Research On Cloud Particle Phase Identification Method Based On "Clustering-fuzzy Logic"

Cloud is a collection of different phases hydrocondensates in the atmosphere.According to the different temperature,the phase of cloud particles can be divided into ice phase,liquid phase and mixed phase.Different phase cloud particles have different scattering and absorbing effects on light,which will directly affect the formation and development of clouds,and then change the global climate.At present,the cloud phase equipment that can be used for detection and analysis mainly includes passive and active remote sensing detection instruments,including multi-channel imager,Lidar,millimeter wave radar,and microwave radiometer.However,there are some problems in cloud phase identification,such as no true value,fuzzy threshold partition and misjudgment of phase state.Based on the above problems,this paper studies the k-means algorithm and fuzzy logic algorithm for cloud phase identification,and establishes a set of adaptive cloud phase classification program on the basis of analyzing the advantages and correlation of the two algorithms,which is called "clustering-fuzzy logic" algorithm.The clustering k-means algorithm can analyze the potential rules in the sample group without true algorithm training;the fuzzy logic algorithm can accurately classify under fuzzy threshold condition.The "clustering-fuzzy logic" algorithm integrates the advantages of clustering k-means algorithm and fuzzy logic algorithm,and uses the ability of clustering k-means algorithm to analyze the potential rules of input parameters to establish targeted affiliation functions for fuzzy logic algorithm,which overcomes the problem that fuzzy logic algorithm has no reliable affiliation function in the field of cloud phase.The affiliation function based on the clustering k-means algorithm can self-adjust according to different environments and different equipment conditions,effectively avoid the possible miscalculation of cloud phase states caused by the difference of experimental conditions.The "clustering-fuzzy logic"algorithm is also applicable to joint observations,and the classification based on data does not limit the detection equipment used.In order to prove the rationality of the algorithm,the observation results of millimeter wave cloud radar and mature product data of millimeter-wave radar were used to classify phase states,and compared with the results of "clustering-fuzzy logic" algorithm.It was proved that the "clustering-fuzzy logic" algorithm can accurately classify the cloud phase states when considering the difference in spatial and temporal resolution of the two devices.In order to identify the phase state of more complete clouds,a splicing method is proposed,which combines the classification results of millimeter wave radar and lidar,and gives full play to the detection advantages of the two devices.In this paper,the cloud precipitation potential detection lidar developed by the Lidar Remote Sensing Research Center of Xi’an University of Technology is used for synchronous observation with radiosonde and microwave radiometer.The backscatter coefficient,declination ratio,radar ratio and temperature data of lidar output are used as algorithm inputs for cloud phase identification research and statistics.The individual case was analyzed with detection results,and the phase identification of the continuous observations in autumn 2022 was conducted.In this continuous observation,clouds were observed for 22642 min,25 sets of continuous cloud evolution data were collected,and cloud phase states were analyzed by the"clustering-fuzzy logic" algorithm.The statistical results show that the autumn cloud phase in Xi’an is mainly dominated by ice clouds,accounting for 48.54%of the total.