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

作　　者：WANG QiJie LIAO DeChun ZHOU YongHong 

机构地区：[1]Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030 China [2]Graduate University of Chinese Academy of Sciences, Beijing 100039, China [3]School of Info-Physics and Geomatics Engineening, Central South University, Changsha 410083, China

出　　处：《Chinese Science Bulletin》2008年第7期969-973,共5页

基　　金：the National Natural Science Foundation of China (Grant Nos. 10673025 and 10633030);Science & Technology Commission of Shanghai Municipality (Grant No. 06DZ22101)

摘　　要：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 con-ventional linear time series analysis methods. This study employs the nonlinear artificial neural net-works (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 pre-diction is significantly improved after introducing the operational prediction series of AAM into the ANN model.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 con- ventional linear time series analysis methods. This study employs the nonlinear artificial neural net-works （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.

关 键 词：地球转动频率 变量 预测方法 地球科学 

分 类 号：P3[天文地球—地球物理学]