Composite Model for Predicting SYM‐H Index
- Resource Type
- article
- Authors
- Yong Ji; Lan Ma; Chao Shen; Gang Zeng; YanYan Yang; Shuo Ti
- Source
- Earth and Space Science, Vol 10, Iss 10, Pp n/a-n/a (2023)
- Subject
- SYM‐H model
geomagnetic storm
neural network
Astronomy
QB1-991
Geology
QE1-996.5
- Language
- English
- ISSN
- 2333-5084
Abstract Predicting SYM‐H index is significant in space weather because it quantifies the degree of perturbation of the geomagnetic field during storm. This study presents a composite model to predict SYM‐H index based on solar wind parameters by combining the empirical magnetospheric dynamical equation with neural networks. The formula for predicted SYM‐H originates from the well‐known empirical relationship between interplanetary conditions and the Dst index. In particular, the coefficients in the empirical relationship are determined by using neural networks that excel at approaching the function linking the coefficients and the solar wind parameters. The 1‐ and 2‐hr forecasts of SYM‐H during storm time are reliable, and the precision of some cases is even better than the latest models solely using deep neural networks. Based on the composite model, the dependence of loss time and injection rates of ring current energy on the solar wind parameters and SYM‐H are investigated.