Implementation of VARMA Model for Ionospheric TEC Forecast over an Indian GNSS Station

Accuracy in positioning services of the Global Navigation Satellite System (GNSS) is majorly affected due to ionospheric signal delays. The forecasting of ionospheric delays is tough and challenging low-latitude regions due to rapid temporal variations in ionospheric electron density irregularities. Hence, in this paper a non-stationary signal decomposition technique based on Variational Mode Decomposition (VMD), combined with Auto Regressive Moving Average (ARMA) called VMD-ARMA (VARMA) model is presented to forecast the ionospheric delay values 1 hour ahead. The performance of the proposed VARMA ionospheric TEC forecasting algorithm is tested during geomagnetic storms that occurred in June 2013. Three months GNSS data i.e., from 1 April 2013- 30 June 2013 is logged using GNSS Ionospheric Scintillation and TEC Monitor (GISTM) receiver located at Koneru Lakshamaiah Education Fondation, (K L E F), Guntur station (geographic: 16.37°N, 80.44°E), India. It is found that the VARMA model is 2-3% more efficient than the ARMA model in providing good forecasting accuracy during storm conditions. The forecasting results demonstrate that the VARMA version can be useful to forecast the ionospheric TEC variations at low-latitude regions during disturbed ionospheric space weather conditions also.

[1]  X. Yue,et al.  Ionospheric Correction Based on Ingestion of Global Ionospheric Maps into the NeQuick 2 Model , 2015, TheScientificWorldJournal.

[2]  J. Klobuchar.  Ionospheric Time-Delay Algorithm for Single-Frequency GPS Users , 1987, IEEE Transactions on Aerospace and Electronic Systems.

[3]  Jun Chen,et al.  Accuracy analysis by using WARIMA model to forecast TEC in China , 2015, Intelligent Earth Observing Systems.

[4]  Andrzej Krankowski,et al.  Wavelet analysis and forecasting of VTEC obtained with GPS observations over European latitudes , 2005 .

[5]  S. Radicella,et al.  Evaluating the accuracy of ionospheric range delay corrections for navigation at low latitude , 2004 .

[6]  Sivavaraprasad Gampala,et al.  Application of Synchrosqueezing Transform (SST) to Forecast Ionospheric Delays using GPS Observations , 2017 .

[7]  Dominique Zosso,et al.  Variational Mode Decomposition , 2014, IEEE Transactions on Signal Processing.

[8]  B. K. De,et al.  Effects of geomagnetic storm on low latitude ionospheric total electron content: A case study from Indian sector , 2015, Journal of Earth System Science.

[9]  Lee-Anne McKinnell,et al.  The international reference ionosphere today and in the future , 2011 .

[10]  Zhan Xiaohon.  Short-term TEC Prediction of Ionosphere Based on ARIMA Model , 2014 .

[11]  D. Venkata Ratnam,et al.  Performance evaluation of ionospheric time delay forecasting models using GPS observations at a low-latitude station , 2017 .

[12]  G. Sivavaraprasad,et al.  Short-term forecasting of ionospheric total electron content over a low-latitude global navigation satellite system station , 2017 .

[13]  Eric Pottiaux,et al.  The influence of space weather on ionospheric total electron content during the 23rd solar cycle , 2013 .