observed layers. During the early morning and early afternoon hours, however, when weak layers appear at upper heights, the virtual to real height differences become larger reaching 20–25 km. The method proposed here for the estimation of can be easily applied to improve the accuracy of the results of sporadic E layer studies.

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.

Prediction of ionospheric TEC using a GRU mechanism method

In this paper, we propose a gate recurrent unit (GRU) method to forecast ionospheric the total electron content (TEC) at different latitudes and longitudes. This method aims to address the issues of low accuracy in traditional neural networks and the challenges faced in training long-term time series. We construct a GRU model using TEC data from the European Orbit Determination Center (CODE) for the years 2016 to 2018, geomagnetic indices, and solar activity indices (16 points along the latitude and longitude lines of 120°E and 30°N). Comparative experiments are carried out with the international reference ionosphere (IRI) model and the long short-term memory (LSTM) model. The results show that the GRU model outperforms other models in terms of accuracy. On geomagnetic quiet days, the 5-day predicted average residual can reach 0.51 TECU, while on storm days it can reach 0.72 TECU. The root mean square error (RMSE) for these days is 0.64 TECU and 1.01 TECU, respectively. The correlation coefficients are 0.98 and 0.93, respectively. The RMSE of the GRU model ranges from 0.60 TECU to 2.62 TECU for both geomagnetic quiet days and storm days. In terms of TEC forecast accuracy in 2018, the RMSE of the GRU model is 1.6 TECU, which is smaller than that of the IRI model and LSTM model. These results demonstrate that the GRU model performs better than the IRI model and LSTM model.

Modeling Ionospheric TEC Using Gradient Boosting Based and Stacking Machine Learning Techniques

AbstractAccurately predicting and modeling the ionospheric total electron content (TEC) can greatly improve the accuracy of satellite navigation and positioning and help to correct ionospheric delay. This study assesses the effectiveness of four different machine learning (ML) models in predicting hourly vertical TEC (VTEC) data for a single‐station study over Ethiopia. The models employed include gradient boosting machine (GBM), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM) algorithms, and a stacked combination of these algorithms with a linear regression algorithm. The models relied on input variables that represent solar activity, geomagnetic activity, season, time of the day, interplanetary magnetic field, and solar wind. The models were trained using the VTEC data from January 2011 to December 2018, excluding the testing data. The testing data comprised the data for the year 2015 and the initial 6 months of 2017. The RandomizedSearchCV algorithm was used to determine the optimal hyperparameters of the models. The predicted VTEC values of the four ML models were strongly correlated with the GPS VTEC, with a correlation coefficient of ∼0.96, which is significantly higher than the corresponding value of the International Reference Ionosphere (IRI 2020) model, which is 0.87. Comparing the GPS VTEC values with the predicted VTEC values based on diurnal and seasonal characteristics showed that the predictions of the developed models were generally in good agreement and outperformed the IRI 2020 model. Overall, the ML models used in this study demonstrated promising potential for accurate single‐station VTEC prediction over Ethiopia.

Ionospheric Error Models for Satellite-Based Navigation—Paving the Road towards LEO-PNT Solutions

Low Earth Orbit (LEO) constellations have recently gained tremendous attention in the navigational field due to their larger constellation size, faster geometry variations, and higher signal power levels than Global Navigation Satellite Systems (GNSS), making them favourable for Position, Navigation, and Timing (PNT) purposes. Satellite signals are heavily attenuated from the atmospheric layers, especially from the ionosphere. Ionospheric delays are, however, expected to be smaller in signals from LEO satellites than GNSS due to their lower orbital altitudes and higher carrier frequency. Nevertheless, unlike for GNSS, there are currently no standardized models for correcting the ionospheric errors in LEO signals. In this paper, we derive a new model called Interpolated and Averaged Memory Model (IAMM) starting from existing International GNSS Service (IGS) data and based on the observation that ionospheric effects repeat every 11 years. Our IAMM model can be used for ionospheric corrections for signals from any satellite constellation, including LEO. This model is constructed based on averaging multiple ionospheric data and reflecting the electron content inside the ionosphere. The IAMM model’s primary advantage is its ability to be used both online and offline without needing real-time input parameters, thus making it easy to store in a device’s memory. We compare this model with two benchmark models, the Klobuchar and International Reference Ionosphere (IRI) models, by utilizing GNSS measurement data from 24 scenarios acquired in several European countries using both professional GNSS receivers and Android smartphones. The model’s behaviour is also evaluated on LEO signals using simulated data (as measurement data based on LEO signals are still not available in the open-access community; we show a significant reduction in ionospheric delays in LEO signals compared to GNSS. Finally, we highlight the remaining open challenges toward viable ionospheric-delay models in an LEO-PNT context.

