Optimized Intelligent Algorithm for Classifying Cloud Particles Recorded by a Cloud Particle Imager

Zepei Wu aDepartment of Electronic Engineering, Chengdu University of Information Technology, Chengdu, China
gCMA Key Laboratory of Atmospheric Sounding, Chengdu, China
dBeijing Weather Modification Office, Beijing, China
fField Experiment Base of Cloud and Precipitation Research in North China, China Meteorological Administration, Beijing, China

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Shuo Liu aDepartment of Electronic Engineering, Chengdu University of Information Technology, Chengdu, China
gCMA Key Laboratory of Atmospheric Sounding, Chengdu, China

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Delong Zhao cDepartment of Atmospheric Sciences, Nanjing University, Nanjing, China
dBeijing Weather Modification Office, Beijing, China
eBeijing Key Laboratory of Cloud, Precipitation and Atmospheric Water Resources, Beijing, China
fField Experiment Base of Cloud and Precipitation Research in North China, China Meteorological Administration, Beijing, China

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Ling Yang aDepartment of Electronic Engineering, Chengdu University of Information Technology, Chengdu, China
bField Key Laboratory for Cloud Physics of China Meteorological Administration, Beijing, China
gCMA Key Laboratory of Atmospheric Sounding, Chengdu, China

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Zixin Xu aDepartment of Electronic Engineering, Chengdu University of Information Technology, Chengdu, China
gCMA Key Laboratory of Atmospheric Sounding, Chengdu, China

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Zhipeng Yang aDepartment of Electronic Engineering, Chengdu University of Information Technology, Chengdu, China
bField Key Laboratory for Cloud Physics of China Meteorological Administration, Beijing, China
gCMA Key Laboratory of Atmospheric Sounding, Chengdu, China

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Dantong Liu hDepartment of Atmospheric Sciences, School of Earth Sciences, Zhejiang University, Hangzhou, China

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Tao Liu aDepartment of Electronic Engineering, Chengdu University of Information Technology, Chengdu, China
gCMA Key Laboratory of Atmospheric Sounding, Chengdu, China

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Yan Ding iAtmospheric Observation Technology Guarantee Center, Shanxi, China

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Wei Zhou dBeijing Weather Modification Office, Beijing, China

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Hui He dBeijing Weather Modification Office, Beijing, China

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Mengyu Huang dBeijing Weather Modification Office, Beijing, China

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Ruijie Li dBeijing Weather Modification Office, Beijing, China
fField Experiment Base of Cloud and Precipitation Research in North China, China Meteorological Administration, Beijing, China

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Deping Ding dBeijing Weather Modification Office, Beijing, China

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Abstract

Cloud particles have different shapes in the atmosphere. Research on cloud particle shapes plays an important role in analyzing the growth of ice crystals and the cloud microphysics. To achieve an accurate and efficient classification algorithm on ice crystal images, this study uses image-based morphological processing and principal component analysis to extract features of images and apply intelligent classification algorithms for the Cloud Particle Imager (CPI). Currently, there are mainly two types of ice-crystal classification methods: one is the mode parameterization scheme, and the other is the artificial intelligence model. Combined with data feature extraction, the dataset was tested on 10 types of classifiers, and the highest average accuracy was 99.07%. The fastest processing speed of the real-time data processing test was 2000 images per second. In actual application, the algorithm should consider the processing speed, because the images are on the order of millions. Therefore, a support vector machine (SVM) classifier was used in this study. The SVM-based optimization algorithm can classify ice crystals into nine classes with an average accuracy of 95%, blurred frame accuracy of 100%, with a processing speed of 2000 images per second. This method has a relatively high accuracy and faster classification processing speed than the classic neural network model. The new method could be also applied in physical parameter analysis of cloud microphysics.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Ling Yang, cimyang@cuit.edu.cn; Delong Zhao, dg1828019@smail.nju.edu.cn

Abstract

Cloud particles have different shapes in the atmosphere. Research on cloud particle shapes plays an important role in analyzing the growth of ice crystals and the cloud microphysics. To achieve an accurate and efficient classification algorithm on ice crystal images, this study uses image-based morphological processing and principal component analysis to extract features of images and apply intelligent classification algorithms for the Cloud Particle Imager (CPI). Currently, there are mainly two types of ice-crystal classification methods: one is the mode parameterization scheme, and the other is the artificial intelligence model. Combined with data feature extraction, the dataset was tested on 10 types of classifiers, and the highest average accuracy was 99.07%. The fastest processing speed of the real-time data processing test was 2000 images per second. In actual application, the algorithm should consider the processing speed, because the images are on the order of millions. Therefore, a support vector machine (SVM) classifier was used in this study. The SVM-based optimization algorithm can classify ice crystals into nine classes with an average accuracy of 95%, blurred frame accuracy of 100%, with a processing speed of 2000 images per second. This method has a relatively high accuracy and faster classification processing speed than the classic neural network model. The new method could be also applied in physical parameter analysis of cloud microphysics.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Ling Yang, cimyang@cuit.edu.cn; Delong Zhao, dg1828019@smail.nju.edu.cn
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