徐海龙, 乔书波, 林家乐. 利用混沌特性和Volterra自适应算法的极移短期预报[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20200505
引用本文: 徐海龙, 乔书波, 林家乐. 利用混沌特性和Volterra自适应算法的极移短期预报[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20200505
XU Hailong, QIAO Shubo, LIN Jiale. Short-term Prediction for Polar Motion Based on Chaos and Volterra Adaptive Algorithm[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20200505
Citation: XU Hailong, QIAO Shubo, LIN Jiale. Short-term Prediction for Polar Motion Based on Chaos and Volterra Adaptive Algorithm[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20200505

利用混沌特性和Volterra自适应算法的极移短期预报

Short-term Prediction for Polar Motion Based on Chaos and Volterra Adaptive Algorithm

  • 摘要: 针对极移序列复杂的时变特点,首次将极移作为混沌考虑并提出Volterra自适应算法的高精度极移短期预报方法。首先利用小数据量法分别计算得到Xp分量和Yp分量的最大Lyapunov指数,证明了极移的混沌特性。然后应用二阶Volterra自适应算法进行两个算例预报实验,结果分别与地球定向参数预报比较活动(EarthOrientation Parameters Prediction Comparison Campaign,EOP PCC)结果和国际地球自转与参考系服务(IERS)官方预报产品Bulletin A对比分析。实验对比发现,与EOP PCC的最佳方法相比本文方法精度更高,Xp分量预报精度提升较为明显,Yp分量预报精度也略有提高;与Bulletin A相比时,两种预报结果的精度互有利弊,本文方法在预报前期精度更高。实例进一步证明了所提出的方法在短期极移预报中可以取得良好的结果,尤其在预报跨度较小时精度更优。

     

    Abstract:   Objectives   The polar motion (PM) is an important part of the Earth rotation parameters (ERP). the prediction error of ERP can be effectively reduced by improving the prediction accuracy of PM.   Methods   Aiming at the complex time variation characteristics of PM, a high-precision prediction method based on the Volterra adaptive algorithm was proposed for the first time, which taken the PM series as chaos. Firstly, the maximum Lyapunov exponent was calculated by using the small data sets method. This analysis proves that the PM has chaotic characteristics. Then two experiments were performed for the second order Volterra adaptive algorithm.   Results   The results of the experimental results were compared with the Earth Orientation Parameters Prediction Comparison Campaign (EOP PCC) and Bulletin A, the official forecast product of IERS. The results show that the prediction accuracy of this method is higher than that of EOP PCC, and Xp component prediction accuracy is improved significantly, Yp component can be also slightly improved. Compared with Bulletin A, the accuracy of the two forecast results has advantages and disadvantages.   Conclusions   The example further proves that our method can obtain good forecast results in the short-term polar motion forecast, especially the prediction period is more accurate than that of the small period.

     

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