JpGU-AGU Joint Meeting 2020

講演情報

[E] ポスター発表

セッション記号 P (宇宙惑星科学) » P-EM 太陽地球系科学・宇宙電磁気学・宇宙環境

[P-EM14] Frontiers in solar physics

コンビーナ:今田 晋亮(名古屋大学宇宙地球環境研究所)、Alphonse Sterling(NASA/MSFC)、横山 央明(東京大学大学院理学系研究科)、清水 敏文(宇宙航空研究開発機構宇宙科学研究所)

[PEM14-P01] Prediction of sunspot number and latitude distribution using machine learning

*道脇 健斗1今田 晋亮1三好 由純1 (1.名古屋大学宇宙地球環境研究所)

キーワード:太陽、予測、機械学習

For space weather study, it is important to estimate the solar activity in near future. Recently, it is believed that the polar magnetic field at the solar minimum is one of the indicators for the next solar activity. Therefore, many studies try to estimate the solar polar magnetic field for the cycle prediction. The temporal variation of the polar magnetic field can be reproduced by using the surface magnetic flux transport calculation model (SFT model). The SFT model consists of advection term due to differential rotation and meridional circulation, magnetic diffusion term, and flux emergence term. The advection and the diffusion coefficients are estimated by modern observations. On the other hand, estimation of future flux emergence is still very difficult. Therefore, estimating when and where the sunspots will be emerge is crucial for cycle prediction study.
In this study, we predicted the sunspot number using RNN (Recurrent Neural Network), which is one of the machine learning technique. Also, the latitude distribution (batterfly diagram) was predicted using CNN (Convolutional Neural Network). As a result, for the prediction of the number of sunspots, by using the sunspot number of former half of the cycle as the input data, we succeeded to predict the sunspot number of latter half of the cycle. For the prediction of the appearance latitude, the transition and the periodicity of the appearance of the sunspot from the middle latitude to the low latitude were able to be reproduced.