Improving the Processing Algorithm of Beijing MST Radar Power Spectral Density Data

Chen Ze; Tian Yufang; Lü Daren

doi:10.11898/1001-7313.20200605

Vol.31, NO.6, 2020

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Article Navigation > Journal of Applied Meteorological Science > 2020 > 31(6): 694-705

Chen Ze, Tian Yufang, Lü Daren. Improving the processing algorithm of Beijing MST radar power spectral density data. J Appl Meteor Sci, 2020, 31(6): 694-705. DOI: 10.11898/1001-7313.20200605.

Citation:

Chen Ze, Tian Yufang, Lü Daren. Improving the processing algorithm of Beijing MST radar power spectral density data. J Appl Meteor Sci, 2020, 31(6): 694-705. DOI: 10.11898/1001-7313.20200605.

Chen Ze, Tian Yufang, Lü Daren. Improving the processing algorithm of Beijing MST radar power spectral density data. J Appl Meteor Sci, 2020, 31(6): 694-705. DOI: 10.11898/1001-7313.20200605.

Citation:

Chen Ze, Tian Yufang, Lü Daren. Improving the processing algorithm of Beijing MST radar power spectral density data. J Appl Meteor Sci, 2020, 31(6): 694-705. DOI: 10.11898/1001-7313.20200605.

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Improving the Processing Algorithm of Beijing MST Radar Power Spectral Density Data

DOI: 10.11898/1001-7313.20200605

Chen Ze^{1, 2, 3},
Tian Yufang^{1, 2, 3
,
,},
Lü Daren^{1, 2, 3}

1.
Key Laboratory of Middle Atmosphere and Global Environment Observation (LAGEO), Institute of Atmospheric Physics, Chinese Academy of Science, Beijing 100029
2.
Xianghe Observatory of Whole Atmosphere, Institute of Atmospheric Physics, Chinese Academy of Sciences, Xianghe 065400
3.
University of Chinese Academy of Sciences, Beijing 100049

Received Date: 2020-04-17
Rev Recd Date: 2020-08-10
Publish Date: 2020-10-27

Abstract

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.
- Beijing MST radar;
- power spectral density data processing;
- log-linear fitting scheme;
- accurate identify of the target signal

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References(45)

Figures and Tables

图 1 2012年1月4日00:10北京MST雷达原始功率谱密度及数据预处理叠加经客观分析法和八段平均法处理的噪声电平(15 km高度东波束)

Fig. 1 Beijing MST radar raw and preprocessed power spectral density and averaged noise value estimated by objective method and segment method at 0010 UTC 4 Jan 2012(eastern beam at height of 15 km)

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图 2 2012年3月16日15:10北京MST雷达观测数据的R₂及lgR₂随dP变化

Fig. 2 R₂ and lgR₂ varying with dP using Beijing MST radar at 1510 UTC 16 Mar 2012

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图 3 2012年5月北京MST雷达7.8~12 km高度内(1715个距离库)功率谱数据应用对数-线性拟合方案与二分法方案估算噪声电平对比

Fig. 3 The comparison of noise level estimated by log-linear scheme and dichotomy scheme using power spectral density at 7.8-12 km height of Beijing MST radar in May 2012

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图 4 2012年1月4日00:10北京MST雷达东波束与西波束经改进算法处理后5个距离库高度功率谱密度(加粗曲线表示识别出的目标回波信号)

Fig. 4 Power spectral density of eastern and western beams at 5 heights of Beijing MST radar data processed by improved algorithm at 0010 UTC 4 Jan 2012(bold curve denotes recognized target echo)

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图 5 2012年3月16日11：40北京MST雷达原算法与改进算法得到的水平风速、水平风向与探空(11:15)对比

Fig. 5 Comparison of the horizontal wind speed and wind direction between radiosonde(1115 UTC) and Beijing MST radar products before and after improved algorithm processed(1140 UTC) on 16 Mar 2012

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图 6 2012年1月1日—12月31日信噪比(a)和谱宽(b)的年平均值(曲线)和标准差(阴影)廓线

Fig. 6 Comparison of annual mean(the curve) and standard deviation(the shaded) of SNR(a) and spectral width(b) between Beijing MST radar data(from 1 Jan to 31 Dec in 2012) obtained using original and improved algorithms

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图 7 2012年1月1日—12月31日北京MST雷达原算法与改进算法得到的经向风、纬向风与探空对比

Fig. 7 Comparison of the zonal and meridional wind between radiosonde and Beijing MST radar data obtained using original and improved algorithms from 1 Jan to 31 Dec in 2012

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图 8 2012年1月1日—12月31日北京MST雷达原算法与改进算法得到的垂直速度与ERA5再分析数据对比

Fig. 8 Comparison of the vertical velocity between ERA5 reanalysis and Beijing MST radar data obtained using original and improved algorithms from 1 Jan to 31 Dec in 2012

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图 9 2012年16组降水天气条件下北京MST雷达原算法与改进算法得到的经向风、纬向风与探空测值平均差值和均方根误差廓线

Fig. 9 Comparison of mean values and root mean square error of zonal and meridional wind between radiosonde and Beijing MST radar data obtained using original and improved algorithms under 16-group precipitation weather conditions in 2012

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