Vol.23, NO.6, 2012
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Article Navigation >  Journal of Applied Meteorological Science  > 2012  >  23(6): 710-720
Du Muyun, Liu Liping, Hu Zhiqun, et al. Quality control of differential propagation phase shift for dual linear polarization radar. J Appl Meteor Sci, 2012, 23(6): 710-720.
Citation: Du Muyun, Liu Liping, Hu Zhiqun, et al. Quality control of differential propagation phase shift for dual linear polarization radar. J Appl Meteor Sci, 2012, 23(6): 710-720.
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Quality Control of Differential Propagation Phase Shift for Dual Linear Polarization Radar

  • 1.

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

  • 2.

    Wuhan Central Meteorological Observatory, Wuhan 430074

  • 3.

    Hubei Lightning Protection Center, Wuhan 430074

  • Received Date: 2012-03-20
  • Rev Recd Date: 2012-08-06
  • Publish Date: 2012-12-31
    Abstract
  • Data processing and quality control is the foundation of the application of dual-linear polarization Doppler radar. Based on the observation in field experiments by a C-band Polarization Doppler Radar on Wheel (CPDRW), the difference of differential propagation phase shift ΦDP between precipitation and ground clutter and its relationship with signal-to-noise ratio SNR are analyzed and a new data analyzing and processing methodology is suggested. According to this new method, the useless ΦDP data can be given up and the KDP data with higher accuracy can be acquired. Analysis indicates that ΦDP data are vulnerable to the influence of the non-meteorological target like ground clutter and usually appears large fluctuations. ΦDP data are also sensitive to the variability of SNR and cross-correlation coefficient ρHV(0), especially the latter. It appears abnormal fluctuations with the quality of related SNR and ρHV(0) becomes poor and that will affect the quality of the estimation of KDP data if no appropriate quality control scheme is adopted. Using this kind of KDP data, obvious errors in the quantitative application of precipitation estimation and precipitation particle morphology recognition can be obtained. In this new method, the abnormal volatility of ΦDP data combining with reflectivity factor ZH and radial velocity Vr information is used to isolate the ground clutter, and then improper data are eliminated in the quantitative application such as quantitative precipitation estimation or attenuation correction. According to SNR and ρHV(0), the meteorological data is divided into good, poor and bad categories. For the good data, the fluctuation is smaller, the increasing trend with distances which accords with theoretical expectations is evident, so the preprocessing algorithms and estimate KDP data can be used directly; for the poor data, although the fluctuation is more pronounced than the good data, the data continuity begins to become poor and there are some ΦDP data "pile" and "depression", however, much weather information remains and the variation trend is also obvious, so the data correction algorithm are applied so as not to affect the estimated KDP data; and for the bad data, it not only has the large fluctuation, the overall variation trend is also difficult to identify, sometimes even negative growth phenomenon appears which is contrary to the theory, so they are eliminated to ensure the overall quality of ΦDP data. After a large number of actual data validation, it reveals that the suggested method can keep the meteorological information to the greatest extent and ensure the overall quality of ΦDP data at the same time, and it can also estimate the high quality of KDP data.
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    References(18)

  • Fig. 1  The PPI of reflectivity CPDRW for large area ground (a) with its range profiles of ΦDP(b) denoted by red solid line in Fig. 1a and sporadic ground (c) with its range profiles of ΦDP (d) denoted by red solid lines in plot Fig. 1c, respectively

    (the elevation is 0.5°; the distance between adjacent circles is 50 km; the degree between adjacent radical lines is 30°)

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    Fig. 2  Range profiles of ΦDP, SNR and ρHV(0)

    (Fig. 2b, Fig. 2c, Fig. 2d corresponding to the elliptic, rounded rectangle, rectangular areas in Fig. 2a, respectively)

    DownLoad: Full-Size Img PowerPoint

    Fig. 3  Comparison of range profiles of CPDRW ΦDP at 0944 BT 6 June 2008 with the elevation of 1.5°, azimuth of 245.8°

    (a) raw data, (b)13-point moving average, (c)13-point median filtering, (d) classification processing

    DownLoad: Full-Size Img PowerPoint

    Fig. 4  The PPI CPDRW for ZH(a), SNR (b), ΦDP (c), ρHV(0)(d) at 0944 BT 6 June 2008 with the elevation of 0.5°

    DownLoad: Full-Size Img PowerPoint

    Fig. 5  The PPI CPDRW of KDP for different ΦDP processing methods at 0944 BT 6 June 2008 with the elevation of 0.5°

    (a)13-point moving average, (b)13-point median filtering, (c) classification processing

    DownLoad: Full-Size Img PowerPoint

    Fig. 6  The scatter plot of ZH, KDP in PPI CPDRW with the elevation of 0.5° at 0944 BT 6 June 2008 for different ΦDP processing methods

    (a)13-point moving average, (b)13-point median filtering, (c) classification processin

    DownLoad: Full-Size Img PowerPoint

    Fig. 7  The PPI CPDRW for ZH(a), SNR (b), ΦDP(c), ρHV(0)(d) at 0127 BT 15 September 2009 with the elevation of 0.5°

    DownLoad: Full-Size Img PowerPoint

    Fig. 8  The PPI CPDRW of KDP for different ΦDP processing methods at 0127 BT 15 September 2009 with the elevation of 0.5°

    (a)13-point moving average, (b)13-point median filtering, (c) classification processing

    DownLoad: Full-Size Img PowerPoint

    Fig. 9  The scatter plot of ZH, KDP in PPI CPDRW with the elevation of 0.5°at 0127 BT 15 September 2009 for different ΦDP processing methods

    (a)13-point moving average, (b)13-point median filtering, (c) classification processing

    DownLoad: Full-Size Img PowerPoint
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    • Received : 2012-03-20
    • Accepted : 2012-08-06
    • Published : 2012-12-31
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