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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.
However, due to the complicated time-variable characteristics of variations of the Earth’s rotational rate (ie, length of day, LOD), it is usually difficult to achieve 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, 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 pred iction series of AAM into the ANN model.