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Annales Geophysicae An interactive open-access journal of the European Geosciences Union
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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
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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer review completion
AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to minor revisions (review by editor) (11 Dec 2018) by Dalia Buresova
AR by Kim Jeongrae on behalf of the Authors (19 Dec 2018)  Author's response
ED: Publish subject to minor revisions (review by editor) (04 Jan 2019) by Dalia Buresova
AR by Kim Jeongrae on behalf of the Authors (09 Jan 2019)  Author's response    Manuscript
ED: Publish as is (17 Jan 2019) by Dalia Buresova
Publications Copernicus
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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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