A Machine Learning-Ready Data Processing Tool for Near Real-Time Forecasting

2025-02-12

AI Summary

Space weather forecasting faces bottlenecks in integrating heterogeneous, multi-source data and time-consuming preprocessing, hindering near-real-time (NRT) radiation risk warning and geomagnetic disturbance mitigation. This paper proposes the first machine learning–ready data processing framework tailored for NRT space weather forecasting. It systematically integrates real-time streaming data—including solar imagery, geomagnetic indices, and energetic particle fluxes—enabling automated download, temporal alignment, physics-informed annotation, and feature engineering, all standardized into HDF5/NetCDF formats with scikit-learn–compatible interfaces. Its key contribution is the first end-to-end, reproducible ML-ready pipeline, reducing average preprocessing time by 83%. The framework supports a coronal mass ejection (CME) arrival time prediction model achieving a mean absolute error <2.1 hours, significantly enhancing both timeliness and reliability of NRT forecasts.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Data Mining & Knowledge Management: Data Stream Mining
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Web Mining and Content Analysis: Large pretrained models with web data
• User Modeling, Personalization and Recommendation: ML for personalized search and recommendations

Abstract

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.

Problem

• Develops ML-ready data processing tool
• Enhances near real-time space weather forecasting
• Merges diverse data sources for predictions

Innovation

• ML-ready data processing
• merges diverse NRT sources
• streamlines ML workflow