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

TL;DR

This paper introduces a data processing tool that consolidates diverse near real-time space weather data, enabling more effective machine learning-based forecasting of solar events and geomagnetic disturbances.

Contribution

The paper presents a novel ML-ready data processing framework that integrates multiple NRT data sources for improved space weather prediction.

Findings

Enhanced data integration for space weather forecasting

Streamlined workflow for ML model development

Potential for improved real-time prediction accuracy

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.