Ionospheric Space Weather Research Articles

Classification of Ionospheric Scintillations during high Solar Activity and Geomagnetic Storm over Visakhapatnam Region using Machine Learning Approach

The ionospheric plasma disturbances typically correlate with irregularities in electron density and ionospheric scintillations are produced in reaction to these variations generating radio signal fluctuations. Geolocation services and space based communication are endangered due to ionospheric scintillation which promptly produces fluctuations in information collected by Global Navigation Satellite Systems and this is at its strongest when the solar cycle is at its peak. Ionospheric space weather has a significant impact on Global Navigation Satellite Systems (GNSS) and one crucial aspect used in investigating ionospheric characteristics is total electron content (TEC). Due to fluctuations in time and space, the TEC obtained from GNSS signals is nonlinear and nonstationary. In this study, machine learning approaches for Classification of the ionospheric scintillations were used during the high solar activity and geomagnetic storm in the month of July 2023. This approach enables the classification of ionospheric phase scintillations using well-known classifiers: Decision Tree and Support Vector Machine.

The correlation between equatorial electrojet and equatorial ionisation anomaly over the east african region during the solar minimum period 2008-2009

This study analyzed the correlation between the equatorial electrojet (EEJ) and the occurrence of equatorial ionisation anomaly (EIA) over the East African region. The study was carried out during both geomagnetically quiet and disturbed conditions when Kp index values were < 2+ and > 5+, respectively. The EEJ data were obtained using a pair of magnetometers located at Adis Ababa (geographic 9.04°N, 38.77°E, geomagnetic 0.17°N, 110.47°E) and Adigrat (geographic 14.281°N, 39.46°E, geomagnetic 6.0°N, 111.06°E), both in Ethiopia while the EIA data were derived from the total electron content (TEC) data that were obtained using a set of Global Navigation Satellite System (GNSS) receivers within the East African region. The data used were for the years 2008 and 2009. The TEC data over the crest of EIA were divided by those over the trough to quantify EIA strength over the region. The EEJ intensity was estimated from the difference in the horizontal component of the geomagnetic field observed by the pair of magnetometers. The results during quiet geomagnetic conditions showed that peak values of EEJ which range from 48nT - 110nT occurred between 10:00 and 14:00 LT. The EIA’s peak which varies from 1.20 to 1.45 occurs between 20:00 to 22:00 LT. The correlation coefficients were found to vary from moderate (0.58) to strongest (0.74). During geomagnetically disturbed conditions, the correlation coefficient ranges from 0.28 to 0.45. The increased eastward electric field and photo-ionization on TEC are responsible for the strong link between EEJ and EIA. This study reveals the trend in the variation of the strength of EEJ and EIA over the East African region which can be used as a basis for developing regional models to forecast or nowcast scintillations and the ionospheric space weather prediction over this region.

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.

A 2D Kaleidoscope of Electron Heat Fluxes Driven by Auroral Electron Precipitation

AbstractElectron heat flux is an important value for ionospheric space weather modeling networks. Utilizing the 2D array of Time History of Events and Macroscale Interactions during Substorms all‐sky‐imager (ASI) observations, Gabrielse et al. (2021, https://doi.org/10.3389/fphy.2021.744298) described a new method that estimates the auroral scale sizes of intense precipitating electron energy fluxes and their mean energies during two substorms on 16 February 2010. These parameters in combination with SuperThermal Electron Transport code were used to develop a new methodology to calculate electron thermal fluxes from data inputs in 2D during one of the substorms at 09:40:00 UT across Canada and Alaska. To test the effect of various precipitation lifetimes on electron heat flux values, boxcar averages ranging from 0 to 900 s were applied to the ASI data. These data are then combined with the newly developed kinetic simulation to determine the thermal fluxes associated with the observed diffuse and discrete precipitation.

