State Of Ionosphere Research Articles

Prediction of Sunspot Activity Based on Differential Evolution Algorithm and BP Neural Network Model

In this research report, the prediction method of sunspot activity is deeply discussed, and a variety of statistical and data science models are used to make comprehensive prediction. Research focuses include the start time and duration of the solar maximum in the solar cycle, as well as the prediction of the number and area of sunspots. By capturing the periodicity of solar activity, Pearson correlation coefficient is used to analyze the relationship between the maximum solar activity and the number of sunspots, and the adaptive multivariate nonlinear regression-BP neural network model and particle swarm optimization BP neural network are combined to make high-precision prediction. The research results provide a scientific basis for the prediction of space weather, ionospheric state and communication system reliability.

Enhanced Forecasting of Global Ionospheric Vertical Total Electron Content Maps Using Deep Learning Methods

The ionospheric state holds significant implications for satellite navigation, radio communication, and space weather; however, precise forecasting of the ionosphere remains a formidable challenge. To improve the accuracy of traditional forecasting models, we developed an enhancement model based on the CODE and IRI forecasting methods, termed the Global Ionospheric Maps Forecast Enhancement Model (GIMs-FEM). The results indicated that by extracting the GIM features from existing forecasts and incorporating additional proxies for geomagnetic and solar activity, the GIMs-FEM provided stable and reliable forecasting outcomes. Compared to the original forecasting models, the overall model error was reduced by approximately 15–17% on the test dataset. Furthermore, we analyzed the model’s performance under different solar activity conditions and seasons. Additionally, the RMSE for the C1pg model ranged from 0.98 TECu in the solar minimum year (2019) to 6.91 TECu in the solar maximum year (2014), while the enhanced GIMs (C1pg) model ranged from 0.91 to 5.75 TECu, respectively. Under varying solar activity conditions, the RMSE of GIMs-FEM for C1pg (C2pg) ranged from 0.98 to 6.91 TECu (0.96 to 7.26 TECu). Seasonally, the GIMs-FEM model performed best in the summer, with the lowest RMSE of 1.9 TECu, and showed the highest error in the autumn, with an RMSE of 2.52 TECu.

Measurements of F1‐ Region Ionosphere State Variables at Arecibo Through Quasi Height‐Independent Exhaustive Fittings of the Incoherent Scatter Ion‐Line Spectra

AbstractWe discuss an exhaustive search approach to fit the incoherent scatter spectrum (ISS) in the F1‐region for molecular ion fraction (fm), ion temperature (Ti), and electron temperature (Te). The commonly used “full profile” approach for F1‐region measurements parameterizes the molecular ion fraction as a function of altitude and fits all the related heights for the state variables. In our approach, we fit the ISS at each height for fm, Ti, Te, and ion velocity (Vi) independently. Our exhaustive search method finds all the major local minima at each altitude. Although a parameterized function is used to guide the algorithm in finding the best solution, the fitting parameters retain their local characteristics. Despite that fitting fm, Ti, and Te without constraints requires Doppler shift to be accurately determined and the ISS signal‐to‐noise ratio higher than the full‐profile method, simulations show that Ti, Te, and fm can be recovered within a few percent accuracy with a moderate signal‐to‐noise ratio. We apply the exhaustive search approach to the Arecibo high‐resolution incoherent scatter radar data taken on 13 September 2014. The derived ion and electron temperatures are sensitive enough to reveal thermosphere gravity waves commonly seen in the electron density previously. Our method is more robust than previous height‐independent fitting methods. Comparison with another Arecibo program indicates our results are likely more accurate. Simultaneous high‐resolution measurements of Ti, Te, fm, Vi, and electron concentration (Ne) in our approach open new opportunities for synergistic studies of the F1‐region dynamics and chemistry.

