Composite Model for Predicting SYM‐H Index

Abstract

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.

Keywords

• SYM‐H model
• geomagnetic storm
• neural network