Inter‐comparison of methods to homogenize daily relative humidity
Inter‐comparison of methods to homogenize daily relative humidity
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DOI：
10.1002/joc.5488
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发表时间：
2018-06
期刊：
影响因子：
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通讯作者：
B. Chimani;V. Venema;A. Lexer;Konrad Andre;I. Auer;J. Nemec
中科院分区：
文献类型：
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作者：
B. Chimani;V. Venema;A. Lexer;Konrad Andre;I. Auer;J. Nemec
三种均匀化方法（ACMANT，MASH和HOMOP）已被评估为他们的效率在10天的每日相对湿度数据。创建并扰动基于奥地利台站的同质替代数据集，以模拟非同质、真实的时间序列（“验证数据集”）。创建了两个验证数据集（“简单”和“复杂”）。在这两组数据中，断裂的幅度取决于一年中的时间和测量值。它们在缺失值的数量上不同，特别是在中断信号是否被白色噪声干扰上。在后一种情况下，考虑到随机测量误差和其他物理因素的变化，噪声也会发生变化。评价表明，真实的数据和替代数据集之间的统计特征高度一致。均质化方法进行了比较，在他们的能力，以检测中断和重现同质的替代数据集。为了评价最终数据集，分析了分布、趋势和均方根误差（RMSE）。改进的时间序列的百分比取决于所考虑的评价参数。在使用“复杂”验证数据集时，得到改进的台站较少。由于大量的断裂和小的信噪比，通过均匀化改善数据对于所有使用的方法都是不理想的，每种方法都有其优点和缺点。ACMANT和HOMOP方法的质量相当，ACMANT解决了较少的站点，但将较少的站点错误地声明为同质。为了了解对真实的数据的影响，ACMANT被应用于均匀化每日奥地利时间序列的相对湿度。虽然一些台站的数据质量可以通过均匀化得到改善，但并非所有时间序列都是如此。在进一步使用前，应对均质化时间序列进行最终评价，以确保其质量。
Three homogenization methods (ACMANT, MASH and HOMOP) have been evaluated for their efficiency in homogenizing daily relative humidity data. A homogeneous surrogate data set based on Austrian stations was created and perturbed to simulate inhomogeneous, realistic time series (“validation data sets”). Two validation data sets (“simple” and “complex”) were created. In both data sets the magnitude of the breaks depends on the time of year and the measured values. They differ in the number of missing values and especially on whether the break signal was perturbed by white noise. In the latter case, the noise also changed to take into account changes in random measurements errors and other physical factors. The evaluation showed high agreement in statistical characteristics between the real data and the surrogate data set. The homogenization methods were compared in their ability both to detect breaks and to reproduce the homogeneous surrogate data set. For the evaluation of the final data set the distribution, trends and root‐mean‐square error (RMSE) were analysed. The percentage of improved time series depends on the evaluation parameter considered. Less stations were improved when using the “complex” validation data set. Because of the large number of breaks and the small signal‐to‐noise ratio, an improvement of the data by homogenization was non‐ideal for all methods used, with each having its advantages and disadvantages. The quality of the ACMANT and HOMOP methods is comparable, with ACMANT solving less stations but declaring less stations falsely as homogeneous. To get an impression of the influence on real data, ACMANT was applied to homogenize daily Austrian time series of relative humidity. While the quality of data from some stations can be improved through the homogenization, this is not the case for all time series. A final evaluation of homogenized time series should be performed to ensure their quality before further use.