| Issue |
J. Space Weather Space Clim.
Volume 11, 2021
Topical Issue - Space Weather research in the Digital Age and across the full data lifecycle
|
|
|---|---|---|
| Article Number | 38 | |
| Number of page(s) | 16 | |
| DOI | https://doi.org/10.1051/swsc/2021021 | |
| Published online | 30 June 2021 | |
Research Article
Operational Dst index prediction model based on combination of artificial neural network and empirical model
1
Korea Meteorological Administration, 61 16-gil Yeouidaebang-ro, Dongjak-gu, Seoul 07062, Republic of Korea
2
Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Republic of Korea
3
Korea Astronomy and Space Science Institute, 776 Daedeok-daero, Yuseong-gu, Daejeon 34055, Republic of Korea
4
University of Science and Technology, 217, Gajeong-ro, Yuseong-gu, Daejeon 34113, Republic of Korea
5
Chungbuk National University, 1 Chungdae-ro, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
6
York University, 4700 Keele St., Toronto, ON M3J 1P3, Canada
7
National Meteorological Satellite Centre, KMA, 64-18, Guam-gil, Gwanghyewon-myeonl, Jincheon-gun, Chungcheongbuk-do 27803, Republic of Korea
* Corresponding author: jjlee@kasi.re.kr
Received:
12
June
2020
Accepted:
23
May
2021
In this paper, an operational Dst index prediction model is developed by combining empirical and Artificial Neural Network (ANN) models. ANN algorithms are widely used to predict space weather conditions. While they require a large amount of data for machine learning, large-scale geomagnetic storms have not occurred sufficiently for the last 20 years, Advanced Composition Explorer (ACE) and Deep Space Climate Observatory (DSCOVR) mission operation period. Conversely, the empirical models are based on numerical equations derived from human intuition and are therefore applicable to extrapolate for large storms. In this study, we distinguish between Coronal Mass Ejection (CME) driven and Corotating Interaction Region (CIR) driven storms, estimate the minimum Dst values, and derive an equation for describing the recovery phase. The combined Korea Astronomy and Space Science Institute (KASI) Dst Prediction (KDP) model achieved better performance contrasted to ANN model only. This model could be used practically for space weather operation by extending prediction time to 24 h and updating the model output every hour.
Key words: Space weather model / Dst index prediction / Artificial neural network
© W. Park et al., Published by EDP Sciences 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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