Retrospect and prospect of ionospheric weather observed by FORMOSAT-3/COSMIC and FORMOSAT-7/COSMIC-2

FORMOSAT-3/COSMIC (F3/C) constellation of six micro-satellites was launched into the circular low-earth orbit at 800 km altitude with a 72-degree inclination angle on 15 April 2006, uniformly monitoring the ionosphere by the GPS (Global Positioning System) Radio Occultation (RO). Each F3/C satellite is equipped with a TIP (Tiny Ionospheric Photometer) observing 135.6 nm emissions and a TBB (Tri-Band Beacon) for conducting ionospheric tomography. More than 2000 RO profiles per day for the first time allows us globally studying three-dimensional ionospheric electron density structures and formation mechanisms of the equatorial ionization anomaly, middle-latitude trough, Weddell/Okhotsk Sea anomaly, etc. In addition, several new findings, such as plasma caves, plasma depletion bays, etc., have been reported. F3/C electron density profiles together with ground-based GPS total electron contents can be used to monitor, nowcast, and forecast ionospheric space weather. The S4 index of GPS signal scintillations recorded by F3/C is useful for ionospheric irregularities monitoring as well as for positioning, navigation, and communication applications. F3/C was officially decommissioned on 1 May 2020 and replaced by FORMOSAT-7/COSMIC-2 (F7/C2). F7/C2 constellation of six small satellites was launched into the circular low-Earth orbit at 550 km altitude with a 24-degree inclination angle on 25 June 2019. F7/C2 carries an advanced TGRS (Tri Gnss (global navigation satellite system) Radio occultation System) instrument, which tracks more than 4000 RO profiles per day. Each F7/C2 satellite also has a RFB (Radio Reference Beacon) on board for ionospheric tomography and an IVM (Ion Velocity Meter) for measuring ion temperature, velocity, and density. F7/C2 TGRS, IVM, and RFB shall continue to expand the F3/C success in the ionospheric space weather forecasting.

Ionospheric Monitoring and Modelling for Space Weather: An Introduction to the Special Issue

Over the last decades, our scientific understanding and user’s community appreciation of the ionospheric space weather and its impacts on Earth’s environment, and some of the technological systems and human beings’ priority areas, have changed considerably [...]

A Bidirectional Deep-Learning Algorithm to Forecast Regional Ionospheric TEC Maps

The rapid evolutions in Artificial Intelligence (AI) and the Machine Learning era have significantly improved accuracy for ionospheric space weather forecasting models. The ionospheric Total Electron Content (TEC) forecasting is necessary to alert Global Navigation Satellite System (GNSS) users about ionospheric space weather influences on satellite-receiver radio communications. Precise modeling and forecasting of the ionospheric TEC are critical for reliable and accurate GNSS applications. In this paper, a deep learning-based Bi-directional Long Short-Term Memory (Bi-LSTM) algorithm is implemented for 26 GPS (Global Positioning System) stations TEC data over the Indian region. The Bi-LSTM ionospheric TEC forecasting maps are generated and compared with ANN, and LSTM models during both geomagnetic quiet and disturbed periods. The potential of Bi-LSTM networks in time sequence processing is improved by having forward and backward connections. The Bi-LSTM results are demonstrated with and without solar and geomagnetic indices as input to the network. This work's outcome would help develop an ionospheric weather alert system for GNSS users.

Long Short‐Term Memory Neural Network for Ionospheric Total Electron Content Forecasting Over China

AbstractAn increasing number of terrestrial‐ and space‐based radio‐communication systems are influenced by the ionospheric space weather, making the ionospheric state increasingly important to forecast. In this study, a novel extended encoder‐decoder long short‐term memory extended (ED‐LSTME) neural network, which can predict ionospheric total electron content (TEC) is proposed. Useful inherent features were automatically extracted from the historical TEC by LSTM layers, and the performance of the proposed model was enhanced by considering solar flux and geomagnetic activity data. The proposed ED‐LSTME model was validated using 15‐min TEC values from GPS measurements over one solar cycle (from January 2006 to July 2018) collected at 15 GPS stations in China. Different assessment experiments were conducted in different geographical locations and seasons as well as under varying geomagnetic activities, to comprehensively evaluate the model's performance. These comparative experiments were conducted using an ED‐LSTM, a traditional LSTM, a deep neural network, autoregressive integrated moving average, and the 2016 International Reference Ionosphere models. The results indicated that the ED‐LSTME model is superior to the other statistical models, with R2 and root mean square error values of 0.89 and 12.09 TECU, respectively. In addition, TEC was adequately predicted under different ionospheric conditions, and satisfactory results were obtained even under geomagnetically disturbed conditions. These results suggest that the prediction performance could be significantly improved by utilizing auxiliary data. These observations confirm that the proposed model outperforms several state‐of‐the‐art models in making predictions at different times and under diverse conditions.

