Three-dimensional Extension of the Unit-Feature Spatial Classification Method for Cloud Type

ZHANG Chengwei; YU Fan; WANG Chenxi; YANG Jianyu

doi:10.1007/s00376-010-9056-9

Impact Factor: 3.9

Volume 28 Issue 3

May 2011

Article Contents

Article > ADVANCES IN ATMOSPHERIC SCIENCES > 2011 > 28(3): 601-611

Three-dimensional Extension of the Unit-Feature Spatial Classification Method for Cloud Type

1.
Key Laboratory of Mesoscale Severe Weather of Ministry of Education, Nanjing University, Nanjing 210093, Meteorological Observatory of Shenzhen Air Traffic Management Station of CAAC, Shenzhen 518128,Key Laboratory of Mesoscale Severe Weather of Ministry of Education, Nanjing University, Nanjing 210093,Key Laboratory of Mesoscale Severe Weather of Ministry of Education, Nanjing University, Nanjing 210093,Meteorological Observatory of Shenzhen Air Traffic Management Station of CAAC, Shenzhen 518128

doi: 10.1007/s00376-010-9056-9

Abstract
References
Related papers
PDF

Abstract:
We describe how the Unit-Feature Spatial Classification Method (UFSCM) can be used operationally to classify cloud types in satellite imagery efficiently and conveniently. By using a combination of Interactive Data Language (IDL) and Visual C++ (VC) code in combination to extend the technique in three dimensions (3-D), this paper provides an efficient method to implement interactive computer visualization of the 3-D discrimination matrix modification, so as to deal with the bi-spectral limitations of traditional two dimensional (2-D) UFSCM. The case study of cloud-type classification based on FY-2C satellite data (0600 UTC 18 and 0000 UTC 10 September 2007) is conducted by comparison with ground station data, and indicates that 3-D UFSCM makes more use of the pattern recognition information in multi-spectral imagery, resulting in more reasonable results and an improvement over the 2-D method.
- cloud-type classification,
- unit-feature spatial classification method,
- three dimensions
References

