Real-time rapid prediction of variations of Earth’s rotational rate
Real-time rapid prediction of variations of Earth’s rotational rate
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DOI：
10.1007/s11434-008-0047-5
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发表时间：
2008-05
影响因子：
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通讯作者：
Qi-jie Wang;D. Liao;Yonghong Zhou
中科院分区：
文献类型：
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作者：
Qi-jie Wang;D. Liao;Yonghong Zhou
实时快速预报地球自转速率的变化具有重要的科学意义和实用价值。然而，由于地球自转速率变化的复杂时变特性（即，日长（LOD），通常很难获得满意的预测传统的线性时间序列分析方法。本研究采用非线性人工神经网络（ANN）来预测LOD的变化。ANN模型的拓扑结构是通过最小化预测的均方根误差（RMSE）来确定的。考虑到LOD变化与大气环流运动的密切关系，将大气轴向角动量（AAM）业务预报序列作为人工神经网络模型的附加输入，对LOD变化进行了1-5天的实时快速预报。结果表明，将AAM业务预报序列引入ANN模型后，LOD预报有了明显的改善。
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.