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Operational Dst index prediction model based on combination of artificial neural network and empirical model
- Source :
- Journal of Space Weather and Space Climate, Vol 11, p 38 (2021)
- Publication Year :
- 2021
- Publisher :
- EDP Sciences, 2021.
-
Abstract
- 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.
- Subjects :
- Geomagnetic storm
Atmospheric Science
010504 meteorology & atmospheric sciences
Meteorology
Artificial neural network
Computer science
Empirical modelling
space weather model
NASA Deep Space Network
Space weather
01 natural sciences
Physics::Geophysics
Space and Planetary Science
Observatory
Meteorology. Climatology
0103 physical sciences
Physics::Space Physics
Coronal mass ejection
dst index prediction
Space Science
QC851-999
010303 astronomy & astrophysics
artificial neural network
0105 earth and related environmental sciences
Subjects
Details
- Language :
- English
- ISSN :
- 21157251
- Volume :
- 11
- Database :
- OpenAIRE
- Journal :
- Journal of Space Weather and Space Climate
- Accession number :
- edsair.doi.dedup.....906ff1500751df02342188a86dc2c5a7