The Effect Assessment of Wind Field Inversion Based on WPR in Precipitation

Lin Xiaomeng; Wei Yinghua; Chen Hong; Wang Yanchun

doi:10.11898/1001-7313.20200310

Vol.31, NO.3, 2020

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Article Navigation > Journal of Applied Meteorological Science > 2020 > 31(3): 361-372

Lin Xiaomeng, Wei Yinghua, Chen Hong, et al. The effect assessment of wind field inversion based on WPR in precipitation. J Appl Meteor Sci, 2020, 31(3): 361-372. DOI: 10.11898/1001-7313.20200310.

Citation:

Lin Xiaomeng, Wei Yinghua, Chen Hong, et al. The effect assessment of wind field inversion based on WPR in precipitation. J Appl Meteor Sci, 2020, 31(3): 361-372. DOI: 10.11898/1001-7313.20200310.

Citation:

Lin Xiaomeng, Wei Yinghua, Chen Hong, et al. The effect assessment of wind field inversion based on WPR in precipitation. J Appl Meteor Sci, 2020, 31(3): 361-372. DOI: 10.11898/1001-7313.20200310.

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The Effect Assessment of Wind Field Inversion Based on WPR in Precipitation

DOI: 10.11898/1001-7313.20200310

Tianjin Meteorological Observatory, Tianjin 300074

Received Date: 2019-12-10
Rev Recd Date: 2020-02-20
Publish Date: 2020-05-31

Abstract

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.
- WPR;
- power spectrum;
- wind field

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References(25)

Figures and Tables

Fig. 1 Power spectrum density of one peak(a), double peaks(b) and three peaks(c)

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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

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Fig. 3 Relative bias along with height between data processed by WPR-HW and ECMWF reanalysis data

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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

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Fig. 5 The barb processed by WIND(a) and WPR-HW(b) at Baodi Station during 1630—2030 BT on 6 Jul 2017

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Fig. 6 Precipitation of Baodi Station during 1630—2030 BT on 6 Jul 2017

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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

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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

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Fig. 9 The barb processed by WIND(a) and WPR-HW(b) at Jinghai Station during 1530—1830 BT on 13 Aug 2018

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Fig. 10 Vertical speed processed by WPR-HW at Jinghai Station during 1530—1830 BT on 13 Aug 2018

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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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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
降水日期	降水量级	WIND方法	WPR-HW方法	WPR-HW方法反演风场特征	WPR-HW方法降水预报提前量/min
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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