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Cokriging based statistical approximation model for forecasting ionospheric VTEC during high solar activity and storm days

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Abstract

The ionospheric storm is a space climate based event that takes place as a function of time, season and location. In spite of that, it is not yet possible to state how the ionosphere will react to a given space climate event since the measurements of the location, time of observation, amount of disturbances, and type of storm vary from event to event. Nevertheless, statistical analysis of observations in terms of Total Electron Content (TEC) enables one to calculate and forecast a most likely upcoming disturbed TEC, based on predictions of geomagnetic activity. The forecast of disturbed TEC is important and it helps users to know how much it affects the satellite communication systems. In this paper, GPS Vertical TEC (VTEC) data during ionospheric storm has been analyzed and storm-influenced features in the VTEC have been studied. Input parameters such as, location (Latitude & Longitude), Time, F10.7, Ap and Dst indices are collected from NASA website and its corresponding TEC values are used for construction of Cokriging (COK) based statistical approximation model. Prediction of VTEC is performed during storm and high solar activity days and for this purpose previous one-week data of different regions are used. The VTEC prediction is performed using COK model during high solar activity (2015) for IISC station. From the prediction, it is found that, our proposed COK model gives good prediction with RMSE of 6.5325 TECU, Correlation Coefficient as 0.9643 and Mean accuracy of 5.1752 TECU. Also VTEC prediction is performed over three different latitude regions during storm days (2003) and from the predicted results it is noted that, in high latitude region RMSE is 6.1848 TECU, correlation coefficient is 0.3958 and mean accuracy is 4.8105 TECU, in mid latitude RMSE is 12.7957 TECU, correlation coefficient is 0.9249 and mean accuracy is 9.2674 TECU and in low latitude RMSE is 11.5348 TECU, correlation coefficient is 0.9299 and mean accuracy is 9.2116 TECU. The results show that COK model provides good forecasting at low and mid latitude regions. Finally, prediction results of COK model is compared with the Empirical Orthogonal Function (EOF) and IRI-Plas-2017 models over Sutherland (−32.21°N, 20.81°E) station in South Africa during storm conditions. It is found that the average RMSE of EOF model is 5.91 TECU, average RMSE of the IRI-Plas-2017 model is 8.2079 TECU and average RMSE of COK model is 5.5971 TECU. Hence the prediction results show that COK model performs 5.6% better than the EOF model and 46.64% better than the IRI-Plas-2017 model. These results of the proposed COK model is encouraging and useful for indicating storm and high solar activity.

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References

  • Acharya, R., Roy, B., Sivaraman, M.R., Dasgupta, A.: Prediction of ionospheric total electron content using adaptive neural network with in-situ learning algorithm. Adv. Space Res. 47(1), 115–123 (2011)

    Article  ADS  Google Scholar 

  • Ahmadi, S.H., Sedghamiz, A.: Application and evaluation of kriging and cokriging methods on groundwater depth mapping. Environ. Monit. Assess. 138(1–3), 357–368 (2008)

    Article  Google Scholar 

  • Arikan, F., Arikan, O., Erol, C.B.: Regularized estimation of TEC from GPS data for certain mid latitude stations and comparisons with IRI model. Adv. Space Res. 39(5), 867–874 (2007)

    Article  ADS  Google Scholar 

  • Badeke, R., Borries, C., Hoque, M.M., Minkwitz, D.: Empirical forecast of quiet time ionospheric total electron content maps over Europe. Adv. Space Res. 61(12), 2881–2890 (2018)

    Article  ADS  Google Scholar 

  • Berdermann, J., Borries, C., Hoque, M., Jakowski, N.: Forecast of Total Electron Content over Europe for disturbed ionospheric conditions. European Space Weather Week. (2012)

  • Bergeot, N., Tsagouri, I., Bruyninx, C., Legrand, J., Chevalier, J.M., Defraigne, P., Baire, Q., Pottiaux, E.: The influence of space weather on ionospheric total electron content during the 23rd solar cycle. J. Space Weather Space Clim. 3, A25 (2013)

    Article  Google Scholar 

  • Bilitza, D., McKinnell, L.A., Reinisch, B., Rowell, T.F.: The international reference ionosphere today and in the future. J. Geod. 85(12), 909–920 (2011)

    Article  ADS  Google Scholar 

  • Hocke, K., Pavelyev, A.G.: General aspect of GPS data use for atmospheric science. Adv. Space Res. 27(6–7), 1313–1320 (2001)

    Article  ADS  Google Scholar 

  • Kenpankho, P., Watthanasangmechai, K., Supnithi, P., Tsugawa, T., Maruyama: Comparison of GPS TEC measurements with IRI TEC prediction at the equatorial latitude station, Chumphon, Thailand. Earth Planets Space 63(4), 365–370 (2011)

    Article  ADS  Google Scholar 

  • Martinez, C.A.: Multivariate geostatistical analysis of evapotranspiration and precipitation in mountainous terrain. J. Hydrol. 174(1–2), 19–35 (1996)