Three‐Dimensional Numerical Modeling of Coseismic Atmospheric Dynamics and Ionospheric Responses in Slant Total Electron Content Observations

AbstractDespite routine detection of coseismic acoustic‐gravity waves (AGWs) in Global Navigation Satellite System (GNSS) total electron content (TEC) observations, models of the earthquake‐atmosphere‐ionosphere dynamics, essential for validating data‐driven studies, remain limited. We present the results of three‐dimensional numerical simulations encompassing the entire coupling from Earth's interior to the ionosphere during the 7.8 2016 Kaikoura earthquake. Incorporating the impact of data/model uncertainties in estimating the ionospheric state, the results show a good agreement between observed and simulated slant TEC (sTEC) signals, assessed through a set of metrics. The signals exhibit intricate waveforms, resulting from the integrated nature of TEC and phase cancellation effects, emphasizing the significance of direct signal comparisons along realistic line‐of‐sight paths. By comparing simulation results initialized with kinematic and dynamic source models, the study demonstrates the quantifiable sensitivity of sTEC to AGW source specifications, pointing to their utility in the analysis of coupled dynamics.

Main features of the geomagnetic effect of the October 14, 2023 annular solar eclipse in the Americas

The purpose of this paper is to investigate temporal variations in the northward, X, eastward, Y, and downward, Z, components of the geomagnetic field recorded during the October 14, 2023 annular solar eclipse, which main features include its annularity, the eclipse occurrence from local dawn to local dusk, its magnitude variation from 0.30 to 0.86, and the longest ever-observed path across the mainland of the Americas, covering latitudes from ∼65°N to 12°S. The analysis was made possible due to the data on temporal variations in the northward, X, eastward, Y, and downward, Z, components of the geomagnetic field collected at thirteen International Real-time Magnetic Observatory Network magnetometer stations (https://imag-data.bgs.ac.uk/GIN_V1/GINForms2). The solar eclipse acted to cause non-sinusoidal and quasi-sinusoidal perturbations having temporal durations of 180–240 min in all geomagnetic field components on a global scale (∼8000 km). The X-component experienced the largest perturbations attaining 10–20 nT, and the Z-component underwent the smallest disturbances. The quasi-sinusoidal perturbation amplitude did not exceed 5–6 nT, and the period most often showed variations within 15–40 min. The magnetic effect exhibited a tendency to increase with solar eclipse magnitude, while the magnitude of the effect has been shown to be significantly dependent on geographic coordinates, local time, ionospheric state, and the patterns of ionospheric currents as well. During the solar eclipse, the electron density depletion was estimated to be ∼0.10 to ∼0.40–0.60 when the eclipse obscuration Amax varied from 19% to 82%. The movement of the lunar shadow was accompanied by the generation of atmospheric gravity waves with period of ∼10–80 min and by electron density perturbations with amplitudes of the order of 0.01–0.03. The estimates made on the assumption that the magnetic effect is due to the ionospheric current disruptions show good agreement with the observations.

GRIMS: global and regional ionosphere monitoring system

The ionosphere shows regular changes such as daily, 27 days, seasonal, semi-annual, annual, and 11 years. These changes can be modeled and their effects largely determined. However, in addition to regular changes, irregular changes occur in the ionosphere due to space weather conditions, natural disasters, and human-induced irregularities. GNSS is one of the instruments along with many others that can give a piece of information on the ionospheric state. Various indices/parameters are used to determine the effect of space weather conditions. The well-known ones are solar activity indices, geomagnetic storm indices, magnetic field components, proton density, and proton flux parameters. It is important to take all of these indices into consideration when investigating the source of the anomaly. Considering only some of them may lead to incorrect inferences about the source of possible anomalies. To carry out comprehensive research in this field, it is necessary to analyze a very large data set. This indicates the requirement for an automatic system. With the Global and Regional Ionosphere Monitoring System (GRIMS) designed within the scope of this study, the ionosphere can be monitored globally and regionally. The GRIMS is online at https://www.online-grims.com/. By using Global ionospheric maps and GNSS receiver data, global, regional, and station-specific anomalies can be detected regularly through methods such as HDI (Highest Density Interval) and ARIMA (Autoregressive Integrated Moving Average). GRIMS gathers space weather-related parameters from ionospheric data centers to help users interpret the situation, and it allows users to download the results and request data for specific days. The details of the experimental results and output products of the system designed during the geomagnetic active days of March 17, 18, 2015 are given in this paper. Moreover, geomagnetic active days that occurred between 2000 and 2023 are given in the GRIMS.

