Vol.32, NO.3, 2021
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Article Navigation >  Journal of Applied Meteorological Science  > 2021  >  32(3): 315-331
Li Feng, Ruan Zheng, Wang Hongyan, et al. A calibration method of wind profile radar echo intensity with doppler velocity spectrum. J Appl Meteor Sci, 2021, 32(3): 315-331. DOI:  10.11898/1001-7313.20210305.
Citation: Li Feng, Ruan Zheng, Wang Hongyan, et al. A calibration method of wind profile radar echo intensity with doppler velocity spectrum. J Appl Meteor Sci, 2021, 32(3): 315-331. DOI:  10.11898/1001-7313.20210305.
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A Calibration Method of Wind Profile Radar Echo Intensity with Doppler Velocity Spectrum

DOI: 10.11898/1001-7313.20210305

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

  • Received Date: 2021-02-09
  • Rev Recd Date: 2021-04-29
  • Publish Date: 2021-05-31
    Abstract
  • The L-band wind profile radar(WPR) detects the Bragg scattering processes from back scattered energy of changes in refractive index, meanwhile it is high sensitive to Rayleigh scattering processes from back scattered energy of hydrometeors in the precipitating clouds. A method named data calibration with noise power (DCNP) is established for calibrating WPR return signal, in which the Doppler velocity spectrum is processed with FFT. The power of unit amplitude in return signal power spectrum is calculated based on radar noise power. Using calibrated power spectrum, echo intensity spectral density, echo intensity and structure parameter of refractive index are derived, and can be used to study vertical structure of precipitating clouds, microphysical properties, and clear air turbulences. The errors derived from noise temperature and noise amplitude are discussed. When the range of actual noise temperature is from 280 to 320 K, the error range caused by using 300 K to calculate noise power is from -0.28 to 0.3 dB. For each observation mode, the fluctuation of monthly average noise amplitude at the last gate is stable, nearly in normal distribution. The error caused by noise amplitude is between -0.3 and 0.3 dB. The method is estimated with data from Beijing (54399) in 2017, Nanjing (58235) in 2016 and Meizhou (59303) in 2018. These WPR types are different, and they are the main types in operation. Three precipitation cases from different stations are used to estimate the calibration method. It shows that the magnitudes between echo intensities calculated with DCNP and weather radars are similar. The evolutions of the two sorts of echo intensity products are also simultaneous. Estimations show that consistence between different observation mode is good. The difference between the high and low mode from Meizhou (59303) is the smallest. The differences between modes from Beijing (54399) are larger than the other two stations. It is consistent with the range of noise amplitude from the farthest gate in each observation mode. Compared with nearby weather radars, the consistence between WPRs and weather radars is also good considering different observation modes. The calibration method is proved stable and reliable. Radar echo intensity calculated with DCNP is compared with that derived from SNR. In most cases, values from the two methods are well consistent. When noise amplitude is large, the echo intensities identified by the method with SNR are usually lower than the values derived from the method using DCNP. The error from turbulence is analyzed with two-peak spectrum from Meizhou (59303). It indictes that the return signal from turbulence can be ignored for the cases.
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    References(28)

  • Fig. 1  Diagram of DCNP for wind profile radar

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    Fig. 2  Spectral density of echo intensity calculated with DCNP for wind profile radars

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    Fig. 3  Noise power deviation from noise temperature

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    Fig. 4  Noise amplitude

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    Fig. 5  Precipitating clouds of Beijing wind profile radar(54399) and weather radar from 0500 UTC to 1700 UTC on 22 Aug 2017

    (a)wind profile radar echo intensity calculated with DCNP, (b)wind profile radar echo intensity calculated with RCSNR, (c)range-corrected wind profile radar SNR, (d)weather radar echo intensity

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    Fig. 6  Precipitating clouds of Nanjing wind profile radar(58235) and wheather radar from 2000 UTC 30 Jun to 1200 UTC 1 Jul in 2016

    (a)wind profile radar echo intensity calculated with DCNP, (b)wind profile radar echo intensity calculated with RCSNR, (c)range-corrected wind profile radar SNR, (d)weather radar echo intensity

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    Fig. 7  Precipitating clouds of Meizhou wind profile radar(59303) and weather radar from 0600 UTC to 2400 UTC on 6 Jun in 2018

    (a)wind profile radar echo intensity calculated with DCNP, (b)wind profile radar echo intensity calculated with RCSNR, (c)range-corrected wind profile radar SNR, (d)weather radar echo intensity

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    Fig. 8  Comparison between different modes from the same wind profile radar using DCNP and RCSNR

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    Fig. 9  Comparison of echo intensity between wind profile radars and weather radars

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    Fig. 10  Comparison in different modes between DCNP and RCSNR

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    Fig. 11  Average noise amplitude distribution under rainy condition

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    Fig. 12  Comparison of air turbulence and precipitation echo intensity in double peak spectrum

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    • Received : 2021-02-09
    • Accepted : 2021-04-29
    • Published : 2021-05-31
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