Abstract
Polar motion depicts the slow changes in the locations of the poles due to the earth’s internal instantaneous axis of rotation. The LS+AR model is recognized as one of the best models for polar motion prediction. Through statistical analysis of the time series of the LS+AR model’s short-term prediction residuals, we found that there is a good correlation of model prediction residuals between adjacent terms. These indicate that the preceding model prediction residuals and experiential adjustment matrixes can be used to correct the next prediction results, thereby forming a new LS+AR model with additional error correction that applies to polar motion prediction. Simulated predictions using this new model revealed that the proposed method can improve the accuracy and reliability of polar motion prediction. In fact, the accuracies of ultra short-term and short-term predictions using the new model were equal to the international best level at present.
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References
Akulenko L D, Kumakshev S A, Markov Y G. Motion of the Earth’s pole. Dokl Phys, 2002, 47: 78–84
Akulenko L D, Kumakshev S A, Markov Y G, et al. A model for the polar motion of the deformable Earth adequate for astrometric data. Astron Rep, 2002, 46: 74–82
Akulenko L D, Kumakshev S A, Markov Y G, et al. Forecasting the polar motions of the deformable Earth. Astron Rep, 2002, 46: 858–866
Akyilmaz O, Kutterer H. Prediction of Earth rotation parameters by fuzzy inference systems. J Geodesy, 2004, 78: 82–93
Kalarus M, Kosek W, Schuh H. Current results of the Earth orientation parameters prediction comparison campaign. In: Capitaine N, ed. Proceedings of the journées 2007, systèmes deréférence spatio-temporels “The celestial reference frame for thefuture”. Observatoire de Paris Systemes de Référence Temps-Espace UMR8630/CNRS, Paris, France. 159–162
Kalarus M, Kosek W, Schuh H. Current results of the Earth orientation parameters prediction comparison campaign. In: AGU Fallmeeting 2007, San Francisco, California, Earth’s Reference System and Rotation: Geodesy and geoscience III posters. Eos Trans AGU 88(52), Fall Meet Suppl, Abstract No. G 43C-1480
Kalarus M, Kosek W, Schuh H. Summary of the Earth orientation parameters prediction comparison campaign. EGU General Assembly 2008, EGU abstract: EGU2008-A-00595
Kalarus M, Schch H, Kozek W, et al. Achievements of Earth orientation parameters prediction comparison campaign. J Geodesy, 2010, 84: 587–596
Freedman A P, Steppe J A, Dickey J O, et al. The short-term predic tion of universal time and length of day using atmospheric angular momentum. J Geophys Res, 1994, 99: 6981–6996
Gross R S, Eubanks T M, Steppe J A, et al. A Kalman filter-based approach to combining independent Earth orientation series. J Geodesy, 1998, 72: 215–235
Chao B F. Predictability of the Earth’s polar motion. J Geodesy, 1985, 59: 81–93
Kosek W. Future improvements in EOP prediction. In: Kenyon S, Pacino M C, Marti U, eds. Proceedings of the 2009 IAG Symposium. Geodesy for Planet Earth. Heildberg: Springer, 2009. 513
Kosek W. Causes of prediction errors of pole coordinates data. In: Proceedings of the 6th Orlov’s Conference “The Study of the Earth as a Planet by Methods of Geophysics, Geodesy and Astronomy”, June 22–24, 2009. MAO NAS of Ukraine, Kiev, Ukraine. 96–103
Schuh H, Ulrich M, Egger D, et al. Prediction of Earth orientation parameters by artificial neural networks. J Geodesy, 2002, 76: 247–258
Zheng D W, Chen Z G. Prediction of the Earth rotation parameters. Ann Shanghai Astron Observ, 1982, 4: 116–120
Wang Q J. The earth rotation changes prediction based on neural network technology. Ph. D. Dissertation. Shanghai: Shanghai Astronomical Observatory, 2007
Yang Y X, Cui X Q. Adaptively robust filter with multi adaptive factors. Surv Rev, 2008, 40: 260–270
Xu X Q, Zhou Y H. The research of the earth orientation parameters high accuracy prediction method. J Flight Contr, 2010, 29: 70–76
Xu J Y. GM (1,1) model based on intercept correction and application to polar motion prediction. J Geodesy Geodyn, 2010, 30: 88–91
Wang X Z, Tao B Z, Qiu W N, et al. Advanced Surveying Adjustment. Beijing: Surveying and Mapping Press, 2006. 41–43
Box G E P, Jenkins G M. Time Series Analysis: Forecasting and Control. San Francisco: Holden-Day, 1970
Yang S Z, Wu Y, Xuan J P, et al. Time Series Analysis in Engineering Application. Wuhan: Huazhong University of Science and Technology Press, 2007. 254–275
Zhang S W, Lei Y J, Feng Y Q, et al. Application of MATLAB in Time Series Analysis. Xi’an: Xi’an Electronic Science and Technology University Press, 2007. 130–133
Chen Z S. Statistical Information Processing Based on to matlab7.0. Changsha: Hunan Science and Technology Press, 2005. 227–245
Giannakis G B, Mendel J M. Cumulant-based order determination of non-Gaussian ARMA models. IEEE Transactions on Acoustics, Speech and Signal Processing, 1990, 38: 1411–1423, doi: 10.1109/29.57576
Qi C J. Algorithm Analysis and Application of Digital Signal Processing. Beijing: China Machine Press, 2005. 183–197
Turner D S. The role of judgement in macroeconomic forecasting. J Forecast, 1990, 9: 315–345
Wallis K F, Whitley J D. Sources of error in forecasts and expectations: UK economic models. J Forecast, 1991, 10: 231–253
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Yao, Y., Yue, S. & Chen, P. A new LS+AR model with additional error correction for polar motion forecast. Sci. China Earth Sci. 56, 818–828 (2013). https://doi.org/10.1007/s11430-012-4572-3
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DOI: https://doi.org/10.1007/s11430-012-4572-3