Achievements of the Earth orientation parameters prediction comparison campaign
Achievements of the Earth orientation parameters prediction comparison campaign
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DOI：
10.1007/s00190-010-0387-1
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发表时间：
2010-10-01
影响因子：
4.4
通讯作者：
Zotov, L.
中科院分区：
文献类型：
--
作者：
Kalarus, M.;Schuh, H.;Zotov, L.
许多先进的大地测量和天文任务，包括地球和空间的定位和导航，都需要在国际天体参考系和地球参考系之间进行精确转换。为了在观测时进行这种转换，即实时应用，需要准确预测地球方向参数（EOP）。2005年10月开始的地球方向参数预测比较运动（EOP PCC）是为了评估EOP预测的准确性而组织的。本文总结了EOP PCC经过近两年半的运行活动所取得的成果。参与者提交的超短期（未来10天）、短期（30天）和中期（500天）EOP预测以IERS EOP 05 C04系列为参考，采用基于平均绝对预测误差的相同统计方法进行评估。在给定的预测历元中，作为所有可用提交的加权平均值计算的EOP预测组合系列也进行了评估。组合的系列表现得非常好，就像一些单独的系列一样，特别是那些使用大气角动量预报的系列。EOP PCC的一个主要结论是，没有一种预测技术对所有EOP分量和所有预测区间都是最好的。
Precise transformations between the international celestial and terrestrial reference frames are needed for many advanced geodetic and astronomical tasks including positioning and navigation on Earth and in space. To perform this transformation at the time of observation, that is for real-time applications, accurate predictions of the Earth orientation parameters (EOP) are needed. The Earth orientation parameters prediction comparison campaign (EOP PCC) that started in October 2005 was organized for the purpose of assessing the accuracy of EOP predictions. This paper summarizes the results of the EOP PCC after nearly two and a half years of operational activity. The ultra short-term (predictions to 10 days into the future), short-term (30 days), and medium-term (500 days) EOP predictions submitted by the participants were evaluated by the same statistical technique based on the mean absolute prediction error using the IERS EOP 05 C04 series as a reference. A combined series of EOP predictions computed as a weighted mean of all submissions available at a given prediction epoch was also evaluated. The combined series is shown to perform very well, as do some of the individual series, especially those using atmospheric angular momentum forecasts. A main conclusion of the EOP PCC is that no single prediction technique performs the best for all EOP components and all prediction intervals.