Advancements and Limitations in Geomagnetic Storm Prediction Models

With the rapid increase in satellite launches and frequent space activities, the impact of space weather on human endeavors has become increasingly significant, prompting greater attention to space weather prediction. Geomagnetic storms, driven by solar wind and coronal mass ejections, significantly affect Earth's magnetosphere and ionosphere, necessitating accurate and timely predictions. Despite advancements in physical models, statistical methods, and machine learning techniques, current prediction methods face challenges in balancing accuracy, timeliness, and computational efficiency. This paper provides a comprehensive review of geomagnetic storm prediction methods, summarizing the principles, advantages, and limitations of these models. By integrating the strengths of these models, such as the theoretical foundation of physical models, the trend analysis capabilities of statistical methods, and the nonlinear processing power of machine learning, hybrid approaches proposes future directions for improving prediction accuracy and real-time performance. This review also highlights recent innovations, including data assimilation, transfer learning, and hybrid modeling, and proposes future directions to optimize model integration, enhance data quality, and address extreme geomagnetic events. These efforts aim to support more reliable early warning systems and foster advancements in space weather research and applications.