Spectral investigation of traveling ionospheric disturbances:IONOLAB-FFT

Ionosphere is an important layer of atmosphere which is under constant forcing from both below due to gravitational, geomagnetic and seismic activities, and above due to solar wind and galactic radiation. Spatio-temporal variability of ionosphere is made up of two major components that can be listed as spatio-temporal trends and secondary variabilities that are due to disturbances in the geomagnetic field, gravitational waves and coupling of seismic activities into the upper atmosphere and ionosphere. Some of these second order variabilities generate wave-like oscillations in the ionosphere which propagate at a certain frequency, duration and velocity. These oscillations cause major problems for navigation and guidance systems that utilize GNSS （Global Navigation Satellite Systems）. In this study, the frequency and duration of wave-like oscillations are determined using a DFT （Discrete Fourier Transform） based algo- rithm over the STEC （slant total electron content） values estimated from single GPS （Global Positioning System） station. The performance of the developed method, namely IONOLAB-FFT, is first determined using synthetic oscillations with known frequencies and durations. Then, IONOLAB-FFr is applied to STEC data from various midlatitude GPS stations for detection of frequency and duration of both medium and large scale TIDs （traveling ionospheric disturbances）. It is observed that IONOLAB-FFr can estimate TIDs with more than 80% accuracy for the following cases： frequencies from 0.6 mHz to 2.4 mHz and durations longer than 10 min; frequencies from 0.15 mHz to 0.6 mHz and durations longer than 50 min; fre- quencies higher than 0.29 mHz and durations longer than 50 rain.

A regional GNSS-VTEC model over Nigeria using neural networks： A novel approach

A neural network model of the Global Navigation Satellite System - vertical total electron content （GNSS-VTEC） over Nigeria is developed. A new approach that has been utilized in this work is the consideration of the International Reference Ionosphere＇s （IRI＇s） critical plasma frequency （foF2） parameter as an additional neuron for the network＇s input layer. The work also explores the effects of using various other input layer neurons like distur- bance storm time （DST） and sunspot number. All available GNSS data from the Nigerian Permanent GNSS Network （NIGNET） were used, and these cover the period from 2011 to 2015, for 14 stations. Asides increasing the learning accuracy of the networks, the inclusion of the IRI＇s foF2 parameter as an input neuron is ideal for making the networks to learn long-term solar cycle variations. This is important especially for regions, like in this work, where the GNSS data is available for less than the period of a solar cycle. The neural network model developed in this work has been tested for time-varying and spatial per- formances. The latest 10% of the GNSS observations from each of the stations were used to test the forecasting ability of the networks, while data from 2 of the stations were entirely used for spatial performance testing. The results show that root-mean-squared-errors were generally less than 8.5 TEC units for all modes of testing performed using the optimal network. When compared to other models, the model developed in this work was observed to reduce the prediction errors to about half those of the NeQuick and the IRI model.

CSES GNSS ionospheric inversion technique, validation and error analysis

With the increased number of low Earth orbit(LEO) satellites equipped with global navigation satellite system(GNSS) receiver,the LEO based GNSS slant total electron content(TEC) and electron density profile(EDP) data play an increasingly important role in space weather and ionospheric research due to improved global coverage. China Seismo-Electromagnetic Satellite(CSES), which was launched in February 2018, is equipped with GNSS receiver for either precise orbit determination(POD) and ionospheric inversion. The purpose of the present paper is to validate CSES GNSS ionospheric inversion technique based on the real observations and verify the accuracy of TEC and EDP retrieval based on the simulated data. The following conclusions can be drawn: the epoch difference inversion(EDI) derived from CSES can successfully retrieve the EDPs without non-occultation side measurements; the technique of EDI and the calibrated TEC inversion(CTI) have similar behaviors in inversion errors,however, the retrieved Nm F2 and hm F2 have a larger systematic error surrounding the equatorial ionization anomaly(EIA)where the assumption of spherical symmetry is often invalid; the precision and accuracy of retrieved TEC have been investigated in the paper based on the simulated data, and it is found that the accuracy of the retrieved TEC is relative to solar activity: the lower the F10.7 index, the higher the accuracy of retrieved TEC.