    Article  ADS  Google Scholar 

  • Meng, X., Mannucci, A.J., Verkhoglyadova, O.P., Tsurutani, B.T.: On forecasting ionospheric total electron content responses to high-speed solar wind streams. J. Space Weather Space Clim. 6, A19 (2016)

    Article  ADS  Google Scholar 

  • Mhmood Dhaher, G., Lee, M.H.: A comparison between the performance of kriging and cokriging in spatial estimation with application. UTM Mathematika 29, 33–41 (2013)

    MathSciNet  Google Scholar 

  • Mukesh, R., Soma, P., Karthikeyan, V., Sindhu, P.: Prediction of ionospheric vertical total electron content from GPS data using ordinary kriging-based surrogate model. Astrophys. Space Sci. 364, 15 (2019)

    Article  ADS  Google Scholar 

  • Nayirl, H., Arikan, F., Arikan, O., Erol, C.B.: GPS/TEC Estimation with IONOLAB Method. International Conference on Recent Advances in Space Technologies. IEEE Xplore (2007)

  • Odeh, I.O.A., McBratney, A.B., Chittleborough, D.J.: Further results on prediction of soil properties from terrain attributes: heterotopic cokriging and regression-kriging. Geoderma 67(3–4), 215–226 (1995)

    Article  ADS  Google Scholar 

  • Sampad, K.P., Shirish, G., Shuanggen, J.: Ionospheric TEC Variations at Low Latitude Indian Region. Satellite Positioning – Methods, Models and Applications. IntechOpen, London (2015)

    Google Scholar 

  • Sobolu, R.: Statistical approximation by an integral type of positive linear operators. Mathematica LII(3), 157–165 (2007)

    MathSciNet  MATH  Google Scholar 

  • Stanislawska, I., Juchnikowski, G., Cander, L.R., Ciraolo, L., Bradley, P.A., Zbyszynski, Z., Swiatek, A.: The kriging method of VTEC instantaneous mapping. Adv. Space Res. 29(6), 945–948 (2002)

    Article  ADS  Google Scholar 

  • Stein, A., Dooremolen, W.V., Bouma, J., Bregt, A.K.: Cokriging point data on moisture deficit. Soil Sci. Soc. Am. J. 52(5), 1418–1423 (1988)

    Article  ADS  Google Scholar 

  • Sunardi, B., Muslim, B., Sakya, A.E., Rohadi, S., Sulastri, Murjaya, J.: Ionospheric earthquake effects detection based on Total Electron Content (TEC) GPS Correlation. IOP Conference Series Earth and Environmental Science 132(1) (2018)

    Article  Google Scholar 

  • Thomas, E.G., Baker, J.B.H., Ruohoniemi, J.M., Coster, A.J., Zhang, S.R.: The geomagnetic storm time response of GPS total electron content in the North American sector. J. Geophys. Res. Space Phys. 121(2), 1744–1759 (2016)

    Article  ADS  Google Scholar 

  • Tsagouri, I., Koutroumbas, K., Elias, P.: A new short-term forecasting model for the total electron content storm time disturbances. J. Space Weather Space Clim. 8, A33 (2018)

    Article  ADS  Google Scholar 

  • Uwamahoro, J.C., Habarulema, J.B.: Modeling total electron content during geomagnetic storm conditions using empirical orthogonal functions and neural networks. J. Geophys. Res. Space Phys. 120(12), 11,000–11,012 (2015)

    Article  Google Scholar 

  • Ya’acob, N., Abdullah, M., Ismail, M.: GPS total electron content (TEC) prediction at ionosphere layer over the equatorial region. In: Bouras, C.J. (ed.) Trends in Telecommunications Technologies. IntechOpen, London (2010)

    Google Scholar 

  • Yates, S.R., Warrick, A.W.: Estimating soil water content using cokriging. Soil Sci. Soc. Am. J. 51(1), 23–30 (1987)

    Article  ADS  Google Scholar 

  • Zhang, R., Shouse, P., Yates, S.: Use of pseudo-crossvariograms and cokriging to improve estimates of soil solute concentrations. Soil Sci. Soc. Am. J. 61(5), 1342–1347 (1997)

    Article  ADS  Google Scholar 

Download references

Acknowledgement

The research work presented in this paper has been carried out under the Project ID “NGP – 13”, funded by Satellite Applications Centre, ISRO, Ahmedabad.

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Authors and Affiliations

  1. Department of Aerospace Engineering, ACS College of Engineering, Bangalore, India

    R. Mukesh

  2. Department of ECE, Dr. MGR Educational and Research Institute, Chennai, India

    R. Mukesh & V. Karthikeyan

  3. Department of Aeronautical Engineering, ACS College of Engineering, Bangalore, India

    P. Soma & P. Sindhu

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  1. R. Mukesh
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  2. V. Karthikeyan
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  3. P. Soma
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  4. P. Sindhu
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Correspondence to R. Mukesh.

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Mukesh, R., Karthikeyan, V., Soma, P. et al. Cokriging based statistical approximation model for forecasting ionospheric VTEC during high solar activity and storm days. Astrophys Space Sci 364, 131 (2019). https://doi.org/10.1007/s10509-019-3612-2

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