| 基于功率谱的风廓线雷达回波强度定标方法 |
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| 引用本文： | 李丰,阮征,王红艳,葛润生.基于功率谱的风廓线雷达回波强度定标方法[J].应用气象学报,2021,32(3):315-331. |
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| 作者姓名： | 李丰 阮征 王红艳 葛润生 |
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| 作者单位： | 中国气象科学研究院灾害天气国家重点实验室, 北京 100081 |
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| 摘 要： | 风廓线雷达已在我国得到大范围的业务布网应用，现有业务产品主要为风场信息。为了充分发挥风廓线雷达的作用，获取更多的天气过程信息，该文提出仅使用风廓线雷达返回信号功率谱进行数据定标（DCNP）的方法。使用雷达系统噪声功率对返回信号功率谱单位幅度进行标校计算，基于标校后的雷达探测功率谱分布数据计算回波强度功率谱密度分布、回波强度、大气折射率结构常数。利用2017年北京风廓线雷达、2016年南京风廓线雷达和2018年梅州风廓线雷达观测数据，对我国业务运行的3种主要型号风廓线雷达进行算法评估试验。定标方法的计算结果稳定，风廓线雷达不同探测模式之间的一致性较好。使用每个测站定标结果与相邻天气雷达数据进行比较，风廓线雷达回波强度定标结果与天气雷达也有较好的一致性。DCNP方法与基于信噪比（SNR）的强度计算方法进行比较，与SNR方法相比，DCNP方法定标结果更加稳定可靠。
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| 关 键 词： | 风廓线雷达 功率谱 数据定标 噪声功率 回波强度 |
| 收稿时间： | 2021-02-09 |
A Calibration Method of Wind Profile Radar Echo Intensity with Doppler Velocity Spectrum |
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| Institution: | State Key Lab of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081 |
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| 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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