Real-time rapid prediction of variations of Earth’s rotational rate

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 satisfacto...

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Bibliographic Details
Published in:Chinese science bulletin 2008-04, Vol.53 (7), p.969-973
Main Authors: Wang, QiJie, Liao, DeChun, Zhou, YongHong
Format: Article
Language:English
Subjects:
Chemistry/Food Science
Earth Sciences
Engineering
Humanities and Social Sciences
Life Sciences
multidisciplinary
Physics
Science
Science (multidisciplinary)
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Summary: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.
ISSN:1001-6538
2095-9273
1861-9541
2095-9281
DOI:10.1007/s11434-008-0047-5