IONOLAB-Fusion: Fusion of Radio Occultation into Computerized Ionospheric Tomography

In this study, a 4-D, computerized ionospheric tomography algorithm, IONOLAB-Fusion, is developed to reconstruct electron density using both actual and virtual vertical and horizontal paths for all ionospheric states. The user-friendly algorithm only requires the coordinates of the region of interest and range with the desired spatio-temporal resolutions. The model ionosphere is formed using spherical voxels in a lexicographical order so that a 4-D ionosphere can be mapped to a 2-D matrix. The model matrix is formed automatically using a background ionospheric model with an optimized retrospective or near-real time manner. The singular value decomposition is applied to extract a subset of significant singular values and corresponding signal subspace basis vectors. The measurement vector is filled automatically with the optimized number of ground-based and space-based paths. The reconstruction is obtained in closed form in the least squares sense. When the performance of IONOLAB-Fusion across Europe was compared with ionosonde profiles, a 26.51% and 32.33% improvement was observed over the background ionospheric model for quiet and disturbed days, respectively. When compared with GIM-TEC, the agreement of IONOLAB-Fusion was 37.89% and 31.58% better than those achieved with the background model for quiet and disturbed days, respectively.

Forecasting 24‐Hr Total Electron Content With Long Short‐Term Memory Neural Network

AbstractAn accurate prediction of the ionospheric state is important for correcting ionospheric propagation effects on Global Navigation Satellite Systems (GNSS) signals used in precise navigation and positioning applications. The main objective of the present work is to find a total electron content (TEC) model which gives a good estimate of ionospheric state not only during quiet but also during perturbed ionospheric conditions. For this, we implemented several long short‐term memory (LSTM)‐based models capable of predicting TEC up to 24 hr ahead. For the first time, we used the solar wind forcing parameters Wprot (a measure of the ionospheric disturbance during storm time) and Econv (measure of the solar wind parameters) as driver parameters. We found that using external drivers does not improve the accuracy of TEC predictions significantly. The final model is trained with data from the last two solar cycles using TEC from the rapid UQRG global ionosphere maps (GIMs). Data from the years 2015 and 2020 were excluded from the training data set and used for testing. The performance of the LSTM‐based TEC model is tested for near real‐time (RT) cases as well by using RT products (IRTG GIMs) as historical TEC inputs. We compared the performance of the LSTM‐based model to a quiet‐time feed forward neural network (FNN)‐based model and the Neustrelitz TEC model (NTCM). The results indicate that the LSTM‐based model proposed here is outperforming the FNN‐based model and NTCM in both cases, that is, using the UQRG or the IRTG GIMs as input for the historical TEC.

GNSS Differential Code Bias Determination Using Rao‐Blackwellized Particle Filtering

AbstractThe Assimilative Canadian High Arctic Ionospheric Model (A‐CHAIM) is a near‐real‐time data assimilation model of the high latitude ionosphere, incorporating measurements from many instruments, including slant Total Electron Content measurements from ground‐based Global Navigation Satellite System (GNSS) receivers. These measurements have receiver‐specific Differential Code Biases (DCB) which must be resolved to produce an absolute measurement, which are resolved simultaneously with the ionospheric state using Rao‐Blackwellized particle filtering. These DCBs are compared to published values and to DCBs determined using eight different Global Ionospheric Maps (GIM), which show small but consistent systematic differences. The potential cause of these systematic biases is investigated using multiple experimental A‐CHAIM test runs, including the effect of plasmaspheric electron content. By running tests using the GIM‐derived DCBs, it is shown that using A‐CHAIM DCBs produces the lowest overall error, and that using GIM DCBs causes an overestimation of the topside electron density which can exceed 100% when compared to in situ measurements from DMSP.

