A short-term forecasting method of foF2 in the ionosphere over the Chinese region based on deep learning
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摘要: 电离层F2层临界频率（foF2）作为电离层的关键参数，其准确预报对于保障高频雷达、短波通信等系统的稳定运行至关重要。本文提出了一种基于深度学习的预报方法，通过采用注意力机制的双向长短期记忆网络（Bidirectional long short-term memory model with attention mechanism, BiLSTM-Attention）算法，结合电离层垂测站foF2观测值、世界时、太阳活动指数及地磁活动指数作为输入，实现了中国区域电离层foF2的准确预报。模型的对比分析结果表明：1）低纬度台站的预报误差显著高于中纬度台站，BiLSTM-Attention模型表现最优，长短期记忆网络（LSTM）模型次之，相比国际参考电离层模型（IRI），BiLSTM-Attention模型的均方根误差（RMSE）降低了54%，平均绝对误差（MAE）降低57%，而决定系数（R2）提升28%；2）磁暴期间，BiLSTM-Attention模型成功捕捉中国区域电离层负暴效应（foF2下降），与观测值非常一致，而IRI模型则无法表征扰动导致的显著偏差；地磁平静期IRI模型虽整体与垂测观测值接近，但在日落后、夜间等时段仍存在系统性误差；3）随着预报时间从1小时增加至24小时，模型预报误差呈系统性上升趋势，RMSE从1.02 MHz增至2.03 MHz，MAE从0.71MHz升至1.55MHz。相关研究为空间天气预警及短波通信系统优化提供了高精度电离层参数预报支撑。
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关键词:
- 电离层 /
- F2层临界频率 /
- 注意力机制的双向长短期记忆网络 /
- 深度学习 /
- 短期预报
Abstract: As a key parameter of the ionosphere, the critical frequency of the F2 layer of the ionosphere (foF2) is of great significance for ensuring the stable operation of systems such as high-frequency radar and short-wave communication. This paper proposes a short-term forecasting method for the ionospheric foF2 based on deep learning. By using the Bidirectional long short-term memory model with attention mechanism (BiLSTM-Attention) algorithm and combining the observed values of the ionospheric foF2 at the ionosonde station for the previous 7 days, universal time, solar activity index, and geomagnetic activity index as inputs, the forecasting of the ionospheric foF2 in the Chinese region is realized. The results of the comparative analysis of the model show that: 1) The forecasting error of the low-latitude stations is significantly higher than that of the mid-latitude stations. The BiLSTM-Attention model performs the best, followed by the long short-term memory network (LSTM) model. Compared with the International Reference Ionosphere model (IRI), the root mean square error (RMSE) of the BiLSTM-Attention model is reduced by 54%, the mean absolute error (MAE) is reduced by 57%, and the coefficient of determination (R2) is increased by 28%. 2) During geomagnetic storms, the BiLSTM-Attention model successfully captures the negative storm effect of the ionosphere in the Chinese region (the decrease of foF2), which is in good agreement with the observed values, while the IRI model cannot represent the significant deviation caused by the disturbance. Although the IRI model is overall close to the vertical sounding observations during the geomagnetically quiet period, there are still systematic errors in the periods after sunset and at night. 3) As the forecasting time increases from 1 hour to 24 hours, the forecasting error of the model shows a systematic upward trend, with the RMSE increasing from 1.02 MHz to 2.03 MHz and the MAE increasing from 0.71 MHz to 1.55 MHz. Relevant research provides high-precision ionospheric parameter forecasting support for space weather warning and short-wave communication system optimization.-
Key words:
- Ionosphere /
- foF2 /
- BiLSTM-Attention /
- Deep learning /
- short-term forecasting
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