As more and more high-technical systems are exposed to the space environment, extreme space weather becomes a great threat to human society. In the solar system, space weather is influenced by the solar wind, such that reliable prediction of solar wind conditions in the near-Earth environment effectively reduces the impact of space weather on human society. Solar wind speed prediction is improved by making full use of OMNI data measured at Lagrangian Point 1 (L1) by the National Aeronautics and Space Administration (NASA) and image data observed by the Solar Dynamics Observatory (SDO) satellite in this work. Specifically, we propose a model based on the “two-dimensional attention mechanism” (TDAM) to predict solar wind speed. In this study, we first analyze and preprocess data from 2011 to 2017. Second, considering the characteristics of time series data, we adopt the gated recurrent units (GRU) model which can deal with long-term dependence as the prediction part of our model. Third, we design a TDAM, which enables our prediction network to focus on important parts. Three performance indices are used: root-mean-square error (RMSE), mean absolute error (MAE), and correlation coefficient (CC). By comparing TDAM with other models, we find that the TDAM model achieves the best prediction results, with RMSE of 62.8 km/s, MAE of 47.8 km/s, and CC of 0.789 24 h in advance. The experimental results show that the proposed TDAM model can improve the prediction accuracy of solar wind speed.

中文翻译：

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基于二维注意力机制的太阳风速预测

随着越来越多的高科技系统暴露在太空环境中，极端太空天气成为对人类社会的巨大威胁。在太阳系中，空间天气受太阳风的影响，因此对近地环境中太阳风状况的可靠预测有效地降低了空间天气对人类社会的影响。在这项工作中，充分利用了美国国家航空航天局（NASA）在拉格朗日点 1（L1）测量的 OMNI 数据和太阳动力学天文台（SDO）卫星观测到的图像数据，改进了太阳风速预测。具体来说，我们提出了一个基于“二维注意力机制”（TDAM）的模型预测太阳风速。在本研究中，我们首先对 2011 年至 2017 年的数据进行分析和预处理。其次，考虑到时间序列数据的特点，我们采用可以处理长期依赖性的门控循环单元 (GRU) 模型作为我们模型的预测部分. 第三，我们设计了一个 TDAM，它使我们的预测网络能够专注于重要部分。使用了三个性能指标：均方根误差 (RMSE)、平均绝对误差 (MAE) 和相关系数 (CC)。通过与其他模型的比较，我们发现TDAM模型取得了最好的预测结果，RMSE为62.8 km/s，MAE为47.8 km/s，CC为0.789 24 h提前。实验结果表明，所提出的TDAM模型能够提高太阳风速的预测精度。