Disappearing Solar Wind at Mars: Changes in the Mars‐Solar Wind Interaction

AbstractOn 26 December 2022 the solar wind density dropped by over an order of magnitude and remained low for about a day. We have utilized in‐situ plasma measurements made by the Mars Atmosphere and Volatile EvolutioN mission to determine how this change affected the Mars‐solar wind interaction. During this time period, on inbound orbit segments, MAVEN sampled the terminator ionosphere, which switched from a magnetized to unmagnetized state immediately following the minimum in solar wind density. The magnetic field amplitude was typically 5–10 nT within the upper ionosphere prior to the event and consistently <1 nT after. During the event the magnetic pressure dominated immediately above the ionosphere while within the ionosphere the ionospheric plasma pressure dominated. The high altitude terminator ionosphere remained in this unmagnetized state throughout the event, suggesting that it was the new equilibrium state of the system. The terminator upper ionosphere returned to its original magnetized state once the solar wind density had recovered. The outbound orbit segments sampled the dayside subsolar region which remained magnetized throughout the event: the magnetization state of the ionosphere varied locally, dependent upon the solar zenith angle and corresponding incident solar wind dynamic pressure. Such conditions are different to the commonly reported unmagnetized ionospheric state at Venus during solar maximum conditions, where the interplanetary magnetic field is repelled from the entire dayside ionosphere. Drastic changes in the upstream solar wind are able to change the Mars‐solar wind interaction state on timescales less than one MAVEN orbit (∼3.5 hr).

Inferring the Ionospheric State With the Far Ultraviolet Imager on the Fengyun‐4C Geostationary Satellite: Retrieval Algorithm and Verification

AbstractThe Multiband Ultraviolet Spectrum Imager (MUSI) is an optical remote sensing instrument scheduled to launch on the Fengyun‐4C meteorological satellite in 2024. MUSI is designed to measure the airglow emissions between ∼120 and 160 nm above the East Asia and Pacific region from a geostationary orbit to infer ionospheric parameters. Here we develop a lookup‐table algorithm for retrieving the peak electron density (nmF2) and the Total Electron Content (TEC) from nighttime observations of the OI 135.6 nm emission. The algorithm takes into account the north‐south asymmetry of the equatorial ionization anomaly and the atmospheric variations with solar activity and local time. Analysis of synthetic observations shows that the assumptions made in the algorithm only lead to ∼2%–4% uncertainties in the retrievals under the modeled ionospheric conditions. The algorithm is verified by retrieving nmF2 and TEC from the Global‐Scale Observations of the Limb and Disk (GOLD) mission and comparing with coincided radio measurements. The comparison indicates almost negligible systematic differences (typically less than ∼3%) between the GOLD and the ionosonde nmF2, which are within the error limit of the GOLD retrievals. The accuracy of the GOLD TEC retrievals is shown to be comparable to (or in some sense even better than) that of the measurements from the Global Navigation Satellite System. Several simplified yet robust retrieval methods are also developed, which can be readily implemented for both existing and upcoming missions. Our results promote far ultraviolet (FUV) remote sensing to be a key technology for accurate determination of the ionospheric state.

F-Region Variations Prior to Magnetic Storms (a Review)

The paper presents a review of variations in ionospheric parameters in the prestorm period obtained from an analysis of particular magnetic storms (case studies). It is demonstrated that, as a result of such analysis, deviations of these parameters (primarily, foF2 and TEC) are often observed on the days preceding the storm commencement (SC). Many authors pay attention to these facts and discuss their relation to space weather parameters, season, local time, and spatial distribution, and even offer suggestions on possible mechanisms of their formation. It is demonstrated that the number of such publications increased substantially in the preceding 4–5 years. It is emphasized that changes in the ionospheric state on prestorm days are detected not only in the “classical” parameters (foF2 and TEC), but also in other ionospheric characteristics. Publications dedicated directly to the problem of ionospheric precursors, their relation to space weather, and their possible role in forecasting coming magnetic storm are considered in detail.

On Some Challenges for National and Global Space Weather Services

Space Weather (SW) hazards are discussed in terms of the operation of national SW services and global SW centers for the International Civil Aviation Organization (ICAO). The definition of threshold values of monitored parameters which are used to identify moderate and severe SW events is one of the critical problems. Due to the lack of both physical data on severe events and user feedback, we tried to approach the problem statistically. In particular, we pursued the answer to the question about what intensity of ionospheric storms and flare effects should be reported by national and global SW entities to their users. We also discussed the possible role of an active region on the Sun, and the cosmic rays’ issues that may be helpful regarding SW operational work. The presented considerations are based on examples of the ionosphere state assessment for the low-latitude American sector with a focus on the Mexican region. This work attempts to argue the possible approaches to resolve the tasks that the SW national services and global centers face.

