论文标题
一种用于GNSS衍生IWV时间序列均质化的新分割方法
A new segmentation method for the homogenisation of GNSS-derived IWV time-series
论文作者
论文摘要
均质化是改善观察数据使用以进行气候分析的重要和关键步骤。这项工作是由对在这种情况下尚未使用的长系列GNSS集成水蒸气（IWV）数据进行分析的动机。本文提出了一种新颖的分割方法，该方法整合了周期性偏见和异质，每月变化的方差。该方法包括首先使用可靠的估计器估算方差，然后在迭代中估算分割和周期性偏置。该策略允许使用动态编程算法，该算法仍然是最有效的精确算法来估计更改点位置的。该方法的统计性能通过数值实验评估。提出了对120个全球GNSS站的真实数据集的应用程序。该方法是在将在cran上可用的r软件包gnssegs中实现的。
Homogenization is an important and crucial step to improve the usage of observational data for climate analysis. This work is motivated by the analysis of long series of GNSS Integrated Water Vapour (IWV) data which have not yet been used in this context. This paper proposes a novel segmentation method that integrates a periodic bias and a heterogeneous, monthly varying, variance. The method consists in estimating first the variance using a robust estimator and then estimating the segmentation and periodic bias iteratively. This strategy allows for the use of the dynamic programming algorithm that remains the most efficient exact algorithm to estimate the change-point positions. The statistical performance of the method is assessed through numerical experiments. An application to a real data set of 120 global GNSS stations is presented. The method is implemented in the R package GNSSseg that will be available on the CRAN.
