摘要
针对目前世界时(UT1)产品滞后的现象,不能满足用户实时性需求,提出将超快速日长变化(ΔLOD)序列与甚长基线干涉测量(VLBI)观测的UT1序列使用Vondrak算法进行融合,并在融合过程中结合马尔科夫链蒙特卡罗模拟方法研究UT1和ΔLOD的权比选择,从而得到优化的近实时UT1产品,填补国内外超快速融合UT1产品的空白,并为UT1预报提供数据支撑。使用该方法将国家授时中心的UT1序列和iGMAS的超快速ΔLOD序列、IVS的UT1序列和IGS的超快速ΔLOD序列分别进行融合,并将融合后的数据和IERS的每日预报数据分别与IERS C04 UT1序列进行精度评估,结论表明,使用超快速ΔLOD序列融合后可得到近实时UT1产品,且国外和国内融合后,UT1精度分别达到45.91μs、87.40μs,精度均优于IERS的每日预报产品。
In view of the lag of universal time(UT1)products,which cannot meet the real-time requirement of users,it is proposed to fuse the ultra-rapid variation of length of day(ΔLOD)sequence with the UT1 sequence observed by very long baseline interferometry(VLBI)through the Vondrak algorithm.In the fusion process,combined with the Markov chain Monte Carlo simulation method,the weight ratio selection of UT1 andΔLOD is studied to obtain the optimal real-time UT1 product.It fills the lack of ultra-rapid UT1 products at home and abroad,which provides data support for UT1 prediction.By using this method,the UT1 sequence of national time service center and ultra-rapid(ΔLOD)sequence of iGMAS,UT1 sequence of national time service center and ultra-rapid(ΔLOD)sequence of IGS,UT1 sequence of IVS and ultra-rapid(ΔLOD)sequence of IGS are fused respectively,and the fused data are compared with IERS C04 UT1 sequence.Results show that real-time UT1 products can be achieved after ultra-rapid sequence fusion,the accuracy of UT1 reaches 45.91μs and 87.40μs,respectively.They are better than the daily forecast products of IERS.
作者
李西顺
吴元伟
叶仁琼
杨旭海
张首刚
Li Xishun;Wu Yuanwei;Ye Renqiong;Yang Xuhai;Zhang Shougang(National Time Service Center,Chinese Academy of Sciences,Xi′an 710600,China;University of Chinese Academy of Sciences School of Electronic,Electrical and Communication Engineering,Beijing 100049,China;Key Laboratory Positioning and Timing Technology,Chinese Academy of Sciences,Xi′an 710600,China;China Women's University,School of Management,Being 100101,China;Key Laboratory of Time and Frequency Primary Standards,Chinese Academy of Seiences,Xi′an 710600,China)
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2022年第8期245-252,共8页
Chinese Journal of Scientific Instrument
基金
国家自然科学基金面上项目(12073034)
中国科学院一带一路团队项目(XAB2018YDYL01)资助