Time-Frequency Characteristics of Ionospheric Clutter in High Frequency Surface Wave Radar during Typhoon Muifa

The ionospheric clutter in High Frequency Surface Wave Radar (HFSWR) is the reflection of electromagnetic waves from the ionosphere back to the receiver, which should be suppressed as much as possible for the primary purpose of target detection in HFSWR. However, ionospheric clutter contains vast quantities of ionospheric state information. By studying ionospheric clutter, some of the relevant ionospheric parameters can be inferred, especially during the period of typhoons, when the ionospheric state changes drastically affected by typhoon-excited gravity waves, and utilizing the time-frequency characteristics of ionospheric clutter at typhoon time, information such as the trend of electron concentration changes in the ionosphere and the direction of the typhoon can be obtained. The results of the processing of the radar data showed the effectiveness of this method.

Локальная диагностика наличия полярных сияний на основе интеллектуального анализа геомагнитных данных

Despite the existing variety of approaches to monitoring space weather and geophysical parameters in the auroral oval region, the issue of effective prediction and diagnostics of auroras as a special state of the upper ionosphere at high latitudes remains virtually unresolved.
 In this paper, we explore the possibility of local diagnostics of auroras through mining of geomagnetic data from ground-based sources. We assess the significance of indicative variables and their statistical relationship.
 So, for example, the application of Bayesian inference to the data from the Lovozero geophysical station for 2012–2020 has shown that the dependence of a posteriori probability of observing auroras in the optical range on the state of geomagnetic parameters is logarithmic, and the degree of its significance is inversely proportional to the discrepancy between empirical data and approximating function.
 The accuracy of the approach to diagnostics of aurora presence based on the random forest method is at least 86 % when using several local predictors and ~80 % when using several global geomagnetic activity indices characterizing the geomagnetic field disturbance in the auroral zone.
 In conclusion, we discuss promising ways to improve the quality metrics of diagnostic models and their scope.

Local diagnostics of aurora presence based on intelligent analysis of geomagnetic data

Despite the existing variety of approaches to monitoring space weather and geophysical parameters in the auroral oval region, the issue of effective prediction and diagnostics of auroras as a special state of the upper ionosphere at high latitudes remains virtually unresolved.
 In this paper, we explore the possibility of local diagnostics of auroras through mining of geomagnetic data from ground-based sources. We assess the significance of indicative variables and their statistical relationship.
 So, for example, the application of Bayesian inference to the data from the Lovozero geophysical station for 2012–2020 has shown that the dependence of a posteriori probability of observing auroras in the optical range on the state of geomagnetic parameters is logarithmic, and the degree of its significance is inversely proportional to the discrepancy between empirical data and approximating function.
 The accuracy of the approach to diagnostics of aurora presence based on the random forest method is at least 86 % when using several local predictors and ~80 % when using several global geomagnetic activity indices characterizing the geomagnetic field disturbance in the auroral zone.
 In conclusion, we discuss promising ways to improve the quality metrics of diagnostic models and their scope.

Toward Real-Time GNSS Single-Frequency Precise Point Positioning Using Ionospheric Corrections

Real−time single−frequency precise point positioning (PPP) is a promising low−cost technique for achieving high−precision navigation with sub−meter or centimeter−level accuracy. However, its effectiveness depends heavily on the availability and quality of the real−time ionospheric state estimations required for correcting the delay in global navigation satellite system (GNSS) signals. In this study, the dynamic mode decomposition (DMD) model is used with global ionospheric vertical total electron content (vTEC) RMS maps to construct 24 h global ionospheric vTEC RMS map forecasts. These forecasts are assimilated with C1P forecast products, and L1 single−frequency positioning solutions are compared with different ionospheric correction models. The study examines the impact of assimilating predicted RMS data and evaluates the presented approach’s practicality in utilizing the IGRG product. The results show that the IGSG RMS prediction−based model improves positioning accuracy up to five hours ahead and achieves comparable results to other models, making it a promising technique for obtaining high−precision navigation.

