Advanced machine learning optimized by the genetic algorithm in ionospheric models using long-term multi-instrument observations

The ionospheric delay is of paramount importance to radio communication, satellite navigation and positioning. It is necessary to predict high-accuracy ionospheric peak parameters for single frequency receivers. In this study, the state-of-the-art artificial neural network (ANN) technique optimized by the genetic algorithm is used to develop global ionospheric models for predicting foF2 and hmF2. The models are based on long-term multiple measurements including ionospheric peak frequency model (GIPFM) and global ionospheric peak height model (GIPHM). Predictions of the GIPFM and GIPHM are compared with the International Reference Ionosphere (IRI) model in 2009 and 2013 respectively. This comparison shows that the root-mean-square errors (RMSEs) of GIPFM are 0.82 MHz and 0.71 MHz in 2013 and 2009, respectively. This result is about 20%-35% lower than that of IRI. Additionally, the corresponding hmF2 median errors of GIPHM are 20% to 30% smaller than that of IRI. Furthermore, the ANN models present a good capability to capture the global or regional ionospheric spatial-temporal characteristics, e.g., the equatorial ionization anomaly andWeddell Sea anomaly. The study shows that the ANN-based model has a better agreement to reference value than the IRI model, not only along the Greenwich meridian, but also on a global scale. The approach proposed in this study has the potential to be a new three-dimensional electron density model combined with the inclusion of the upcoming Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC-2) data

Validation of University of New Brunswick Ionospheric Modeling Technique with ionosonde TEC estimation over South Africa

For more than a decade, ionospheric research over South Africa has been carried out using data from ionosondes geographically located at Madimbo (28.38°S, 30.88°E), Grahamstown (33.32°S, 26.50°E), and Louisvale (28.51°S, 21.24°E). The objective has been modelling the bottomside ionospheric characteristics using neural networks. The use of Global Navigation Satellite System (GNSS) data is described as a new technique to monitor the dynamics and variations of the ionosphere over South Africa, with possible future application in high frequency radio communication. For this task, the University of New Brunswick Ionospheric Modelling Technique (UNB-IMT) was applied to compute midday (10:00 UT) GNSS-derived total electron content (GTEC). GTEC values were computed using GNSS data for stations located near ionosondes for the years 2002 and 2005 near solar maximum and minimum, respectively. The GTEC was compared with the midday ionosonde-derived TEC (ITEC) measurements to validate the UNB-IMT results. It was found that the variation trends of GTEC and ITEC over all stations are in good agreement and show a pronounced seasonal variation for the period near solar maximum, with maximum values ( 80 TECU) around autumn and spring equinoxes, and minimum values ( 22 TECU) around winter and summer. Furthermore, the residual ΔTEC = GTEC − ITEC was computed. It was evident that ΔTEC, which is believed to correspond to plasmaspheric electron content, showed a pronounced seasonal variation with maximum values ( 20 TECU) around equinoxes and minimum ( 5 TECU) around winter near solar maximum. The equivalent ionospheric and total slab thicknesses were also computed and comprehensively discussed. The results verified the use of UNB-IMT as one of the tools for future ionospheric TEC research over South Africa

The European COST (Co-operation in the field of Scientific and Technical Research) Actions: an important chance to cooperate and to grow for all the international ionospheric community

The current COST (Co-operation in the field of Scientific and Technical Research) Action 296 on Mitigation of Ionospheric Effects on Radio Systems, along with previous COST238 (Prediction and Retrospective Ionospheric Modelling over Europe), COST251 (Improved Quality of Service in Ionospheric Telecommunication Systems Planning and Operation) and COST271 (Effects of the Upper Atmosphere on Terrestrial and Earth-Space Communications) Actions have addressed investigations of the different effects of the ionosphere on terrestrial telecommunication systems and on Earth-space systems. Throughout their lifetime of 20 years, these COST actions have achieved a great deal in long-term archiving of synoptic soundings of the state of the ionosphere, in enhancing understanding of the morphology of the ionosphere and its dependence on space weather and in producing ionosphere-plasmasphere as well as propagation models for terrestrial radio services available to variety of radio users. Besides the formal contributions to ITU-R and the contributions to international organisations such as URSI, COSPAR, EGU and ESA, these COST Actions have provided a forum for the establishment of collaborative European initiatives, a centre of expertise and excellence in ionosphere knowledge when none other equivalent in Europe or elsewhere exists. In this paper, we review the main achievements of the COST 238, 251 and 271 actions as developed in the past studies

