| AMSR-E卫星资料陆面干扰的气候特征分析 |
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| 引用本文： | 冯呈呈,董慧杰.AMSR-E卫星资料陆面干扰的气候特征分析[J].大气科学学报,2016,39(4):536-545. |
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| 作者姓名： | 冯呈呈 董慧杰 |
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| 作者单位： | 南京信息工程大学大气科学学院;大连市气象台 |
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| 基金项目： | 公益性行业(气象)科研专项(GYHY201406008);江苏省普通高校研究生科研创新计划资助项目(CXLX13_483;KYLX_0822);江苏高校优势学科建设工程资助项目(PAPD) |
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| 摘 要： | 先进微波辐射计(AMSR-E)的低频特性使其更容易受到无线电频率干扰(RFI),本文首先对比了谱差法和双主成分分析(Double Principal Component Analysis,DPCA)方法这两种RFI识别算法的差异,验证了DPCA方法的适用性,然后选用DPCA方法对2002年9月—2011年9月AMSR-E陆面升轨亮温月平均数据中的RFI进行识别,并分析其多年变化特征。DPCA方法可以有效地识别出AMSR-E全球范围内陆地亮温数据中的RFI分布情况。识别结果显示:1)C波段中的RFI主要分布在美国、东亚及印度—阿拉伯半岛地区;X波段主要分布在欧洲及东亚地区。2)各区域的RFI空间位置及强度随时间会发生变化,其原因可能是由于基础设施及使用无线电频谱的变化造成。3)不同区域月平均亮温数据中识别出的RFI信号数量存在季节变化,并且C波段中识别出的RFI数量随时间减少,X波段中随时间增加。
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| 关 键 词： | 卫星资料 先进微波辐射计 无线电频率干扰 双主成分分析 气候趋势分析 |
| 收稿时间： | 2014/12/15 0:00:00 |
| 修稿时间： | 2015/6/15 0:00:00 |
Trend analysis of radio-frequency interference signals of Advanced Microwave Scanning Radiometer data over land |
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| Feng Chengcheng and Dong Huijie.Trend analysis of radio-frequency interference signals of Advanced Microwave Scanning Radiometer data over land[J].大气科学学报,2016,39(4):536-545. |
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| Authors: | Feng Chengcheng and Dong Huijie |
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| Institution: | Feng Chengcheng;Dong Huijie;School of Atmospheric Science,Nanjing University of Information Science & Technology;Dalian Meteorological Observatory; |
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| Abstract: | With the improvement of weather forecasting and the development of technology,the measurements of meteorological satellites have gradually become an important supplement to conventional observations.Owing to its advantage of all-day observation,Advanced Microwave Scanning Radiometer (AMSR-E) measurements have been widely used in research on global environmental change.The AMSR-E data record is very important in climate change monitoring and data assimilation in numerical weather prediction,but man-made radiative signals are also received by the microwave instrument and interfere with the natural thermal emissions of Earth.This phenomenon of satellite observations being mixed with signals from active microwave transmitters is referred to as Radio-Frequency Interference (RFI).RFI causes severe contamination of passive and active microwave sensing observations and corresponding retrieval products over some continents.The presence of RFI signals will reduce the scientific value of satellite measurements,so RFI signals should be detected and filtered before applying the microwave data in retrieval and data assimilation.With the long-term AMSR-E data being used in climate research,the characteristics of RFI signals also need to be analyzed.This paper focuses on the characteristics of RFI signals from AMSR-E data and their trend from September 2002 to September 2011.In this study,firstly,the spectral difference method and double principal component analysis (DPCA) method are used to obtain the spatial distribution of RFI signals.Compared to the spectral difference method,the DPCA method can detect RFI signals even over snow-covered areas,by taking advantage of the correlation of different channels for natural and snow radiation,and the de-correlation caused by RFI,and it is more robust and suitable for application worldwide.Then,the DPCA method is chosen to detect the RFI signals from the AMSR-E data over land from September 2002 to September 2011,and the trend of RFI signals with time is analyzed.The results show that the DPCA method can identify the RFI signals from brightness temperature of AMSR-E over land effectively,and can avoid the scattering effect of snow surfaces.The RFI signals from AMSR-E detected by the DPCA method are distributed mainly over the United States,East Asia and the India-Arabia Peninsula on C-band channels,and over Europe and East Asia on X-band.Strong RFI signals are mainly concentrated in populated cities.The locations of RFI signals are almost the same for horizontal polarization and vertical polarization channels;and RFI for horizontal polarization is stronger than that for vertical polarization.The areas and positions of RFI signals change with time,and their intensities are also not constant.The variation of RFI signals is probably due to changes in the human utilization of the radio spectrum,and obvious position changes may be related to the replacement of the infrastructure.The number of RFI signals detected by the DPCA method also varies with the seasons,being high in summer and low in winter in different regions except East Asia.From September 2002 to September 2011,the number of RFI signals decreased with time on C-band channels and increases on X-band. |
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| Keywords: | satellite data AMSR-E RFI DPCA trend analysis |
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