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ANGEO | Articles | Volume 37, issue 1
Ann. Geophys., 37, 77–87, 2019
https://doi.org/10.5194/angeo-37-77-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Ann. Geophys., 37, 77–87, 2019
https://doi.org/10.5194/angeo-37-77-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Regular paper 31 Jan 2019

Regular paper | 31 Jan 2019

Extending the coverage area of regional ionosphere maps using a support vector machine algorithm

Mingyu Kim and Jeongrae Kim
Related subject area  
Subject: Earth's ionosphere & aeronomy | Keywords: Modelling and forecasting
High-resolution vertical total electron content maps based on multi-scale B-spline representations
Andreas Goss, Michael Schmidt, Eren Erdogan, Barbara Görres, and Florian Seitz
Ann. Geophys., 37, 699–717, https://doi.org/10.5194/angeo-37-699-2019,https://doi.org/10.5194/angeo-37-699-2019, 2019
Short summary
Validation and application of optimal ionospheric shell height model for single-site estimation of total electron content
Jiaqi Zhao and Chen Zhou
Ann. Geophys., 37, 263–271, https://doi.org/10.5194/angeo-37-263-2019,https://doi.org/10.5194/angeo-37-263-2019, 2019
Cited articles  
Akhoondzadeh, M.: Support vector machines for TEC seismo-ionospheric anomalies detection, Ann. Geophys., 31, 173–186, https://doi.org/10.5194/angeo-31-173-2013, 2013. 
Ban, P. P., Sun, S. J., Chen, C., and Zhao, Z. W.: Forecasting of low-latitude storm-time ionospheric f0F2 using support vector machine, Radio Sci., 46, 1–9, https://doi.org/10.1029/2010RS004633, 2011. 
Borovsky, J. E. and Denton, M. H.: Differences between CME-driven storms and CIR-driven storms, J. Geophys. Res., 111, A07S08, https://doi.org/10.1029/2005JA011447, 2006. 
Chen, C., Wu, Z. S., Ban, P. P., Sun, S. J., Xu, Z. W., and Zhao, Z. W.: Diurnal specification of the ionospheric f0F2 parameter using a support vector machine, Radio Sci., 45, 1–13, https://doi.org/10.1029/2010RS004393, 2010. 
Cristianini, N.: Support vector and kernel machines, Tutorial at the 18th Int. Conf. Mach. Learn., 2001. 
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Short summary
Spatial extrapolation of an ionosphere TEC map was carried out using a SVM learning algorithm. There has been much research on the temporal extrapolation or prediction of TEC time series, but the spatial extrapolation has rarely been attempted. Some researchers have performed simultaneous extrapolation both in time and in spatial domains, but this research covers the spatial extrapolation only by using an inner TEC map. This spatial TEC extrapolation can be useful for small countries.
Spatial extrapolation of an ionosphere TEC map was carried out using a SVM learning algorithm....
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