Thermospheric variability investigation and thermospheric prediction using machine learning techniques

Jiuhou Lei, Denxin Ren, Libing Weng

Presentation information

[E] Oral

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

[P-EM11] Coupling Processes in the Atmosphere-Ionosphere System

Thu. Jun 3, 2021 10:45 AM - 12:15 PM Ch.05 (Zoom Room 05)

convener:Huixin Liu(Earth and Planetary Science Division, Kyushu University SERC, Kyushu University), Loren Chang(Institute of Space Science, National Central University), Yuichi Otsuka(Institute for Space-Earth Environmental Research, Nagoya University), Yue Deng(University of Texas at Arlington), Chairperson:Yuichi Otsuka(Institute for Space-Earth Environmental Research, Nagoya University), Atsuki Shinbori(Institute for Space-Earth Environment Research (ISEE), Nagoya University)

11:15 AM - 11:30 AM

[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)

Keywords: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.