Investigation and Forecasting of Variation in lonospheric TEC in the Equatrial Region

Abstract

newlineEstimation and prediction of the ionospheric Total Electron Content (TEC) are crucial in improving the accuracy of the Global Navigation satellite systems. The ionospheric TEC causes a group delay in the navigation signal which deteriorates the accuracy of the estimated position. Further, the ionosphere in the equatorial region is highly dynamic and driven by complex electrodynamics like the equatorial ionization anomaly (EIA), equatorial electrojet (EEJ), plasma fountain, etc. which further deteriorate the accuracy and add uncertainties in the estimation of range delays. Currently, the range delay is estimated by using global and regional models based on mathematical formulation and a fixed correction is used to mitigate the error. But these empirical models do not consider the complex spatial and temporal variations in the equatorial ionosphere. To improve accuracy, many data-driven models have also been proposed by erstwhile researchers. Most of the models are long-term prediction models and depend on initial training with large historical data. Since the number of quiet days is much higher as compared to the geo-magnetically disturbed days, the models based on historical data are likely to be biased towards the majority quiet day TEC patterns and may suffer from data bias. Also, such models are usually trained with data for a particular period and require regular retraining. Further, these models are trained on long-term data and hence cannot track the dynamic short-term variations and changing statistical nature of the TEC in the equatorial region. Developing a short-term TEC prediction model trained using the recent variations and irregularities would help improve the prediction accuracy of TEC in an equatorial region. This research work thus focuses on developing forecasting algorithms to extract short-term patterns in TEC in the equatorial region which can provide a basis for range error correction. newlineFor developing a short-term prediction model for TEC, the variation pattern, anomalies, correla