A long-range forecasting model for the thermosphere based on the intelligent optimized particle filtering  

作　　者：Dexin REN Jiuhou LEI 

机构地区：[1]CAS Key Laboratory of Geospace Environment,School of Earth and Space Sciences,University of Science and Technology of China,Hefei 230026,China [2]Mengcheng National Geophysical Observatory,University of Science and Technology of China,Mengcheng 233500,China [3]CAS Center for Excellence in Comparative Planetology,University of Science and Technology of China,Hefei 230026,China

出　　处：《Science China Earth Sciences》2022年第1期75-86,共12页中国科学（地球科学英文版）

基　　金：supported by the Project of Stable Support for Youth Team in Basic Research Field,CAS(Grant No.YSBR-018);the B-type Strategic Priority Program of the Chinese Academy of Sciences(Grant No.XDB41000000);the China Postdoctoral Science Foundation(Grant No.2021TQ0318)。

摘　　要：The uncertainties associated with the variations in the thermosphere are responsible for the inaccurate prediction of the orbit decay of low Earth orbiting space objects due to the drag force.Accurate forecasting of the thermosphere is urgently required to avoid satellite collisions,which is a potential threat to the rapid growth of spacecraft applications.However,owing to the imperfections in the physics-based forecast model,the long-range forecast of the thermosphere is still primitive even if the accurate prediction of the external forcing is achieved.In this study,we constructed a novel methodology to forecast the thermosphere for tens of days by specifying the uncertain parameters in a physics-based model using an intelligent optimized particle filtering algorithm.A comparison of the results suggested that this method has the capability of providing a more reliable forecast with more than 30-days leading time for the thermospheric mass density than the existing ones under both weak and severe disturbed conditions,if solar and geomagnetic forcing is known.Moreover,the accurate estimation of the state of thermosphere based on this technique would further contribute to the understanding of the temporal and spatial evolution of the upper atmosphere.

关 键 词：THERMOSPHERE FORECAST Intelligent optimized particle filter Uncertain parameters 

分 类 号：V419[航空宇航科学与技术—航空宇航推进理论与工程] V52