Hydrometeor classification from polarimetric radar measurements: a clustering approach
Hydrometeor classification from polarimetric radar measurements: a clustering approach
复制标题
极化雷达测量的水凝物分类：聚类方法
DOI：
10.5194/amt-8-149-2015
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发表时间：
2015
影响因子：
3.8
通讯作者：
A. Berne
中科院分区：
文献类型：
--
作者：
J. Grazioli;D. Tuia;A. Berne
抽象。提出了一种数据驱动的方法，从偏振天气雷达收集的测量水凝物的分类。在第一步骤中，确定可以从大的偏振数据集可靠地识别的水凝物类的最佳数量（nopt）。这是通过一个无监督的聚类技术指导下的标准相关的数据相似性和空间平滑的分类图像。在第二步中，nopt集群被分配到适当的水凝物类通过人类的解释和比较与其他分类技术的输出。所提出的方法的主要创新是无监督的部分：水凝物类不是先验定义的，而是从数据中学习的。该方法适用于由X波段偏振天气雷达在两个领域的活动（约50降水事件中使用的本研究）收集的数据。在数据集中已经发现了七种水凝物类别（nopt = 7），它们被识别为小雨（LR）、雨（RN）、大雨（HR）、融化的雪（MS）、冰晶/小聚集体（CR）、聚集体（AG）和雾凇冰粒（RI）。
Abstract. A data-driven approach to the classification of hydrometeors from measurements collected with polarimetric weather radars is proposed. In a first step, the optimal number of hydrometeor classes (nopt) that can be reliably identified from a large set of polarimetric data is determined. This is done by means of an unsupervised clustering technique guided by criteria related both to data similarity and to spatial smoothness of the classified images. In a second step, the nopt clusters are assigned to the appropriate hydrometeor class by means of human interpretation and comparisons with the output of other classification techniques. The main innovation in the proposed method is the unsupervised part: the hydrometeor classes are not defined a priori, but they are learned from data. The approach is applied to data collected by an X-band polarimetric weather radar during two field campaigns (from which about 50 precipitation events are used in the present study). Seven hydrometeor classes (nopt = 7) have been found in the data set, and they have been identified as light rain (LR), rain (RN), heavy rain (HR), melting snow (MS), ice crystals/small aggregates (CR), aggregates (AG), and rimed-ice particles (RI).