Modeling Total Electron Content and Critical Frequency at F1 and E Layer Boundary

This work investigates the variation of the total electron content TEC and the critical frequency fo in the boundary zone of the F1 and E layers at the low-latitude in the ionosphere. This study takes place at the Ouagadougou station (12.4°N and 358.5°E), in West Africa during the quiet geomagnetic activity of solar cycle 23. Ionosphere is the upper layer of the Earth's atmosphere ionized mainly by solar X- and UV-rays, extending from around 80km altitude up to 1000km [1] [2]. Ultraviolet light from the sun ionizes the atoms and molecules in the Earth's upper atmosphere [3]. For this study we use the 2016 version of International Reference Ionosphere (IRI) model. The quiet periods of maximum and minimum phase of solar cycle 23 are considered [4] [5]. From this study, it emerges that at the E and F1 layer boundary zone, TEC and fo increase during the day as solar irradiance increases and decrease as solar irradiance decreases.

Evaluating F2-region long-term trends using the International Reference Ionosphere (IRI) model: is this a feasible approximation for experimental trends?

Abstract. The International Reference Ionosphere (IRI) is a widely used empirical ionospheric model based on observations from a worldwide network of ionospheric stations. Therefore, it would be reasonable to expect it to capture long-term changes in key ionospheric parameters, such as foF2 and hmF2 linked to trend forcings like greenhouse gas increasing concentrations and the Earth's magnetic field secular variation. Despite the numerous reported trends in foF2 and hmF2 derived from experimental data and model results, there are inconsistencies that require continuous refinement of trend estimation methods and regular data updates. This ongoing effort is crucial to address the difficulties posed by the weak signal-to-noise ratio characteristic of ionospheric long-term trends. Furthermore, the experimental verification of these trends remains challenging, primarily due to time and spatial coverage limitations of measured data series. Achieving these needs for accurate detection of long-term trends requires extensive global coverage and high resolution of ionospheric measurements together with long enough periods spanning multiple solar cycles to properly filter out variations of shorter terms than the sought trend. Considering these challenges, IRI-modeled foF2 and hmF2 parameters offer a valuable alternative for assessing trends and obtaining a first approximation of a plausible global picture representative of experimental trends. This work presents these global trend patterns, considering the period 1960–2022 using the IRI-Plas 2020 version, which are consistent with other model predictions. While IRI explicitly takes into account the Earth's magnetic field variations, the increase in the concentrations of greenhouse gases appears indirectly through the Ionospheric Global index (IG) which is derived from ionospheric measurements. F2-region trends induced by the first mechanism should be important only around the magnetic equator at the longitudinal range with the strongest displacement, and it should be negligible out of this region. Conversely, trends induced by the greenhouse effect, which are the controversial ones, should be dominant away from the geomagnetic equator and should globally average to negative values in both cases, i.e., foF2 and hmF2. Effectively, these negative global means are verified by trends based on IRI-Plas, even though not for the correct reasons in the hmF2 case. In addition, a verification was performed for more localized foF2 trend values, considering data from nine mid-latitude stations, and a reasonable level of agreement was observed. It is concluded that the IRI model can be a valuable tool for obtaining preliminary approximations of the Earth's magnetic-field-induced long-term changes in foF2 and hmF2, as well as of experimental trends only in the foF2 case. The latter does not hold for hmF2, even if the trends obtained are close to the expected values.

Forecasting Ionospheric foF2 Using Bidirectional LSTM and Attention Mechanism

AbstractThe critical frequency of ionospheric F2 layer (foF2) is an important ionospheric characteristic parameter. In this paper, a deep learning model based on Bidirectional long short‐term memory (BiLSTM) and attention mechanism is implemented for predicting the foF2 parameter. The inputs of models are the foF2 of globally available ionospheric ionosonde stations, geographic longitude and latitude, world time (UT), geomagnetic activity index, and solar activity index from 2015 to 2017. The superiority of the model is analyzed from different latitudes, seasons, and geomagnetic conditions. The results show that the prediction performance of the Bidirectional long short‐term memory model based on attention mechanism (BiLSTM‐Attention) is better than other models. The performance of the prediction model is optimal at high latitudes. The root mean square error (RMSE) and correlation coefficient (R) of the BiLSTM‐Attention model are 0.539 MHZ and 0.908 MHz at high latitudes, respectively. In terms of RMSE, it is 25.243%, 18.209%, and 11.203% lower than those of the international reference ionosphere (IRI), LSTM, and BiLSTM models, respectively. The prediction results of the four seasons show that the models are more applicable in winter. Compared with the IRI model, the RMSE of the BiLSTM‐Attention model in spring, summer, autumn, and winter is reduced by 24.344%, 21.181%, 25.058%, and 30.948%, respectively. The prediction effect of the BiLSTM‐Attention model is improved in the magnetic quiet period, the magnetic moderate period and the magnetic storm period. Also, the improvement effect is more obvious in the magnetostatic day, and the RMSE is reduced by 27.462% compared with the IRI model.