GNSS-based hardware-in-the-loop simulations of spacecraft formation flying with the global ionospheric model TIEGCM

Low-earth-orbit (LEO) spacecraft formation flying (SFF) simulated on the Virginia Tech Formation Flying Testbed (VTFFTB) demonstrated the feasibility of detecting simplified ionospheric electron density (Ne) structures (i.e., one-dimensional equatorial plasma bubbles/EPBs) utilizing a differential total electron content (TEC) method. The Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIEGCM) is used to greatly enhance the ionospheric Ne simulation capability and fidelity (e.g., dimension, resolution, more realistic space weather processes) of GNSS-based hardware-in-the-loop (HIL) simulations in the current work. An algorithm is developed to incorporate TIEGCM 4D ionospheric Ne profiles into the VTFFTB HIL infrastructure by simulating the TEC impacts on multi-band GNSS signals. First, a series of first-principle analyses on ionospheric Ne retrieval accuracy are presented using software-based LEO SFF simulations in the TIEGCM ionosphere. Subsequently, two HIL ionospheric space weather observation simulation test cases, EPB and Tongues-of-Ionization (TOI), are simulated on the VTFFTB to demonstrate the enhanced 4D ionospheric simulation capability and evaluate the Ne retrieval characteristics. The Ne retrieval techniques are validated and characterized in the TIEGCM ionosphere via both software and HIL simulations. In principle, the technique developed is expected to be applicable to other similar simulation platforms, as well as other global or regional ionospheric models. This is advantageous for future GNSS testing, HIL simulations, and technology developments.

Performance analysis of Neural Networks with IRI-2016 and IRI-2012 models over Indian low-latitude GPS stations

The Global Positioning System (GPS) applications are highly vulnerable to the ionospheric space weather effects. Modelling and forecasting the ionospheric effects such as time delays for GPS signals are important for real time alerts of space weather effects on GPS services. In the present work, the performance of Neural Networks (NN) model is compared with International Reference Ionosphere (IRI) models. The ionospheric Total Electron Content (TEC) observations have been collected during 2015 year, descending phase of 24th solar cycle, over three Indian low latitude GPS stations namely, Bengaluru (Geographic coordinates: 13.02°N and 77.57°E), near to geomagnetic equator, Guntur (Geographic coordinates: 16.37°N and 80.37°E), which is at Equatorial Ionization Anomaly (EIA) and Lucknow (Geographic coordinates: 26.83°N and 80.92°E), which is beyond EIA region. The performance of NN model in predicting the ionospheric TEC values is compared with IRI (IRI-2012 and IRI-2016) models during test period, October–December 2015 over three Indian low latitude regions. It is observed that IRI models (IRI-2012 and IRI-2016) have shown more the temporal differences with GPS-VTEC during sunrise hours compared to sunset hours over three low latitude regions. The performance of IRI-2016 model has apparently better than IRI-2012 model. However, it is observed that IRI-2016 model has large discrepancies over Bengaluru and Guntur station due to high VTEC fluctuations at equatorial and low latitudes. The NN models are well predicted the measured diurnal mean VTEC variations with the less errors, ±5 TECU but the differences of IRI models are ±15 TECU over all the three stations. Later, GPS-data for 10 years, 2009–2018, is collected over Bengaluru station during 24th solar cycle. The performance of NN model is validated during 2016, 2017 and 2018 years over Bengaluru GPS station. The error measurements and experimental results reported that the measured GPS-VTEC values are well predicted by NN model compared to IRI-2016 model over equatorial and low latitude GPS stations.

Machine learning algorithm to forecast ionospheric time delays using Global Navigation satellite system observations

Global Navigation satellite systems (GNSS) are predominantly affected by Ionospheric space weather. The GNSS signal delays due to ionospheric Total Electron Content (TEC) can be forecasted using advances in the emerging mathematics tools and algorithms. Machine learning algorithms such as Gaussian Process Regression (GPR) is considered in the present paper to implement in the forecasting of low-latitude ionospheric conditions. The GPS receiver data is obtained for 8 years (2009–2016) during 24th solar cycle from International GNSS Services (IGS) station located in Bengaluru (Geographical latitude: 12.97° N, Geographical longitude: 77.59° E), India. The performance of GPR model is validated using statistical parameters such as Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and correlation coefficient. The results of the proposed GPR model were compared with the Auto Regressive Moving Average (ARMA) model and Artificial Neural Networks (ANN) model during solar maximum period and descending phase of 24th solar cycle. The experiment results are evident that GPR model is significantly providing the promising results in forecasting the ionospheric time delays for GNSS signals. The outcome of the work can be useful to develop web based Ionospheric TEC forecasting system to alert the GNSS users.

