We assess the performance of different break detection methods on three sets of benchmark data sets, each consisting of 120 daily time series of integrated water vapor differences. These differences are generated from the Global Positioning System (GPS) measurements at 120 sites worldwide, and the numerical weather prediction reanalysis (ERA‐Interim) integrated water vapor output, which serves as the reference series here. The benchmark includes homogeneous and inhomogeneous sections with added nonclimatic shifts (breaks) in the latter. Three different variants of the benchmark time series are produced, with increasing complexity, by adding autoregressive noise of the first order to the white noise model and the periodic behavior and consecutively by adding gaps and allowing nonclimatic trends. The purpose of this “complex experiment” is to examine the performance of break detection methods in a more realistic case when the reference series are not homogeneous. We evaluate the performance of break detection methods with skill scores, centered root mean square errors (CRMSE), and trend differences relative to the trends of the homogeneous series. We found that most methods underestimate the number of breaks and have a significant number of false detections. Despite this, the degree of CRMSE reduction is significant (roughly between 40% and 80%) in the easy to moderate experiments, with the ratio of trend bias reduction is even exceeding the 90% of the raw data error. For the complex experiment, the improvement ranges between 15% and 35% with respect to the raw data, both in terms of RMSE and trend estimations.

中文翻译：

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均质化GPS集成水汽时间序列：基于综合数据集的基准破损检测方法

我们在三组基准数据集上评估不同的中断检测方法的性能，每组基准数据集由120个每日的综合水汽差异时间序列组成。这些差异是由全球120个站点的全球定位系统（GPS）测量结果以及数字天气预报重新分析（ERA-Interim）集成水蒸气输出产生的，在此用作参考序列。该基准包括均质和非均质部分，后者中增加了非气候变化（断裂）。通过将一阶自回归噪声添加到白噪声模型和周期性行为中，并通过添加间隙并允许非气候趋势来连续生成基准时间序列的三种不同变体，其复杂性不断提高。此“复杂实验”的目的是在参考序列不一致的情况下，在更实际的情况下检查中断检测方法的性能。我们用技能评分，居中均方根误差（CRMSE）和相对于同类序列趋势的趋势差异来评估突破检测方法的性能。我们发现大多数方法低估了中断次数，并且有大量错误检测。尽管如此，在易于适度的实验中，CRMSE降低的程度还是很明显的（大约在40％到80％之间），趋势偏差降低的比例甚至超过了原始数据误差的90％。对于复杂的实验，就RMSE和趋势估计而言，相对于原始数据而言，改进幅度在15％至35％之间。