高原气象

高原气象 >

2018 , Vol. 37 >Issue 4: 1033 - 1041

DOI: https://doi.org/10.7522/j.issn.1000-0534.2017.00084

论文

机载微波辐射计亮温数据中RFI识别方法研究

  • 王婉 ,
  • 雷恒池 ,
  • 王兆宇 ,
  • 郭晓军 ,
  • 聂皓浩
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  • 天津市人工影响天气办公室, 天津 300074;中国气象局云雾物理环境重点实验室, 北京 100081;中国科学院大气物理研究所云降水物理与强风暴重点实验室, 北京 100029

收稿日期: 2017-07-06

  网络出版日期: 2018-08-28

基金资助

中国气象局云雾物理环境重点实验室开放科研课题（2017Z01609）；天津市气象局科研课题（201707ybxm07）；国家自然科学基金项目（41675046，41675078）

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Method Study on Identification of Radio-Frequency Interference Signal from Airborne Microwave Radiometer

  • WANG Wan ,
  • LEI Hengchi ,
  • WANG Zhaoyu ,
  • GUO Xiaojun ,
  • NIE Haohao
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  • TianJin Weather Modification Office, TianJin 300074, China;Key Laboratory for Cloud Physics of China Meteorological Administration, Beijing 100081, China;Key Laboratory of Cloud-Precipitation Physics and Severe Storms, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China

Received date: 2017-07-06

  Online published: 2018-08-28

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摘要

机载微波辐射计（G-band water Vapor Radiometer，GVR）在使用过程中发现存在无线电频率干扰信号（Radio-Frequency Interference，RFI），为准确使用数据，需对干扰信号进行识别和订正。在分析多种RFI识别方法在GVR数据中的适用性基础上，根据GVR探测及定标原理提出适用于GVR的RFI识别和订正方案。采用该方案对天津市2016年11月20日一次GVR亮温数据进行识别和订正，结果表明，该方法能较好的识别出各通道亮温数据中的RFI信号；RFI存在于多个通道中，时空分布无规律，具有随机性，为干扰源确定带来较大困难；RFI在亮温数据中除少数以孤立点形式存在，多数为连续干扰点，连续干扰点越多，订正效果越差，当连续干扰点较多时建议剔除该部分数据。RFI订正前后的反演结果对比分析表明，多数情况下，RFI的存在使得垂直累积水汽和垂直累积液态水含量值被高估，少数值被低估，单个通道做订正对反演结果的影响不同。

关键词： GVR; 无线电频率干扰; 识别

本文引用格式

王婉 , 雷恒池 , 王兆宇 , 郭晓军 , 聂皓浩 . 机载微波辐射计亮温数据中RFI识别方法研究[J]. 高原气象, 2018 , 37(4) : 1033 -1041 . DOI: 10.7522/j.issn.1000-0534.2017.00084

Abstract

Radio-frequency interference (RFI) signals were found in the brightness temperature datas of G-band water Vapor Radiometer (GVR). It is necessary to identify and correct the interference datas for better use. Based on the analysis of the applicability of various RFI identification methods of GVR data, the RFI recognition and correction method for GVR was proposed according to the principle of GVR detection and calibration. This method is used to identify the RFI from GVR brightness temperature datas on November 20, 2016 and the results show that:(1)The method works well for identifying the RFI from the brightness temperature datas of GVR different channels; (2) RFI signals exist in multiple channels and have no regular distribution in spatial and temporal that brings great difficulties to determine the source of RFI; (3) In addition to a few signals exist in the form of isolated points, majority of RFI signals in the brightness temperature datas exist in the form of continuity points. The more the continuity interference points are, the worse the correction results are. The datas are recommended to be eliminated when there are many continuity interference points. The retrival results analysis of before and after correction of RFI from GVR datas show that most of the PWV (Precipitable Water Vapor) and LWP (Liquid Water Path) values are overestimated because of the RFI and the correction of individual channels, which has different effects on the retrival results.

Key words： GVR; radio-frequency inte; identification

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