Forbush Decreases and Associated Geomagnetic Storms: Statistical Comparison in Solar Cycles 23 and 24

Melkumyan, A. A.; Belov, A. V.; Shlyk, N. S.; Abunina, M. A.; Abunin, A. A.; Oleneva, V. A.; Yanke, V. G.

doi:10.1007/s11207-024-02281-3

Sol Phys

2024

DOI: 10.1007/s11207-024-02281-3

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Forbush Decreases and Associated Geomagnetic Storms: Statistical Comparison in Solar Cycles 23 and 24

A. A. Melkumyan,

A. V. Belov,

N. S. Shlyk

et al.

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Cited by 2 publications

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Prediction of Geoeffective CMEs Using SOHO Images and Deep Learning

Alobaid,

Wang,

Wang

et al. 2024

Sol Phys

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The application of machine learning to the study of coronal mass ejections (CMEs) and their impacts on Earth has seen significant growth recently. Understanding and forecasting CME geoeffectiveness are crucial for protecting infrastructure in space and ensuring the resilience of technological systems on Earth. Here we present GeoCME, a deep-learning framework designed to predict, deterministically or probabilistically, whether a CME event that arrives at Earth will cause a geomagnetic storm. A geomagnetic storm is defined as a disturbance of the Earth’s magnetosphere during which the minimum Dst index value is less than −50 nT. GeoCME is trained on observations from the instruments including LASCO C2, EIT, and MDI on board the Solar and Heliospheric Observatory (SOHO), focusing on a dataset that includes 136 halo/partial halo CMEs in Solar Cycle 23. Using ensemble and transfer learning techniques, GeoCME is capable of extracting features hidden in the SOHO observations and making predictions based on the learned features. Our experimental results demonstrate the good performance of GeoCME, achieving a Matthew’s correlation coefficient of 0.807 and a true skill statistics score of 0.714 when the tool is used as a deterministic prediction model. When the tool is used as a probabilistic forecasting model, it achieves a Brier score of 0.094 and a Brier skill score of 0.493. These results are promising, showing that the proposed GeoCME can help enhance our understanding of CME-triggered solar-terrestrial interactions.

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Prediction of Geoeffective CMEs Using SOHO Images and Deep Learning

Alobaid,

Wang,

Wang

et al. 2024

Sol Phys

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Precursory Signs of Large Forbush Decreases: The Criterion of Anisotropy

Papailiou,

Abunina,

Mavromichalaki

et al. 2024

Sol Phys

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Forbush Decreases and Associated Geomagnetic Storms: Statistical Comparison in Solar Cycles 23 and 24

Cited by 2 publications

References 44 publications

Prediction of Geoeffective CMEs Using SOHO Images and Deep Learning

Prediction of Geoeffective CMEs Using SOHO Images and Deep Learning

Precursory Signs of Large Forbush Decreases: The Criterion of Anisotropy

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