Automatic Identification of Clear-Air Echoes Based on Millimeter-wave Cloud Radar Measurements

被引:0
|
作者
Ling Yang
Yun Wang
Zhongke Wang
Qian Yang
Xingang Fan
Fa Tao
Xiaoqiong Zhen
Zhipeng Yang
机构
[1] Chengdu University of Information Technology,Electronic Engineering College
[2] Chengdu University of Information Technology,Information Security Engineering College
[3] Western Kentucky University,Department of Geography and Geology
[4] Chinese Academy of Sciences,Institute of Atmospheric Physics
[5] Chengdu University of Information Technology,CMA Key Laboratory of Atmospheric Sounding
[6] Nanjing University of Information Science and Technology,Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters
[7] CMA,Meteorological Observation Centre
来源
关键词
millimeter-wave cloud radar; clear-air echoes; neural network; laser ceilometer; all-sky camera; feature extraction; feature selection; 毫米波云雷达; 晴空回波; 神经网络; 激光云高仪; 全天空成像仪; 特征提取; 特征选择;
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中图分类号
学科分类号
摘要
Millimeter-wave cloud radar (MMCR) provides the capability of detecting the features of micro particles inside clouds and describing the internal microphysical structure of the clouds. Therefore, MMCR has been widely applied in cloud observations. However, due to the influence of non-meteorological factors such as insects, the cloud observations are often contaminated by non-meteorological echoes in the clear air, known as clear-air echoes. It is of great significance to automatically identify the clear-air echoes in order to extract effective meteorological information from the complex weather background. The characteristics of clear-air echoes are studied here by combining data from four devices: an MMCR, a laser-ceilometer, an L-band radiosonde, and an all-sky camera. In addition, a new algorithm, which includes feature extraction, feature selection, and classification, is proposed to achieve the automatic identification of clear-air echoes. The results show that the recognition algorithm is fairly satisfied in both simple and complex weather conditions. The recognition accuracy can reach up to 95.86% for the simple cases when cloud echoes and clear-air echoes are separate, and 88.38% for the complicated cases when low cloud echoes and clear-air echoes are mixed.
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页码:912 / 924
页数:12
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