Structural characterization of the equatorial F region plasma irregularities in the multifractal context

Abstract. In the emerging ionosphere–space–weather paradigm, investigating the dynamical properties of ionospheric plasma irregularities using advanced computational nonlinear algorithms provide new insights into their turbulent-seeming nature, for instance, the evidence of energy distribution via a multiplicative cascade. In this study, we present a multifractal analysis of the equatorial F region in situ data obtained from two different experiments performed at Alcântara (2.4∘ S, 44.4∘ W), Brazil, to explore their scaling structures. The first experiment observed several medium- to large-scale plasma bubbles whereas the second experiment observed vertical uplift of the base of the F region. The multifractal detrended fluctuation analysis and the p-model fit are used to analyze the plasma density fluctuation time series. The result shows the presence of multifractality with degree of multifractality 0.53–0.93 and 0.3≤p≤0.4 cascading probability for the first experiment. Other experimental data also exhibit multifractality with degree of multifractality 0.19–0.27 and 0.42≤p≤0.44 cascading probability in ionospheric plasma irregularities. Our results confirm the nonhomogeneous nature of plasma irregularities and characterize the underlying nonhomogeneous multiplicative cascade hypothesis in the ionospheric medium. Differences in terms of scaling and complexity in the data belonging to different types of phenomena are also addressed.

Recent ionospheric investigations in China (2018–2019)

Since the release of the 2018 National Report of China on ionospheric research ( <xref ref-type="bibr" rid="bLiu2018e">Liu LB and Wan WX, 2018</xref>) to the Committee on Space Research (COSPAR), scientists from Mainland China have made many new fruitful investigations of various ionospheric-related issues. In this update report, we briefly introduce more than 130 recent reports (2018–2019). The current report covers the following topics: ionospheric space weather, ionospheric structures and climatology, ionospheric dynamics and couplings, ionospheric irregularity and scintillation, modeling and data assimilation, and radio wave propagation in the ionosphere and sounding techniques.

Generation of Assimilated Indian Regional Vertical TEC Maps

Regional ionospheric total electron content maps have become a prominent tool for understanding the dynamic behavior of the ionosphere. In recent times, ionospheric data assimilation methods have enhanced the prediction capabilities of global ionospheric models by embedding several standalone ionospheric remote sensing observations. In this study, an attempt is made to generate hourly Assimilated Indian Regional Vertical Total Electron Content (AIRAVAT) Maps by the process of data assimilation using the Kalman filter exclusively for the Indian region (longitude: 65° to 100°; latitude: 5° to 40°). Observations from the ground-based Global Positioning System aided Geo-Stationary Orbit Augmented Network and radio occultations from the space-based Constellation Observing System for Meteorology, Ionosphere, and Climate are introduced into the global ionospheric map developed by the Centre for Orbit Determination in Europe with a temporal resolution of 1 h. The AIRAVAT maps are created for a day of quiet (September 16, 2016) and disturbed (October 25, 2016) geomagnetic conditions. A new methodology is provided for the covariance matrix of initial background model errors through multivariate principal component analysis of solar and geomagnetic parameters. The equatorial ionization anomaly features are clearly captured in the developed AIRAVAT maps by using both the updated and forecasted steps of the Kalman filter. The AIRAVAT model is validated with an independent GNSS receiver through root-mean-square error analysis for both quiet and disturbed geomagnetic conditions to showcase the efficiency of the model. The quiet day RMSEs between the estimated TEC of proposed AIRAVAT model and the true data are approximately 2 TECU and 2.66 TECU for a disturbed day, respectively. The proposed AIRAVAT maps are useful for monitoring the impact of ionospheric space weather on satellite-based navigation and communication systems.

