A Machine Learning-Ready Data Processing Tool for Near Real-Time Forecasting
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866929712083763200 |
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| author | Dayeh, Maher A Starkey, Michael J Chatterjee, Subhamoy Elliott, Heather Hart, Samuel Moreland, Kimberly |
| author_facet | Dayeh, Maher A Starkey, Michael J Chatterjee, Subhamoy Elliott, Heather Hart, Samuel Moreland, Kimberly |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_08555 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Machine Learning-Ready Data Processing Tool for Near Real-Time Forecasting Dayeh, Maher A Starkey, Michael J Chatterjee, Subhamoy Elliott, Heather Hart, Samuel Moreland, Kimberly Solar and Stellar Astrophysics Instrumentation and Methods for Astrophysics Machine Learning 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. |
| title | A Machine Learning-Ready Data Processing Tool for Near Real-Time Forecasting |
| topic | Solar and Stellar Astrophysics Instrumentation and Methods for Astrophysics Machine Learning |
| url | https://arxiv.org/abs/2502.08555 |