Reconstructing solar wind profiles associated with extreme magnetic storms: A machine learning approach

片岡 龍峰; 中野 慎也

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-EM09] 宇宙天気・宇宙気候

2022年5月31日(火) 11:00 〜 13:00 オンラインポスターZoom会場 (3) (Ch.03)

コンビーナ:片岡龍峰(国立極地研究所)、コンビーナ:Pulkkinen Antti A(NASA Goddard Space Flight Center)、坂口歌織(情報通信研究機構)、コンビーナ:塩田大幸(国立研究開発法人情報通信研究機構)、座長:片岡龍峰(国立極地研究所)、Antti A Pulkkinen(NASA Goddard Space Flight Center)、坂口歌織(情報通信研究機構)、塩田大幸(国立研究開発法人情報通信研究機構)

11:00 〜 13:00

[PEM09-P13] Reconstructing solar wind profiles associated with extreme magnetic storms: A machine learning approach

*片岡龍峰¹、中野慎也² (1.国立極地研究所、2.統計数理研究所)

キーワード：磁気嵐、太陽風、機械学習

The lack of data on solar wind have prevented a detailed understanding of extreme magnetic storms. To address this issue, we apply a machine learning technique in the form of an Echo State Network (ESN) to reconstruct solar wind data for several extreme magnetic storms for which little or no solar wind data were previously available. Multiple geomagnetic activity indices are used as the input data for the ESN, which produces a continuous time series of solar wind parameters as output. As a result, the solar wind parameters for the largest storm event in March 1989 are obtained, and the minimum Bz is estimated to be −95 nT ±10 nT. Two different types of solar wind profiles are discussed for the extreme magnetic storms, a sheath-driven profile and a magnetic cloud-driven profile. The results reported here will be highly useful as input data for future simulation studies modeling extreme magnetic storms.