Performance evaluation of IRI-Plas 2017 model with ionosonde data measurements of ionospheric parameters

This study presents an evaluation of the performance of the International Reference Ionosphere Model augmented with the Plasmasphere (IRI Plas 2017) from the Addis Ababa Ionosonde Station, Ethiopia, for measuring ionospheric parameters at geographic (9.00o N, 38.70o E) and geomagnetic (0.16o N, 110.44o E) location on selected days of 2014. During comparison hourly and daily variability of the ionospheric parameters of the electron density profile (Neh), maximum electron density (NmF2) and maximum height (hmF2) measurements are considered. When evaluating the IRI Plas model using ionosonde data, the percentage deviation and correlation coefficient (R) were used as measures of IRI Plas model performance. In general, the overall results of the study show that the IRI Plas 2017 model mostly overestimates at most altitudes and hours of electron density measurements. The IRI Plas model has an acceptable fit with the ionosonde electron density measurements at altitudes of 100 km–200 km, mostly during the hours between 03LT and 09 LT and 15 LT–21 LT, while the model biases in other altitudes and hours with overestimates or underestimates in the ionosonde electron density measurements. The IRI-Plas 2017 model has a good correlation after midnight and around midday hours, with about ±2% point deviation from the ionosonde electron density measurement. The model has a high percentage deviation value for electron density measurements, mostly between altitudes of 200 km and 450 km and during early nighttime and before midnight hours. IRI-Plas model measurements of NmF2 and hmF2 are mostly underestimated from the ionosonde data during the nighttime (21 LT–09 LT) and overestimated during the daytime (09 LT–21 LT). The NmF2 values measured with the model are more consistent with ionosonde values than hmF2 values.

A Comparison of a GNSS‐GIM and the IRI‐2020 Model Over China Under Different Ionospheric Conditions

AbstractThe ionosphere is a crucial factor affecting Global Navigation Satellite System positioning. The Global Ionosphere Map (GIM) or the International Reference Ionosphere (IRI) model can be used for regional ionospheric correction. Since southern China is located near the electron density equatorial anomaly, this study evaluates the performance of the Wuhan University GIM (WHU‐GIM) and the IRI‐2020 from 2008 to 2020 over the China region. The comparison indicates that the Total Electron Content (TEC) from IRI‐2020 is lower than that from WHU‐GIM overall, the discrepancy is more obvious in high solar conditions and low‐latitude regions. The differential Slant TEC (dSTEC) during a phase‐arc with about 0.1 TECU accuracy derived from Global Positioning System (GPS) observations is used for model validation, the results show that the accuracies of WHU‐GIM and IRI‐2020 are 3.14 and 4.57 TECU, respectively. The dSTEC error is larger at low latitudes and decreases with increasing latitude. GPS‐derived TEC is taken for reference to evaluate the model reliability. Results show that both models can reproduce the diurnal TEC variations, but IRI‐2020 is more influenced by geomagnetic activities. The TEC correction percentage for IRI‐2020 is about 60%–80% under different ionospheric conditions, while for WHU‐GIM is 80%–90%. The Single‐Frequency Precise Point Positioning is performed with the ionosphere delay corrected by the two models, respectively. The positioning errors show that using IRI‐2020 has a lower accuracy, and the TEC discrepancy of the IRI‐2020 can cause a large bias in the up direction, especially at low‐latitude regions.

Performance evaluation for vertical TEC predictions over the East Africa and South America: IRI-2016 and IRI-2020 versions

In this paper, we evaluate and report the International Reference Ionosphere (IRI) model's vertical Total Electron Content (TEC) regional profile. Diurnal, monthly, seasonal, and storm-time characteristics of IRI estimates over the equatorial region's ionosphere are validated. We compared the vertical TEC derived from IRI-2020 and its predecessor, IRI-2016, with the GPS-TEC measurements. Results show that IRI (both versions) agrees with observed TEC during solar cycle maximum periods. Exceptionally, over Turkwel station, IRI-2020 produced double peak profiles on the March equinox, June solstice, and September equinox and overestimated with larger discrepancies. Over the other three stations, both IRI versions reproduced the seasonal averaged observed TEC with only slight time lags and discrepancies. During geomagnetically perturbed times, IRI-2020 better indicated positively enhanced GPS-TEC than IRI-2016. However, during the March 17, 2015 storm, IRI-2020 underestimated the storm-enhanced TEC over Bahir Dar station at all phases of the storm. IRI-2016 shows good performances for negative storms where lower TEC measurements are recorded.

