| 北京MST雷达功率谱密度处理算法改进 |
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| 引用本文： | 陈泽,田玉芳,吕达仁.北京MST雷达功率谱密度处理算法改进[J].应用气象学报,2020,31(6):694-705. |
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| 作者姓名： | 陈泽 田玉芳 吕达仁 |
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| 作者单位： | 1.中国科学院大气物理研究所中层大气和全球环境探测重点实验室, 北京 100029 |
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| 摘 要： | 北京MST（mesosphere-stratosphere-troposphere，中间层-平流层-对流层）雷达是我国“子午工程”一期中探测大气动力结构的独特大型设备。雷达自2011年建成以来，已获取较好的大气风场数据，但在其他要素提取方面仍有改进需求。从噪声电平估算与目标回波识别这两个关键步骤改进雷达原始功率谱密度处理算法，以期得到更准确的大气要素信息。在噪声电平估算方面，提出应用对数-线性拟合方案快速实现客观分析法，与二分法方案差值的标准差为0.43 dB，表明对数-线性拟合方案能兼顾时效性与准确性。改进后的数据处理算法能够精确识别目标回波。利用改进算法处理2012年1—12月数据结果与雷达、探空以及ERA5再分析数据进行比较，各高度纬向风与探空测值的均方根误差均为2~3 m·s-1，优于雷达产品和探空测值均方根误差（3~4 m·s-1），信噪比、谱宽和垂直速度质量也有明显提高，表明改进算法可靠、有效且相对易于实现。
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| 关 键 词： | 北京MST雷达 功率谱密度处理 对数-线性拟合方案 目标回波精确识别 |
| 收稿时间： | 2020-04-17 |
Improving the Processing Algorithm of Beijing MST Radar Power Spectral Density Data |
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| Affiliation: | 1.Key Laboratory of Middle Atmosphere and Global Environment Observation (LAGEO), Institute of Atmospheric Physics, Chinese Academy of Science, Beijing 1000292.Xianghe Observatory of Whole Atmosphere, Institute of Atmospheric Physics, Chinese Academy of Sciences, Xianghe 0654003.University of Chinese Academy of Sciences, Beijing 100049 |
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| Abstract: | Beijing MST radar is a unique large instrument for atmospheric dynamic structure detection in Chinese Meridian Project. It plays an essential role in the in-depth understanding of the vertical structure of atmospheric wind, waves and turbulence in the troposphere, lower stratosphere, mesosphere, and lower thermosphere in North China. Since the completion of Beijing MST radar in 2011, wind data have been well acquired. However, there is still a need for improving the extraction of some elements. To achieve this goal, the power spectral density data processing algorithms is improved mainly from two aspects including accurate noise level estimation and target signal recognition. The improved algorithm derived data, radar products, radiosonde data and ERA5 reanalysis data from 1 January to 31 December in 2012 are statistically analyzed and compared. A log-linear fitting scheme is put forward and applied to realize rapid implementation of objective determination of the noise level. The root mean square error(RMSE) of noise values between the log-linear fitting and the conventional scheme is about 0.43 dB and mean values are 168.6 dB and 168.5 dB, respectively. Results show that the noise level estimation can be fast and accurate using the log-linear fitting scheme. Based on the property that atmospheric signals have spatio-temporal consistency and diffident signals show different spectral characteristics, the target signal can be accurately identified and extracted by the improved algorithms. The RMSE of zonal wind speed between the improved algorithm derived data and radiosonde data at different height are in the range of 2-3 m·s-1 while the RMSE of zonal wind speed between radar products and radiosonde data at different height are 3-4 m·s-1. Moreover, the mean value of the spectral width derived by the improved algorithm is 2.5 m·s-1, which is less than the mean value of radar products. Under precipitation weather condition, the mean bias and RMSE of horizontal wind speed between the improved algorithm derived data and radiosonde data at different height are both less than values between radar products and radiosonde data. Results show that the improved algorithm can reduce non-atmospheric signals such as noise and intermittent clutter and effectively suppress signals caused by precipitation. Thus the effectiveness and reliability of the improved algorithm are verified, and it is relatively easy to implement. |
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