GeoDGP: One‐Hour Ahead Global Probabilistic Geomagnetic Perturbation Forecasting Using Deep Gaussian Process
Abstract Accurately predicting the horizontal component of ground magnetic field perturbation (dBH), a key quantity for calculating the geomagnetically induced currents (GICs), is crucial for assessing the space weather impact of geomagnetic disturbances. The current operational first‐principles Mic...
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| Format: | Article |
| Language: | English |
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Wiley
2025-06-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2024SW004301 |
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| _version_ | 1849415812861919232 |
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| author | Hongfan Chen Gabor Toth Yang Chen Shasha Zou Zhenguang Huang Xun Huan |
| author_facet | Hongfan Chen Gabor Toth Yang Chen Shasha Zou Zhenguang Huang Xun Huan |
| author_sort | Hongfan Chen |
| collection | DOAJ |
| description | Abstract Accurately predicting the horizontal component of ground magnetic field perturbation (dBH), a key quantity for calculating the geomagnetically induced currents (GICs), is crucial for assessing the space weather impact of geomagnetic disturbances. The current operational first‐principles Michigan Geospace model provides effective forecasts of dBH, but requires significant computational resources to achieve real‐time speeds. Existing data‐driven methods tend to underpredict dBH and lack uncertainty quantification, which is either overlooked or treated as secondary. In this work, we introduce GeoDGP, a novel and efficient data‐driven model based on the deep Gaussian process. GeoDGP provides global probabilistic forecasts of dBH with a lead time of at least 1 hr, at 1‐min time cadence, and at arbitrary spatial locations. The model takes solar wind measurements, the Dst index, and the prediction location in solar magnetic coordinate system as inputs, and is trained on 28 years of data from SuperMAG global magnetometer stations. Additionally, GeoDGP is also trained to predict the north (dBN) and east (dBE) components of perturbations. We evaluate GeoDGP's performance at over 200 stations worldwide during 24 geomagnetic storms, including the Gannon extreme storm of May 2024. Comparisons with the first‐principles Michigan Geospace model and the data‐driven DAGGER model revealed that GeoDGP significantly outperforms both across multiple performance metrics. |
| format | Article |
| id | doaj-art-8c6f69a3417545fd97a8b328b9b436cb |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-8c6f69a3417545fd97a8b328b9b436cb2025-08-20T03:33:25ZengWileySpace Weather1542-73902025-06-01236n/an/a10.1029/2024SW004301GeoDGP: One‐Hour Ahead Global Probabilistic Geomagnetic Perturbation Forecasting Using Deep Gaussian ProcessHongfan Chen0Gabor Toth1Yang Chen2Shasha Zou3Zhenguang Huang4Xun Huan5Department of Mechanical Engineering University of Michigan Ann Arbor MI USADepartment of Climate and Space Sciences and Engineering University of Michigan Ann Arbor MI USADepartment of Statistics University of Michigan Ann Arbor MI USADepartment of Climate and Space Sciences and Engineering University of Michigan Ann Arbor MI USADepartment of Climate and Space Sciences and Engineering University of Michigan Ann Arbor MI USADepartment of Mechanical Engineering University of Michigan Ann Arbor MI USAAbstract Accurately predicting the horizontal component of ground magnetic field perturbation (dBH), a key quantity for calculating the geomagnetically induced currents (GICs), is crucial for assessing the space weather impact of geomagnetic disturbances. The current operational first‐principles Michigan Geospace model provides effective forecasts of dBH, but requires significant computational resources to achieve real‐time speeds. Existing data‐driven methods tend to underpredict dBH and lack uncertainty quantification, which is either overlooked or treated as secondary. In this work, we introduce GeoDGP, a novel and efficient data‐driven model based on the deep Gaussian process. GeoDGP provides global probabilistic forecasts of dBH with a lead time of at least 1 hr, at 1‐min time cadence, and at arbitrary spatial locations. The model takes solar wind measurements, the Dst index, and the prediction location in solar magnetic coordinate system as inputs, and is trained on 28 years of data from SuperMAG global magnetometer stations. Additionally, GeoDGP is also trained to predict the north (dBN) and east (dBE) components of perturbations. We evaluate GeoDGP's performance at over 200 stations worldwide during 24 geomagnetic storms, including the Gannon extreme storm of May 2024. Comparisons with the first‐principles Michigan Geospace model and the data‐driven DAGGER model revealed that GeoDGP significantly outperforms both across multiple performance metrics.https://doi.org/10.1029/2024SW004301space weatheruncertainty quantificationmachine learning |
| spellingShingle | Hongfan Chen Gabor Toth Yang Chen Shasha Zou Zhenguang Huang Xun Huan GeoDGP: One‐Hour Ahead Global Probabilistic Geomagnetic Perturbation Forecasting Using Deep Gaussian Process Space Weather space weather uncertainty quantification machine learning |
| title | GeoDGP: One‐Hour Ahead Global Probabilistic Geomagnetic Perturbation Forecasting Using Deep Gaussian Process |
| title_full | GeoDGP: One‐Hour Ahead Global Probabilistic Geomagnetic Perturbation Forecasting Using Deep Gaussian Process |
| title_fullStr | GeoDGP: One‐Hour Ahead Global Probabilistic Geomagnetic Perturbation Forecasting Using Deep Gaussian Process |
| title_full_unstemmed | GeoDGP: One‐Hour Ahead Global Probabilistic Geomagnetic Perturbation Forecasting Using Deep Gaussian Process |
| title_short | GeoDGP: One‐Hour Ahead Global Probabilistic Geomagnetic Perturbation Forecasting Using Deep Gaussian Process |
| title_sort | geodgp one hour ahead global probabilistic geomagnetic perturbation forecasting using deep gaussian process |
| topic | space weather uncertainty quantification machine learning |
| url | https://doi.org/10.1029/2024SW004301 |
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