摘要
为准确且精细地识别云相态,提出一种基于模糊逻辑识别云相态的优化算法,基于不同云粒子特征参数对T函数系数进行了调整。考虑了回波反射率因子衰减和温度对云相态识别准确性的影响,利用毫米波云雷达订正后的回波反射率因子、径向速度、谱宽和微波辐射计探测的连续时空温度,作为优化后的模糊逻辑算法的输入参数。优化后的模糊逻辑算法在原有云粒子相态(冰晶、雪花、混合相态、液态云滴、毛毛雨和雨滴)识别的基础上,还可实现对过冷水和暖云滴的识别。利用该算法对2022年2月6日陕西省西安市一次降雪过程的云粒子相态进行识别,将近地面的云粒子相态结果与同址地面降水现象仪记录的降水粒子相态进行对比,二者探测的相态有较高的一致性,说明优化后的算法能准确且精细地识别云粒子相态。
Objective Phase state recognition of cloud particles is an important content in cloud physics research and also significant for inverting other cloud microphysical parameters.With the development of remote sensing detection technology,researchers have developed various recognition methods of cloud phase particles,such as decision tree recognition,classic statistical decision recognition,neural networks,clustering algorithms,and fuzzy logic algorithms.However,due to the complex characteristics of cloud particles,the radar information corresponding to different particles does not have absolute features,and there may be some overlap degree.Thus,recognition algorithms based on rigid threshold conditions are not well suitable for phase recognition and classification of cloud particles.Fortunately,the fuzzy logic recognition algorithm can improve this rigid threshold defect,but the accuracy of the T-function coefficients in fuzzy logic will directly determine the accuracy of the recognition results.To accurately and finely identify cloud phase states,we propose an optimization algorithm based on fuzzy logic to recognize the phase states of cloud particles.The optimized fuzzy logic algorithm can also recognize supercooled water and warm cloud droplets compared to the original fuzzy logic algorithm which can only recognize ice crystals,snow,mixed phases,liquid cloud droplets,drizzle,and raindrops.Methods Based on the induction and summary of a large number of aircraft and remote sensing instruments simultaneously observed data and comprehensive characteristic consideration of different cloud types,we adjust and optimize the T-function coefficients of fuzzy logic.A table of T-function coefficient parameters for different cloud phase particles is constructed as shown in Table 2.The corrected reflectivity factor,radial velocity,and spectral width detected by millimeter wave cloud radars with high spatiotemporal resolution,as well as the temperature detected by microwave radiometer,are adopted as input parameters for the opt
作者
袁云
狄慧鸽
高宇星
曹梅
华灯鑫
Yuan Yun;Di Huige;Gao Yuxing;Cao Mei;Hua Dengxin(School of Mechanical and Precision Instrument Engineering,Xi'an University of Technology,Xi'an 710048,Shaanxi,China;Xi'an Meteorological Administration,Xi'an 710016,Shaanxi,China)
出处
《光学学报》
EI
CAS
CSCD
北大核心
2024年第12期92-100,共9页
Acta Optica Sinica
基金
国家自然科学基金重点项目(42130612)
国家自然科学基金国家重大科研仪器研制项目(41627807)
西安市科协青年人才托举计划项目(959202313017)
西安理工大学博士创新基金(310-252072106)。
关键词
大气光学
云粒子相态识别
模糊逻辑优化
过冷水
毫米波云雷达
atmospheric optics
cloud particle phase recognition
fuzzy logic optimization
supercooled water
millimeter wave cloud radar