Estimated Impacts Induced by The Magnetic Activity Index (Dst) on the Local Total Electronic Content (TEC) in Africa Equatorial Zone

The state of equatorial ionosphere has been studied from the determination of the Total Electron Content (TEC) based on the correction of errors propagation of GPS signals due to ionospheric disturbances using the dual frequency method. In our study, we considered tree stations of the equatorial area (adis, bjco and nklg) in January 2012. Firstly, the monthly survey of the TEC has revealed that the magnetic storm days coincide with the days that the TEC Rinex around 70 UCET. Secondly, the daily survey of the TEC reveals two TEC ( TEC/RINEX and the TEC/CODG) has revealed that during magnetics storms days, the interval between curves of TEC observed between 10: 00PM to 16:00PM (UT) is more important (around 10 UCET) when the Dst is strongly negative whatever the nature of the magnetic storm, and the interval between curves of TEC observed between 17:00 to 20:00PM (UT) is function of the nature of the magnetic storm.

Forecasting of Ionospheric Total Electron Content Data Using Multivariate Deep LSTM Model for Different Latitudes and Solar Activity

The ionospheric state is becoming increasingly important to forecast for the reliable operation of terrestrial and space-based radio-communication systems which are influenced by ionospheric space weather. In this study, we have investigated and tested a multivariate long short-term memory (LSTM) deep learning model for its forecasting accuracy over different latitudinal regions during the solar quiet and solar active years. We also tested its prediction capability during the occurrence of a geomagnetic storm. Four stations qaq1 (60.7°N, 46.04°W), baie (49.18°N, 68.26°W), mas1 (27.76°N, 15.63°W), and bogt (4.64°N, 74.08°W) in the northern hemisphere were used in this study. To optimize the feature extraction process, we used heat map to find the correlation between TEC and the various exogenous parameters and finally nine correlated parameters were used as inputs to train the LSTM model. The performance of the LSTM model was validated by comparing it with the multilayer perceptron (MLP) machine learning algorithm using root mean square error (RMSE) and mean absolute error (MAE) as evaluation indices. The results showed an accuracy improvement of 70% and 64% over MLP during the solar quiet and active years, respectively. The prediction accuracy of our LSTM model was also 74% better than MLP during the geomagnetic storm event. These findings demonstrate the effectiveness of the developed LSTM model and the right selection of the exogenous parameters in estimating TEC, and suggest that this LSTM model can be used for short-term TEC forecasting.

Klobuchar, NeQuickG, BDGIM, GLONASS, IRI-2016, IRI-2012, IRI-Plas, NeQuick2, and GEMTEC Ionospheric Models: A Comparison in Total Electron Content and Positioning Domains

Global navigation satellite systems (GNSS) provide a great data source about the ionosphere state. These data can be used for testing ionosphere models. We studied the performance of nine ionospheric models (Klobuchar, NeQuickG, BDGIM, GLONASS, IRI-2016, IRI-2012, IRI-Plas, NeQuick2, and GEMTEC) both in the total electron content (TEC) domain—i.e., how precise the models calculate TEC—and in the positioning error domain—i.e., how the models improve single frequency positioning. The whole data set covers 20 years (2000–2020) from 13 GNSS stations, but the main analysis involves data during 2014–2020 when calculations are available from all the models. We used single-frequency positioning without ionospheric correction and with correction via global ionospheric maps (IGSG) data as expected limits for errors. Improvements against noncorrected solution were as follows: GIM IGSG—22.0%, BDGIM—15.3%, NeQuick2—13.8%, GEMTEC—13.3%, NeQuickG and IRI-2016—13.3%, Klobuchar—13.2%, IRI-2012—11.6%, IRI-Plas—8.0%, GLONASS—7.3%. TEC bias and mean absolute TEC errors for the models are as follows: GEMTEC—−0.3 and 2.4 TECU, BDGIM—−0.7 and 2.9 TECU, NeQuick2—−1.2 and 3.5 TECU, IRI-2012—−1.5 and 3.2 TECU, NeQuickG—−1.5 and 3.5 TECU, IRI-2016—−1.8 and 3.2 TECU, Klobuchar—1.2 and 4.9 TECU, GLONASS—−1.9 and 4.8 TECU, and IRI-Plas—3.1 and 4.2 TECU. While TEC and positioning domains differ, new-generation operational models (BDGIM and NeQuickG) could overperform or at least be at the same level as classical empirical models.