A new global model for the ionospheric F2 peak height for radio wave propagation

The F2-layer peak density height <I>hm</I>F2 is one of the most important ionospheric parameters characterizing HF propagation conditions. Therefore, the ability to model and predict the spatial and temporal variations of the peak electron density height is of great use for both ionospheric research and radio frequency planning and operation. For global <I>hm</I>F2 modelling we present a nonlinear model approach with 13 model coefficients and a few empirically fixed parameters. The model approach describes the temporal and spatial dependencies of <I>hm</I>F2 on global scale. For determining the 13 model coefficients, we apply this model approach to a large quantity of global <I>hm</I>F2 observational data obtained from GNSS radio occultation measurements onboard CHAMP, GRACE and COSMIC satellites and data from 69 worldwide ionosonde stations. We have found that the model fits to these input data with the same root mean squared (RMS) and standard deviations of 10%. In comparison with the electron density NeQuick model, the proposed Neustrelitz global <I>hm</I>F2 model (Neustrelitz Peak Height Model – NPHM) shows percentage RMS deviations of about 13% and 12% from the observational data during high and low solar activity conditions, respectively, whereas the corresponding deviations for the NeQuick model are found 18% and 16%, respectively

A new method for improving the performance of an ionospheric model developed by multi-instrument measurements based on artificial neural network

There are remarkable ionospheric discrepancies between space-borne (COSMIC) measurements and ground-based (ionosonde) observations, the discrepancies could decrease the accuracies of the ionospheric model developed by multi-source data seriously. To reduce the discrepancies between two observational systems, the peak frequency (foF2) and peak height (hmF2) derived from the COSMIC and ionosonde data are used to develop the ionospheric models by an artificial neural network (ANN) method, respectively. The averaged root-mean-square errors (RMSEs) of COSPF (COSMIC peak frequency model), COSPH (COSMIC peak height model), IONOPF (Ionosonde peak frequency model) and IONOPH (Ionosonde peak height model) are 0.58 MHz, 19.59 km, 0.92 MHz and 23.40 km, respectively. The results indicate that the discrepancies between these models are dependent on universal time, geographic latitude and seasons. The peak frequencies measured by COSMIC are generally larger than ionosonde's observations in the nighttime or middle-latitudes with the amplitude of lower than 25%, while the averaged peak height derived from COSMIC is smaller than ionosonde's data in the polar regions. The differences between ANN-based maps and references show that the d

Ionosphere sounding for pre-seismic anomalies identification (INSPIRE): results of the project and perspectives for the short-term earthquake forecast

