Prediction of Ionograms With/Without Spread‐F at Hainan by a Combined Spatio‐Temporal Neural Network
Abstract An intelligent high‐definition and short‐term prediction of ionograms with/without Spread‐F for the observation at Hainan (19.5°N, 109.1°E, magnetic 11°N) is presented in this paper, which comprises a spatio‐temporal ConvGRU network and a super‐resolution EDSR network. Our prediction is bas...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
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| Series: | Space Weather |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2023SW003727 |
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| Summary: | Abstract An intelligent high‐definition and short‐term prediction of ionograms with/without Spread‐F for the observation at Hainan (19.5°N, 109.1°E, magnetic 11°N) is presented in this paper, which comprises a spatio‐temporal ConvGRU network and a super‐resolution EDSR network. Our prediction is based on spatio‐temporal features in the ionogram graph only. There are 469,227 ionograms classified into 5 categories, that is, frequency/range/mix/strong range/no Spread F, over a solar cycle (14 years) labeled manually by the research group, and we process these ionograms into two data sets for training the two networks mentioned above. A series of comprehensive experiments have been designed and conducted to determine the optimal super‐parameters. Our method inputs 8 consecutive authentic ionograms (lasting 2 hr) and generates the next 2 figures (next 30 min). Remarkably, all predicted figures achieve a high accuracy rate of over 94% in predicting the occurrence of Spread‐F. |
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| ISSN: | 1542-7390 |