顾及大气角动量的Prophet-VAR日长变化预报方法

针对日长变化参数序列中蕴含的复杂非线性特征会严重影响其预报精度的问题，同时为探讨引入大气角动量序列是否有助于提升预报精度，提出一种Prophet拟合外推联合向量自回归(vector autoregression，VAR)残差补偿的组合模型用于日长预报。选用2008~2020年的日长变化参数序列进行实验，同时设计不顾及大气角动量序列的Prophet-AR以及传统的LS-AR两种方案进行对比。结果表明，3种方案的预报精度依次降低，既说明Prophet算法比LS算法能更好地拟合非线性信号，从而降低组合模型的预报误差，也说明当预报模型一致时，引入大气角动量序列能够有效提升预报精度。综上可知，顾及大气角动量的Prophet-VAR组合预报模型可以应用于高精度的日长变化预报。

Prophet-VAR Length-of-Day Variations Prediction Method Considering Atmospheric Angular Momentum

The complex nonlinear characteristics contained in the length-of-day variations parameter sequence seriously affects prediction accuracy.In order to explore whether the introduction of atmospheric angular momentum sequence can help improve the prediction accuracy,this paper proposes a Prophet fitting extrapolation joint vector autoregression residual compensation combined model to predict length-of-day variations.The sequence between 2008 and 2020 is selected for predicting experiments.At the same time,two schemes of Prophet-AR and traditional LS-AR,which ignore the atmospheric angular momentum sequence,are designed for comparison.The results show that the prediction accuracy of the three schemes decreases successively,which shows that Prophet algorithm can better fit the nonlinear signal than the LS algorithm to reduce the prediction error of the combined model.It also shows that the introduction of the atmospheric angular momentum sequence can effectively improve the prediction accuracy when the prediction model is consistent.It is comprehensively shown that the Prophet-VAR combined prediction model,which takes into account the atmosphere angular momentum,can be applied to high-precision prediction of length-of-day variations.