The INSPIRE project was dedicated to the study of physical processes and their effects in ionosphere which could be determined as earthquake precursors together with detailed description of the methodology of ionospheric pre-seismic anomalies definition. It was initiated by ESA and carried out by an international consortium. The full set of key parameters of the ionospheric plasma was selected based on the retrospective analysis of the ground-based and satellite measurements of pre-seismic anomalies. Using this classification the multi-instrumental database of worldwide relevant ionospheric measurements (ionosonde and GNSS networks, LEO-satellites with in situ probes including DEMETER and FORMOSAT/COSMIC ROC missions) was developed for the time intervals related to selected test cases. As statistical processing shows, the main ionospheric precursors appear approximately 5 days before the earthquake within the time interval of 30 days before and 15 days after an earthquake event. The physical mechanisms of the ionospheric pre-seismic anomalies generation from ground to the ionosphere altitudes were formulated within framework of the Lithosphere-Atmosphere- Ionosphere Coupling (LAIC) model. The processes of precursor’s development were analyzed starting from the crustal movements, radon emission and air ionization, thermal and atmospheric anomalies, electric field and electromagnetic emissions generation, variations of the ionospheric plasma parameters, in particular vertical TEC and vertical profiles of the electron concentration. The assessment of the LAIC model performance with definition of performance criteria for earthquake forecasting probability has been done in statistical and numerical simulation domains of the Global Electric Circuit. The numerical simulations of the earthquake preparation process as an open complex system from start of the final stage of earthquake preparation up to the final point–main shock confirms that in the temporal domain the ionospheric precursors are one of the most late in the sequence of precursors. The general algorithm for the identification of the ionospheric precursors was formalized which also takes into account the external Space Weather factors able to generate the false alarms. The importance of the special stable pattern called the “precursor mask” was highlighted which is based on self-similarity of pre-seismic ionospheric variations. The role of expert decision in pre-seismic anomalies interpretation for generation of seismic warning is important as well. The algorithm performance of the LAIC seismo-ionospheric effect detection module has been demonstrated using the L’Aquila 2009 earthquake as a case study. The results of INSPIRE project have demonstrated that the ionospheric anomalies registered before the strong earthquakes could be used as reliable precursors. The detailed classification of the pre-seismic anomalies was presented in different regions of the ionosphere and signatures of the pre-seismic anomalies as detected by ground and satellite based instruments were described what clarified methodology of the precursor’s identification from ionospheric multi-instrumental measurements. Configuration for the dedicated multiobservation experiment and satellite payload was proposed for the future implementation of the INSPIRE project results. In this regard the multi-instrument set can be divided into two groups: space equipment and ground-based support, which could be used for realtime monitoring. Together with scientific and technical tasks the set of political, logistic and administrative problems (including certification of approaches by seismological community, juridical procedures by the governmental authorities) should be resolved for the real earthquake forecast effectuation.In years 2014–2016 works were supported by the ESA Project “INSPIRE, ionosphere Sounding for Pre-seismic anomalies Identification Research (INSPIRE)” nr 4000,111,456/14/NL/ MV. The work is supported by the National Center for Research and Development, Poland, through Grant ARTEMIS (decision no. DWM/PL-CHN/97/2019, WPC1/ ARTEMIS/2019); The authors thank also the Ministry of Science and Higher Education (MSHE), Poland for granting funds for the Polish contribution to the International LOFAR Telescope “(MSHE decision no. DIR/ WK/2016/2017/05–1)” and for maintenance of the LOFAR PL-612 Baldy (MSHE decisions: no. 59/E-383/SPUB/SP/ 2019.1). This work is supported by the National Science Centre, Poland, through Grants 2017/25/B/ST10/00479 and 2017/27/B/ST10/02190.Peer ReviewedPostprint (published version

Single station TEC modelling during storm conditions

It has been shown in ionospheric research that modelling total electron content (TEC) during storm conditions is a big challenge. In this study, mathematical equations were developed to estimate TEC over Sutherland (32.38oS, 20.81oE), during storm conditions, using the Empirical Orthogonal Function (EOF) analysis, combined with regression analysis. TEC was derived from GPS observations and a geomagnetic storm was defined for Dst ≤ -50 nT. The inputs for the model were chosen based on the factors that influence TEC variation, such as diurnal, seasonal, solar and geomagnetic activity variation, and these were represented by hour of the day, day number of the year, F10.7 and A index respectively. The EOF model was developed using GPS TEC data from 1999 to 2013 and tested on different storms. For the model validation (interpolation), three storms were chosen in 2000 (solar maximum period) and three others in 2006 (solar minimum period), while for extrapolation six storms including three in 2014 and three in 2015 were chosen. Before building the model, TEC values for the selected 2000 and 2006 storms were removed from the dataset used to construct the model in order to make the model validation independent on data. A comparison of the observed and modelled TEC showed that the EOF model works well for storms with non-significant ionospheric TEC response and storms that occurred during periods of low solar activity. High correlation coefficients between the observed and modelled TEC were obtained showing that the model covers most of the information contained in the observed TEC. Furthermore, it has been shown that the EOF model developed for a specific station may be used to estimate TEC over other locations within a latitudinal and longitudinal coverage of 8.7o and 10.6o respectively. This is an important result as it reduces the data dimensionality problem for computational purposes. It may therefore not be necessary for regional storm-time TEC modelling to compute TEC data for all the closest GPS receiver stations since most of the needed information can be extracted from measurements at one location

