A Spatial Verification Method for Integrating Wind Speed and Direction

Zhang Bo; Zhao Bin

doi:10.11898/1001-7313.20190203

Vol.30, NO.2, 2019

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Zhang Bo, Zhao Bin. A spatial verification method for integrating wind speed and direction. J Appl Meteor Sci, 2019, 30(2): 154-163. DOI: 10.11898/1001-7313.20190203.

Citation:

Zhang Bo, Zhao Bin. A spatial verification method for integrating wind speed and direction. J Appl Meteor Sci, 2019, 30(2): 154-163. DOI: 10.11898/1001-7313.20190203.

Zhang Bo, Zhao Bin. A spatial verification method for integrating wind speed and direction. J Appl Meteor Sci, 2019, 30(2): 154-163. DOI: 10.11898/1001-7313.20190203.

Citation:

Zhang Bo, Zhao Bin. A spatial verification method for integrating wind speed and direction. J Appl Meteor Sci, 2019, 30(2): 154-163. DOI: 10.11898/1001-7313.20190203.

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A Spatial Verification Method for Integrating Wind Speed and Direction

DOI: 10.11898/1001-7313.20190203

Zhang Bo^{1
,
,},
Zhao Bin^1,2

1.
National Meteorological Center, Beijing 100081
2.
Numerical Weather Prediction Center of China Meteorological Administration, Beijing 100081

Received Date: 2018-10-22
Rev Recd Date: 2018-12-27
Publish Date: 2019-03-31

Abstract

Abstract

In traditional statistical analyses, the vector wind field is always verified by wind speed and wind direction separately. However, assessment results of wind speed are often contrary to those of wind direction, which then makes it difficult to obtain a uniform conclusion. To solve this problem, a novel selection scheme of wind speed thresholds is defined based on the probability distribution of wind speed, which is not affected by geographical and seasonal factors and it can keep universality in different complex environments. A vector wind classification is established based on integrating wind speed classes and wind directions. Using the spatial verification technique of fraction skill score (FSS), a vector wind verification method is developed by integrating wind speed and wind direction together. Based on hourly forecast products with different resolution (10 km and 3 km) simulated by GRAPES_Meso model from 1 April 2018 to 30 April 2018, assessment results show that the randomness of the wind direction forecast will decrease with the increasing of wind speed, which indicates it's difficult to predict the wind direction of weak wind speed successfully. By the comprehensive analysis of the vector wind field, it is found that the high-resolution (GRAPES_3 km) forecasting performance has a maximum score advantage of 0.24 on the 170 km spatial scale than the lower one (GRAPES_10 km). Scores in adjacent region are highly consistent, and it does not change the evolution of the score with the time series. And therefore, a comprehensive score can be calculated and used to assess the modelling performance by averaging skill scores in each spatial scale. In this way, deficiencies of artificial definitions of spatial scale can be avoided, which guarantee the spatial verification score of vector wind better practical application value. Simultaneously, different vector wind field classification methods have an impact on evaluation results. By sensitivity analysis, the higher wind classification method can make the score more stable with moderate wind classification one, and the magnitude of comprehensive scores are basically equivalent. The lower wind classification method has a relatively low overall score due to its single classification score weight, and it leads to the weak consistency with results of higher classification method. Therefore, under conditions of computing ability, choosing an encrypted wind direction classification to obtain a vector wind classification method will help to obtain a more stable verification result and improve the convergence and stability of the comprehensive evaluation.
- wind direction;
- wind speed;
- spatial verification method;
- probability distribution;
- comprehensive evaluation

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References(27)

Figures and Tables

图 1 17种包含风向风速的矢量风分类示意图

Fig. 1 The definition of 17-class basic wind speed and direction

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图 2 GRAPES_3 km模式2018年4月16日12 h预报风向误差随实况风速分布

Fig. 2 GRAPES_3 km 12-hour wind direction forecast change with observed wind speed initialed on 16 Apr 2018

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图 3 2018年4月1—30日24 h预报的风向及风速均方根误差分布

Fig. 3 Root mean square errors of wind speed and wind direction for 24-hour forecast from 1 Apr to 30 Apr in 2018

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图 4 2018年4月1—30日36 h逐时预报平均F_w分布(黑线为F_w=0.5)(a)GRAPES_10 km, (b)GRAPES_3 km

Fig. 4 Hourly F_w for 36-hour forecast averaged from 1 Apr to 30 Apr in 2018 with GRAPES_10 km(a) and GRAPES_3 km(b)(the black line denotes F_w =0.5)

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图 5 2018年4月1—30日170 km邻域空间尺度下24 h预报F_w逐日演变

Fig. 5 F_w of 24-hour forecast from 1 Apr to 30 Apr in 2018 with 170 km spatial scale

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图 6 2018年4月1—30日不同邻域空间尺度24 h预报F_w逐日分布(a)50 km, (b)90 km, (c)130 km, (d)330 km

Fig. 6 F_w for 24-hour forecast from 1 Apr to 30 Apr in 2018 with different spatial scales of 50 km(a), 90 km(b), 130 km(c), 330 km(d)

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图 7 2018年4月1—30日24 h预报F_c逐日分布

Fig. 7 F_c for 24-hour forecast from 1 Apr to 30 Apr in 2018

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图 8 2018年4月6日24 h预报的各风场分类中实况和预报所占样本百分比

Fig. 8 Sample percentage of observation and forecasts in each basic wind class for 24-hour forecast initialed on 6 Apr 2018

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图 9 9种(a)及33种(b)包含风向风速的矢量风示意图

Fig. 9 The definition of 9-class(a) and 33-class(b) basic wind speed and direction

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图 10 2018年4月1—30日不同矢量风分类方法36 h逐时预报平均F_w分布(黑线为F_w=0.5)

Fig. 10 Hourly F_w for 36-hour forecast averaged from 1 Apr to 30 Apr 2018(the black line denotes F_w=0.5)

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图 11 2018年4月1—30日9种及33种分类方法F_c综合指标24 h预报逐日分布

Fig. 11 Daily F_c for 24-hour forecast from 1 Apr to 30 Apr in 2018

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