摘要
水凝物相态识别(Hydrometeor Classification Algorithm,HCA)是从微物理角度分析天气特征的一个重要研究方向,研究水凝物粒子的分类,对于冰雹、降雨、降雪的观测均有重大意义。分析并总结了目前模糊逻辑水汽分类算法(Fuzzy logic Hydrometeor Classification,FHC)的优势,但是其局限性在于FHC较大地依赖于散射模拟,对冰相粒子相态识别的结果不确定。此外,目前也缺少有效的相态识别验证方法,这进一步限制了雷达双极化特征的应用。因此,发展其他聚类算法进行相态识别并建立分类标准体系是未来主要研究方向。
The Hydrometeor Classification Algorithm(HCA)is one of the main vegetation research direction to analyze weather characteristics from microphysical perspective. The hydrometeor classification algorithm is of great significance for the observation of hail,rainfall and snowfall. This paper reviewed the advantages of the classical Fuzzy logic Hydrometeor Classification(FHC),including Neuro-Fuzzy Hydrometeor Classification(NF-HC),Support Vector Machine Hydrometeor Classification(SVM-HC)and deep learning methods. We also introduced the details of the application of hydrometeor classification in the hail,rainfall and snowfall and summarized the verification method,including aircraft measurement verification,numerical simulation verification,and ground observation comparison. In addition,the current problems of FHC are proposed,including the setting of membership function parameters and the hydrometeor phase identification of ice phase particles. The current research lacks an effective and convenient method for HCA,which limits the application of dual-polarization weather radar. Finally,directions for future research to HCA were forecasted.
作者
林青云
何建新
王皓
史朝
陈婉婷
Lin Qingyun;He Jianxin;Wang Hao;Shi Zhao;Chen Wanting(Chengdu University of Infonnati on Technology,Chengdu 610225,China)
出处
《遥感技术与应用》
CSCD
北大核心
2020年第3期517-526,共10页
Remote Sensing Technology and Application
基金
国家重点研发计划(2018YFC1506104)
四川省科技厅应用基础研究项目(2019YJ0316)
成都信息工程大学科研基金(J201502)。
关键词
水凝物相态识别
双偏振天气雷达
模糊逻辑
Hydrometeor phase identification
Dual-polarization weather radar
Fuzzy logic
