Relation between Latitude-dependent Sunspot Data and Near-Earth Solar Wind Speed
Relation between Latitude-dependent Sunspot Data and Near-Earth Solar Wind Speed
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DOI：
10.3847/1538-4357/acfc21
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发表时间：
2023-11
期刊：
影响因子：
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通讯作者：
Qirong Jiao;Wenlong Liu;Dianjun Zhang;Jinbin Cao
中科院分区：
文献类型：
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作者：
Qirong Jiao;Wenlong Liu;Dianjun Zhang;Jinbin Cao
太阳风对太阳与地球之间的空间环境具有重要意义，并随太阳黑子周期的变化而变化，而太阳黑子周期又受太阳内部动力学的影响。我们使用格兰杰因果检验方法和机器学习预测方法研究了与纬度相关的太阳黑子数据对太阳风速度的影响。结果表明，低纬度太阳黑子数对太阳风速度的影响较大。年平均太阳风速度与黑子数之间的时间差随纬度范围的减小而减小。建立了考虑纬度和时间影响的太阳风速度预测的机器学习模型。发现该模型对与纬度相关的太阳黑子数据有不同的表现。结果表明，低纬度黑子对太阳风速度的时间尺度影响更大，黑子资料对30天平均太阳风速度的影响大于日平均太阳风速度。加上7以下的太阳黑子数据。°2纬度，日平均和30天平均预报分别提高0.23%和12%。日太阳风预报模型的最佳相关系数为0.787。
Solar wind is important for the space environment between the Sun and the Earth and varies with the sunspot cycle, which is influenced by solar internal dynamics. We study the impact of latitude-dependent sunspot data on solar wind speed using the Granger causality test method and a machine-learning prediction approach. The results show that the low-latitude sunspot number has a larger effect on the solar wind speed. The time delay between the annual average solar wind speed and sunspot number decreases as the latitude range decreases. A machine-learning model is developed for the prediction of solar wind speed considering latitude and time effects. It is found that the model performs differently with latitude-dependent sunspot data. It is revealed that the timescale of the solar wind speed is more strongly influenced by low-latitude sunspots and that sunspot data have a greater impact on the 30 day average solar wind speed than on a daily basis. With the addition of sunspot data below 7.°2 latitude, the prediction of the daily and 30 day averages is improved by 0.23% and 12%, respectively. The best correlation coefficient is 0.787 for the daily solar wind prediction model.