Abstract
Real-time rapid prediction of variations of the Earth’s rotational rate is of great scientific and practical importance. However, due to the complicated time-variable characteristics of variations of the Earth’s rotational rate (i.e., length of day, LOD), it is usually difficult to obtain satisfactory predictions by conventional linear time series analysis methods. This study employs the nonlinear artificial neural networks (ANN) to predict the LOD variations. The topology of the ANN model is determined by minimizing the root mean square errors (RMSE) of the predictions. Considering the close relationships between the LOD variations and the atmospheric circulation movement, the operational prediction series of axial atmospheric angular momentum (AAM) is incorporated into the ANN model as an additional input in the real-time rapid prediction of LOD variations with 1–5 days ahead. The results show that the LOD prediction is significantly improved after introducing the operational prediction series of AAM into the ANN model.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
McCarthy D D, Luzum B J. Prediction of Earth orientation. Bull Geod, 1991, 65: 18–22
Akyilmaz O, Kutterer H. Prediction of Earth rotation parameters by fuzzy inference systems. J Geodesy, 2004, 8: 82–93
Kalarus M, Schuh H, Kosek W, et al. The application of artificial neural networks and autoregressive techniques for Earth orientation parameters prediction. Geophys Res Abs, 2005, 7: 01753, SRef-ID: 1607-7962/gra/EGU05-A-01753
Jacek M Z. Introduction to Artificial Neural Systems. New York: West Pub Company, 1992
Jiao L C. Theory of Artificial Neural System. Xi’an: Xi’an University of Electronic Techology Press, 1996
Ojo A K, Games G K. Selecting ANN models for geoidal undulation prediction. In: Proceedings of the 2003 International Conference on Artificial Intelligence. Vol 1, 2003 June 23–26, USA. Las Vegas: CSREA Press, 2003. 191–192
Xu D, Wu Z. System Analysis and Design Based on Matlab 6.x-Articial Neural System. Xi’an: Xi’an University of Electronic Techology Press, 2002
Egger D. Neuronales Netz prädiziert Erdrotation. In: Allgemeine Vermessungsnachrichten (AVN), 1992, 11/12, S: 517–524
Schuh H, Ulrich M, Egger D, et al. Prediction of Earth orientation parameters by artificial neural networks. J Geodesy, 2002, 76:247–258
Zhou Y H, Zheng D W, Yu N H, et al. Movement of Earth rotation and activities of atmosphere and ocean. Chin Sci Bull, 2001, 46: 881–888
Zhong M, Yan H M, Zhu Y Z. The investigation of atmospheric angular momentum as a contributor to polar wobble and length of day change with AMIP II GCM data. Adv Atm Sci, 2002, 19(2):287–296
Ma L H, Han Y B. Atmospheric excitation of time variable length-of-day on seasonal scales. Chin J Astro Astrophys, 2006, 6(1):120–124
McCarthy D D, Petit G. IERS Convention (2003). IERS Technical Note 32. Paris: Observatoire de Paris, 2003
Zhou Y H, Salstein D A, Chen J L. Revised atmospheric excitation function series related to Earth’s variable rotation under consideration of surface topography. J Geophys Res, 2005, 111, D12108
Moorthi S, Pan H L, Caplan P. Changes to the 2001 NCEP operational MRF/AVN global analysis/forecast system. NWS Tech Procedures Bull, 2001, 484: 1–14
Author information
Authors and Affiliations
Corresponding author
Additional information
Supported by the National Natural Science Foundation of China (Grant Nos. 10673025 and 10633030) and Science & Technology Commission of Shanghai Municipality (Grant No. 06DZ22101)
About this article
Cite this article
Wang, Q., Liao, D. & Zhou, Y. Real-time rapid prediction of variations of Earth’s rotational rate. Chin. Sci. Bull. 53, 969–973 (2008). https://doi.org/10.1007/s11434-008-0047-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11434-008-0047-5