OBJECT-ORIENTED HYDROMETEOR CLASSIFICATION BASED ON POPLARIMETRIC RADAR OBSERVATIONS
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摘要: 双偏振雷达的主要用途之一就是降水粒子识别，目前主流的方法为模糊逻辑分类(FL)，但是该方法仅使用单个距离库的信息，易受到噪声的影响。基于模糊逻辑方法的不足，利用聚类分析，提出了一种面向对象的降水粒子分类方法，即在模糊逻辑分类基础上考虑距离库与不同降水粒子的距离以及周围距离库类别信息。基于广州S波段双偏振雷达的观测数据进行降水粒子识别验证，结果表明使用的面向对象的降水粒子识别方法可有效地降低噪声对分类结果的影响，且符合降水粒子的微物理特征。Abstract: One of the main uses of dual-polarization radar is hydrometeor classification. At present, the mainstream hydrometeor classification algorithm is fuzzy logic classification (FL), but this method only uses information from a range bin, which is vulnerable to noise. Based on the limitation of the fuzzy logic method, this paper employs cluster analysis and proposes an object-oriented precipitation particle classification method, which considers the distance between the distance database and different hydrometeor and the surrounding range bin category information based on fuzzy logic classification. To verify the accuracy of hydrometeor classification, the observed data from the Guangzhou S-band dual-polarization radar is utilized in this paper. The results show that the object-oriented hydrometeor classification algorithm used in this paper can effectively reduce the impact of noise on the classification results, and the classification results are consistent with the microphysical characteristics of hydrometeors.
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