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Experimental Quantification of the Sampling Uncertainty Associated with Measurements from PARSIVEL Disdrometers

机译:与来自PARSIVEL测速仪的测量相关的采样不确定度的实验量化

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The variability of the (rain) drop size distribution (DSD) in time and space is an intrinsic property of rainfall, which is of primary importance for various environmental fields such as remote sensing of precipitation, for example. DSD observations are usually collected using disdrometers deployed at the ground level. Like any other measurement of a physical process, disdrometer measurements are affected by noise and sampling effects. This uncertainty must be quantified and taken into account in further analyses. This paper addresses this issue for the Particle Size Velocity (PARSIVEL) optical disdrometer by using a large dataset corresponding to light and moderate rainfall and collected from two collocated PARSIVELs deployed during 15 months in Lausanne, Switzerland. The relative sampling uncertainty associated with quantities characterizing the DSD-namely the total concentration of drops N(t) and the median-volume diameter D(0)-is quantified for different temporal resolutions. Similarly, the relative sampling uncertainty associated with the estimates of the most commonly used weighted moments of the DSD (i.e., the rain-rate R, the radar reflectivity at horizontal polarization Z(h), and the differential reflectivity Z(dr)) is quantified as well for different weather radar frequencies. The relative sampling uncertainty associated with estimates of N(t) is below 13% for time steps longer than 60 s. For D(0), it is below 8% for D(0) values smaller than 1 mm. The associated sampling uncertainty for estimates of R is on the order of 15% at a temporal resolution of 60 s. For Z(h), the sampling uncertainty is below 9% for Z(h) values below 35 dBZ at a temporal resolution of 60 s. For Z(dr) values below 0.75 dB, the sampling uncertainty is below 36% for all temporal resolutions. These analyses provide relevant information for the accurate quantification of the variability of the DSD from disdrometer measurements.
机译:(雨)滴大小分布(DSD)在时间和空间上的可变性是降雨的固有属性,这对于各种环境领域(例如,降雨的遥感)至关重要。 DSD观测值通常是使用部署在地面上的测距仪收集的。像物理过程的任何其他测量一样,测速仪的测量也会受到噪声和采样效应的影响。必须对这种不确定性进行量化,并在进一步分析中加以考虑。本文通过使用与轻度和中度降雨相对应的大型数据集解决了粒径速度(PARSIVEL)光学测速仪的问题,该数据集是从在瑞士洛桑15个月内部署的两个并置的PARSIVEL收集的。对于不同的时间分辨率,量化了与表征DSD的量相关的相对采样不确定性,即DSD的总浓度N(t)和中位体积直径D(0)。类似地,与DSD最常用加权矩的估计相关的相对采样不确定性(即降雨率R,水平极化Z(h)时的雷达反射率和差分反射率Z(dr))为还可以针对不同的天气雷达频率进行量化。对于超过60 s的时间步长,与N(t)的估计值相关的相对采样不确定度低于13%。对于D(0),小于1mm的D(0)值低于8%。在60 s的时间分辨率下,估计R的相关采样不确定度约为15%。对于Z(h),在60 s的时间分辨率下,对于低于35 dBZ的Z(h)值,采样不确定度低于9%。对于低于0.75 dB的Z(dr)值,所有时间分辨率的采样不确定度均低于36%。这些分析提供了相关信息,可准确地从DSD测量中量化DSD的可变性。

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