Attributes of Several Methods for Detecting Discontinuities in Mean Temperature Series
Attributes of Several Methods for Detecting Discontinuities in Mean Temperature Series
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DOI：
10.1175/jcli3662.1
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发表时间：
2006-03
影响因子：
4.9
通讯作者：
A. Degaetano
中科院分区：
文献类型：
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作者：
A. Degaetano
摘要采用模拟年温度序列，比较了7种均质化方法。采用似然比检验的两种方法在识别平均值的适度（0.33°C； 0.6标准差异常）变化方面通常优于其他方法。通过这些方法检测到的强加移位的百分比与基于依赖于有关潜在移位位置的先验元数据信息的测试的百分比相似。这些方法，以及两阶段回归方法，也最适合于在单个时间序列中识别和放置多个位移。虽然回归过程能够更好地检测由相对较短的时间间隔分隔的多个中断，但在其公布的形式中，它的I型错误率高于预期。在当前操作使用的基于元数据的过程中也发现了这个问题。似然检验受到差异序列和短序列趋势存在的强烈影响。
Abstract Simulated annual temperature series are used to compare seven homogenization procedures. The two that employ likelihood ratio tests routinely outperform other methods in their ability to identify modest (0.33°C; 0.6 standard deviation anomaly) shifts in the mean. The percentage of imposed shifts that are detected by these methods is similar to that based on tests that rely on a priori metadata information concerning the position of potential shifts. These methods, along with a two-phase regression approach, are also best at identifying and placing multiple shifts within a single time series. Although the regression procedure is better able to detect multiple breaks that are separated by relatively short time intervals, in its published form it suffers from a higher-than-expected Type I error rate. This was also found to be a problem with a metadata-based procedure currently in operational use. The likelihood tests are strongly influenced by the presence of trends in the difference series and shor...