Accurate Solar Wind Speed Prediction with Multimodality Information

Abstract

When the solar wind passes over the Earth, it will cause geomagnetic storms, affect short-wave communications, and threaten the safety of pipelines such as electricity and oil. Accurate prediction of the solar wind speed will allow people to make adequate preparations to avoid wasting resources and affecting people’s life. Most existing methods only use single-modality data as input and do not consider the information complementarity between different modalities. This paper proposes a multimodality prediction (MMP) method that jointly learns vision and sequence information in a unified end-to-end framework for solar wind speed prediction. MMP includes three modules: Vmodule, Tmodule, and Fusion module. Vmodule, which uses pretrained GoogLeNet, is proposed to learn visual representations from the extreme ultraviolet (EUV) images. Tmodule combining one-dimensional CNN with bidirectional long short-term memory (BiLSTM) is applied for learning sequence representation from multivariate time series. Finally, a multimodality fusion method is applied to improve the overall performance. We adopt the EUV images observed by the solar dynamics observatory (SDO) satellite and the OMNIWEB dataset measured at Lagrangian point 1 (L1) to experiment. Comparative experiments have shown that the proposed MMP achieves best performance in many metrics. The ablation experiments also verify the validity of each module and the rationality of the hyperparameter setting.