Estimates of the statistical two-dimensional spatial structure in rain over a small network of disdrometers
Estimates of the statistical two-dimensional spatial structure in rain over a small network of disdrometers
复制标题
小型测速仪网络上降雨统计二维空间结构的估计
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
M. L. Larsen
中科院分区：
文献类型：
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作者：
A. R. Jameson;M. L. Larsen
摘要
对降雨可变性的微观物理理解需要对时间和空间所有维度上的不同水滴大小进行统计表征。在时间上，已经有了几种雨滴计数的统计特征。然而，时间结构和空间结构既不对等，也不容易翻译。虽然最近有关于雨中一维空间相关函数的报道，但在空间各向同性的假设下，它们只能被假定为表示二维相关函数。然而，到目前为止，还没有实际观测到降雨地区的(2D)空间相关函数。造成这一不足的两个原因是，财政上和物质上不可能组装一个即使是数百米的密集仪器网络，更不用说超过几公里了。因此，必须对区域上的所有测量结果进行稀疏采样。然后，必须使用来自可用观测的插值法来估计密集的数据网络。在这项工作中，一个由19个光学碟形仪组成的网络在100米乘71米的区域内每分钟都能产生液滴光谱的观测结果。然后将这些数据内插到1米分辨率的网格中。然后，傅立叶技术产生2D空间相关函数的估计。使用这种技术的初步例子发现，更稳定的小雨在空间上去相关的速度比对流雨快，但在这两种情况下，2D空间相关函数都是各向异性的，反映了影响降雨到达地面的物理过程的不对称性，而数值微物理模式没有考虑到这种不对称性。
Abstract
Microphysical understanding of the variability in rain requires a statistical characterization of different drop sizes both in time and in all dimensions of space. Temporally, there have been several statistical characterizations of raindrop counts. However, temporal and spatial structures are neither equivalent nor readily translatable. While there are recent reports of the one-dimensional spatial correlation functions in rain, they can only be assumed to represent the two-dimensional (2D) correlation function under the assumption of spatial isotropy. To date, however, there are no actual observations of the (2D) spatial correlation function in rain over areas. Two reasons for this deficiency are the fiscal and the physical impossibilities of assembling a dense network of instruments over even hundreds of meters much less over kilometers. Consequently, all measurements over areas will necessarily be sparsely sampled. A dense network of data must then be estimated using interpolations from the available observations. In this work, a network of 19 optical disdrometers over a 100 m by 71 m area yield observations of drop spectra every minute. These are then interpolated to a 1 m resolution grid. Fourier techniques then yield estimates of the 2D spatial correlation functions. Preliminary examples using this technique found that steadier, light rain decorrelates spatially faster than does the convective rain, but in both cases the 2D spatial correlation functions are anisotropic, reflecting an asymmetry in the physical processes influencing the rain reaching the ground not accounted for in numerical microphysical models.