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

Ryuho Kataoka; Shin ya Nakano

Presentation information

[E] Poster

P (Space and Planetary Sciences ) » P-EM Solar-Terrestrial Sciences, Space Electromagnetism & Space Environment

[P-EM09] Space Weather and Space Climate

Tue. May 31, 2022 11:00 AM - 1:00 PM Online Poster Zoom Room (3) (Ch.03)

convener:Ryuho Kataoka(National Institute of Polar Research), convener:Antti A Pulkkinen(NASA Goddard Space Flight Center), Kaori Sakaguchi(National Institute of Information and Communications Technology), convener:Daikou Shiota(National Institute of Information and Communications Technology (NICT)), Chairperson:Ryuho Kataoka(National Institute of Polar Research), Antti A Pulkkinen(NASA Goddard Space Flight Center), Kaori Sakaguchi(National Institute of Information and Communications Technology), Daikou Shiota(National Institute of Information and Communications Technology (NICT))

11:00 AM - 1:00 PM

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

*Ryuho Kataoka¹, Shin ya Nakano² (1.National Institute of Polar Research, 2.The Institute of Statistical Mathematics)

Keywords:magnetic storm, solar wind, machine learning

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.