Short-term prediction of geomagnetic secular variation with an echo state network

Abstract

Abstract A technique for predicting the secular variation (SV) of the geomagnetic field based on the echo state network (ESN) model is proposed. SV is controlled by the geodynamo process in the Earth’s outer core, and modeling its nonlinear behaviors can be challenging. This study employs an ESN to represent the short-term temporal evolution of the geomagnetic field on the Earth’s surface. The hindcast results demonstrate that the ESN enables us to predict SV for a duration of several years with satisfactory accuracy. It is also found that the prediction is robust to the length of the the training data period. This suggests that the recent features of the SV are important for short-term prediction and that the ESN effectively learns these features. Graphical Abstract

Keywords

• Geomagnetic secular variation
• Machine learning