Probabilistic Prediction of Dst Storms One‐Day‐Ahead Using Full‐Disk SoHO Images
Abstract We present a new model for the probability that the disturbance storm time (Dst) index exceeds −100 nT, with a lead time between 1 and 3 days. Dst provides essential information about the strength of the ring current around the Earth caused by the protons and electrons from the solar wind,...
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| Format: | Article |
| Language: | English |
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Wiley
2022-08-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2022SW003064 |
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| author | A. Hu C. Shneider A. Tiwari E. Camporeale |
| author_facet | A. Hu C. Shneider A. Tiwari E. Camporeale |
| author_sort | A. Hu |
| collection | DOAJ |
| description | Abstract We present a new model for the probability that the disturbance storm time (Dst) index exceeds −100 nT, with a lead time between 1 and 3 days. Dst provides essential information about the strength of the ring current around the Earth caused by the protons and electrons from the solar wind, and it is routinely used as a proxy for geomagnetic storms. The model is developed using an ensemble of Convolutional Neural Networks that are trained using Solar and Heliospheric Observatory (SoHO) images (Michelson Doppler Imager, Extreme ultraviolet Imaging Telescope, and Large Angle and Spectrometric Coronagraph). The relationship between the SoHO images and the solar wind has been investigated by many researchers, but these studies have not explicitly considered using SoHO images to predict the Dst index. This work presents a novel methodology to train the individual models and to learn the optimal ensemble weights iteratively, by using a customized class‐balanced mean square error (CB‐MSE) loss function tied to a least‐squares based ensemble. The proposed model can predict the probability that Dst < −100 nT 24 hr ahead with a True Skill Statistic (TSS) of 0.62 and Matthews Correlation Coefficient (MCC) of 0.37. The weighted TSS and MCC is 0.68 and 0.47, respectively. An additional validation during non‐Earth‐directed CME periods is also conducted which yields a good TSS and MCC score. |
| format | Article |
| id | doaj-art-3cf87c3d2cba43588e1438ce0c6ab90b |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2022-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-3cf87c3d2cba43588e1438ce0c6ab90b2025-01-14T16:27:07ZengWileySpace Weather1542-73902022-08-01208n/an/a10.1029/2022SW003064Probabilistic Prediction of Dst Storms One‐Day‐Ahead Using Full‐Disk SoHO ImagesA. Hu0C. Shneider1A. Tiwari2E. Camporeale3Centrum Wiskunde & Informatica Amsterdam The NetherlandsCentrum Wiskunde & Informatica Amsterdam The NetherlandsCentrum Wiskunde & Informatica Amsterdam The NetherlandsCIRES University of Colorado Boulder CO USAAbstract We present a new model for the probability that the disturbance storm time (Dst) index exceeds −100 nT, with a lead time between 1 and 3 days. Dst provides essential information about the strength of the ring current around the Earth caused by the protons and electrons from the solar wind, and it is routinely used as a proxy for geomagnetic storms. The model is developed using an ensemble of Convolutional Neural Networks that are trained using Solar and Heliospheric Observatory (SoHO) images (Michelson Doppler Imager, Extreme ultraviolet Imaging Telescope, and Large Angle and Spectrometric Coronagraph). The relationship between the SoHO images and the solar wind has been investigated by many researchers, but these studies have not explicitly considered using SoHO images to predict the Dst index. This work presents a novel methodology to train the individual models and to learn the optimal ensemble weights iteratively, by using a customized class‐balanced mean square error (CB‐MSE) loss function tied to a least‐squares based ensemble. The proposed model can predict the probability that Dst < −100 nT 24 hr ahead with a True Skill Statistic (TSS) of 0.62 and Matthews Correlation Coefficient (MCC) of 0.37. The weighted TSS and MCC is 0.68 and 0.47, respectively. An additional validation during non‐Earth‐directed CME periods is also conducted which yields a good TSS and MCC score.https://doi.org/10.1029/2022SW003064machine learningensemble methodsolar imagesgeomagnetic stormDst |
| spellingShingle | A. Hu C. Shneider A. Tiwari E. Camporeale Probabilistic Prediction of Dst Storms One‐Day‐Ahead Using Full‐Disk SoHO Images Space Weather machine learning ensemble method solar images geomagnetic storm Dst |
| title | Probabilistic Prediction of Dst Storms One‐Day‐Ahead Using Full‐Disk SoHO Images |
| title_full | Probabilistic Prediction of Dst Storms One‐Day‐Ahead Using Full‐Disk SoHO Images |
| title_fullStr | Probabilistic Prediction of Dst Storms One‐Day‐Ahead Using Full‐Disk SoHO Images |
| title_full_unstemmed | Probabilistic Prediction of Dst Storms One‐Day‐Ahead Using Full‐Disk SoHO Images |
| title_short | Probabilistic Prediction of Dst Storms One‐Day‐Ahead Using Full‐Disk SoHO Images |
| title_sort | probabilistic prediction of dst storms one day ahead using full disk soho images |
| topic | machine learning ensemble method solar images geomagnetic storm Dst |
| url | https://doi.org/10.1029/2022SW003064 |
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