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Volume 31 Issue 4

May  2014

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Article >  ADVANCES IN ATMOSPHERIC SCIENCES  > 2014 >  31(4): 825-835

Applications of Wavelet Analysis in Differential Propagation Phase Shift Data De-noising

  • 1.

    State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081

Fund Project:

doi: 10.1007/s00376-013-3095-y

  • Using numerical simulation data of the forward differential propagation shift (DP) of polarimetric radar, the principle and performing steps of noise reduction by wavelet analysis are introduced in detail. Profiting from the multiscale analysis, various types of noises can be identified according to their characteristics in different scales, and suppressed in different resolutions by a penalty threshold strategy through which a fixed threshold value is applied, a default threshold strategy through which the threshold value is determined by the noise intensity, or a DP penalty threshold strategy through which a special value is designed for DP de-noising. Then, a hard- or soft-threshold function, depending on the de-noising purpose, is selected to reconstruct the signal. Combining the three noise suppression strategies and the two signal reconstruction functions, and without loss of generality, two schemes are presented to verify the de-noising effect by dbN wavelets: (1) the penalty threshold strategy with the soft threshold function scheme (PSS); (2) the DP penalty threshold strategy with the soft threshold function scheme (PPSS). Furthermore, the wavelet de-noising is compared with the mean, median, Kalman, and finite impulse response (FIR) methods with simulation data and two actual cases. The results suggest that both of the two schemes perform well, especially when DP data are simultaneously polluted by various scales and types of noises. A slight difference is that the PSS method can retain more detail, and the PPSS can smooth the signal more successfully.
    摘要: Using numerical simulation data of the forward differential propagation shift (DP) of polarimetric radar, the principle and performing steps of noise reduction by wavelet analysis are introduced in detail. Profiting from the multiscale analysis, various types of noises can be identified according to their characteristics in different scales, and suppressed in different resolutions by a penalty threshold strategy through which a fixed threshold value is applied, a default threshold strategy through which the threshold value is determined by the noise intensity, or a rm DP penalty threshold strategy through which a special value is designed for rm DP de-noising. Then, a hard- or soft-threshold function, depending on the de-noising purpose, is selected to reconstruct the signal. Combining the three noise suppression strategies and the two signal reconstruction functions, and without loss of generality, two schemes are presented to verify the de-noising effect by dbN wavelets: (1) the penalty threshold strategy with the soft threshold function scheme (PSS); (2) the rm DP penalty threshold strategy with the soft threshold function scheme (PPSS). Furthermore, the wavelet de-noising is compared with the mean, median, Kalman, and finite impulse response (FIR) methods with simulation data and two actual cases. The results suggest that both of the two schemes perform well, especially when rm DP data are simultaneously polluted by various scales and types of noises. A slight difference is that the PSS method can retain more detail, and the PPSS can smooth the signal more successfully.
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    • Manuscript History

    Manuscript received: 18 July 2013
    Manuscript revised: 22 October 2013
    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

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    Applications of Wavelet Analysis in Differential Propagation Phase Shift Data De-noising

      Corresponding author: HU Zhiqun; 
    • 1. State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081
    • Manuscript received: 2013-07-18
    • Manuscript revised: 2013-10-22
    Fund Project:  This work was funded by National Natural Science Foundation of China (41375038), and China Meteorological Administration Special Public Welfare Research Fund (GYHY201306040, GYHY201306075).

    Abstract: Using numerical simulation data of the forward differential propagation shift (DP) of polarimetric radar, the principle and performing steps of noise reduction by wavelet analysis are introduced in detail. Profiting from the multiscale analysis, various types of noises can be identified according to their characteristics in different scales, and suppressed in different resolutions by a penalty threshold strategy through which a fixed threshold value is applied, a default threshold strategy through which the threshold value is determined by the noise intensity, or a DP penalty threshold strategy through which a special value is designed for DP de-noising. Then, a hard- or soft-threshold function, depending on the de-noising purpose, is selected to reconstruct the signal. Combining the three noise suppression strategies and the two signal reconstruction functions, and without loss of generality, two schemes are presented to verify the de-noising effect by dbN wavelets: (1) the penalty threshold strategy with the soft threshold function scheme (PSS); (2) the DP penalty threshold strategy with the soft threshold function scheme (PPSS). Furthermore, the wavelet de-noising is compared with the mean, median, Kalman, and finite impulse response (FIR) methods with simulation data and two actual cases. The results suggest that both of the two schemes perform well, especially when DP data are simultaneously polluted by various scales and types of noises. A slight difference is that the PSS method can retain more detail, and the PPSS can smooth the signal more successfully.

    摘要: Using numerical simulation data of the forward differential propagation shift (DP) of polarimetric radar, the principle and performing steps of noise reduction by wavelet analysis are introduced in detail. Profiting from the multiscale analysis, various types of noises can be identified according to their characteristics in different scales, and suppressed in different resolutions by a penalty threshold strategy through which a fixed threshold value is applied, a default threshold strategy through which the threshold value is determined by the noise intensity, or a rm DP penalty threshold strategy through which a special value is designed for rm DP de-noising. Then, a hard- or soft-threshold function, depending on the de-noising purpose, is selected to reconstruct the signal. Combining the three noise suppression strategies and the two signal reconstruction functions, and without loss of generality, two schemes are presented to verify the de-noising effect by dbN wavelets: (1) the penalty threshold strategy with the soft threshold function scheme (PSS); (2) the rm DP penalty threshold strategy with the soft threshold function scheme (PPSS). Furthermore, the wavelet de-noising is compared with the mean, median, Kalman, and finite impulse response (FIR) methods with simulation data and two actual cases. The results suggest that both of the two schemes perform well, especially when rm DP data are simultaneously polluted by various scales and types of noises. A slight difference is that the PSS method can retain more detail, and the PPSS can smooth the signal more successfully.

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