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- Title
Prediction of Solar Wind Speed Through Machine Learning From Extrapolated Solar Coronal Magnetic Field.
- Authors
Lin, Rong; Luo, Zhekai; He, Jiansen; Xie, Lun; Hou, Chuanpeng; Chen, Shuwei
- Abstract
An accurate solar wind (SW) speed model is important for space weather predictions, catastrophic event warnings, and other issues concerning SW—magnetosphere interaction. In this work, we construct a model based on convolutional neural network (CNN) and Potential Field Source Surface (PFSS) magnetic field maps, considering a SW source surface of RSS = 2.5R⊙, aiming to predict the SW speed at the Lagrange‐1 (L1) point of the Sun‐Earth system. The input of our model consists of four PFSS magnetic field maps at RSS, which are three, four, five, and six days before the target epoch. Reduced maps are used to promote the model's efficiency. We use the Global Oscillation Network Group (GONG) photospheric magnetograms and the potential field extrapolation model to generate PFSS magnetic field maps at the source surface. The model provides predictions of the quasi‐continuous test data set, which is generated by randomly assigning 120 data segments that are individually continuous in time, with an averaged correlation coefficient (CC) of 0.53 ± 0.07 and a root mean square error (RMSE) of 80.8 ± 4.8 km/s in an eight‐fold validation training scheme with the time resolution of the data as small as one hour. The model also has the potential to forecast high speed streams of the SW, which can be quantified with a general threat score of 0.39. Plain Language Summary: The dynamic pressure of the solar wind (SW) is a crucial condition for solar activity to affect Earth's space weather. The strength of the SW's dynamic pressure depends on the SW's speed. Therefore, predicting SW velocity upstream of the Earth is one of the essential topics of space weather research. The distribution of the solar coronal magnetic field is an important factor in regulating the speed of the SW. In this work, we explore the mapping from the variation of the extrapolated solar coronal magnetic field at the source surface, where the open field lines are assumed to direct radially along with the nascent SW flow, to the variation of SW speed at the Lagrange‐1 (L1) point of the Sun‐Earth system. We use the photospheric magnetograms from the Global Oscillation Network Group (GONG) to extrapolate the coronal magnetic field up to the source surface. We construct a prediction model based on convolutional neural networks (CNN) with five hidden layers. The model proves that the mapping relation is reliable and has an advantage in predicting when the SW speed starts to grow. Key Points: A model based on convolutional neural network (NN) is trained to predict the solar wind (SW) speed upstream of the EarthThis model uses four potential field source surface (PFSS) magnetic maps three, four, five, and six days before the prediction as the inputThe model achieves a performance of correlation coefficient (CC) = 0.53 ± 0.07 and root mean square error (RMSE) = 80.8 ± 4.8 km/s on the quasi‐continuous test data set
- Subjects
SOLAR wind; SOLAR magnetic fields; WIND speed; SOLAR cycle; CONVOLUTIONAL neural networks; HELIOSEISMOLOGY; MACHINE learning; SPACE environment
- Publication
Space Weather: The International Journal of Research & Applications, 2024, Vol 22, Issue 6, p1
- ISSN
1539-4956
- Publication type
Article
- DOI
10.1029/2023SW003561