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

Kim, Mingyu; Kim, Jeongrae

doi:https://doi.org/10.5194/angeo-37-77-2019

Articles | Volume 37, issue 1

https://doi.org/10.5194/angeo-37-77-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/angeo-37-77-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 37, issue 1

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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Final revised paper (published on 31 Jan 2019)
Preprint (discussion started on 25 Sep 2018)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Revision', Anonymous Referee #1, 18 Oct 2018
- AC1: 'Response to Referee#1', Kim Jeongrae, 25 Oct 2018
- RC2: 'Revision', Anonymous Referee #1, 26 Oct 2018
  - AC5: 'Thank you for your comments', Kim Jeongrae, 26 Nov 2018
- RC3: 'Revision', Anonymous Referee #1, 26 Oct 2018
  - AC2: 'Thanks for your comments.', Kim Jeongrae, 26 Oct 2018
RC4: 'Review Comments', Anonymous Referee #2, 30 Oct 2018
- AC3: 'Response to Referee#2', Kim Jeongrae, 12 Nov 2018
RC5: 'Extending the Coverage Area of Regional Ionosphere Maps Using a Support Vector Machine Algorithm', Anonymous Referee #3, 05 Nov 2018
- AC4: 'Response to Referee#3', Kim Jeongrae, 19 Nov 2018

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

AR by Kim Jeongrae on behalf of the Authors (18 Jan 2019)

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.