3-Day Solar Wind Speed Prediction based on Deep Learning using SDO/AIA Images

In this study, we predict solar wind speed for the next 3 days with 6 hours cadence using a deep learning model. For this we use SDO/AIA 211 and 193Å images together with solar wind speeds for the last five days as input data. Total period of the data is from 2010 May to 2020 December. We divide them into 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. Root mean square error (RMSE) of our model is from 37.4 km/s (for 6h prediction) to 68.2 km/s (for 72h prediction), and correlation coefficient (CC) is from 0.92 to 0.67. These results are much better than those of the 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 WSA-ENLIL model, especially in high speed stream regions. It is also noted that our model cannot predict solar wind speed enhancement by CMEs. Our study demonstrates the effectiveness of deep learning for solar wind speed prediction, with potential applications in space weather forecasting.