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Radar subpixel-scale rainfall variability and uncertainty: Lessons learned from observations of a dense rain-gauge network

机译:雷达亚像素级降雨的变异性和不确定性:从密集雨量计网络的观测中学到的经验教训

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Runoff and flash flood generation are very sensitive to rainfall's spatial and temporal variability. The increasing use of radar and satellite data in hydrological applications, due to the sparse distribution of rain gauges over most catchments worldwide, requires furthering our knowledge of the uncertainties of these data. In 2011, a new superdense network of rain gauges containing 14 stations, each with two side-by-side gauges, was installed within a 4 km~2 study area near Kibbutz Galed in northern Israel. This network was established for a detailed exploration of the uncertainties and errors regarding rainfall variability within a common pixel size of data obtained from remote sensing systems for timescales of 1 min to daily. In this paper, we present the analysis of the first year's record collected from this network and from the Shacham weather radar, located 63 km from the study area. The gauge-rainfall spatial correlation and uncertainty were examined along with the estimated radar error. The nugget parameter of the inter-gauge rainfall correlations was high (0.92 on the 1 min scale) and increased as the timescale increased. The variance reduction factor (VRF), representing the uncertainty from averaging a number of rain stations per pixel, ranged from 1.6% for the 1 min timescale to 0.07% for the daily scale. It was also found that at least three rain stations are needed to adequately represent the rainfall (VRF < 5%) on a typical radar pixel scale. The difference between radar and rain gauge rainfall was mainly attributed to radar estimation errors, while the gauge sampling error contributed up to 20% to the total difference. The ratio of radar rainfall to gauge-areal-averaged rainfall, expressed by the error distribution scatter parameter, decreased from 5.27 dB for 3 min timescale to 3.21 dB for the daily scale. The analysis of the radar errors and uncertainties suggest that a temporal scale of at least 10 min should be used for hydrological applications of the radar data. Rainfall measurements collected with this dense rain gauge network will be used for further examination of small-scale rainfall's spatial and temporal variability in the coming years.
机译:径流和山洪暴发对降雨的时空变化非常敏感。由于全世界大多数集水区雨量计的稀疏分布,雷达和卫星数据在水文应用中的使用越来越多,这需要进一步了解这些数据的不确定性。 2011年,在以色列北部基布兹·加莱德(Kibbutz Galed)附近一个4 km〜2的研究区域内,安装了一个新的超稠密雨量计网络,该网络包含14个站,每个雨量计具有两个并列的雨量计。该网络的建立是为了详细探索与降雨变化有关的不确定性和误差,这些不确定性和误差在从遥感系统获取的数据的普通像素大小内,持续时间为1分钟到每天。在本文中,我们对从该网络和距研究区63公里的Shacham天气雷达收集的第一年记录进行了分析。检验了雨量雨量的空间相关性和不确定性以及估计的雷达误差。表间降雨相关性的金块参数较高(1分钟尺度上为0.92),并且随着时间尺度的增加而增大。方差减小因子(VRF)表示平均每个像素雨点数量的不确定性,范围从1分钟时间范围的1.6%到每日范围的0.07%。还发现,至少需要三个雨站才能在典型的雷达像素尺度上充分表示降雨(VRF <5%)。雷达与雨量计降雨之间的差异主要归因于雷达估计误差,而雨量计采样误差占总差异的比例高达20%。用误差分布散射参数表示的雷达降雨量与平均面积降雨量之比,从3分钟时标的5.27 dB降至每日标度的3.21 dB。对雷达误差和不确定性的分析表明,对于雷达数据的水文应用,应使用至少10分钟的时间尺度。通过这种密集的雨量计网络收集的降雨测量值将用于进一步检查未来几年小规模降雨的空间和时间变化。

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