Validation of a neural network based model to predict foF2

This work presents the application of Neural Networks (NNs) techniques to develop an empirical model for foF2, the critical frequency of the F2 ionospheric layer. Prediction of foF2 parameter is important because of the paucity of data in some areas of the globe (due to uneven distributions of ground- based measuring equipment) and, also, because of the roles it plays in high frequency communications and navigation purposes. Hourly values of foF2 corresponding to the period 1985–2007 and obtained from 105 World-wide distributed ionospheric stations, have been considered in this work. A statistical analysis of the difference between NN model-derived and the corresponding experimental foF2 values was carried out for eleven (11) selected ionospheric stations during periods of very high (year 2000), relatively low (year 2006) and very low (year 2008) solar activity. In particular, the ionospheric behavior during the years 2000 and 2008 appeared to be very challenging to be captured for any empirical model. For comparison purposes, the same kind of statistical analysis was performed with foF2 values as obtained from the International Reference Ionosphere (IRI) model and from NeQuick 2 model. Results of the statistical comparison confirmed that the neural network modelling approach is suitable to describe in a climatological way the main characteristics of the ionosphere F2 layer peak frequency, also over the extended geographic region where the additional sensor stations were located. The results obtained also indicate that the NN predictions and NeQuick 2 algorithm driven by solar flux (F10.7) index have similar performance.

Ionospheric TEC variation over Svalbard archipelago, Norway and assessment of Bilinear interpolated GIM model

The Svalbard archipelago region is a group of islands in the Arctic Ocean, located between the North Pole and the northern coastal region of Norway at high latitudes. The current study focuses on the spatiotemporal distribution of total electron content (TEC) over the Svalbard archipelago region with the ground-based global positioning system (GPS) tracking stations during the years 2020–2022. The spatial ionospheric TEC characteristics from GPS observables and their correlation with the Bilinear interpolated global ionospheric map (GIM) and the latest edition of the international reference ionosphere (IRI- 2020) model are investigated. The results showed that both GPS-TEC and GIM-TEC have minimum ionospheric TEC during the winter season while they have maximum TEC occurrence in the summer and spring seasons with a higher difference of magnitude. We suspect such discrepancy could be due to expected uncertainties in the estimation of GIM-TEC interpolation over the region. The correlation coefficients between GPS-TEC versus GIM-TEC and GPS-TEC versus IRI-TEC showed that they are correlated with an almost equivalent correlation coefficient of approximately 0.76 which is reasonably acceptable but relatively lower than other middle latitude regions. This poor correlation happened because of the diurnal variation in the polar cap region during the summer season which is minimal. Finally, a Multitaper spectral analysis is employed on the frequency spectra of the time-varying periodograms of the four oscillation components. The outcome of the spectral analysis confirms that the first principal component (PC) of GPS-TEC is well correlated with the respective PC of GIM and IRI models. Although the higher order PCs of GIM models remain well-correlated with GPS-TEC, the subdued correlation between IRI model outputs and GPS-TEC suggests further improvements in the IRI model representation with respect to the high latitude characteristics.

Accuracy of hmF2 estimations, including IRI-2020 options and ionograms validated parameters, compared to ISR measurements at Millstone Hill

Several empirical formulations used over time to estimate the fundamental ionospheric parameter hmF2 have been compared in this study. These are the first formulation proposed by Shimazaki (1955) (SHI-1955) as a function of the propagation parameter M(3000)F2, the more accurate BSE-1979 formula proposed by Bilitza et al. (1979) and firstly adopted by the International Reference Ionosphere (IRI) model, and the newest Altadill-Magdaleno-Torta-Blanch (AMTB-2013) (Altadill et al., 2013) and SHU-2015 (Shubin, 2015) models, obtained with a different approach with no explicit dependence on any ionospheric parameter and added as alternative options in the IRI-2016. The evaluation of the accuracy of the available formulation is performed by comparing the modeled values of hmF2 with those simultaneously obtained with independent measurements from the Incoherent Scatter Radar (ISR) installed at the Millstone Hill ionospheric station. The database considered consists of 3626 measurements, thus allowing the evaluation of the results for different heliogeophysical conditions. SHI-1955 and BSE-1979 formulations are evaluated also using input data manually scaled from ionograms recorded at the same location, with the aim of evaluating their accuracy when updated with validated data rather than modeled ones. The SHU-2015 is confirmed the best option in any condition, while AMTB-2013 turns out to perform poorly during night, when SHI-1955 and BSE-1979 fed by validated data can be used for trend analyses due to the high correlation with ISR data. Despite this, BSE-1979 performs better with modeled parameters as input, in terms of RMSE and mean deviation from ISR data. The use of SHI-1955 with CCIR-modeled M(3000)F2 is discouraged under daytime conditions even for long trend analyses.

