Forecasting of ionospheric TEC for different latitudes, seasons and solar activity conditions based on OKSM
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Ionosphere is the upper atmosphere region that contains sufficient number of electrons which disturb the propagation of radio signal travel from navigational satellite to ground/user receiver. Ionospheric delay in range measurement is related to its Total Electron Content (TEC). Ionospheric delay results in range error and degrades the user position accuracy of navigational satellite systems such as Global Positioning System (GPS) and Indian Regional Navigational Satellite System (IRNSS). Hence a suitable TEC prediction model to correct the range delay in single frequency range measurement is necessary. In dual frequency receiver, ionospheric delay is estimated and eliminated using the two range measurements performed at the same time. This paper describes the TEC prediction methodology using Ordinary Kriging based Surrogate Model (OKSM). OKSM is evaluated using the data received and collected from the IRNSS receiver station installed at ACS College of Engineering (ACSCE), Bengaluru (12.8913 °N, 77.4658 °E), India and other International GNSS Service (IGS) network stations. IRNSS TEC data (January 2018) is calculated by using dual frequency (L5 & S) pseudo range method and TEC is smoothed by normal cubic smoothing spline method. IRNSS Vertical TEC (VTEC) is predicted from 16 January 2018 to 26 January 2018 by using previous six days of estimated VTEC values. Similarly, GPS VTEC for IGS station at IISC, Bengaluru is also predicted for same duration to validate the developed OKSM. In order to evaluate the performance of the developed forecasting model for different geographic locations, solar activity conditions and seasons, the VTEC is predicted and analyzed for different latitude regions such as low-latitude PHON station (6.9599 °N, 158.2101 °E), mid-latitude ALGO station (45.9588 °N, −78.0714 °E) and high-latitude NRIL station (69.3618 °N, 88.3597 °E) during different solar activity conditions (Low-2008, Medium-2011 and High-2013 solar activity) and during different seasons (spring, summer, rainy and winter) in the year 2017. From the analysis of OKSM prediction results, it is observed that, RMSE of predicted TEC varies from 0.79 to 3.6 TECU, MAE is 0.4 to 3 TECU and MAPE is within 40% for ionospheric quiet days. VTEC is also predicted during storm days (26 October 2003 to 31 October 2003). To study the performance of the model, VTEC prediction results of OKSM are compared with prediction results from Standard Persistence Model (SPM) and VTEC derived from International Reference Ionosphere (IRI-2016) model. The RMSE of OKSM is 1.9679 TECU, MAE is 1.245 TECU and MAPE is 9%, whereas for SPM, RMSE is 4.8372 TECU, MAE is 3.7496 TECU and MAPE is 36%. Similarly, for IRI-2016 model, RMSE is 7.9 TECU, MAE is 7.1976 TECU and MAPE is 66%. Therefore, TEC predictions by OKSM are better than SPM and IRI-2016. The results show that the OKSM is useful for applications in ionospheric studies.
KeywordsTotal electron content VTEC forecast OKSM GPS IRNSS
The research work presented in this paper has been carried out under the project entitled “Surrogate model for ionospheric studies using IRNSS/GPS Data”, funded by SAC, ISRO, Ahmedabad. We also thankful to NASA website, for providing input parameters which are necessary for our database creation.
Compliance with Ethical Standards
Availability of data and Material The datasets used and analyzed in the current study are taken from the NavIC SPS receiver located at ACSCE station and IISC station, Bangalore. The input parameters are taken from https://www.nasa.gov/ and http://www.ionolab.org/ website.
Competing Interests The authors declare that there are no financial and non-financial competing interests with respect to the current research work. The authors confirm that there are no known conflicts of interest associated with this publication.
Funding This research work is funded by SAC, ISRO, Ahmedabad (Project ID–NGP–13).
Author’s Contribution R. Mukesh and P. Soma constructed the Surrogate Model; P. Sindhu analyzed and interpreted the data regarding the VTEC prediction. V. Karthikeyan performed the validation of VTEC prediction and checked the formation and quality of manuscript. All authors read and approved the final manuscript.
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