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The Effect Assessment of Wind Field Inversion Based on WPR in Precipitation
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摘要: 风廓线雷达（wind profile radar，WPR）因具有高时空分辨率特点，成为当前短时临近预报的重要参考工具。降水时WPR同时接收大气湍流回波和降水粒子散射回波，现有技术不能有效分离叠加在一起的湍流信号和降水信号，导致降水期间风廓线雷达反演的风场数据严重缺失或失真。根据风廓线雷达探测技术原理及降水天气的功率谱特点，提出了降水天气时风廓线雷达湍流信号提取方法（WPR-HW），并选取2015—2018年天津10次降水过程对WPR-HW方法进行模式检验及个例效果评估。结果表明：WPR-HW方法对改善降水期间风廓线雷达风场数据缺失问题效果明显，在选取的10次降水过程中，目前通用的风廓线雷达风场反演方法（WIND）风场数据平均缺失率为25.4%，WPR-HW方法未出现风场数据缺失现象；WPR-HW方法较WIND方法反演风场数据可信度有显著提高，反演数据与再分析数据的风速均方根误差由WIND方法的2.3 m·s-1降至WPR-HW方法的1.6 m·s-1，风向均方根误差由WIND方法的45°降至WPR-HW方法的22°，从而验证WPR-HW方法在降水期间适用。Abstract:
Wind profile radar(WPR), taking atmospheric turbulence of clear air as main detecting object, is the main reference tool currently for short-time forecast because of its high spatial and temporal resolution. In the past few decades, WPR spectral data processing mainly focuses on the wind spectrum estimation. In recent years, with the use of WPR data expansion, there are increasingly high demand for WPR data accuracy, but because of ground clutter and external noise, flying objects, the presence of disturbances such as precipitation and limitations of Fourier Transform method itself, there are often multiple overlapped peaks, which makes it difficult to judge the spectral meaning, resulting in large error detection products. WPR has a large dynamic reception range, so it can receive the echo of scattering of atmosphere turbulence and precipitation particles simultaneously. However, the superimposed spectrum of atmosphere and precipitation cannot be separated effectively. In the meantime, the wind field calculation is based on the hypothesis of local uniform and isotropy, which cannot be met during precipitation with great spatial variability and leads to data of WPR serious deficiency or distortion. It's of great importance to establish an effective spectral extraction programs under different weather conditions to improve the accuracy of spectral estimation for wind field data after the inversion, thereby enhancing the wind profile accuracy of radar detection.A method of WPR-HW is developed for the case of precipitation according to the principle of WPR detection and the feature of spectrum, and then the effectiveness of the method is tested using ECMWF ERA Interim data. 10 precipitation cases in Tianjin are investigated to verify the significance of wind field data processed by WPR-HW in severe convection prediction. Results show that the WPR-HW has significant advantage compared with the recent WIND method (the universal method of wind field inversion from WPR) in integrity and reliability. For the wind field data in 10 precipitation cases, the leakage rate of WIND is 25.4% while that of WPR-HW is 0. The root mean square error in wind speed of WPR-HW is 1.6 m·s-1 while that of WIND is 2.3 m·s-1. The RMSE in wind direction of WPR-HW is 22° while that of WIND is 45°. The wind field processed by WPR-HW is able to make up for the deficiency and distortion of WPR data effectively in precipitation, which thus benefit to improve the timeliness and accuracy in strong convective weather forecasting.
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Key words:
- WPR;
- power spectrum;
- wind field
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图 1 单峰谱(a)、双峰谱(b)、三峰谱(c)的功率谱密度
Fig. 1 Power spectrum density of one peak(a), double peaks(b) and three peaks(c)
图 2 WPR-HW方法反演风场与EC再分析风场对比统计
(a)WPR-HW方法反演风速与EC再分析风速数据对比，(b)风向均方根误差占有率分布
Fig. 2 Comparison of data processed by WPR-HW and ECMWF reanalysis data
(a)comparison of wind speed, (b)distribution of root mean square error of wind direction
图 3 WPR-HW方法反演风场与EC再分析风场平均相对偏差随高度变化
Fig. 3 Relative bias along with height between data processed by WPR-HW and ECMWF reanalysis data
图 4 2017年7月6日17：41宝坻站功率谱密度
(a)北波束，(b)南波束
Fig. 4 Power spectrum density of Baodi Station at 1740 BT 6 Jul 2017
(a)north beam, (b)south beam
图 5 2017年7月6日16:30—20:30 WIND方法(a)和WPR-HW方法(b)反演的宝坻站风羽图
Fig. 5 The barb processed by WIND(a) and WPR-HW(b) at Baodi Station during 1630—2030 BT on 6 Jul 2017
图 6 2017年7月6日16:30—20:30宝坻站降水量随时间分布
Fig. 6 Precipitation of Baodi Station during 1630—2030 BT on 6 Jul 2017
图 7 2017年7月6日17:00—18:30宝坻站WPR-HW方法反演产品
(a)垂直速度时间-高度剖面，(b)速度谱宽时间-高度剖面
Fig. 7 Vertical products processed by WPR-HW at Baodi Station during 1700—1830 BT on 6 Jul 2017
(a)time-altitude section of vertical speed, (b)time-altitude section of spectrum width
图 8 2018年8月13日17：40静海站功率谱密度
(a)北波束，(b)南波束
Fig. 8 The power spectrum density of Jinghai Station at 1740 BT 13 Aug 2018
(a)north beam, (b)south beam
图 9 2018年8月13日15:30—18:30 WIND方法(a)和WPR-HW方法(b)反演的静海站风羽图
Fig. 9 The barb processed by WIND(a) and WPR-HW(b) at Jinghai Station during 1530—1830 BT on 13 Aug 2018
图 10 2018年8月13日15:30—18:30利用WPR-HW方法反演的静海站垂直速度
Fig. 10 Vertical speed processed by WPR-HW at Jinghai Station during 1530—1830 BT on 13 Aug 2018
表 1 风廓线雷达参数
Table 1 Parameters of WPR
参数 低模式 高模式 起始采样库的高度/m 60 600 宝坻站终止采样库的高度/m 1200 5280 静海站终止采样库的高度/m 1500 7080 距离库长/m 60 120 相干累积 100 64 fft点数 256 512 谱平均数 8 4 
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表 2 10次降水过程WPR-HW方法风场反演效果统计
Table 2 Statistics of case evaluation for data processed WPR-HW
降水日期 降水量级 数据缺失率/% WPR-HW方法反演风场特征 WPR-HW方法降水预报提前量/min WIND方法 WPR-HW方法 2015-09-04 大雨 26 0 风向切变 105 2016-06-27 小雨 20 0 风向切变 150 2016-07-20 暴雨 28 0 边界层急流 15 2016-08-07 大雨 30 0 边界层急流，风速辐合 60 2017-05-22 小雨 21 0 风向切变 150 2017-06-22 小雨 23 0 边界层急流 150 2017-06-23 中到大雨 27 0 边界层急流，风向切变 15 2017-07-06 暴雨 22 0 边界层急流，风速辐合 30 2017-08-02 大雨 26 0 边界层急流 45 2018-08-13 暴雨 31 0 风向切变，龙卷 30
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