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 ca...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 934μs in 1-10 days predictions.In comparison to the traditional LS+AR hybrid method,the MAEs show a reduction of 7-53μs 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 5-42μs 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.展开更多
现有的UT1-UTC预报模式在进行周期项与残差项拟合分离时,通常没有考虑最小二乘拟合序列的端部畸变现象(数据处理中称为端部效应),预报精度难以取得较大改善。针对最小二乘拟合存在的端部畸变现象,首先采用时序分析方法在UT1-UTC序列两...现有的UT1-UTC预报模式在进行周期项与残差项拟合分离时,通常没有考虑最小二乘拟合序列的端部畸变现象(数据处理中称为端部效应),预报精度难以取得较大改善。针对最小二乘拟合存在的端部畸变现象,首先采用时序分析方法在UT1-UTC序列两端进行数据延拓,形成一个新序列,然后用新序列求解最小二乘外推模型系数,最后再联合最小二乘外推模型及神经网络对UT1-UTC序列进行预测。结果表明,在UT1-UTC序列端部增加延拓数据,可以有效地抑制最小二乘拟合序列的端部畸变,相对于常规的最小二乘外推模型,基于端部效应改善的最小二乘(Edge-effect Corrected Least Squares,ECLS)外推模型的UT1-UTC中长期预报精度改善明显。展开更多
As the participants of Earth Orientation Parameters Combination of Prediction Pilot Project(EOPC PPP),Sternberg Astronomical Institute of Moscow State University(SAI) and Shanghai Astronomical Observatory(SHAO) have a...As the participants of Earth Orientation Parameters Combination of Prediction Pilot Project(EOPC PPP),Sternberg Astronomical Institute of Moscow State University(SAI) and Shanghai Astronomical Observatory(SHAO) have accumulated ～1800 days of Earth Orientation Parameters(EOP) predictions since2012 till 2017, which were up to 90 days into the future, and made by four techniques: auto-regression(AR), least squares collocation(LSC), and neural network(NNET) forecasts from SAI, and least-squares plus auto-regression(LS+AR) forecast from SHAO. The predictions were finally combined into SAISHAO COMB EOP prediction. In this work we present five-year real-time statistics of the combined prediction and compare it with the uncertainties of IERS bulletin A predictions made by USNO.展开更多
基于2010-2019年公开发布的国际测地/天体测量学VLBI服务组织(International VLBI Service for Geodesy and Astrometry,IVS)加强观测资料,本文对加强型UT1观测数据及快速服务产品进行了系统介绍,对不同分析中心采用的软件及解算策略进...基于2010-2019年公开发布的国际测地/天体测量学VLBI服务组织(International VLBI Service for Geodesy and Astrometry,IVS)加强观测资料,本文对加强型UT1观测数据及快速服务产品进行了系统介绍,对不同分析中心采用的软件及解算策略进行总结;在此基础上,按照“EOPI”产品中不同类型观测序列进行分类,对比分析了不同分析中心、不同观测类型的UT1-UTC结果差异。结果表明,不同分析中心的解算精度最大差异在0.02ms左右;对比不同观测类型,INT2观测解算精度优于“INT1”、“INT3”和“VLBA”观测,与IERS C04产品相比,UT1-UTC精度为0.02-0.03ms,其余EOP产品精度约0.04ms。展开更多
基金supported by China Natural Science Fund,China(No.42004016)the science and technology innovation Program of Hunan Province,China(No.2023RC3217)+1 种基金Research Foundation of the Department of Natural Resources of Hunan Province(Grant No:20240105CH)HuBei Natural Science Fund,China(No.2020CFB329).
文摘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 934μs in 1-10 days predictions.In comparison to the traditional LS+AR hybrid method,the MAEs show a reduction of 7-53μs 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 5-42μs 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.
文摘现有的UT1-UTC预报模式在进行周期项与残差项拟合分离时,通常没有考虑最小二乘拟合序列的端部畸变现象(数据处理中称为端部效应),预报精度难以取得较大改善。针对最小二乘拟合存在的端部畸变现象,首先采用时序分析方法在UT1-UTC序列两端进行数据延拓,形成一个新序列,然后用新序列求解最小二乘外推模型系数,最后再联合最小二乘外推模型及神经网络对UT1-UTC序列进行预测。结果表明,在UT1-UTC序列端部增加延拓数据,可以有效地抑制最小二乘拟合序列的端部畸变,相对于常规的最小二乘外推模型,基于端部效应改善的最小二乘(Edge-effect Corrected Least Squares,ECLS)外推模型的UT1-UTC中长期预报精度改善明显。
基金supported by Discipline Innovative Engineering Plan of Modern Geodesy and Geodynamics(grant No.B17033)NSFC grants(11673049,11773057)RFBR grant(N16-05-00753)
文摘As the participants of Earth Orientation Parameters Combination of Prediction Pilot Project(EOPC PPP),Sternberg Astronomical Institute of Moscow State University(SAI) and Shanghai Astronomical Observatory(SHAO) have accumulated ～1800 days of Earth Orientation Parameters(EOP) predictions since2012 till 2017, which were up to 90 days into the future, and made by four techniques: auto-regression(AR), least squares collocation(LSC), and neural network(NNET) forecasts from SAI, and least-squares plus auto-regression(LS+AR) forecast from SHAO. The predictions were finally combined into SAISHAO COMB EOP prediction. In this work we present five-year real-time statistics of the combined prediction and compare it with the uncertainties of IERS bulletin A predictions made by USNO.
文摘基于2010-2019年公开发布的国际测地/天体测量学VLBI服务组织(International VLBI Service for Geodesy and Astrometry,IVS)加强观测资料,本文对加强型UT1观测数据及快速服务产品进行了系统介绍,对不同分析中心采用的软件及解算策略进行总结;在此基础上,按照“EOPI”产品中不同类型观测序列进行分类,对比分析了不同分析中心、不同观测类型的UT1-UTC结果差异。结果表明,不同分析中心的解算精度最大差异在0.02ms左右;对比不同观测类型,INT2观测解算精度优于“INT1”、“INT3”和“VLBA”观测,与IERS C04产品相比,UT1-UTC精度为0.02-0.03ms,其余EOP产品精度约0.04ms。
