Vol.27, NO.2, 2016
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Article Navigation >  Journal of Applied Meteorological Science  > 2016  >  27(2): 148-159
Gao Zhuyu, Ruan Zheng, Wei Ming, et al. Quality factors and processing algorithm for wind profiling radar data. J Appl Meteor Sci, 2016, 27(2): 148-159. DOI:  10.11898/1001-7313.20160203.
Citation: Gao Zhuyu, Ruan Zheng, Wei Ming, et al. Quality factors and processing algorithm for wind profiling radar data. J Appl Meteor Sci, 2016, 27(2): 148-159. DOI:  10.11898/1001-7313.20160203.
shu

Quality Factors and Processing Algorithm for Wind Profiling Radar Data

DOI: 10.11898/1001-7313.20160203
  • 1.

    Nanjing University of Information Science & Technology, Nanjing 210044

  • 2.

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

  • Received Date: 2015-05-21
  • Rev Recd Date: 2015-11-04
  • Publish Date: 2016-03-31
    Abstract
  • In recent years, wind profiling radar (WPR) network in China is under rapid development. To take advantage of the network measurements in weather analysis and numerical prediction, it's of great significance to make full aware of quality factors and improve the current processing algorithm for WPR data.Many factors affect the quality of horizontal wind data detected by WPR, especially system error and meteorological background. According to five-beam WPR, a new method for examining system error from radar Doppler measurements is proposed. As for meteorological background, wind filed is assumed homogenous when it is detected by WPR, and the accuracy of horizontal wind data will decline when the assumption is not satisfied. During the period of precipitation, scattering caused by raindrops is much stronger than turbulence detected by WPR. And the assumption of homogenous wind breaks down easily for the cause that fall terminal velocity of precipitation particles changes rapidly in space when convective precipitation happens, which is a significant problem for WPR data quality control algorithm.However, two independent wind profiles can be measured with a five-beam WPR and differences between measured zonal winds and meridional winds can reflect errors caused by the inhomogeneity of wind field. In order to reduce such errors, all observations are examined to make sure data detected under circumstances where the wind filed is extremely inhomogenous are deleted. Besides, different averaging methods, such as consensus average and simple average, used to calculate hourly averaged winds also affect the accuracy of it and comparisons are conducted on two averaging methods.Combined with 10 radars of Guangdong WPR network, evaluation of the new methods for processing basic data is analyzed from March to May in 2014. Results show that 10 radars in Guangdong WPR network, including 8 boundary radars (LC), 1 troposphere radar Ⅰ(PA) and 1 troposphere radar Ⅱ(PB), meet the designed requirements respectively in terms of the maximum height of credible data in clear air, which is 3 km for LC radar, 6 km for PB radar and 10 km for PA radar. Furthermore, there are no large system errors in 10 radars except that the examining consequence is unsatisfactory during 1-2 km for PA radar. It is necessary to consider the atmospheric inhomogeneities that may cause great errors especially when it rains heavily, and consensus averaged wind is superior to simple averaged wind in median and high heights. Therefore, an improved algorithm according to examination of atmospheric inhomogeneities and consensus average is proposed to obtain hourly averaged winds. It is proved that winds obtained from the improved algorithm show better representation than the currently used data during precipitation, as the stand deviation of differences between two independent measured zonal wind values and meridional wind values are both close to 1 m·s-1.
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  • Fig. 1  The distribution of Vew and Vns in low and high modes at Zhuhai Station in Mar 2014

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    Fig. 2  The statistical average of Vew and Vns of different radars

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    Fig. 3  The distribution of Δu and Δv for all observations at Zhuhai Station in Mar 2014

    (a) low mode, (b) high mode

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    Fig. 4  The distribution of Δu and Δv in low and high modes using different schemes

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    Fig. 5  The stand deviation of Δu and Δv changing with height in low and high modes using different schemes

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    Table  1  Parameters of wind profile radars

