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.

翻译：空间天气预报对于减轻太空探索中的辐射风险以及保护地面技术免受地磁扰动至关重要。本文介绍了一种用于近实时空间天气预报的机器学习就绪数据处理工具的开发。该工具通过整合来自多种近实时数据源（如太阳图像、磁场测量和高能粒子通量）的数据，解决了当前空间天气预报能力中的关键缺口。该工具为机器学习模型处理和结构化数据，重点关注极端太阳事件的时间序列预报和事件检测。它为用户提供了一个用于机器学习应用的下载、处理和标注数据的框架，从而简化了工作流程，以改进近实时空间天气预报和科学研究。