Cosmic-Ray-Constrained LSTM Model for Geomagnetic Storm Prediction
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866914221337346048 |
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| author | Ge, Zongyuan Zhang, Chenwaner Zhou, Wei Zeng, Hongyu Zhou, Guiping |
| author_facet | Ge, Zongyuan Zhang, Chenwaner Zhou, Wei Zeng, Hongyu Zhou, Guiping |
| contents | Geomagnetic storms (GSTs) driven by solar wind-magnetosphere coupling can severely disrupt technological systems, motivating the need for improved prediction accuracy and longer warning times. In this study, we develop a physics-informed Long Short-Term Memory (LSTM) model that incorporates cosmic-ray flux modulation as an additional precursor signal. As coronal mass ejection (CME)-driven disturbances propagate through the heliosphere, enhanced turbulence and magnetic-field compression reduce galactic cosmic-ray (GCR) flux measured by ground-based neutron monitors (Forbush decreases), providing early information that can precede near-Earth solar-wind signatures by 1--3 days. We integrate multi-source space-weather data, spanning 1995-2020, including cosmic-ray observations, solar wind plasma parameters, interplanetary magnetic-field data, and geomagnetic indices. Based on these data, we construct a 19-dimensional feature vector that includes flux background levels, decrease-related indicators, and inter-station correlation measures as model inputs. Employing a 50-unit LSTM architecture, the proposed model achieves root-mean-square errors (RMSE) of 5.106 nT, 8.315 nT, 10.854 nT, 12.883 nT, and 14.788 nT for 2-, 6-, 12-, 24-, and 48-hour predictions, respectively. Incorporating cosmic-ray information further improves 48-hour forecast skill by up to 25.84% (from 0.178 to 0.224). These results demonstrate the value of physics-informed deep learning and cosmic-ray precursors for advancing space-weather forecasting. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_22003 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cosmic-Ray-Constrained LSTM Model for Geomagnetic Storm Prediction Ge, Zongyuan Zhang, Chenwaner Zhou, Wei Zeng, Hongyu Zhou, Guiping Solar and Stellar Astrophysics Instrumentation and Methods for Astrophysics Geomagnetic storms (GSTs) driven by solar wind-magnetosphere coupling can severely disrupt technological systems, motivating the need for improved prediction accuracy and longer warning times. In this study, we develop a physics-informed Long Short-Term Memory (LSTM) model that incorporates cosmic-ray flux modulation as an additional precursor signal. As coronal mass ejection (CME)-driven disturbances propagate through the heliosphere, enhanced turbulence and magnetic-field compression reduce galactic cosmic-ray (GCR) flux measured by ground-based neutron monitors (Forbush decreases), providing early information that can precede near-Earth solar-wind signatures by 1--3 days. We integrate multi-source space-weather data, spanning 1995-2020, including cosmic-ray observations, solar wind plasma parameters, interplanetary magnetic-field data, and geomagnetic indices. Based on these data, we construct a 19-dimensional feature vector that includes flux background levels, decrease-related indicators, and inter-station correlation measures as model inputs. Employing a 50-unit LSTM architecture, the proposed model achieves root-mean-square errors (RMSE) of 5.106 nT, 8.315 nT, 10.854 nT, 12.883 nT, and 14.788 nT for 2-, 6-, 12-, 24-, and 48-hour predictions, respectively. Incorporating cosmic-ray information further improves 48-hour forecast skill by up to 25.84% (from 0.178 to 0.224). These results demonstrate the value of physics-informed deep learning and cosmic-ray precursors for advancing space-weather forecasting. |
| title | Cosmic-Ray-Constrained LSTM Model for Geomagnetic Storm Prediction |
| topic | Solar and Stellar Astrophysics Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2512.22003 |