Application of Singular Spectrum Analysis Using Artificial Neural Networks in TEC Predictions for Ionospheric Space Weather

The prediction of the ionospheric state has become increasingly important as more and more terrestrial and space-based radiocommunication systems rely on the ionospheric space weather. This article presents a new ionospheric prediction model, named the singular spectrum analysis-artificial neural network (SSA-ANN) model. SSA is used as a preprocessing tool for the ionosphere total electron content (TEC) prediction based on the ANN approach. The hourly global positioning system (GPS)-TEC observations from the period of 2009–2017 at the Bangalore (13.02° N and 77.57° E) station are taken into account for analysis. The quick convergence of the SSA decomposition makes it possible to use the first four SSA modes to represent 99.57% of the total variance of the GPS-TEC dataset. The root mean square error between the observations and the SSA-ANN model TEC value is 1.40 TECU for the period of 2009–2017, and the correlation coefficient is 0.99. The performance of SSA-ANN is evaluated using autoregressive moving average (ARMA) and International Reference Ionosphere-16 (IRI-16) models in three different cases of solar phase periods, namely the ascending solar phase (ASP), high solar phase (HSP), and descending solar phase (DSP), and in different seasons. The SSA-ANN model predicts the absence of winter anomaly during the ASP period (2012), but the ARMA model and IRI models failed to predict the absence of the anomaly. The proposed SSA-NN model resulted in higher prediction accuracy and reduced training and testing times, which would be useful for the development of an ionospheric predicting system.

Implementation of Hybrid Ionospheric TEC Forecasting Algorithm Using PCA-NN Method

Forecasting the ionospheric space weather is crucial for improving the accuracy of the global navigation satellite systems (GNSS). Nonetheless, comprehending the nonhomogeneous ionospheric variability under space earth environmental conditions is a major challenge, and so is developing an accurate ionospheric forecasting model. The complex spatial and temporal variations in the ionospheric region are the results of the solar and interplanetary activities, in addition to the magnetosphere, mesosphere, thermosphere, stratosphere, troposphere, and lithosphere processes. Thus, this calls for an urgent need to develop a suitable ionospheric forecasting algorithm to capture the ionospheric perturbations. Total electron content (TEC) is the key parameter derived from GNSS receivers to represent the status of the ionosphere. This paper introduces a novel ionospheric forecasting algorithm based on the fusion of principal component analysis and artificial neural networks (PCA–NN) methods to forecast the ionospheric TEC values. Solar index (F10.7), geomagnetic index (Ap index), and 20-year TEC data (1997–2016) over a Japan Grid Point (34.95 °N and 134.05 °E) were used to apply artificial intelligence methodologies. The experimental results underscore the reliability of the proposed algorithm in forecasting the ionospheric time delay effects.

Validation results of maximum S4 index in F-layer derived from GNOS on FY3C satellite

The first Global Navigation Satellite System (GNSS) Occultation Sounder (GNOS) compatible with both BeiDou Navigation Satellite System (BDS) and Global Positioning System was successfully launched into orbit on September 23, 2013, which was mounted on the FengYun-3C (FY3C) satellite. FY3C/GNOS has produced a lot of ionospheric radio occultation (RO) scintillation data until now. This work was conducted to verify the accuracy of the scintillation data observed by FY3C/GNOS for monitoring of the GNSS signal attenuation and early warning of the possible GNSS signal interruption, thus laying the foundation for research on ionospheric space weather. In this work, the ionospheric F-layer maximum scintillation indexes S4 (\(S4_{{{\text{max}}}}^{{\text{F}}}\)) probed by FY3C/GNOS and Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) are matched into data pairs by data matching criterions during January 10, 2014 and January 9, 2015, which allow the comparison and error analysis of \(S4_{{{\text{max}}}}^{{\text{F}}}\) indexes between FY3C/GNOS and COSMIC. We find that FY3C/GNOS overestimates the \(S4_{{{\text{max}}}}^{{\text{F}}}\) data compared to that of COSMIC, and the biases and standard deviations (Stds) of the differences between the two \(S4_{{{\text{max}}}}^{{\text{F}}}\) indexes (FY3C/GNOS-COSMIC) in data pairs are 0.004 and 0.063 in the whole day, 0.007 and 0.080 at nighttime, 0.001 and 0.046 at daytime, respectively. The statistical error results verify the precision consistency of the ionospheric RO scintillation data between FY3C/GNOS and COSMIC. The errors at nighttime are larger than those in the whole day and daytime, which is probably due to time-varying ionospheric irregularities after midnight and disparity of the data matching criterions. Since the deployment of FY3 series satellites, GNOS can contribute more scintillation data to the international ionospheric research and forecasting of ionospheric space weather.

