Establishment of European Regional Ionosphere Model Based on Spherical Harmonic Functions

In order to study the temporal and spatial variation characteristics of the regional ionosphere and the modeling accuracy, the experiment is based on the spherical harmonic function model, using the GPS, Glonass, and Galileo dual-frequency observation data from the 305th-334th day of the European CORS network in 2019 to establish a global ionospheric model. By analyzing and evaluating the accuracy of the global ionospheric puncture points, VTEC, and comparing code products, the test results showed that the GPS system has the most dense puncture electricity distribution, the Glonass system is the second, and the Galileo system is the weakest. The values of ionospheric VTEC calculated by GPS, Glonass and Galileo are slightly different, but in terms of trends, they are the same as those of ESA, JPL and UPC. GPS data has the highest accuracy in global ionospheric modeling. GPS, Glonass and Galileo have the same trend, but Glonass data is unstable and fluctuates greatly.

Forecasting Ionospheric foF2 Based on Deep Learning Method

In this paper, a deep learning long-short-term memory (LSTM) method is applied to the forecasting of the critical frequency of the ionosphere F2 layer (foF2). Hourly values of foF2 from 10 ionospheric stations in China and Australia (based on availability) from 2006 to 2019 are used for training and verifying. While 2015 and 2019 are exclusive for verifying the forecasting accuracy. The inputs of the LSTM model are sequential data for the previous values, which include local time (LT), day number, solar zenith angle, the sunspot number (SSN), the daily F10.7 solar flux, geomagnetic the Ap and Kp indices, geographic coordinates, neutral winds, and the observed value of foF2 at the previous moment. To evaluate the forecasting ability of the deep learning LSTM model, two different neural network forecasting models: a back-propagation neural network (BPNN) and a genetic algorithm optimized backpropagation neural network (GABP) were established for comparative analysis. The foF2 parameters were forecasted under geomagnetic quiet and geomagnetic disturbed conditions during solar activity maximum (2015) and minimum (2019), respectively. The forecasting results of these models are compared with those of the international reference ionosphere model (IRI2016) and the measurements. The diurnal and seasonal variations of foF2 for the 4 models were compared and analyzed from 8 selected verification stations. The forecasting results reveal that the deep learning LSTM model presents the optimal performance of all models in forecasting the time series of foF2, while the IRI2016 model has the poorest forecasting performance, and the BPNN model and GABP model are between two of them.

Space Weather Services for Civil Aviation—Challenges and Solutions

This paper presents a review on the PECASUS service, which provides advisories on enhanced space weather activity for civil aviation. The advisories are tailored according to the Standards and Recommended Practices of the International Civil Aviation Organization (ICAO). Advisories are disseminated in three impact areas: radiation levels at flight altitudes, GNSS-based navigation and positioning, and HF communication. The review, which is based on the experiences of the authors from two years of running pilot ICAO services, describes empirical models behind PECASUS products and lists ground- and space-based sensors, providing inputs for the models and 24/7 manual monitoring activities. As a concrete example of PECASUS performance, its products for a post-storm ionospheric F2-layer depression event are analyzed in more detail. As PECASUS models are particularly tailored to describe F2-layer thinning, they reproduce observations more accurately than the International Reference Ionosphere model (IRI(STORM)), but, on the other hand, it is recognized that the service performance is much affected by the coverage of its input data. Therefore, more efforts will be directed toward systematic measuring of the availability, timeliness and quality of the data provision in the next steps of the service development.

Development of a local empirical model of ionospheric total electron content (TEC) and its application for studying solar-ionospheric effects

Regular and irregular variations in total electron content (TEC) are one of the most significant observables in ionospheric studies. During the solar cycle 24, the variability of ionosphere is studied using global positioning system derived TEC at a mid-latitude station, Tehran (35.70N, 51.33E). Based on solar radio flux and seasonal and local time-dependent features of TEC values, a semi-empirical model is developed to represent its monthly/hourly mean values. Observed values of TEC and the results of our semi-empirical model then are compared with estimated values of a standard plasmasphere–ionosphere model. The outcome of this model is an expected mean TEC value considering the monthly/hourly regular effects of solar origin. Thus, it is possible to use it for monitoring irregular effects induced by solar events. As a result, the connection of TEC variations with solar activities are studied for the case of coronal mass ejections accompanying extreme solar flares. TEC response to solar flares of class X is well reproduced by this model. Our resulting values show that the most powerful flares (i.e. class X) induce a variation of more than 20 percent in daily TEC extent.

