Zhang Fugui, Fan Xiao, He Jianxin. A modified method of removing ground clutter from wind profiler radar based on adaptive wavelet threshold. J Appl Meteor Sci, 2015, 26(4): 472-481. DOI:  10.11898/1001-7313.20150409.
Citation: Zhang Fugui, Fan Xiao, He Jianxin. A modified method of removing ground clutter from wind profiler radar based on adaptive wavelet threshold. J Appl Meteor Sci, 2015, 26(4): 472-481. DOI:  10.11898/1001-7313.20150409.

A Modified Method of Removing Ground Clutter from Wind Profiler Radar Based on Adaptive Wavelet Threshold

DOI: 10.11898/1001-7313.20150409
  • Received Date: 2014-11-13
  • Rev Recd Date: 2015-03-18
  • Publish Date: 2015-07-31
  • Wind profiler radar (WPR) can be used to retrieve real-time atmospheric wind field data of high resolution. Backscattered echo caused by irregularities of atmospheric refractive index is received by radar antenna and wind velocities is calculated with Doppler frequency shifting speed formula. It is widely used in fields of very short-term weather forecasting, airport operations and public protection, air pollution monitoring, wind field analyses and forecasts of toxic plume trajectories resulting from chemical or nuclear incidents. As a result of being widely used in different situations, WPR is always sited near the city with a large population and complicated geographical environment. Transmission of electromagnetic wave during WPR detecting period is often interfered by various clutters that contaminate WPR data introduce bias in moments and wind velocity estimation. Of all clutters, ground clutter is the primary source because it happens more often than the others. Ground clutter is radar return from more or less stationary targets such as trees, buildings near the cited place. How to eliminate the influence of ground clutter is a most concerned aspect. Ground clutter mainly concentrates around the zero-frequency and it has high amplitude on the power spectrum. The most frequently used methods, such as traditional wavelet threshold processing and zero-frequency elimination of 3 points, both have the ability to separate the meteorology echo from the ground clutter when the turbulent peak is away from the zero-frequency and not covered with ground clutter peak. However, when the near zero-frequency echo is taken into consideration, both of the traditional methods meet their limitation. Based on the wavelet high frequency coefficients, a method of determining threshold adaptively is proposed and the validation of the method is done by using of simulated data and WPR measured data. The corresponding power spectrum before and after self-adapting wavelet threshold processing are compared. Results show that this method performs well even when the signal is close to the zero-frequency and covered completely. Meanwhile, the method has some important features, such as simple theory, small amount of calculation and easy to implement. Cases analysis shows that self-adapting threshold processing can increase the accuracy of peak identification, also provide approach and basis for improving the WPR products.
  • Fig. 1  The flow chart of signal decomposition

    Fig. 2  The figure of data simulation environment

    Fig. 3  Wavelet coefficients of 1.5 Hz signal

    (a) traditional threshold processing, (b) self-adapting threshold processing

    Fig. 4  Power spectrum of 1.5 Hz signal

    (a) original power spectrum, (b) power spectrum after zero-frequency elimination of 3 points, (c) power spectrum after traditional threshold processing, (d) power spectrum after self-adapting threshold processing

    Fig. 5  Power spectrum of 50 Hz signal

    (a) original power spectrum, (b) power spectrum after self-adapting threshold processing

    Fig. 6  The time series of Ⅰ component and Q component and wavelet coefficients of WPR at CUIT at 1250 BT 17 May 2014

    a) original time series of Ⅰ component and Q component, (b) reconstructed time series of Ⅰ component and Q component, (c) original wavelet coefficients, (d) wavelet coefficients after self-adapting threshold processing

    Fig. 7  Power spectrum of WPR at CUIT at 1250 BT 17 May 2014

    (a) original power spectrum, (b) power spectrum after self-adapting threshold processing

    Fig. 8  Power spectrumof WPR at CUIT at 0930 BT 25 Apr 2014

    (a) original power spectrum, (b) power spectrum after self-adapting threshold processing, (c) power spectrum after zero-frequency elimination of 3 points

    Fig. 9  Spectral distribution of WPR at CUIT with height at 1200 BT 17 May 2014

    (a) original spectral distribution with height, (b) spectral distribution with height after self-adapting threshold processing

