A study, led by PhD candidate JiaWen Yue and Dr XiaoCheng Guo from the National Space Science Center at the Chinese Academy of Sciences, has introduced a novel semi-empirical approach to simulating the Dst index and developed a model that it says outperforms traditional empirical approaches in accuracy while retaining computational efficiency. The study also highlights the model’s adaptability and that its modular design enables seamless integration into existing global MHD simulation frameworks.
Integrating empirical methods
Credit: Beijing Zhongke Journal Publising
The Dst index, widely used for decades to quantify the strength of geomagnetic storms, is influenced by complex interactions between the solar wind and Earth’s magnetosphere. The team highlighted that traditional empirical models, such as Burton’s, rely on statistical correlations but lack the ability to capture dynamic physical processes. Physics-based models typically require a significant number of computational resources and pose challenges in describing ring currents. The new semi-empirical model aims to bridge this gap by combining the strengths of both methodologies.
In this approach, the ring current contribution to the Dst index is derived from Burton’s empirical framework, while contributions from other current systems – such as the magnetotail current – are calculated using high-resolution global MHD simulations. This hybrid strategy enables the model to retain the speed of empirical methods while incorporating the physical realism of MHD simulations.
Improving space weather forecasting
To validate the model, the team tested it against a series of recent geomagnetic storm events, comparing simulated results with observed Dst data using metrics such as correlation coefficient (CC), prediction efficiency (PE), root mean square error (RMSE) and central RMSE (CRMSE).
The team found that during moderate to intense geomagnetic storms, the semi-empirical model achieved significantly higher CC and PE values and lower RMSE and CRMSE compared to purely empirical models. These results demonstrate the model’s robustness in capturing both the timing and magnitude of Dst variations.
“This model provides more possibilities for space weather forecasting,” said Yue. “By merging empirical simplicity with MHD physics, we’ve created a tool that is both accurate and practical for space environment applications.”
“The ability to simulate the Dst index within a global MHD framework opens new avenues for understanding storm-time magnetospheric dynamics,” said Guo. “As space weather becomes an increasingly critical area of study, this semi-empirical approach offers a scalable solution for improving the accuracy and reliability of geomagnetic storm predictions.”
In related news, read about a proposal to launch a UK-led satellite mission concept named UK-ODESSI (UK-Orbital pathfinDEr for Space-borne, Space-weather Instrumentation), which was presented at the Royal Astronomical Society’s National Astronomy Meeting 2025 in the UK
