A Machine Learning-Ready Data Processing Tool for Near Real-Time Forecasting
This tool addresses gaps in space weather prediction for space exploration and Earth-based technologies, but it is incremental as it focuses on data processing rather than novel forecasting methods.
The paper tackles the problem of space weather forecasting by developing a machine learning-ready data processing tool that merges data from diverse near real-time sources, resulting in a streamlined workflow for improved forecasting and scientific research.
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.