[1]	Yu Fan, Liu Changsheng, Chen Weimin, 1997: Man-Computer Interactive Method on Cloud Classification Based on Bispectral Satellite Imagery, ADVANCES IN ATMOSPHERIC SCIENCES, 14, 389-398. doi: 10.1007/s00376-997-0058-1
[2]	Li Jun, Zhou Fengxian, Wang Luyi, 1992: Automatic Classification and Compression of GMS Cloud Imagery in Heavy Rainfall Monitoring Application, ADVANCES IN ATMOSPHERIC SCIENCES, 9, 458-464. doi: 10.1007/BF02677078
[3]	Juan HUO, Yongheng BI, Daren Lü, Shu DUAN, 2019: Cloud Classification and Distribution of Cloud Types in Beijing Using Ka-Band Radar Data, ADVANCES IN ATMOSPHERIC SCIENCES, , 793-803. doi: 10.1007/s00376-019-8272-1
[4]	Xiaoli LIU, Kerui MIN, Jianren SANG, Simin MA, 2023: Classification of Hailstone Trajectories in a Hail Cloud over a Semi-Arid Region in China, ADVANCES IN ATMOSPHERIC SCIENCES. doi: 10.1007/s00376-023-2156-0
[5]	Jianli MA, Zhiqun HU, Meilin YANG, Siteng LI, 2020: Improvement of X-Band Polarization Radar Melting Layer Recognition by the Bayesian Method and ITS Impact on Hydrometeor Classification, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 105-116. doi: 10.1007/s00376-019-9007-z
[6]	MEI Shuangli, Tim LI, CHEN Wen, 2015: Three-type MJO Initiation processes over the Western Equatorial Indian Ocean, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 1208-1216. doi: 10.1007/s00376-015-4201-0
[7]	ZHANG Lei, QIU Chongjian, HUANG Jianping, 2008: A Three-Dimensional Satellite Retrieval Method for Atmospheric Temperature and Moisture Profiles, ADVANCES IN ATMOSPHERIC SCIENCES, 25, 897-904. doi: 10.1007/s00376-008-0897-4
[8]	Zhou Jiabin, 1985: A NEW TYPE OF TIME-SERIES-FORECASTING METHOD, ADVANCES IN ATMOSPHERIC SCIENCES, 2, 385-401. doi: 10.1007/BF02677255
[9]	Bozhen LI, Gen ZHANG, Lingjun XIA, Ping KONG, Mingjin ZHAN, Rui SU, 2020: Spatial and Temporal Distributions of Atmospheric CO₂ in East China Based on Data from Three Satellites, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 1323-1337. doi: 10.1007/s00376-020-0123-6
[10]	Eun-Han KWON, Jinlong LI, B. J. SOHN, Elisabeth WEISZ, 2012: Use of Total Precipitable Water Classification of A Priori Error and Quality Control in Atmospheric Temperature and Water Vapor Sounding Retrieval, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 263-273. doi: 10.1007/s00376-011-1119-z
[11]	Chong WU, Liping LIU, Ming WEI, Baozhu XI, Minghui YU, 2018: Statistics-based Optimization of the Polarimetric Radar Hydrometeor Classification Algorithm and Its Application for a Squall Line in South China, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 296-316. doi: 10.1007/s00376-017-6241-0
[12]	WANG Lili, WANG Yuesi, SUN Yang, LI Yuanyuan, 2012: Using Synoptic Classification and Trajectory Analysis to Assess Air Quality during the Winter Heating Period in Urumqi, China, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 307-319. doi: 10.1007/s00376-011-9234-4
[13]	Jeong-Eun LEE, Sung-Hwa JUNG, Hong-Mok PARK, Soohyun KWON, Pay-Liam LIN, GyuWon LEE, 2015: Classification of Precipitation Types Using Fall Velocity-Diameter Relationships from 2D-Video Distrometer Measurements, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 1277-1290. doi: 10.1007/s00376-015-4234-4
[14]	Yihe FANG, Haishan CHEN, Yi LIN, Chunyu ZHAO, Yitong LIN, Fang ZHOU, 2021: Classification of Northeast China Cold Vortex Activity Paths in Early Summer Based on K-means Clustering and Their Climate Impact, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 400-412. doi: 10.1007/s00376-020-0118-3
[15]	Hongli LI, Xiangde XU, 2017: Application of a Three-dimensional Variational Method for Radar Reflectivity Data Correction in a Mudslide-inducing Rainstorm Simulation, ADVANCES IN ATMOSPHERIC SCIENCES, 34, 469-481. doi: 10.1007/s00376-016-6010-5
[16]	Chenbin XUE, Zhiying DING, Xinyong SHEN, Xian CHEN, 2022: Three-Dimensional Wind Field Retrieved from Dual-Doppler Radar Based on a Variational Method: Refinement of Vertical Velocity Estimates, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 145-160. doi: 10.1007/s00376-021-1035-9
[17]	ZHANG Yi, and LI Jian, 2013: Shortwave Cloud Radiative Forcing on Major Stratus Cloud Regions in AMIP-type Simulations of CMIP3 and CMIP5 Models, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 884-907. doi: 10.1007/s00376-013-2153-9
[18]	GAO Shouting, Xiaofan LI, 2009: Dependence of the Accuracy of Precipitation and Cloud Simulation on Temporal and Spatial Scales, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 1108-1114. doi: 10.1007/s00376-009-8143-2
[19]	WU Wei, CHEN Jilong, HUANG Ronghui, 2013: Water Budgets of Tropical Cyclones: Three Case Studies, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 468-484. doi: 10.1007/s00376-012-2050-7
[20]	PENG Jie, ZHANG Hua, Zhanqing LI, 2014: Temporal and Spatial Variations of Global Deep Cloud Systems Based on CloudSat and CALIPSO Satellite Observations, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 593-603. doi: 10.1007/s00376-013-3055-6

PDF

Get Citation+

Export:

Article Metrics

Article Views: 937 Times

PDF downloads: 713 Times

Cited by: Times

Proportional views

Manuscript History

Manuscript received: 10 May 2011

Manuscript revised: 10 May 2011

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Three-dimensional Extension of the Unit-Feature Spatial Classification Method for Cloud Type

1. Key Laboratory of Mesoscale Severe Weather of Ministry of Education, Nanjing University, Nanjing 210093, Meteorological Observatory of Shenzhen Air Traffic Management Station of CAAC, Shenzhen 518128,Key Laboratory of Mesoscale Severe Weather of Ministry of Education, Nanjing University, Nanjing 210093,Key Laboratory of Mesoscale Severe Weather of Ministry of Education, Nanjing University, Nanjing 210093,Meteorological Observatory of Shenzhen Air Traffic Management Station of CAAC, Shenzhen 518128

Manuscript received: 2011-05-10
Manuscript revised: 2011-05-10

Abstract: We describe how the Unit-Feature Spatial Classification Method (UFSCM) can be used operationally to classify cloud types in satellite imagery efficiently and conveniently. By using a combination of Interactive Data Language (IDL) and Visual C++ (VC) code in combination to extend the technique in three dimensions (3-D), this paper provides an efficient method to implement interactive computer visualization of the 3-D discrimination matrix modification, so as to deal with the bi-spectral limitations of traditional two dimensional (2-D) UFSCM. The case study of cloud-type classification based on FY-2C satellite data (0600 UTC 18 and 0000 UTC 10 September 2007) is conducted by comparison with ground station data, and indicates that 3-D UFSCM makes more use of the pattern recognition information in multi-spectral imagery, resulting in more reasonable results and an improvement over the 2-D method.

Keywords: