Performance of GPS slant total electron content and IRI-Plas-STEC for days with ionospheric disturbance

Total Electron Content （TEC） is an important observable parameter of the ionosphere which forms the main source of error for space based navigation and positioning systems. Since the deployment of Global Navigation Satellite Systems （GNSS）, cost-effective esti- mation of TEC between the earth based receiver and Global Positioning System （GPS） sat- ellites became the major means of investigation of local and regional disturbance for earthquake precursor and augmentation system studies. International Reference Iono- sphere （IRI） extended to plasmasphere （IRI-Plas） is the most developed ionospheric and plasmaspheric climatic model that provides hourly, monthly median of electron density distribution globally. Recently, IONOLAB group {www.ionolab.org） has presented a new online space weather service that can compute slant TEC （STEC） on a desired ray path for a given date and time using IRI-Plas model （IRI-Plas-STEC）. In this study, the performance of the model based STEC is compared with GPS-STEC computed according to the estimation method developed by the IONOLAB group and includes the receiver bias as IONOLAB-BIAS （IONOLAB-STEC）. Using Symmetric Kullback-Leibler Distance （SKLD）, Cross Correlation （CC） coefficient and the metric norm （L2N） to compare IRI-Plas-STEC and IONOLAB-STEC for the month of October 2011 over the Turkish National Permanent GPS Network （TNPGN- Active）, it has been observed that SKLD provides a good indicator of disturbance for both earthquakes and geomagnetic storms.

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.