利用端部效应改正的LS+AR模型进行日长变化预报
Prediction of LOD Change Based on the LS and AR Model with Edge Effect Corrected
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摘要: 针对LS+AR模型在日长变化预报过程中存在的端部效应现象,采用时间序列分析方法对日长变化的序列进行外推,形成一个新的序列,用这个新序列求得LS模型的系数,然后再用LS+AR模型对日长变化原始序列进行预报。实验结果表明,利用端部效应改正的LS+AR模型与LS+AR模型相比,在日长变化的预报精度上有一定的改善,尤其在跨度为中长期时改善更为明显。Abstract: Aiming to resolve the edge effect in the process of predicting length of day(LOD) by the least squares and autoregressive(LS+AR) model,we employed a time series analysis model to extrapolate LOD series and produce a new series.Then,we used the new series to solve the coefficients for the LS model.At last,we used the LS+AR model to predict the LOD series again.By comparing the accuracy of LOD prediction by edge-effect corrected LS+AR and that by LS+AR,we conclude that edge-effect corrected LS+AR can improve the prediction accuracy,especially for medium-term and long-term predictions.
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