Automatic recognition and drawing of low-level jet based on wind field information of MICAPS

doi:10.3778/j.issn.1002-8331.1509-0350

Computer Engineering and Applications ›› 2017, Vol. 53 ›› Issue (8): 230-234.DOI: 10.3778/j.issn.1002-8331.1509-0350

Previous Articles Next Articles

Automatic recognition and drawing of low-level jet based on wind field information of MICAPS

LU Zhiying1, DIAO Changying1, LU Huanzhen2, JIA Huizhen2

1.School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China
2.Tianjin Meteorological Bureau, Tianjin Meteorological Observatory, Tianjin 300074, China

Online:2017-04-15 Published:2017-04-28

基于MICAPS风场信息的低空急流自动识别及绘制

路志英1，刁长莹1，卢焕珍2，贾惠珍2

1.天津大学电气与自动化工程学院，天津 300072
2.天津市气象局天津市气象台，天津 300074

Abstract

Abstract: It is too inefficient to draw low level jet artificially and there are a lot of subjective factors. In order to solve this problem effectively, a method that can achieve automatic identification of low-level jet is presented. Conventional meteorological data, intensive Automatic Weather Stations（AWS） observation data, wind field information of MICAPS and feature of low level jet are used in this method. Automatic recognition of low-level jet on 850 hPa constant-pressure surface in coastal areas of east China has been realized by pretreatment, clustering and fitting. A large number of experiments show that the automatic recognized jet stream satisfies the meteorological requirement. In addition, the efficiency is also improved.

Key words: wind field, low-level?jet, automatic recognition, clustering, fitting

摘要： 人工绘制低空急流效率过低并且存在很多主观因素，为有效地解决这一问题提出了低空急流自动识别的方法。该方法利用常规观测资料、加密自动站资料和MICAPS风场信息，结合低空急流特征，通过预处理、聚类、拟合等步骤实现了850 hPa等压面上中国东部沿海地区低空急流的自动识别。测试结果表明：利用该方法自动识别的急流能够满足气象要求，并且识别效率得到提高。

关键词: 风场, 低空急流, 自动识别, 聚类, 拟合

LU Zhiying1, DIAO Changying1, LU Huanzhen2, JIA Huizhen2. Automatic recognition and drawing of low-level jet based on wind field information of MICAPS[J]. Computer Engineering and Applications, 2017, 53(8): 230-234.

路志英1，刁长莹1，卢焕珍2，贾惠珍2. 基于MICAPS风场信息的低空急流自动识别及绘制[J]. 计算机工程与应用, 2017, 53(8): 230-234.

[1]	LAN Hong, HUANG Min. Fusion of KNN Optimized Density Peaks and FCM Clustering Algorithm [J]. Computer Engineering and Applications, 2021, 57(9): 81-88.
[2]	GUO Xiaojing, SUI Haoda. Application of Improved YOLOv3 in Foreign Object Debris Target Detection on Airfield Pavement [J]. Computer Engineering and Applications, 2021, 57(8): 249-255.
[3]	LI Li, JI Xinyuan, SONG Song. Prediction Model for Number of Software Defects in Loop [J]. Computer Engineering and Applications, 2021, 57(7): 158-163.
[4]	HUO Guangyu, ZHANG Yong, SUN Yanfeng, YIN Baocai. Research on Archive Data Intelligent Classification Based on Semantic [J]. Computer Engineering and Applications, 2021, 57(6): 247-253.
[5]	YANG Fang, YIN Xi, SI Jianhui, LIU Hongyuan, WANG Xue. Mathematical Expression Similarity Calculation Method Based on Focus Clustering [J]. Computer Engineering and Applications, 2021, 57(6): 88-93.
[6]	ZHAO Fan, ZHANG Lin, WEN Zhiquan, YANG Linlin, LIN Guangfeng. Direct and Efficient Natural Scene Chinese Character Approaching Spotting Method [J]. Computer Engineering and Applications, 2021, 57(6): 159-167.
[7]	PENG Qihui, XUAN Shibin, GAO Qing. Distribution Automatic Threshold Density Peak Clustering Algorithm [J]. Computer Engineering and Applications, 2021, 57(5): 71-78.
[8]	LI Yongzhen, LIAO Husheng. Multi-view Clustering via Graph Convolutional Neural Network [J]. Computer Engineering and Applications, 2021, 57(5): 115-122.
[9]	WANG Changlong, ZHANG Yuandong, MIAO Hong, YANG Yuheng. Application of Double Channel Convolutional Neural Network in Pumpkin Diseases Identification [J]. Computer Engineering and Applications, 2021, 57(5): 183-189.
[10]	HU Xiaomin, WANG Mingfeng, ZHANG Shourong, LI Min. New Differential Evolution with Particle Swarm Optimization Algorithm for Text Clustering [J]. Computer Engineering and Applications, 2021, 57(4): 61-67.
[11]	WANG Junling, LU Xinming. Video Key Frame Extraction Algorithm Based on Semantic Correlation [J]. Computer Engineering and Applications, 2021, 57(4): 192-198.
[12]	WANG Fuyin, ZHANG Desheng, ZHANG Xiao. Adaptive Density Peaks Clustering Algorithm Combining with Whale Optimization Algorithm [J]. Computer Engineering and Applications, 2021, 57(3): 94-102.
[13]	CHEN Junfeng, ZHENG Zhongtuan. Over-Sampling Method on Imbalanced Data Based on WKMeans and SMOTE [J]. Computer Engineering and Applications, 2021, 57(23): 106-112.
[14]	ZHANG Zhonglin, ZHAO Yu, YAN Guanghui. Natural Neighbor Density Extremum Clustering Algorithm [J]. Computer Engineering and Applications, 2021, 57(23): 200-210.
[15]	MEI Jie, WEI Yuanyuan, XU Taosheng. Fusion Clustering Algorithm Based on Multi-Prototypes Using Density Peaks [J]. Computer Engineering and Applications, 2021, 57(22): 78-85.

Automatic recognition and drawing of low-level jet based on wind field information of MICAPS

基于MICAPS风场信息的低空急流自动识别及绘制

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics