Thermospheric variability investigation and thermospheric prediction using machine learning techniques

Jiuhou Lei

講演情報

[E] 口頭発表

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-EM11] Coupling Processes in the Atmosphere-Ionosphere System

2021年6月3日(木) 10:45 〜 12:15 Ch.05 (Zoom会場05)

コンビーナ:Liu Huixin(九州大学理学研究院地球惑星科学専攻九州大学宙空環境研究センター)、Chang Loren(Institute of Space Science, National Central University)、大塚雄一(名古屋大学宇宙地球環境研究所)、Yue Deng(University of Texas at Arlington)、座長:大塚雄一(名古屋大学宇宙地球環境研究所)、新堀淳樹(名古屋大学宇宙地球環境研究所)

11:15 〜 11:30

[PEM11-09] Thermospheric variability investigation and thermospheric prediction using machine learning techniques

★Invited Papers

*Jiuhou Lei¹、Denxin Ren¹、Libing Weng¹ (1.USTC University of Science and Technology of China)

キーワード：Upper atmosphere, Thermosphere prediction, machine learning technique

In recent years, the deep learning technology has become increasingly popular and successfully applied in space physics. In this talk, we will present long-term trend of thermospheric density by using a Machine-Learning approach. Also, we will introduce a novel methodology, which is utilized to forecast the thermosphere by specifying the uncertain parameters in a physics-based model using an intelligent optimized particle filter. This method has the capability of providing a reliable forecast with multi-days leading time for the thermospheric mass density.