GITM‐Data Comparisons of the Depletion and Enhancement During the 2017 Solar Eclipse

The total solar eclipse of 21 August 2017 was simulated with the Global Ionosphere‐Thermosphere Model (GITM), and the results were compared with the total electron content (TEC) measurements provided by the Global Navigation Satellite System, as well as F2 layer peak electron density (NmF2) derived from six ionosondes. TEC decreased over North America by ~54.3% in the model and ~57.6% in measurements, and NmF2 decreased by ~20–50% in the model and ~40–60% in the measurements. GITM predicted a posteclipse enhancement of ~10% in TEC and NmF2, consistent with observations which suggested an increase of ~10–25% in TEC and ~10–40% in NmF2. GITM showed that the divergence of horizontal winds drove the increase in Oxygen after the eclipse allowing an increase in the ionization rate. The slower charge exchange due to both the decreased ion temperature and N2 density allowed an increase of O+ density in the F region also.Plain Language SummaryThe total solar eclipse of 21 August 2017 was explored with the Global Ionosphere‐Thermosphere Model, which simulates weather in space. The results were compared with ionospheric observations from the Global Navigation Satellite System and six ground‐based measurements. Both the model and data showed a decrease in ionospheric densities during the eclipse and an enhancement above what would happen without the eclipse after the passage of the moon. The analysis showed that the posteclipse enhancement in the ionosphere was caused by the enhanced neutral Oxygen density, which was driven by the horizontal winds, as well as decreased neutral N2 density, which was driven primarily by the cooling of the atmosphere.Key PointsModel‐data comparisons showed a relatively consistent depletion and enhancement in the ionosphere during and after the eclipseGITM showed that the divergence of horizontal winds drove the increased O after the eclipse allowing an increase in the ionization rateSlower charge exchange due to both the decreased ion temperature and N2 density allowed an increase of O+ density in the F region alsoPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/144250/1/grl57159_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/144250/2/grl57159.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/144250/3/grl57159-sup-0001-2018GL077409-SI.pd

Progress in space weather modeling in an operational environment

This paper aims at providing an overview of latest advances in space weather modeling in an operational environment in Europe, including both the introduction of new models and improvements to existing codes and algorithms that address the broad range of space weather’s prediction requirements from the Sun to the Earth. For each case, we consider the model’s input data, the output parameters, products or services, its operational status, and whether it is supported by validation results, in order to build a solid basis for future developments. This work is the output of the Sub Group 1.3 ‘‘Improvement of operational models’’ of the European Cooperation in Science and Technology (COST) Action ES0803 ‘‘Developing Space Weather Products and services in Europe’’ and therefore this review focuses on the progress achieved by European research teams involved in the action

Regional reference total electron content model over Japan based on neural network mapping techniques

A regional reference model of total electron content (TEC) was constructed using data from the GPS Earth Observation Network (GEONET), which consists of more than 1000 Global Positioning System (GPS) satellite receivers distributed over Japan. The data covered almost one solar activity period from April 1997 to June 2007. First, TECs were determined for 32 grid points, expanding from 27 to 45&amp;deg; N in latitude and from 127 to 145&amp;deg; E in longitude at 15-min intervals. Secondly, the time-latitude variation averaged over three days was determined by using the surface harmonic functional expansion. The coefficients of the expansion were then modeled by using a neural network technique with input parameters of the season (day of the year) and solar activity (F10.7 index and sunspot number). Thus, two-dimensional TEC maps (time vs. latitude) can be obtained for any given set of solar activity and day of the year