Finite element method study on VLF propagation in the fine Earth‐ionosphere waveguide

AbstractA fast and highly efficient frequency‐domain finite element method (FEM) is developed to simulate propagation characteristics of very low frequency (VLF) waves in the fine Earth‐ionosphere waveguide structure containing the transition period between day and night. The FEM is accelerated by embedding the GPU‐based parallel LU factorization method. We present an approach for determining the waveguide structure with the VLF transmitter and receiver locations. After validating the method, effects of the time‐ and location‐varying electric density on the amplitude and phase of the VLF wave are investigated. The electric density of the International Reference Ionosphere (IRI) is revised by coupling with the solar zenith angle. Numerical results simulated using the revised IRI model show excellent agreement with the measured data for the VLF wave from Novosibirsk to Qingdao. This work provides a promising way to research VLF electromagnetic properties of the Earth‐ionosphere waveguide and improve the prediction accuracy of VLF navigation and communication.

A Novel Technique for High-Precision Ionospheric VTEC Estimation and Prediction at the Equatorial Ionization Anomaly Region: A Case Study over Haikou Station

This paper introduces a novel technique that uses observation data from GNSS to estimate the ionospheric vertical total electron content (VTEC) using the Kriging–Kalman method. The technique provides a method to validate the accuracy of the Ionospheric VTEC analysis within the Equatorial Ionization anomaly region. The technique developed uses GNSS VTEC alongside solar parameters, such as solar radio flux (F10.7 cm), Disturbance Storm Time (Dst) and other data, and Long Short Term Memory (LSTM) Networks to predict the occurrence time of the ionospheric equatorial anomaly and ionospheric VTEC changes. The LSTM method was applied to GNSS data from Haikou Station. A comparison of this technique with the neural network (NN) model and International Reference Ionosphere model shows that the LSTM outperforms all of them at VTEC estimation and prediction. The results, which are based on the root mean square error (RMSE) between GNSS VTEC and GIM VTEC outside the equatorial anomaly region, was 1.42 TECU, and the results of GNSS VTEC and VTEC from Beidou geostationary orbit satellite, which lies inside the equatorial ionization anomaly region, was 1.92 TECU. The method developed can be used in VTEC prediction and estimation in real time space operations.

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.

A Prediction Method of Ionospheric hmF2 Based on Machine Learning

The ionospheric F2 layer is the essential layer in the propagation of high-frequency radio waves, and the peak electron density height of the ionospheric F2 layer (hmF2) is one of the important parameters. To improve the predicted accuracy of hmF2 for further improving the ability of HF skywave propagation prediction and communication frequency selection, we present an interpretable long-term prediction model of hmF2 using the statistical machine learning (SML) method. Taking Moscow station as an example, this method has been tested using the ionospheric observation data from August 2011 to October 2016. Only by inputting sunspot number, month, and universal time into the proposed model can the predicted value of hmF2 be obtained for the corresponding time. Finally, we compare the predicted results of the proposed model with those of the International Reference Ionospheric (IRI) model to verify its stability and reliability. The result shows that, compared with the IRI model, the predicted average statistical RMSE decreased by 5.20 km, and RRMSE decreased by 1.78%. This method is expected to provide ionospheric parameter prediction accuracy on a global scale.

Assimilation and Inversion of Ionospheric Electron Density Data Using Lightning Whistlers

The data assimilation algorithm is a common algorithm in space weather research. In this paper, the time-frequency information in the dispersion spectrum of lightning whistlers received by the ZH-1 satellite is used as the observed value, and the international reference ionospheric model serves as the background model to construct the calculation model of the propagation time of lightning whistlers in the ionosphere. Kalman filtering is adopted to assimilate the electron density distribution along the propagation path of lightning whistlers. The results show that the situation where the electron density of the background model deviates greatly from the true value is significantly improved through data assimilation. The electron density after assimilation is in good agreement with the true value, which effectively helps realize the process of using observed values to correct the background value. On this basis, the influence of the frequency difference on the assimilation inversion effect is studied, and the results show that the assimilation effect is worse when the frequency difference between frequency points is less than 1 kHz.