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首页> 外文学位 >Development of a polarimetric radar based hydrometeor classification algorithm for winter precipitation.
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Development of a polarimetric radar based hydrometeor classification algorithm for winter precipitation.

机译：基于极化雷达的冬季降水水汽分类算法的开发。

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摘要

The nation-wide WSR-88D radar network is currently being upgraded for dual-polarized technology. While many convective, warm-season fuzzy-logic hydrometeor classification algorithms based on this new suite of radar variables and temperature have been refined, less progress has been made thus far in developing hydrometeor classification algorithms for winter precipitation. Unlike previous studies, the focus of this work is to exploit the discriminatory power of polarimetric variables to distinguish the most common precipitation types found in winter storms without the use of temperature as an additional variable. For the first time, detailed electromagnetic scattering of plates, dendrites, dry aggregated snowflakes, rain, freezing rain, and sleet are conducted at X-, C-, and S-band wavelengths. These physics-based results are used to determine the characteristic radar variable ranges associated with each precipitation type. A variable weighting system was also implemented in the algorithm's decision process to capitalize on the strengths of specific dual-polarimetric variables to discriminate between certain classes of hydrometeors, such as wet snow to indicate the melting layer.;This algorithm was tested on observations during three different winter storms in Colorado and Oklahoma with the dual-wavelength X- and S-band CSU-CHILL, C-band OU-PRIME, and X-band CASA IP1 polarimetric radars. The algorithm showed success at all three frequencies, but was slightly more reliable at X-band because of the algorithm's strong dependence on KDP. While plates were rarely distinguished from dendrites, the latter were satisfactorily differentiated from dry aggregated snowflakes and wet snow. Sleet and freezing rain could not be distinguished from rain or light rain based on polarimetric variables alone. However, high-resolution radar observations illustrated the refreezing process of raindrops into ice pellets, which has been documented before but not yet explained. Persistent, robust patterns of decreased ρ HV, enhanced ZDR, and an inflection point around enhanced ZH occurred over the exact depth of the surface cold layer indicated by atmospheric soundings during times when sleet was reported at the surface. It is hypothesized that this refreezing signature is produced by a modulation of the drop size distribution such that smaller drops preferentially freeze into ice pellets first. The melting layer detection algorithm and fall speed spectra from vertically pointing radar also captured meaningful trends in the melting layer depth, height, and mean ρHV during this transition from freezing rain to sleet at the surface. These findings demonstrate that this new radar-based winter hydrometeor classification algorithm is applicable for both research and operational sectors.
机译：目前，针对双极化技术的WSR-88D雷达网络正在升级。虽然已经改进了许多基于这种新的雷达变量和温度对流的暖季模糊逻辑水文气象分类算法，但迄今为止，在开发冬季降水的水文气象分类算法方面进展甚微。与以前的研究不同，这项工作的重点是利用极化变量的区分能力来区分冬季暴风雨中最常见的降水类型，而无需使用温度作为附加变量。首次在X波段，C波段和S波段对板，树枝状晶体，干聚集的雪花，雨，冰冻雨和雨夹雪进行详细的电磁散射。这些基于物理学的结果用于确定与每种降水类型相关的特征性雷达变量范围。该算法的决策过程中还采用了可变加权系统，以利用特定的双极化变量的优势来区分某些类型的水凝物，例如湿雪来指示融化层。双波长X波段和S波段CSU-CILL，C波段OU-PRIME和X波段CASA IP1极化雷达在科罗拉多州和俄克拉荷马州的不同冬季风暴。该算法在所有三个频率上均显示出成功，但是由于该算法对 K DP 的强烈依赖性，因此在X波段上的可靠性稍高。尽管极少将板与树突区分开来，后者与干聚集的雪花和湿的雪花令人满意地区分。仅仅根据极化变量不能将雨夹雪和冻雨与雨或小雨区分开。但是，高分辨率的雷达观测显示了雨滴重新冻结为冰粒的过程，该过程以前已有记录，但尚未解释。持久，稳健的模式降低ρ HV ，增强 Z DR ，并在增强的周围出现拐点Z H 发生在表面冰雹被报告期间的大气探测所指示的表面冷层的确切深度上。假设通过调节液滴大小分布来产生这种重新冻结的特征，使得较小的液滴优先首先冻结成冰块。垂直指向雷达的融化层检测算法和坠落速度谱还捕获了从冰冻雨点到冰雪过渡期间融化层深度，高度和平均ρ HV 的有意义趋势。雨夹雪在表面。这些发现表明，这种基于雷达的新型冬季水汽流分类算法适用于研究和运营部门。

著录项

  • 作者

    Thompson, Elizabeth Jennifer.;

  • 作者单位

    Colorado State University.;

  • 授予单位 Colorado State University.;
  • 学科 Engineering Electronics and Electrical.;Remote Sensing.;Atmospheric Sciences.
  • 学位 M.S.
  • 年度 2012
  • 页码 161 p.
  • 总页数 161
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-17 11:42:34

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