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

被引:0
|
作者
Nakano, Shin'ya [1 ,2 ,3 ]
Sato, Sho [4 ]
Toh, Hiroaki [4 ]
机构
[1] Inst Stat Math, Tachikawa, Tokyo 1908562, Japan
[2] Ctr Data Assimilat Res & Applicat, Joint Support Ctr Data Sci Res, Tachikawa, Japan
[3] SOKENDAI, Grad Univ Adv Studies, Hayama, Japan
[4] Kyoto Univ, Grad Sch Sci, Kyoto, Japan
来源
EARTH PLANETS AND SPACE | 2024年 / 76卷 / 01期
基金
日本学术振兴会;
关键词
Geomagnetic secular variation; Machine learning;
D O I
10.1186/s40623-024-02064-x
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
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.
引用
收藏
页数:10
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