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A technique to reduce the edge-effect in least squares extrapolation for enhanced Earth orientation prediction
Studia Geophysica Et Geodaetica ( IF 0.9 ) Pub Date : 2020-05-30 , DOI: 10.1007/s11200-021-0546-2
Danning Zhao , Yu Lei

A well-known property of the classical least squares (LS) extrapolation is that a fit is best in the middle of the time span of observed data, but worse near the beginning and end of the time span. This phenomenon is called the edge effect in data processing. The goal of this work is to reduce the edge effect to improve predictions of the Earth rotation parameters (ERP), which comprise the Earth’s polar motion and rotation angle (the difference between the smoothed principal form of universal time UT1 and the coordinated universal time UTC) because a best LS fitting near the end of the data used is better for extrapolation. We first use the LS extrapolation for models consisting of one polynomial and two sinusoids in combination with an autoregressive (AR) technique to extend the observed time series forward. We then re-estimate the LS extrapolation model from the extended time series to reduce the edge-effect. ERP predictions are subsequently generated by combining of the edge effect reduced LS extrapolation and AR technique, denoted as ERLS + AR. Through an example, we demonstrate that the edge-effect in the observed data fitting can be reduced by re-estimating the LS extrapolation model with the extended time series. To validate the ERLS + AR method, we calculate the ERP predictions up to 365 days into the future year-by-year for the 4-year period from 2014 to 2017 using the data from the previous 8 years. The results show that the accuracy of the short-term predictions obtained by the ERLS + AR method is comparable with that achieved by the LS + AR approach in terms of the mean absolute error (MAE). However, an accuracy improvement is found mostly for long-term predictions based on the ERLS + AR method. The MAE for the UT1 ? UTC and polar motion predictions can decrease by approximately 15% to 20%, respectively. It is therefore suggested embedding the ERLS extrapolation algorithm into the existing ERP prediction procedure.



中文翻译:

减少边缘效应最小二乘外推以增强地球方向预测的技术

经典最小二乘(LS)外推法的一个众所周知的属性是,拟合在观测数据的时间跨度的中间最好,但在时间跨度的开始和结束时更差。这种现象称为数据处理中的边缘效应。这项工作的目的是减少边缘效应,以改善对地球自转参数(ERP)的预测,该参数包括地球的极运动和自转角(世界时UT1的平滑主形式与世界时协调时UTC之间的差) ),因为在所使用数据的末尾附近最好的LS拟合对于外推更好。我们首先将LS外推用于由一个多项式和两个正弦曲线组成的模型,并结合自回归(AR)技术将观察到的时间序列向前扩展。然后,我们从扩展的时间序列中重新估计LS外推模型,以减少边缘效应。随后,通过将边缘效应缩减的LS外推法和AR技术(称为ERLS + AR)相结合,生成ERP预测。通过一个示例,我们证明了可以通过使用扩展的时间序列重新估计LS外推模型来减少观测数据拟合中的边缘效应。为了验证ERLS + AR方法,我们使用前8年的数据,计算了2014年至2017年这4年期间,未来365年(逐年)的ERP预测。结果表明,就平均绝对误差(MAE)而言,通过ERLS + AR方法获得的短期预测的准确性与通过LS + AR方法获得的短期预测的准确性可比。然而,发现精度提高主要是基于ERLS + AR方法进行的长期预测。UT1的MAE?UTC和极移预测可以分别减少大约15%到20%。因此,建议将ERLS外推算法嵌入到现有的ERP预测过程中。

更新日期:2020-05-30
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