当前位置：
X-MOL 学术
›
Int. J. Climatol.
›
论文详情
Our official English website, www.x-mol.net, welcomes your
feedback! (Note: you will need to create a separate account there.)
Uncertainty evaluation of Climatol's adjustment algorithm applied to daily air temperature time series
International Journal of Climatology ( IF 3.5 ) Pub Date : 2020-09-29 , DOI: 10.1002/joc.6854 Oleg Skrynyk 1, 2 , Enric Aguilar 1 , Jose Guijarro 3 , Luc Yannick Andreas Randriamarolaza 1, 4 , Sergiy Bubin 5
International Journal of Climatology ( IF 3.5 ) Pub Date : 2020-09-29 , DOI: 10.1002/joc.6854 Oleg Skrynyk 1, 2 , Enric Aguilar 1 , Jose Guijarro 3 , Luc Yannick Andreas Randriamarolaza 1, 4 , Sergiy Bubin 5
Affiliation
|
The present study investigated the uncertainty associated with Climatol's adjustment algorithm applied to daily minimum and maximum air temperature. The uncertainty quantification was performed based on several numerical experiments and the benchmark data that were created in the framework of the INDECIS project. Using a complex approach, the uncertainty was evaluated at different levels of detail (day‐to‐day evaluation through formalism of random functions and six statistical metrics) and time resolution (daily and yearly). However, only the main source of potential residual errors was considered, namely station signals introduced into a raw data set to be homogenized/adjusted. Other influencing factors, such as the averaged correlation between a candidate and references, were removed from the analysis or kept almost unchanged. According to our calculations, the Climatol's adjustment uncertainty, evaluated on the daily scale, varies over time. The width of the residual errors distribution in summer months is substantially less compared with wintertime. The slight seasonality is also observed in the means of the residual errors. The uncertainty evaluation based on the statistical metrics usually neglect such seasonal non‐stationarity of the residual errors providing only assessments averaged over time. On the other hand, the metrics provide detailed information regarding both types of the residual errors, systematic and scatter. The metrics values confirmed good capability of the Climatol software to remove the systematic errors related to jumps in the means, while the scatter errors are removed from the raw time series less efficiently. On the yearly scale, the uncertainty evaluation was performed for the yearly temperature data and several climate extreme indices. Both types of errors are removed well in the yearly time series of the air temperature and the extreme indices. The metrics values showed a significant reduction of the Climatol's adjustment uncertainty. A substantial decrease of the linear trend errors in the yearly time series can also be observed.
中文翻译：
用于日常气温时间序列的Climatol调整算法的不确定性评估
本研究调查了应用于每日最低和最高气温的Climatol调整算法的不确定性。基于INDECIS项目框架中创建的几个数值实验和基准数据进行了不确定性量化。使用复杂的方法，对不确定性进行了不同程度的详细评估（通过随机函数和六个统计指标的形式进行日常评估）和时间分辨率（每日和每年）。但是，仅考虑了潜在残留误差的主要来源，即将站信号引入要进行均质/调整的原始数据集中。其他影响因素，例如候选人与参考文献之间的平均相关性，已从分析中删除或几乎保持不变。根据我们的计算，按日标评估的Climatol调整不确定性随时间变化。与冬季相比，夏季剩余误差分布的宽度要小得多。在残留误差的方法中也观察到轻微的季节性变化。基于统计指标的不确定性评估通常忽略了残留误差的季节性非平稳性，仅提供了随时间平均的评估。另一方面，度量标准提供有关两种类型的系统误差和分散误差的详细信息。指标值证实了Climatol软件具有良好的能力，可以消除与均值跳跃相关的系统误差，而从原始时间序列中消除散射误差的效率较低。在每年的规模上 对年度温度数据和几个气候极端指数进行了不确定性评估。在空气温度和极端指数的年度时间序列中，两种类型的误差都可以很好地消除。指标值显示出Climatol调整不确定性的显着降低。还可以观察到年度时间序列中线性趋势误差的显着减少。
更新日期：2020-09-29
中文翻译：
用于日常气温时间序列的Climatol调整算法的不确定性评估
本研究调查了应用于每日最低和最高气温的Climatol调整算法的不确定性。基于INDECIS项目框架中创建的几个数值实验和基准数据进行了不确定性量化。使用复杂的方法，对不确定性进行了不同程度的详细评估（通过随机函数和六个统计指标的形式进行日常评估）和时间分辨率（每日和每年）。但是，仅考虑了潜在残留误差的主要来源，即将站信号引入要进行均质/调整的原始数据集中。其他影响因素，例如候选人与参考文献之间的平均相关性，已从分析中删除或几乎保持不变。根据我们的计算，按日标评估的Climatol调整不确定性随时间变化。与冬季相比，夏季剩余误差分布的宽度要小得多。在残留误差的方法中也观察到轻微的季节性变化。基于统计指标的不确定性评估通常忽略了残留误差的季节性非平稳性，仅提供了随时间平均的评估。另一方面，度量标准提供有关两种类型的系统误差和分散误差的详细信息。指标值证实了Climatol软件具有良好的能力，可以消除与均值跳跃相关的系统误差，而从原始时间序列中消除散射误差的效率较低。在每年的规模上 对年度温度数据和几个气候极端指数进行了不确定性评估。在空气温度和极端指数的年度时间序列中，两种类型的误差都可以很好地消除。指标值显示出Climatol调整不确定性的显着降低。还可以观察到年度时间序列中线性趋势误差的显着减少。
京公网安备 11010802027423号