    参数 边界层雷达 (LC) 对流层Ⅱ型雷达 (PB) 对流层Ⅰ型雷达 (PA)
    低模式 高模式 低模式 高模式 低模式 中模式 高模式
    波长/mm 232 232 674 674 674 674 674
    探测起始高度/m 100 1050 150 2070 150 3030 4950
    探测终止高度/m 2740 5970 3630 10470 3750 6630 16950
    距离库数 45 83 30 36 31 16 26
    距离库长/m 60 60 120 240 120 240 480
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    Table  2  Valid height of Guangdong wind profiler network from Mar to May in 2014 of clear sky (unit:km)

    站名 3月 4月 5月
    珠海 3.8 4.4 3.2
    潮州 4.4 4.4 3.6
    从化 4.2 4.4 3.6
    龙门 3.6 4.2 4.2
    新会 4.6 4.6 3.6
    罗定 4.2 4.5 3.6
    连州 4.2 4.6 4.2
    增城 4.0 4.6 3.4
    湛江 6.0 7.0 8.0
    萝岗 10.0 12.0 12.0
    DownLoad: Download CSV

    Table  3  Standard deviation of Δu and Δv using simple average and consensus average

    高度/km 数学平均 一致性平均
    Δu标准差/(m·s-1) Δv标准差/(m·s-1) Δu标准差/(m·s-1) Δv标准差/(m·s-1)
    (0，1] 1.85 1.70 2.29 2.25
    (1，2] 2.52 2.72 2.46 2.59
    (2，3] 3.97 4.38 3.20 3.62
    (3，4] 5.06 6.52 4.68 4.65
    (4，5] 8.73 9.69 4.44 5.37
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    Table  4  Comparison between improved algorithm data and OOBS data

    高度/km 业务OOBS数据 算法处理结果
    u离差标准差/
    (m·s-1)    		 v离差标准差/
    (m·s-1)    		 u离差标准差/
    (m·s-1)    		 v离差标准差/
    (m·s-1)
    (0, 1] 2.2 2.4 1.6 1.6
    (1, 2] 2.4 2.5 1.5 1.6
    (2, 3] 2.7 2.7 1.8 1.7
    (3, 4] 2.6 3.1 1.6 1.5
    (4, 5] 3.7 3.3 1.4 1.5
    DownLoad: Download CSV

    Table  5  The standard deviation of Δu and Δv in Guangdong wind profiler network from Mar to May in 2014(unit:m·s-1)

    站点 探测模式 Δu标准差 Δv标准差
    3月 4月 5月 3月 4月 5月
    珠海 低 1.07 1.12 1.12 1.06 1.12 1.08
    高 1.04 1.05 1.14 1.11 1.08 1.15
    潮州 低 1.17 1.24 1.36 1.09 1.13 1.30
    高 1.16 1.27 1.38 1.14 1.25 1.35
    从化 低 1.18 1.23 1.32 1.17 1.21 1.33
    高 1.14 1.18 1.31 1.15 1.23 1.30
    龙门 低 1.13 1.21 1.32 1.14 1.13 1.29
    高 1.14 1.14 1.39 1.10 1.14 1.37
    新会 低 1.12 1.09 1.23 1.15 1.29 1.21
    高 1.00 1.05 1.24 1.04 1.20 1.28
    罗定 低 1.10 1.19 1.24 1.15 1.19 1.24
    高 1.10 1.16 1.29 1.20 1.19 1.30
    连州 低 1.36 1.40 1.30 1.37 1.40 1.29
    高 1.19 1.26 1.28 1.21 1.29 1.26
    增城 低 1.12 1.18 1.20 1.22 1.24 1.26
    高 1.12 1.19 1.29 1.22 1.27 1.29
    湛江 低 0.96 0.94 0.91 1.03 0.91 0.87
    高 1.24 1.10 1.11 1.17 1.09 1.10
    萝岗 低 1.36 1.25 1.37 0.94 1.00 0.92
    中 0.68 0.78 1.03 0.71 0.74 1.29
    高 1.22 1.15 1.30 1.30 1.20 1.31
    DownLoad: Download CSV
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    • Received : 2015-05-21
    • Accepted : 2015-11-04
    • Published : 2016-03-31
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