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Quality Factors and Processing Algorithm for Wind Profiling Radar Data
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摘要: 风廓线雷达系统误差和探测数据时空代表性影响风的数据质量。针对五波束探测风廓线雷达，提出雷达系统误差检测方法并分析风的空间不均匀分布和时间代表性对风数据质量的影响。在此基础上，通过比较4组三波束计算的两组水平风u，v分量离差进行风的空间均匀性判别，并比较了一致性平均和数学平均两种时间代表性处理算法间的测风精度差异。利用广东风廓线雷达站网2014年3—5月10部雷达数据进行方法应用和评估。结果表明：稳定大气条件下，3种型号雷达 (LC，PB，PA) 的有效数据高度分别达到3，6 km和10 km的雷达系统功能设计需求。经空间均匀性检验与时间一致性平均处理的风数据在降水期间质量优于业务雷达数据，3—5月10部雷达获取的两组u，v分量离差标准差约为1 m·s-1，表明经过空间一致性检验和时间一致性平均处理后的数据质量较好。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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图 1 2014年3月珠海站低模式和高模式Vew，Vns频数分布图
Fig. 1 The distribution of Vew and Vns in low and high modes at Zhuhai Station in Mar 2014
图 2 不同型号雷达Vew，Vns平均值随高度分布
Fig. 2 The statistical average of Vew and Vns of different radars
图 3 2014年3月珠海站所有探测时次的Δu，Δv频率分布
(a) 低模式，(b) 高模式
Fig. 3 The distribution of Δu and Δv for all observations at Zhuhai Station in Mar 2014
(a) low mode, (b) high mode
图 4 4种方案低、高模式探测下Δu，Δv的频率分布
Fig. 4 The distribution of Δu and Δv in low and high modes using different schemes
图 5 4种方案低、高模式探测Δu，Δv标准差随高度分布
Fig. 5 The stand deviation of Δu and Δv changing with height in low and high modes using different schemes
表 1 风廓线雷达参数
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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表 2 2014年3—5月晴空稳定大气条件下广东风廓线雷达站网有效探测高度 (单位：km)
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
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表 3 数学平均法与一致性平均法的Δu，Δv标准差
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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表 4 算法处理结果与业务OOBS数据比对
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
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表 5 广东风廓线雷达站网2014年3—5月Δu，Δv标准差 (单位：m·s-1)
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
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