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Electric Potential Patterns Forecasting in the Southern Hemisphere with Deep Learning Techniques — arXiv:2609.31804

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.31804
License (as archived): CC0-1.0
Archived: 2026-10-07T23:55:01.516Z
SHA-256 of the archived text: 7f4550e222a9ce7c243a80a8780cdeffbc023b4245c9b69ef712da28d25c927c
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How to cite

Electric Potential Patterns Forecasting in the Southern Hemisphere with Deep Learning Techniques — arXiv:2609.31804 — arXiv (en), https://arxiv.org/abs/2609.31804 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ff0952dc3afa (Metraipolis archive dc_ff0952dc3afa, sha256 7f4550e222a9ce7c243a80a8780cdeffbc023b4245c9b69ef712da28d25c927c)

BibTeX

@misc{dcff0952dc3afa,
  title = {Electric Potential Patterns Forecasting in the Southern Hemisphere with Deep Learning Techniques — arXiv:2609.31804},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.31804},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ff0952dc3afa; sha256 7f4550e222a9ce7c243a80a8780cdeffbc023b4245c9b69ef712da28d25c927c},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.31804. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Space weather disturbances driven by the solar wind can degrade satellite navigation, disrupt radio communications, and threaten power infrastructure, making accurate forecasting of the high-latitude ionosphere's response a critical operational need. Existing approaches, empirical climatological models and physics-based magnetohydrodynamic simulations, either smooth out the ionosphere's time-dependent, non-linear response or are too computationally expensive for real-time use, while prior machine learning efforts have mostly targeted scalar indices rather than the full spatial structure of ionospheric convection. Here we train and compare three deep learning architectures, a probabilistic diffusion model conditioned on multi-variate solar wind and interplanetary magnetic field measurements at the L1 Lagrange point, an unconditioned diffusion ablation, and a deterministic U-Net baseline, to forecast Southern Hemisphere high-latitude electric potential maps derived from SuperDARN radar observations. Using five years (2020--2025) of SuperDARN data synchronised with DSCOVR L1 measurements, we evaluate the models under a single-pass regime, an extended autoregressive rollout of up to 350 frames, and an out-of-distribution case study on the intense March 2015 St.\ Patrick's Day storm, unseen during training. We find that the relative advantage of the deterministic and probabilistic approaches is not fixed: the deterministic model is competitive over short horizons and calm conditions, while the diffusion model's advantage grows and eventually dominates as the forecast horizon lengthens and the event becomes more dynamic, evidence that probabilistic, sample-based generative models are the more promising direction for operational, long-horizon space weather forecasting.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-29 in cs.CV).
Categorie arXiv: Space Physics (physics.space-ph) ; Earth and Planetary Astrophysics (astro-ph.EP); Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2609.31804 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T23:29:27.654Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-29?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).