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cs.LG updates on arXiv.org

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Connecting the Dots: A Machine Learning Ready Dataset for...
Linnea M. Wo · 2026-05-14 · via cs.LG updates on arXiv.org

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Abstract:Operational forecasting of the ionosphere remains a critical space weather challenge due to sparse observations, complex coupling across geospatial layers, and a growing need for timely, accurate predictions that support Global Navigation Satellite System (GNSS), communications, aviation safety, as well as satellite operations. As part of the 2025 NASA Heliolab, we present a curated, open-access dataset that integrates diverse ionospheric and heliospheric measurements into a coherent, machine learning-ready structure, designed specifically to support next-generation forecasting models and address gaps in current operational frameworks. Our workflow integrates a large selection of data sources comprising Solar Dynamic Observatory data, solar irradiance indices (F10.7), solar wind parameters (velocity and interplanetary magnetic field), geomagnetic activity indices (Kp, AE, SYM-H), and NASA JPL's Global Ionospheric Maps of Total Electron Content (GIM-TEC). We also implement geospatially sparse data such as the TEC derived from the World-Wide GNSS Receiver Network and crowdsourced Android smartphone measurements. This novel heterogeneous dataset is temporally and spatially aligned into a single, modular data structure that supports both physical and data-driven modeling. Leveraging this dataset, we train and benchmark several spatiotemporal machine learning architectures for forecasting vertical TEC under both quiet and geomagnetically active conditions. This work presents an extensive dataset and modeling pipeline that enables exploration of not only ionospheric dynamics but also broader Sun-Earth interactions, supporting both scientific inquiry and operational forecasting efforts.
Comments: 8 pages, 2 figures, 2 tables. Accepted as a poster presentation in the Machine Learning for the Physical Sciences workshop at NeurIPS 2025. Dataset can be found on Zenodo (this https URL) or GitHub (this https URL)
Subjects: Machine Learning (cs.LG); Earth and Planetary Astrophysics (astro-ph.EP); Instrumentation and Methods for Astrophysics (astro-ph.IM)
Cite as: arXiv:2511.15743 [cs.LG]
  (or arXiv:2511.15743v2 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2511.15743

arXiv-issued DOI via DataCite

Submission history

From: Linnea Wolniewicz M [view email]
[v1] Tue, 18 Nov 2025 20:13:25 UTC (3,037 KB)
[v2] Tue, 12 May 2026 23:26:49 UTC (3,037 KB)

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