Total Electron Content Observations by Dense Regional and Worldwide International Networks of GNSS

Two-dimensional ionospheric total electron content (TEC) maps have been derived from ground-based Global Navigation Satellite System (GNSS) receiver networks and applied to studies of various ionospheric disturbances since the mid-1990s. For the purpose of monitoring and researching ionospheric conditions and ionospheric space weather phenomena, we have developed TEC maps of areas over Japan using the dense GNSS network, GNSS Earth Observation NETwork (GEONET), which consists of about 1300 stations and is operated by the Geospatial Information Authority of Japan (GSI). Currently, we are providing high-resolution, two-dimensional maps of absolute TEC, detrended TEC, rate of TEC change index (ROTI), and loss-of-lock on GPS signal over Japan on a real-time basis. Such high-resolution TEC maps using dense GNSS receiver networks are one of the most effective ways to observe, on a scale of several 100 km to 1000 km, ionospheric variations caused by traveling ionospheric disturbances and/or equatorial plasma bubbles, which can degrade single-frequency and differential GNSS positioning/navigation. We have collected all the available GNSS receiver data in the world to expand the TEC observation area. Currently, however, dense GNSS receiver networks are available in only limited areas, such as Japan, North America, and Europe. To expand the two-dimensional TEC observation with high resolution, we have conducted the Dense Regional and Worldwide International GNSS TEC observation (DRAWING-TEC) project, which is engaged in three activities: (1) standardizing GNSS-TEC data, (2) developing a new high-resolution TEC mapping technique, and (3) sharing the standardized TEC data or the information of GNSS receiver network. We have developed a new standardized TEC format, GNSS-TEC EXchange (GTEX), which is included in the Formatted Tables of ITU-R SG 3 Databanks related to Recommendation ITU-R P.311. Sharing the GTEX TEC data would be easier than sharing the GPS/GNSS data among those in the international ionospheric researcher community. The DRAWING-TEC project would promote studies of medium-scale ionospheric variations and their effect on GNSS.

Chinese ionospheric investigations in 2016–2017

After the release of the previous report to the Committee on Space Research (COSPAR) on progress achieved by Chinese scientists in ionospheric researches (Liu LB and Wan WX, 2016), in the recent two years (2016–2017) many interesting new investigations into various ionospheric related issues have been completed. In this report, about 100 publications are summarized. The topics highlighted are as follows: Ionospheric space weather, ionospheric dynamics, ionospheric climatology and modelling, ionospheric irregularity and scintillation, Global Navigation Satellite System (GNSS) related ionospheric issues and other techniques, and radio wave propagation in the ionosphere. An outstanding feature is that more and more observations from the Meridional Project supported the ionospheric investigations.

An ionospheric index suitable for estimating the degree of ionospheric perturbations

Space weather can strongly affect trans-ionospheric radio signals depending on the used frequency. In order to assess the strength of a space weather event from its origin at the sun towards its impact on the ionosphere a number of physical quantities need to be derived from scientific measurements. These are for example the Wolf number sunspot index, the solar flux density F10.7, measurements of the interplanetary magnetic field, the proton density, the solar wind speed, the dynamical pressure, the geomagnetic indices Auroral Electrojet, Kp, Ap and Dst as well as the Total Electron Content (TEC), the Rate of TEC, the scintillation indices S4 and σ(ϕ) and the Along-Arc TEC Rate index index. All these quantities provide in combination with an additional classification an orientation in a physical complex environment. Hence, they are used for brief communication of a simplified but appropriate space situation awareness. However, space weather driven ionospheric phenomena can affect many customers in the communication and navigation domain, which are still served inadequately by the existing indices. We present a new robust index, that is able to properly characterize temporal and spatial ionospheric variations of small to medium scales. The proposed ionospheric disturbance index can overcome several drawbacks of other ionospheric measures and might be suitable as potential driver for an ionospheric space weather scale.