  • [1]
    阮征, 吴志根.风廓线仪探测降水云体结构方法的研究.应用气象学报, 2002, 13(3):330-338 http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20020343&flag=1
    [2]
    何平, 朱小燕, 阮征, 等.风廓线雷达探测降水过程的初步研究.应用气象学报, 2009, 20(4):465-470 doi:  10.11898/1001-7313.200904011
    [3]
    郭虎, 段丽, 杨波, 等.0679香山局地大暴雨的中小尺度天气分析.应用气象学报, 2008, 19(3):265-275 doi:  10.11898/1001-7313.20080302
    [4]
    王莎, 阮征, 葛润生.风廓线雷达探测大气返回信号谱的仿真模拟.应用气象学报, 2012, 23(1):20-29 http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20120103&flag=1
    [5]
    王欣, 卞林根, 彭浩, 等.风廓线仪系统探测试验与应用.应用气象学报, 2006, 16(5):693-698 http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20050590&flag=1
    [6]
    何平.相控阵风廓线雷达.北京:气象出版社, 2006:11-12
    [7]
    Benjamin S G, Schwartz B E, Koch S E, et al.The value of wind profiler data in US weather forecasting. Bull Amer Meteor Soc, 2004, 85(12):1871-1886 doi:  10.1175/BAMS-85-12-1871
    [8]
    Kitamura Y, Nakagawa K, Sekizawa S, et al.Vertical Profile of Raindrop Size Distribution by Using 400 MHz Wind Profiler in Stratiform Rainfall//32nd Conference on Radar Meteorology.2005:P7R
    [9]
    Wang M Z, Wei W S, He Q, et al.Application of wind profiler data to rainfall analyses in Tazhong Oilfield region, Xinjiang, China. Journal of Arid Land, 2012, 4(4):369-377 doi:  10.3724/SP.J.1227.2012.00369
    [10]
    Russell C A, Jordan J R.Portable Clutter Fence for UHF Wind Profiling Radar//7th Symposium on Meteorological Observations and Instrumentations.1991:152
    [11]
    Strauch R G, Merritt D A, Moran K P, et al.The Colorado wind-profiling network. J Atmos Ocean Technol, 1984, 1(1):37-49 doi:  10.1175/1520-0426(1984)001<0037:TCWPN>2.0.CO;2
    [12]
    刘涛, 曾祥利, 曾军.实用小波分析入门.北京:国防工业出版社, 2006:73-75
    [13]
    何平, 李柏, 吴蕾, 等.确定风廓线雷达功率谱噪声功率方法.应用气象学报, 2013, 24(3):297-303 doi:  10.11898/1001-7313.20130305
    [14]
    Sifuzzaman M, Islam M R, Ali M Z.Application of wavelet transform and its advantages compared to Fourier transform. Journal of Physical Sciences, 2009, 13:121-134 http://www.ijmetmr.com/olfebruary2016/BasimNasih-A-4.pdf
    [15]
    Ingrid Daubechies. 小波十讲. 李建平, 杨万年, 译. 北京: 国防工业出版社, 2005: 29-31
    [16]
    Jordan J R, Lataitis R J, Carter D A.Removing ground and intermittent clutter contamination from wind profiler signals using wavelet transforms. J Atmos Ocean Technol, 1997, 14(6):1280-1297 doi:  10.1175/1520-0426(1997)014<1280:RGAICC>2.0.CO;2
    [17]
    Ai W, Huang Y, Hu M, et al.Ground Clutter Removing for Wind Profiler Radar Signal Using Adaptive Wavelet Threshold//2010 International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), IEEE, 2010:370-373
    [18]
    Justen L A, Teschke G, Lehmann V.Wavelet-based Methods for Clutter Removal from Radar Wind Profiler Data//Photonics Technologies for Robotics, Automation, and Manufacturing.International Society for Optics and Photonics, 2004:157-168
    [19]
    Lehmann V, Teschke G.Wavelet based methods for improved wind profiler signal processing. Annale Geophysicae, 1999, 19(8):825-836 http://www.oalib.com/paper/2551055
    [20]
    丁敏, 卜祥元.提升小波去地杂波方法.现代电子技术, 2006, 29(11):32-34 doi:  10.3969/j.issn.1004-373X.2006.11.011
  • 加载中
  • -->

Catalog

    Figures(9)

    Article views (3389) PDF downloads(796) Cited by()
    • Received : 2014-11-13
    • Accepted : 2015-03-18
    • Published : 2015-07-31

    /

    DownLoad:  Full-Size Img  PowerPoint