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The impact of tipping-bucket raingauge measurement errors on design rainfall for urban-scale applications

机译:自卸桶雨量计测量误差对城市规模应用设计雨量的影响

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The tipping-bucket rain gauge is known to underestimate rainfall at high intensities because of the rain water amount that is lost during the tipping movement of the bucket. The related biases are known as systematic mechanical errors and, since their effect increases with rain intensity, have a significant influence on the derived statistics of rainfall extremes. A correction procedure for rain intensity data sets is proposed in this paper based on the dynamic calibration of the gauge at both fine and coarse resolution, either in direct form or after proper downscaling of the available figures. The effect of systematic mechanical errors on the assessment of design rainfall for urban-scale applications is quantified based on two rain-rate data sets recorded at very different resolutions in time. A random cascade downscaling algorithm is used for the processing of coarse-resolution data so that correction can be applied at suitable time scales. The resulting depth-duration-frequency curves obtained from the original and corrected data sets are used to quantify the impact of uncorrected rain-intensity measurements on design rainfall and the related statistical parameters.
机译:known斗雨量计已知会低估高强度下的降雨,因为在the斗的倾覆运动中会损失掉雨水。相关的偏差被称为系统性机械误差,由于其影响随着降雨强度的增加而增加,因此对得出的极端降雨统计数据具有重大影响。本文提出了一种雨量数据集的校正程序,该方法基于量规在精细和粗糙分辨率下的动态校准,可以是直接形式,也可以是在适当缩小现有数据的尺度后进行的。基于以不同分辨率及时记录的两个降雨率数据集,量化了系统机械误差对城市规模设计降雨评估的影响。随机级联缩减算法用于处理粗分辨率数据,以便可以在适当的时间尺度上应用校正。从原始数据集和校正后的数据集获得的深度-持续时间-频率曲线将用于量化未经校正的雨强测量值对设计降雨和相关统计参数的影响。

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