A new hybrid method to improve the ultra-short-term prediction of LOD

被引:41
|
作者
Modiri, Sadegh [1 ,2 ]
Belda, Santiago [3 ,4 ]
Hoseini, Mostafa [5 ]
Heinkelmann, Robert [1 ]
Ferrandiz, Jose M. [4 ]
Schuh, Harald [1 ,2 ]
机构
[1] GFZ German Res Ctr Geosci, Potsdam, Germany
[2] Tech Univ Berlin, Inst Geodesy & Geoinformat Sci, Berlin, Germany
[3] Univ Valencia, IPL, LEO, Valencia, Spain
[4] Univ Alicante, UAVAC, Alicante, Spain
[5] Norwegian Univ Sci & Technol, Dept Civil & Environm Engn, Trondheim, Norway
基金
欧洲研究理事会;
关键词
LOD; EOP; Copula-based analysis; Prediction; EARTH ORIENTATION PARAMETERS; SINGULAR SPECTRUM ANALYSIS; FREE CORE NUTATION; AUTOCOVARIANCE PREDICTION; ROTATION; PRECIPITATION; COMBINATION; LENGTH; MODEL; TIME;
D O I
10.1007/s00190-020-01354-y
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
Accurate, short-term predictions of Earth orientation parameters (EOP) are needed for many real-time applications including precise tracking and navigation of interplanetary spacecraft, climate forecasting, and disaster prevention. Out of the EOP, the LOD (length of day), which represents the changes in the Earth's rotation rate, is the most challenging to predict since it is largely affected by the torques associated with changes in atmospheric circulation. In this study, the combination of Copula-based analysis and singular spectrum analysis (SSA) method is introduced to improve the accuracy of the forecasted LOD. The procedure operates as follows: First, we derive the dependence structure between LOD and the Z component of the effective angular momentum (EAM) arising from atmospheric, hydrologic, and oceanic origins (AAM + HAM + OAM). Based on the fitted theoretical Copula, we then simulate LOD from the Z component of EAM data. Next, the difference between LOD time series and its Copula-based estimation is modeled using SSA. Multiple sets of short-term LOD prediction have been done based on the IERS 05 C04 time series to assess the capability of our hybrid model. The results illustrate that the proposed method can efficiently predict LOD.
引用
收藏
页数:14
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