Research on UT1-UTC and LOD Prediction Algorithm Based on Denoised EAM Dataset

doi:10.3390/rs15194654

	Research on UT1-UTC and LOD Prediction Algorithm Based on Denoised EAM Dataset
	Li, Xishun 1,2,3; Wu, Yuanwei 1,3,4; Yao, Dang 1,3; Liu, Jia 1,3; Nan, Kai 1,3; Ma, Langming 1,3; Cheng, Xuan1,3 ; Yang, Xuhai 1,3,4; Zhang, Shougang 1,5
	2023-10-01
发表期刊	REMOTE SENSING
卷号	15 期号:19 页码:17
摘要	The components of EAM are strongly correlated with LOD and play an important role in UT1-UTC and LOD prediction. However, the EAM dataset is prone to be noisy. In this study, we propose a hybrid method to reduce the noise of the EAM data and improve the accuracy of UT1-UTC and LOD predictions. We use the EOP data to denoise the EAM data, and use Kalman filtering to denoise the 1-6 days forecast of EAM. Then, we use the denoised EAM dataset to improve the UT1-UTC and LOD prediction. The denoised EAM dataset improved the prediction of UT1-UTC within 10 days by 20%. In addition, we found that by introducing two additional periodic (23.9 days and 91.3 days) components for the least-squares fitting, the accuracy of UT1-UTC and LOD prediction in the range of 30-80 days is significantly improved. In more than 430 UT1-UTC and LOD prediction experiments conducted during 2021-2022, the improvements in the 1-6 days forecast were significant. For the 6th day, 30th day, and 60th day, the MAE of UT1-UTC was 0.1592, 2.9169, and 6.7857 ms, respectively, corresponding to improvements of 31.35, 12.60, and 12.93%, respectively, when compared to predictions of Bulletin A. The MAE of LOD predictions on the 1st day, 6th day, 30th day, and 90th day was 0.0255, 0.0432, 0.1694, and 0.2505 ms, respectively, which improved by 26.09, 14.29, 6.36, and 3.76% when compared with our second EOPPCC method.
关键词	UT1-UTC LOD EAM GAM LS AR
资助者	This article uses IERS C04 sequences, Bulletin A sequences, and GFZ EAM (AAM, OAM, HAM, and SLAM) sequences. We would like to express our gratitude to the data provider. ; This article uses IERS C04 sequences, Bulletin A sequences, and GFZ EAM (AAM, OAM, HAM, and SLAM) sequences. We would like to express our gratitude to the data provider. ; This article uses IERS C04 sequences, Bulletin A sequences, and GFZ EAM (AAM, OAM, HAM, and SLAM) sequences. We would like to express our gratitude to the data provider. ; This article uses IERS C04 sequences, Bulletin A sequences, and GFZ EAM (AAM, OAM, HAM, and SLAM) sequences. We would like to express our gratitude to the data provider. ; This article uses IERS C04 sequences, Bulletin A sequences, and GFZ EAM (AAM, OAM, HAM, and SLAM) sequences. We would like to express our gratitude to the data provider. ; This article uses IERS C04 sequences, Bulletin A sequences, and GFZ EAM (AAM, OAM, HAM, and SLAM) sequences. We would like to express our gratitude to the data provider. ; This article uses IERS C04 sequences, Bulletin A sequences, and GFZ EAM (AAM, OAM, HAM, and SLAM) sequences. We would like to express our gratitude to the data provider. ; This article uses IERS C04 sequences, Bulletin A sequences, and GFZ EAM (AAM, OAM, HAM, and SLAM) sequences. We would like to express our gratitude to the data provider.
DOI	10.3390/rs15194654
关键词[WOS]	EARTH ORIENTATION PARAMETERS ; SHORT-TERM PREDICTION ; LEAST-SQUARES ; COMBINATION ; ROTATION ; LENGTH ; MODEL ; MOTION ; TIME ; VLBI
语种	英语
资助项目	This article uses IERS C04 sequences, Bulletin A sequences, and GFZ EAM (AAM, OAM, HAM, and SLAM) sequences. We would like to express our gratitude to the data provider.
资助者	This article uses IERS C04 sequences, Bulletin A sequences, and GFZ EAM (AAM, OAM, HAM, and SLAM) sequences. We would like to express our gratitude to the data provider. ; This article uses IERS C04 sequences, Bulletin A sequences, and GFZ EAM (AAM, OAM, HAM, and SLAM) sequences. We would like to express our gratitude to the data provider. ; This article uses IERS C04 sequences, Bulletin A sequences, and GFZ EAM (AAM, OAM, HAM, and SLAM) sequences. We would like to express our gratitude to the data provider. ; This article uses IERS C04 sequences, Bulletin A sequences, and GFZ EAM (AAM, OAM, HAM, and SLAM) sequences. We would like to express our gratitude to the data provider. ; This article uses IERS C04 sequences, Bulletin A sequences, and GFZ EAM (AAM, OAM, HAM, and SLAM) sequences. We would like to express our gratitude to the data provider. ; This article uses IERS C04 sequences, Bulletin A sequences, and GFZ EAM (AAM, OAM, HAM, and SLAM) sequences. We would like to express our gratitude to the data provider. ; This article uses IERS C04 sequences, Bulletin A sequences, and GFZ EAM (AAM, OAM, HAM, and SLAM) sequences. We would like to express our gratitude to the data provider. ; This article uses IERS C04 sequences, Bulletin A sequences, and GFZ EAM (AAM, OAM, HAM, and SLAM) sequences. We would like to express our gratitude to the data provider.
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001083264900001
出版者	MDPI
引用统计
文献类型	期刊论文
条目标识符	http://210.72.145.45/handle/361003/14238
专题	高精度时间传递与精密测定轨研究室
通讯作者	Zhang, Shougang
作者单位	1.Chinese Acad Sci, Natl Time Serv Ctr, Xian 710600, Shaanxi, Peoples R China 2.Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Key Lab Positioning & Timing Technol, Xian 710600, Peoples R China 4.Univ Chinese Acad Sci, Sch Astron & Space Sci, Beijing 100049, Peoples R China 5.Chinese Acad Sci, Key Lab Time & Frequency Primary Stand, Xian 710600, Peoples R China
推荐引用方式 GB/T 7714	Li, Xishun,Wu, Yuanwei,Yao, Dang,et al. Research on UT1-UTC and LOD Prediction Algorithm Based on Denoised EAM Dataset[J]. REMOTE SENSING,2023,15(19):17.
APA	Li, Xishun.,Wu, Yuanwei.,Yao, Dang.,Liu, Jia.,Nan, Kai.,...&Zhang, Shougang.(2023).Research on UT1-UTC and LOD Prediction Algorithm Based on Denoised EAM Dataset.REMOTE SENSING,15(19),17.
MLA	Li, Xishun,et al."Research on UT1-UTC and LOD Prediction Algorithm Based on Denoised EAM Dataset".REMOTE SENSING 15.19(2023):17.