构建可直接用于机器学习的近实时空间天气数据处理工具
A Machine Learning-Ready Data Processing Tool for Near Real-Time Forecasting
- 整合太阳图像、磁场和高能粒子等多源近实时数据
- 支持时间序列预测与极端太阳事件检测，提升预报效率
- 适合空间天气研究者与机器学习应用开发者使用
空间天气预报对减轻太空探索中的辐射风险及保护地球技术免受地磁扰动至关重要。本文开发了一款面向机器学习的近实时（NRT）空间天气数据处理工具。该工具融合来自太阳影像、磁场测量和高能粒子通量等多源近实时数据，填补了当前空间天气预测能力的空白。它对数据进行处理与结构化，专为时序预测和极端太阳事件检测设计，提供数据下载、处理与标注的一体化框架，显著优化了近实时空间天气预报与科研工作流程。
原文摘要 · Abstract (English)
Space weather forecasting is critical for mitigating radiation risks in space exploration and protecting Earth-based technologies from geomagnetic disturbances. This paper presents the development of a Machine Learning (ML)- ready data processing tool for Near Real-Time (NRT) space weather forecasting. By merging data from diverse NRT sources such as solar imagery, magnetic field measurements, and energetic particle fluxes, the tool addresses key gaps in current space weather prediction capabilities. The tool processes and structures the data for machine learning models, focusing on time-series forecasting and event detection for extreme solar events. It provides users with a framework to download, process, and label data for ML applications, streamlining the workflow for improved NRT space weather forecasting and scientific research.
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