The Important Role Played by Atmospheric Angular Momentum in the Non-linear Prediction of the Variations in the Length of Day

The variation in the length of day has complicated time-varying characteristics and the traditional method for linear time series analysis is always difficult to obtain good effect of prediction. If the non-linear artificial neural network technique is adopted to predict the variation in the length of day, the topological structure of the network model is determined by the least square error method. By taking into account the close relation between the variation in the length of day and the general circulation of atmosphere, the axial sequence of atmospheric angular momentum is introduced into the forecasting model of neural network. The results show that the forecast accuracy is significantly improved by taking advantage of the combination of the length of day and the atmospheric angular momentum sequence in comparison with the individual adoption of the data of the length of day.