Sampling Issues in Estimating Radar Variables from Disdrometer Data
Sampling Issues in Estimating Radar Variables from Disdrometer Data
复制标题
从测距仪数据估计雷达变量的采样问题
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Paul L. Smith
中科院分区：
文献类型：
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作者：
Paul L. Smith
从伽玛分布的雨滴种群抽样的模拟表明，显着的偏差和实质性的错误可能会出现在偏振雷达变量的估计的基础上的雨滴种群的样本与disdrometers。在差分反射率Zdr的估计中，即使是几百滴的样本也会出现0.5 dB或更大的偏差和RMS误差;在反射率ZH或特定差分相位Kdp的估计中也会出现显著的偏差和误差。结果表明，将需要非常大的样本，以获得足够的代表性的人口特征的许多雷达应用。他们还建议，需要更多地关注用于开发极化率估计器或水凝物分类算法的disdrometer数据的样本大小。
AbstractSimulation of sampling from gamma-distributed raindrop populations demonstrates that significant biases and substantial errors can occur in estimates of polarimetric radar variables based on samples of raindrop populations obtained with disdrometers. Biases and RMS errors of 0.5 dB or more in estimates of differential reflectivity Zdr can occur with samples of even a few hundred drops; significant biases and errors also occur in estimates of reflectivity ZH or specific differential phase Kdp. The results indicate that very large samples would be required to obtain adequate representation of the population characteristics for many radar applications. They also suggest that greater attention is needed to the sample sizes in the disdrometer data used in developing polarimetric rainfall-rate estimators or hydrometeor classification algorithms.