构建太阳活动区生成数据集，助力机器学习预测空间天气。
SolARED: Solar Active Region Emergence Dataset for Machine Learning Aided Predictions
- 基于太阳动力学观测站数据，构建时序追踪的活动区演化数据集。
- 覆盖50个大型活动区，包含磁通量、振荡功率等多维度特征变化。
- 适合从事空间天气预测与机器学习应用的研究者使用。
准确预报太阳爆发活动对防范空间技术风险日益重要，关键在于在活动区形成前及时检测。为此，我们构建了太阳活动区生成数据集（SolARED），基于太阳动力学观测站（SDO）上日震与磁成像仪（HMI）获取的多普勒速度、磁场和连续谱强度全盘图，涵盖2010至2023年间50个大型活动区在出现前后及其周围区域的演化数据。数据经重映射、追踪与分箱处理，包含声波功率、无符号磁通量和连续谱强度的时间序列，可直接用于机器学习模型训练，支持提升活动区生成的预测能力，推动业务化预报发展。数据集可通过https://sun.njit.edu/sarportal/在线交互可视化平台获取。
原文摘要 · Abstract (English)
The development of accurate forecasts of solar eruptive activity has become increasingly important for preventing potential impacts on space technologies and exploration. Therefore, it is crucial to detect Active Regions (ARs) before they start forming on the solar surface. This will enable the development of early-warning capabilities for upcoming space weather disturbances. For this reason, we prepared the Solar Active Region Emergence Dataset (SolARED). The dataset is derived from full-disk maps of the Doppler velocity, magnetic field, and continuum intensity, obtained by the Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamics Observatory (SDO). SolARED includes time series of remapped, tracked, and binned data that characterize the evolution of acoustic power of solar oscillations, unsigned magnetic flux, and continuum intensity for 50 large ARs before, during, and after their emergence on the solar surface, as well as surrounding areas observed on the solar disc between 2010 and 2023. The resulting ML-ready SolARED dataset is designed to support enhancements of predictive capabilities, enabling the development of operational forecasts for the emergence of active regions. The SolARED dataset is available at https://sun.njit.edu/sarportal/, through an interactive visualization web application.
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