使用来自IGS、Code和GFZ数据中心的快速产品,使用Arima和Kriging预测地球自转参数——比较

IF 0.7 Q4 ASTRONOMY & ASTROPHYSICS
Maciej Michalczak, M. Ligas, J. Kudrys
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引用次数: 1

摘要

摘要地球方位参数的实时预测是许多先进的大地测量和天文任务所必需的,包括地球和空间的定位和导航。地球自转参数(ERP)是地球自转参数(EOP)的一个子集,由地球极点坐标(PMx, PMy)和UT1-UTC(或日长- LOD)组成。本文介绍了利用普通克里格地质统计学方法和自回归综合移动平均(ARIMA)模型进行超短期(未来15天)和短期(未来30天)ERP预测。该贡献使用IGS, CODE和GFZ的快速GNSS产品EOP 14 12h以及IERS最终产品- IERS EOP 14 C04 12h (IAU2000A)。结果表明,ARIMA对各ERP的预测精度在超短预测中较好。ARIMA模式对15天预报的前几天的最大差异约为0.32 mas (PMx)、0.23 mas (PMy)和0.004 ms (LOD)。平均绝对预测误差(mape)在30 d预报最后几天的最大差值分别为1.91 ma (PMx)、0.30 ma (PMy)和0.026 ms (LOD),均优于克里金方法。对于所有erp,来自不同分析中心的时间序列mape差异不显著,其最大值约为0.05 mas (PMx), 0.04 mas (PMy)和0.005 ms (LOD)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of Earth Rotation Parameters with the Use of Rapid Products from IGS, Code and GFZ Data Centres Using Arima and Kriging – A Comparison
Abstract Real-time prediction of Earth Orientation Parameters is necessary for many advanced geodetic and astronomical tasks including positioning and navigation on Earth and in space. Earth Rotation Parameters (ERP) are a subset of EOP, consisting of coordinates of the Earth’s pole (PMx, PMy) and UT1-UTC (or Length of Day – LOD). This paper presents the ultra-short-term (up to 15 days into the future) and short-term (up to 30 days into the future) ERP prediction using geostatistical method of ordinary kriging and autoregressive integrated moving average (ARIMA) model. This contribution uses rapid GNSS products EOP 14 12h from IGS, CODE and GFZ and also IERS final products – IERS EOP 14 C04 12h (IAU2000A). The results indicate that the accuracy of ARIMA prediction for each ERP is better for ultra-short prediction. The maximum differences between methods for first few days of 15-day predictions are around 0.32 mas (PMx), 0.23 mas (PMy) and 0.004 ms (LOD) in favour of ARIMA model. The maximum differences of Mean Absolute Prediction Errors (MAPEs) on the last few days of 30-day predictions are 1.91 mas (PMx), 0.30 mas (PMy) and 0.026 ms (LOD) with advantage to kriging method. For all ERPs the differences of MAPEs for time series from various analysis centres are not significant and vary up to maximum value of around 0.05 mas (PMx), 0.04 mas (PMy) and 0.005 ms (LOD).
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