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 th...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.展开更多
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 ...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.展开更多
文摘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.
基金supported by Spark Program from China Earthquake Administration(Grant No.XH18032)the Scientific Research Fund of Institute of Seismology,China Earthquake Administration(Grant No.IS201716161)
文摘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.
