Solar activity drives space weather, affecting Earth's magnetosphere and technological infrastructure, which makes accurate solar flare forecasting critical. Current space weather models under-utilize multi-modal solar data, lack iterative enhancement via expert knowledge, and rely heavily on human forecasters under the Observation-Orientation-Decision-Action (OODA) paradigm. Here we present the "Solar Activity AI Forecaster", a scalable dual data-model driven framework built on foundational models, integrating expert knowledge to autonomously replicate human forecasting tasks with quantifiable outputs. It is implemented in the OODA paradigm and comprises three modules: a Situational Perception Module that generates daily solar situation awareness maps by integrating multi-modal observations; In-Depth Analysis Tools that characterize key solar features (active regions, coronal holes, filaments); and a Flare Prediction Module that forecasts strong flares for the full solar disk and active regions. Executed within a few minutes, the model outperforms or matches human forecasters in generalization across multi-source data, forecast accuracy, and operational efficiency. This work establishes a new paradigm for AI-based space weather forecasting, demonstrating AI's potential to enhance forecast accuracy and efficiency, and paving the way for autonomous operational forecasting systems.
@article{arxiv.2508.06892,
title = {Large Model Driven Solar Activity AI Forecaster: A Scalable Dual Data-Model Framework},
author = {Jingjing Wang and Pengyu Liang and Tingyu Wang and Ming Li and Yanmei Cui and Siwei Liu and Xin Huang and Xiang Li and Minghui Zhang and Yunshi Zeng and Zhu Cao and Jiekang Feng and Qinghua Hu and Bingxian Luo and Bing Cao},
journal= {arXiv preprint arXiv:2508.06892},
year = {2025}
}