Assessing the Performance of Artificial Neural Networks to Predict Ionospheric TEC over Nigeria During Different Space Weather events.

Ionosphere model is much essential to satellite-based system in order to accurately correct the ionospheric error encountered by the satellite signals’ en-route. Levenberg-Marquardt backpropagation (LMBP) algorithm in the Artificial Neural Network (ANN) was used in this work to predict the Total Electron Content (TEC) within the trough of Equatorial Ionization Anomaly (EIA) over Nigeria. Two sets of data were used over the period of three consecutive years (2011-2013) of high solar activity. The first set was used as an input to the ANN model and the second set of data was used as a target. 70% of the data sets were used to train the network, 15% of the data were used for validation and 15% used for testing. The performance of the model was assessed during specific quiet and disturbed geomagnetic conditions. The regression analysis of the model output was optimized by minimizing a cost function of the Mean Square Error (MSE). The results of the errors, regression and comparative analyses have revealed that ANN model is able to predict accurate and reliable TEC that compares well with the actual experimental at any geophysical conditions. Hence, this model would be useful to forecast TEC over Nigeria to a reliable threshold.

AIM-E: E-Region Auroral Ionosphere Model

The auroral oval is the high-latitude region of the ionosphere characterized by strong variability of its chemical composition due to precipitation of energetic particles from the magnetosphere. The complex nature of magnetospheric processes cause a wide range of dynamic variations in the auroral zone, which are difficult to forecast. Knowledge of electron concentrations in this highly turbulent region is of particular importance because it determines the propagation conditions for the radio waves. In this work we introduce the numerical model of the auroral E-region, which evaluates density variations of the 10 ionospheric species and 39 reactions initiated by both the solar extreme UV radiation and the magnetospheric electron precipitation. The chemical reaction rates differ in more than ten orders of magnitude, resulting in the high stiffness of the ordinary differential equations system considered, which was solved using the high-performance Gear method. The AIM-E model allowed us to calculate the concentration of the neutrals NO, N(4S), and N(2D), ions N+, N2+, NO+, O2+, O+(4S), O+(2D), and O+(2P), and electrons Ne, in the whole auroral zone in the 90‒150 km altitude range in real time. The model results show good agreement with observational data during both the quiet and disturbed geomagnetic conditions.

AIM-E auroral ionosphere model adjustment for the regular E layer

The E-Region Auroral Ionosphere Model (AIM-E) was developed to determine the chemical composition and electron density in the auroral zone at E-layer heights (90–150 km). Solar and magnetic activity input parameters for AIM-E are the three-hour Ap index and the daily solar radio flux at a wavelength of 10.7 cm (index F10.7). In this paper, we compare AIM-E calculations of the electron density for the daytime with EUV radiation spectrum specified in two different ways: 1) the EUV spectrum theoretically calculated using the F10.7 index as an input parameter; 2) using TIMED satellite direct measurements of the EUV spectrum. We have corrected the EUVAC EUV radiation model to specify a photoionization source in AIM-E. Calculations of regular E-region critical frequencies show good agreement with the vertical sounding data from Russian high-latitude stations. Results we obtained make it possible to do a quick on-line assessment of the regular E layer, using the daily index F10.7 as an input parameter.

AIM-E auroral ionosphere model adjustment for the regular E layer

The E-Region Auroral Ionosphere Model (AIM-E) was developed to determine the chemical composition and electron density in the auroral zone at E-layer heights (90–150 km). Solar and magnetic activity input parameters for AIM-E are the three-hour Ap index and the daily solar radio flux at a wavelength of 10.7 cm (index F10.7). In this paper, we compare AIM-E calculations of the electron density for the daytime with EUV radiation spectrum specified in two different ways: 1) the EUV spectrum theoretically calculated using the F10.7 index as an input parameter; 2) using TIMED satellite direct measurements of the EUV spectrum. We have corrected the EUVAC EUV radiation model to specify a photoionization source in AIM-E. Calculations of regular E-region critical frequencies show good agreement with the vertical sounding data from Russian high-latitude stations. Results we obtained make it possible to do a quick on-line assessment of the regular E layer, using the daily index F10.7 as an input parameter.