首页> 外文期刊>Journal of Applied Meteorology and Climatology >Quantification of the Small-Scale Spatial Structure of the Raindrop Size Distribution from a Network of Disdrometers
【24h】

Quantification of the Small-Scale Spatial Structure of the Raindrop Size Distribution from a Network of Disdrometers

机译:从测速仪网络量化雨滴大小分布的小规模空间结构

获取原文
获取原文并翻译 | 示例
           

摘要

The spatial structure of the raindrop size distribution (DSD) conveys crucial information for reliable quantitative estimation of rainfall using remote sensing techniques. To investigate this question, a network of 16 optical disdrometers has been deployed over a typical weather radar pixel (similar to 1 x 1 km(2)) in Lausanne, Switzerland. A set of 36 rainfall events has been classified according to three types: convective, transitional, and frontal. In a first step, the spatial structure of the DSD is quantified using spatial correlation for comparison with the literature, showing good agreement with previous studies. The spatial structure of important quantities related to the DSD-namely, the total concentration of drops N-t, the mass-weighted diameter D-m, and the rain rate R-is quantified using variograms. Results clearly highlight that DSD fields are organized and not randomly distributed even at a scale below 1 km. Moreover, convective-type rainfall exhibits larger variability of the DSD than do transitional and frontal rainfall. The temporal resolution is shown to have an influence on the results: increasing time steps tend to decrease the spatial variability. This study presents a possible application of such information by quantifying the error associated with the use of point measurements as areal estimates at larger scales. Analyses have been conducted for different sizes of domain ranging from 100 x 100 to 1000 x 1000 m(2). As expected, this error is increasing with the size of the domain. For instance, for a domain of similar to 1000 x 1000 m(2), the error associated with rain-rate estimates is on the order of 25% for all types of rain.
机译:雨滴大小分布(DSD)的空间结构传达了至关重要的信息,从而可以使用遥感技术对雨量进行可靠的定量估计。为了调查这个问题,在瑞士洛桑的一个典型的天气雷达像素(类似于1 x 1 km(2))上部署了一个由16个光学测距仪组成的网络。根据三种类型对36个降雨事件进行了分类:对流,过渡和额叶。第一步,使用空间相关性对DSD的空间结构进行量化,以便与文献进行比较,表明与先前的研究有很好的一致性。重要的量的空间结构与DSD相关,即滴的总浓度N-t,质量加权直径D-m和降雨率R-,使用方差图量化。结果清楚地表明,即使在小于1 km的范围内,DSD场也是有组织的,并且不是随机分布的。此外,对流型降雨比过渡和正面降雨表现出更大的DSD变异性。结果表明时间分辨率对结果有影响:增加时间步长往往会降低空间变异性。这项研究通过量化与使用点测量作为较大比例的面积估计值相关的误差,提出了此类信息的可能应用。已经对范围从100 x 100到1000 x 1000 m(2)的不同大小的域进行了分析。如预期的那样,此错误随着域的大小而增加。例如,对于类似于1000 x 1000 m(2)的域,对于所有类型的降雨,与降雨率估算相关的误差约为25%。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号