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Small-Scale Variability of the Raindrop Size Distribution and Its Effect on Areal Rainfall Retrieval

机译:雨滴大小分布的小尺度变异及其对地区降水量反演的影响

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The drop size distribution (DSD) describes the microstructure of liquid precipitation. The high variability of the DSD reflects the variety of microphysical processes controlling raindrop properties and affects the retrieval of rainfall. An analysis of the effects of DSD subgrid variability on areal estimation of precipitation is presented. Data used were recorded with a network of disdrometers in Ardeche, France. DSD variability was studied over two typical scales: 5 km x 5 km, similar to the ground footprint size of the Global Precipitation Measurement (GPM) spaceborne weather radar, and 2.8 km x 2.8 km, an operational pixel size of the Consortium for Small-Scale Modeling (COSMO) numerical weather model. Stochastic simulation was used to generate high-resolution grids of DSD estimates over the regions of interest, constrained by experimental DSDs measured by disdrometers. From these grids, areal DSD estimates were derived. The error introduced by assuming a point measurement to be representative of the areal DSD was quantitatively characterized and was shown to increase with the size of the considered area and with drop size and to decrease with the integration time. The controlled framework allowed for the accuracy of retrieval algorithms to be investigated. Rainfall variables derived by idealized simulations of GPM- and COSMO-style algorithms were compared to subgrid distributions of the same variables. While rain rate and radar reflectivity were well represented, the estimated drop concentration and mass-weighted mean drop diameter were often less representative of subgrid values.
机译:液滴尺寸分布(DSD)描述了液体沉淀的微观结构。 DSD的高可变性反映了控制雨滴特性的各种微物理过程,并影响了降雨的恢复。提出了DSD亚网格变化对降水面积估算的影响的分析。使用的数据通过法国Ardeche的测速仪网络记录。在两种典型的尺度上研究了DSD的可变性:5 km x 5 km(与全球降水测量(GPM)太空气象雷达的地面足迹大小相似)和2.8 km x 2.8 km(小规模联盟的运行像素大小)比例模型(COSMO)数值天气模型。随机模拟被用来生成感兴趣区域上DSD估计值的高分辨率网格,并受到由测速仪测量的实验性DSD的约束。从这些网格中,得出了区域DSD估计。定量表征假设点测量代表区域DSD所引入的误差,并显示该误差随所考虑区域的大小和液滴大小而增加,并随积分时间而减小。受控框架允许研究检索算法的准确性。通过GPM和COSMO风格算法的理想模拟得出的降雨变量与相同变量的子网格分布进行了比较。虽然可以很好地表示降雨率和雷达反射率,但估计的液滴浓度和质量加权平均液滴直径通常不足以代表次网格值。

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