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一种集成风向风速的风场空间检验方法

张博 赵滨

张博, 赵滨. 一种集成风向风速的风场空间检验方法. 应用气象学报, 2019, 30(2): 154-163. DOI: 10.11898/1001-7313.20190203..
引用本文: 张博, 赵滨. 一种集成风向风速的风场空间检验方法. 应用气象学报, 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.

一种集成风向风速的风场空间检验方法

DOI: 10.11898/1001-7313.20190203
资助项目: 

公益性行业(气象)科研专项 GYHY20150600

国家重点研究发展计划 2017YFA0604500

国家重点研究发展计划 2017FYC150190X

国家科技支撑计划 2015BAC03B04

国家科技支撑计划 2015BAC03B07

详细信息
    通信作者:

    张博, 邮箱:zhangb81@yeah.net

A Spatial Verification Method for Integrating Wind Speed and Direction

  • 摘要: 基于概率分布特征定义全新风速阈值选取方案,不受地域及季节性影响,并综合风向信息建立兼顾风向风速的风场分类列表,采用邻域空间检验技术构建可集成风向风速的矢量风场检验方法。基于2018年4月1—30日GRAPES_Meso模式不同分辨率(10 km及3 km)逐小时预报产品,利用所开发的矢量风场检验方法分析表明:模式风向预报的随机性随着风速的增大而减小,即弱风的风向难以成功预报。通过矢量风场综合分析发现高分辨率预报效果在170 km空间尺度上24 h预报最大评分优势可达0.24,各邻域空间尺度上评分分布趋势保持一致。通过敏感性分析发现,所获取的综合指标可用于反映风场预报性能。同时,不同矢量风场分类方法将对评估结果产生影响,高分类方法评分稳定性更好,低分类方法受限于单一分类权重过大而影响评估一致性。因此,在计算能力允许的条件下,选择较高分类方式将有助于获得更为稳定的检验效果。
  • 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

    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

    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

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

    Fig. 4  Hourly Fw 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 Fw =0.5)

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

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

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

    Fig. 6  Fw 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)

    图  7  2018年4月1—30日24 h预报Fc逐日分布

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

    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

    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

    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Fw分布(黑线为Fw=0.5)

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

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

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

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出版历程
  • 收稿日期:  2018-10-22
  • 修回日期:  2018-12-27
  • 刊出日期:  2019-03-31

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