Three-day Forecasting of Solar Wind Speed Using SDO/AIA Extreme-ultraviolet Images by a Deep-learning Model

Abstract

In this study, we forecast solar wind speed for the next 3 days with a 6 hr cadence using a deep-learning model. For this we use Solar Dynamics Observatory/Atmospheric Imaging Assembly 211 and 193 Å images together with solar wind speeds for the last 5 days as input data. The total period of the data is from 2010 May to 2020 December. We divide them into a training set (January–August), validation set (September), and test set (October–December), to consider the solar cycle effect. The deep-learning model consists of two networks: a convolutional layer–based network for images and a dense layer–based network for solar wind speeds. Our main results are as follows. First, our model successfully predicts the solar wind speed for the next 3 days. The rms error (RMSE) of our model is from 37.4 km s−1 (for the 6 hr prediction) to 68.2 km s−1 (for the 72 hr prediction), and the correlation coefficient is from 0.92 to 0.67. These results are much better than those of previous studies. Second, the model can predict sudden increase of solar wind speeds caused by large equatorial coronal holes. Third, solar wind speeds predicted by our model are more consistent with observations than those by the Wang–Sheely–Arge–ENLIL model, especially in high-speed-stream regions. It is also noted that our model cannot predict solar wind speed enhancement by coronal mass ejections. Our study demonstrates the effectiveness of deep learning for solar wind speed prediction, with potential applications in space weather forecasting.