Global Geomagnetic Perturbation Forecasting Using Deep Learning
Abstract Geomagnetically Induced Currents (GICs) arise from spatio‐temporal changes to Earth's magnetic field, which arise from the interaction of the solar wind with Earth's magnetosphere, and drive catastrophic destruction to our technologically dependent society. Hence, computational mo...
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| Format: | Article |
| Language: | English |
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Wiley
2022-06-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2022SW003045 |
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| author | Vishal Upendran Panagiotis Tigas Banafsheh Ferdousi Téo Bloch Mark C. M. Cheung Siddha Ganju Asti Bhatt Ryan M. McGranaghan Yarin Gal |
| author_facet | Vishal Upendran Panagiotis Tigas Banafsheh Ferdousi Téo Bloch Mark C. M. Cheung Siddha Ganju Asti Bhatt Ryan M. McGranaghan Yarin Gal |
| author_sort | Vishal Upendran |
| collection | DOAJ |
| description | Abstract Geomagnetically Induced Currents (GICs) arise from spatio‐temporal changes to Earth's magnetic field, which arise from the interaction of the solar wind with Earth's magnetosphere, and drive catastrophic destruction to our technologically dependent society. Hence, computational models to forecast GICs globally with large forecast horizon, high spatial resolution and temporal cadence are of increasing importance to perform prompt necessary mitigation. Since GIC data is proprietary, the time variability of the horizontal component of the magnetic field perturbation (dB/dt) is used as a proxy for GICs. In this work, we develop a fast, global dB/dt forecasting model, which forecasts 30 min into the future using only solar wind measurements as input. The model summarizes 2 hr of solar wind measurement using a Gated Recurrent Unit and generates forecasts of coefficients that are folded with a spherical harmonic basis to enable global forecasts. When deployed, our model produces results in under a second, and generates global forecasts for horizontal magnetic perturbation components at 1 min cadence. We evaluate our model across models in literature for two specific storms of 5 August 2011 and 17 March 2015, while having a self‐consistent benchmark model set. Our model outperforms, or has consistent performance with state‐of‐the‐practice high time cadence local and low time cadence global models, while also outperforming/having comparable performance with the benchmark models. Such quick inferences at high temporal cadence and arbitrary spatial resolutions may ultimately enable accurate forewarning of dB/dt for any place on Earth, resulting in precautionary measures to be taken in an informed manner. |
| format | Article |
| id | doaj-art-1edde87a7ab4496b9ff1d7df9b564e6b |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2022-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-1edde87a7ab4496b9ff1d7df9b564e6b2025-01-14T16:27:09ZengWileySpace Weather1542-73902022-06-01206n/an/a10.1029/2022SW003045Global Geomagnetic Perturbation Forecasting Using Deep LearningVishal Upendran0Panagiotis Tigas1Banafsheh Ferdousi2Téo Bloch3Mark C. M. Cheung4Siddha Ganju5Asti Bhatt6Ryan M. McGranaghan7Yarin Gal8Frontier Development Lab Sunnyvale CA USAFrontier Development Lab Sunnyvale CA USAFrontier Development Lab Sunnyvale CA USAFrontier Development Lab Sunnyvale CA USAFrontier Development Lab Sunnyvale CA USAFrontier Development Lab Sunnyvale CA USAFrontier Development Lab Sunnyvale CA USAFrontier Development Lab Sunnyvale CA USAFrontier Development Lab Sunnyvale CA USAAbstract Geomagnetically Induced Currents (GICs) arise from spatio‐temporal changes to Earth's magnetic field, which arise from the interaction of the solar wind with Earth's magnetosphere, and drive catastrophic destruction to our technologically dependent society. Hence, computational models to forecast GICs globally with large forecast horizon, high spatial resolution and temporal cadence are of increasing importance to perform prompt necessary mitigation. Since GIC data is proprietary, the time variability of the horizontal component of the magnetic field perturbation (dB/dt) is used as a proxy for GICs. In this work, we develop a fast, global dB/dt forecasting model, which forecasts 30 min into the future using only solar wind measurements as input. The model summarizes 2 hr of solar wind measurement using a Gated Recurrent Unit and generates forecasts of coefficients that are folded with a spherical harmonic basis to enable global forecasts. When deployed, our model produces results in under a second, and generates global forecasts for horizontal magnetic perturbation components at 1 min cadence. We evaluate our model across models in literature for two specific storms of 5 August 2011 and 17 March 2015, while having a self‐consistent benchmark model set. Our model outperforms, or has consistent performance with state‐of‐the‐practice high time cadence local and low time cadence global models, while also outperforming/having comparable performance with the benchmark models. Such quick inferences at high temporal cadence and arbitrary spatial resolutions may ultimately enable accurate forewarning of dB/dt for any place on Earth, resulting in precautionary measures to be taken in an informed manner.https://doi.org/10.1029/2022SW003045geomagnetic perturbationssolar windgeomagnetically induced currentsdeep learningspherical harmonicsspace weather |
| spellingShingle | Vishal Upendran Panagiotis Tigas Banafsheh Ferdousi Téo Bloch Mark C. M. Cheung Siddha Ganju Asti Bhatt Ryan M. McGranaghan Yarin Gal Global Geomagnetic Perturbation Forecasting Using Deep Learning Space Weather geomagnetic perturbations solar wind geomagnetically induced currents deep learning spherical harmonics space weather |
| title | Global Geomagnetic Perturbation Forecasting Using Deep Learning |
| title_full | Global Geomagnetic Perturbation Forecasting Using Deep Learning |
| title_fullStr | Global Geomagnetic Perturbation Forecasting Using Deep Learning |
| title_full_unstemmed | Global Geomagnetic Perturbation Forecasting Using Deep Learning |
| title_short | Global Geomagnetic Perturbation Forecasting Using Deep Learning |
| title_sort | global geomagnetic perturbation forecasting using deep learning |
| topic | geomagnetic perturbations solar wind geomagnetically induced currents deep learning spherical harmonics space weather |
| url | https://doi.org/10.1029/2022SW003045 |
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