关键词: 双偏振雷达, 降水粒子分类, 贝叶斯网络, 贝叶斯分类, 不确定知识, 离散化, BNT

Abstract: The over-reliance on expert experience and model preset errors in traditional precipitation particle classification algorithms are discussed. This paper proposes a dual-polarization hydrometeor classification (HC) method based on discrete attribute Bayesian NeTwork (BNT). Firstly, the value of polarization parameters obtained by the dual-polarization meteorological radar is discretized to generate a discretization standard, and the training data set is made according to the discretization standard. Then the training data set is used to learn the structure of the Bayesian network and the conditional probability table matching the structure of the Bayesian network. At last, additional information is added to calculate the prior probability of each precipitation particle class, and the Bayesian network classifier is composed of Bayesian network structure and conditional probability table. The trained Bayesian network classifier classifies the precipitation particles according to the maximum posterior probability criterion and compares the evaluation results with the fuzzy logic algorithm. Experiments show that this method can effectively distinguish different precipitation particles.

Key words: dual polarization radar, hydrometeor classification, Bayesian networks, Bayesian classification, uncertain knowledge, discretization, BNT(Bayesian NeTwork )