Advances and Challenges in Solar Flare Prediction: A Review

TL;DR

This review paper discusses recent progress in solar flare prediction, highlighting data-driven methods from traditional statistics to advanced machine learning and multimodal models, and evaluates current forecasting platforms' performance and limitations.

Contribution

It provides a comprehensive overview of the evolution of solar flare prediction techniques, emphasizing the transition to multimodal large models and assessing operational system limitations.

Findings

Progress from statistical to deep learning methods in flare prediction.

Emergence of multimodal large models for improved forecasting.

Current platforms face limitations in operational accuracy.

Abstract

Solar flares, as one of the most prominent manifestations of solar activity, have a profound impact on both the Earth's space environment and human activities. As a result, accurate solar flare prediction has emerged as a central topic in space weather research. In recent years, substantial progress has been made in the field of solar flare forecasting, driven by the rapid advancements in space observation technology and the continuous improvement of data processing capabilities. This paper presents a comprehensive review of the current state of research in this area, with a particular focus on tracing the evolution of data-driven approaches -- which have progressed from early statistical learning techniques to more sophisticated machine learning and deep learning paradigms, and most recently, to the emergence of Multimodal Large Models (MLMs). Furthermore, this study examines the realistic performance of existing flare forecasting platforms, elucidating their limitations in operational space weather applications and thereby offering a practical reference for future advancements in technological optimization and system design.