首页|Improved LS+MAR hybrid method to UT1-UTC ultra-short-term prediction by using first-order-difference UT1-UTC

Improved LS+MAR hybrid method to UT1-UTC ultra-short-term prediction by using first-order-difference UT1-UTC

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Accurate ultra-short-term prediction of the Earth rotation parameters(ERP)holds paramount impor-tance for real-time applications,particularly in reference frame conversion.Among them,diurnal rota-tion(UT1-UTC)which cannot be directly estimated through Global Navigation Satellite System(GNSS)techniques,significantly affects the rapid and ultra-rapid orbit determination of GNSS satellites.Pres-ently,the traditional LS(least squares)+AR(autoregressive)and LS+MAR(multivariate autoregressive)hybrid methods stand as primary approaches for UT1-UTC ultra-short-term predictions(1-10 days).The LS+MAR hybrid method relies on the UT1-UTC and LOD(length of day)series.However,the correlation between LOD and first-order-difference UT1-UTC is stronger than that between LOD and UT1-UTC.In light of this,and with the aid of the first-order-difference UT1-UTC,we propose an enhanced LS+MAR hybrid method to UT1-UTC ultra-short-term prediction.By using the UT1-UTC and LOD data series of the IERS(International Earth Rotation and Reference Systems Service)EOP 14 C04 product,we conducted a thorough analysis and evaluation of the improved method's prediction performance compared to the traditional LS+AR and LS+MAR hybrid methods.According to the numerical results over more than 210 days,they demonstrate that,when considering the correlation information between the LOD and the first-order-difference UT1-UTC,the mean absolute errors(MAEs)of the improved LS+MAR hybrid method range from 21 to 934 μs in 1-10 days predictions.In comparison to the traditional LS+AR hybrid method,the MAEs show a reduction of 7-53 μs in 1-10 days predictions,with corresponding improvement percentages ranging from 1 to 28％.Similarly,when compared to the traditional LS+MAR hybrid method,the MAEs have a reduction of 5-42 μs in 1-10 days predictions,with corresponding improvement percentages ranging from 4-20％.Additionally,when aided by GNSS-derived LOD data series,the MAEs of improved LS+MAR hybrid method experience further reduction.

UT1-UTC ultra-short-term predictionRapid and ultra-rapid productsLS+AR and LS+MARCorrelationFirst-order-difference UT1-UTC

Fei Ye、Yunbin Yuan

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BeiDou High-precision Satellite Navigation and Location Service Hunan Engineering Research Center,Hunan Institute of Geomatics Sciences and Technology,Changsha 410007,China

State Key Laboratory of Geodesy and Earth's Dynamics,Innovation Academy for Precision Measurement Science and Technology,Chinese Academy of Sciences,Wuhan 430077,China

HNCORS Data Center,Hunan Institute of Geomatics Sciences and Technology,Changsha 410007,China

International Earth Rotation and Reference Systems Service(IERS)International GNSS Services(IGS)China Natural Science Fund,Chinascience and technology innovation Program of Hunan Province,ChinaResearch Foundation of the Department of Natural Resources of Hunan ProvinceHuBei Natural Science Fund,China

420040162023RC321720240105CH2020CFB329

2024

10.1016/j.geog.2024.01.003

大地测量与地球动力学(英文版)
中国地震局地震研究所

大地测量与地球动力学(英文版)

EI
影响因子：0.568
ISSN：1674-9847
年,卷(期)：2024.15(4)
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