Correct, timely and meaningful interpretation of polarimetric weather radar observations requires an accurate understanding of hydrometeors and their associated microphysical processes along with well-developed techniques that automatize their recognition in both the spatial and temporal dimensions of the data. This study presents a novel technique for identifying different types of hydrometeors from quasi-vertical profiles (QVPs). In this new technique, the hydrometeor types are identified as clusters belonging to a hierarchical structure. The number of different hydrometeor types in the data is not predefined, and the method obtains the optimal number of clusters through a recursive process. The optimal clustering is then used to label the original data. Initial results using observations from the National Centre for Atmospheric Science (NCAS) X-band dual-polarization Doppler weather radar (NXPol) show that the technique provides stable and consistent results. Comparison with available airborne in situ measurements also indicates the value of this novel method for providing a physical delineation of radar observations. Although this demonstration uses NXPol data, the technique is generally applicable to similar multivariate data from other radar observations.

中文翻译：

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使用自上而下的迭代层次聚类方法对极化雷达测量的准垂直剖面进行水流分类

正确，及时和有意义地解释偏振气象雷达的观测结果，需要对水凝物及其相关的微物理过程有一个准确的了解，同时还要有先进的技术来使它们在数据的时空维度上都能够自动识别。这项研究提出了一种从准垂直剖面（QVP）识别不同类型水凝物的新颖技术。在这种新技术中，水凝物类型被识别为属于一个层次结构的簇。数据中不同水凝物类型的数量尚未预定义，并且该方法通过递归过程获得了最佳的簇数。然后，使用最佳聚类来标记原始数据。使用来自美国国家大气科学中心（NCAS）X波段双极化多普勒天气雷达（NXPol）的观测结果的初步结果表明，该技术提供了稳定且一致的结果。与可用机载原位测量的比较还表明，这种新颖的方法可提供雷达观测结果的物理描述。尽管此演示使用了NXPol数据，但该技术通常适用于来自其他雷达观测的类似多元数据。