一种集成风向风速的风场空间检验方法
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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。
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.
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.
引文
[1] Atger F. Verification of intense precipitation forecasts from single models and ensemble prediction systems. Nonlinear Processes Geophys,2001,8:401-417.
[2] Weisman M L,Davis C.Wang W,et al. Experiences with O-36-h explicit convective forecasts with the WRF-ARW model. Wea Forecasting, 2008,23:407-437.
[3]沈学顺,苏勇,胡江林,等.GRAPES_GFS全球中期预报系统的研发和业务化.应用气象学报,2017,28(1):1-10.
[4]谭桂容,范艺媛,牛若芸.江淮地区强降水分型及其环流演变.应用气象学报,2018,29(4):396-409.
[5]张萌,于海鹏,黄建平,等.GRAPES_GFS2.0模式系统误差评估.应用气象学报,2018,29(5):571-583.
[6]杨挺,端义宏,徐晶,等.城市效应对登陆热带气旋妮妲降水影响的模拟.应用气象学报,2018,29(4):410-422.
[7] Murphy A H. A note on the ranked probability score. J Appl Meteor, 1971,10:155-156.
[8]洪伟,郑玉兰.基于ECMWF产品福建省前汛期短时强降水预报方法.应用气象学报,2018,29(5):584-595.
[9] Ahijevych D,Gilleland E, Barbara G B,et al. Application of spatial verification methods to idealized and NWP-gridded precipitation forecasts. Wea Forecasting, 2009,24:1485-1497.
[10] Brill K F,Mesinger F. Applying a general analytic,method for assessing bias sensitivity to bias-adjusted threat, and equitable threat scores. Wea Forecasting,2009,24:1748-1754.
[11] Baldwin M E,Kain J S. Sensitivity of several performance measures to displacement error,bias,and event frequency. Wea Forecasting,2006,21:636-648.
[12] Elizabeth E. Neighborhood verification:A strategy for rewardingclose forecasts. Wea Forecasting,2009,24:1498-1510.
[13] Casati B. New developments of the intensity-scale technique within the spatial verification methods intercomparison project. Wea Forecasting,2010,25:113-143.
[14] Zepeda-Arce J, Foufoula-Georgiou E, Droegemeier K K. Spacetime rainfall organization and its role in validating quantitative precipitation forecasts. J Geophys Res, 2000,105(8):10129-10146.
[15] Yates E,Anquetin S,Ducrocq V,et al. Point and areal validation of forecast precipitation fields. Meleorol Appl,2006, 13:1-20.
[16] Roberts N M,Lean H W. Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events. Mon Wea Rev, 2008,136:78-97.
[17] Zhao B,Zhang B. Assessing hourly precipitation forecast skill with the fractions skill score. J Meteor Res,2018,32(1):135-145.
[18]赵滨,张博.邻域空间检验方法在降水评估中的应用.暴雨灾害,2018,37(1):1-7.
[19] Skok G,Roberts N. Analysis of Fractions Skill Score properties for random precipitation fields and ECMWF forecasts. Q J R Meteor Soc,2016,142:2599-2610.
[20]唐文苑,郑永光,张小雯.基于FSS的高分辨率模式华北对流预报能力评估.应用气象学报,2018,29(5):513-523.
[21]毕宝贵,代刊,王毅,等.定量降水预报技术进展.应用气象学报,2016,27(5):534-549.
[22] Davies B M, Thomson D J. Comparisons of some parameterizations of wind direction variability with observations. Atmos Environ,1999,33:4909-4917.
[23] Mahrt L. Surface wind direction variability. J Appl Meteor Climatol,2011,50:144-152.
[24] Dorninger M, Mittermaier M P,Gilleland E, et al. MesoVICT:Mesoscale Verification Inter-Comparison over Complex Terrain. NCAR Technical Note NCAR/TN-505+STR, 2013:1-23.
[25] Rodwell M J,Richardson D S,IIewson T D,et al. A new equitable score suitable for verifying precipitation in numerical weather prediction. Quart J Roy Meteor Soc,2010,136:1344-1363.
[26] Haiden T M, Rodwell M J, Richardson D S. Intercomparison of global model precipitation forecast skill in 2010/11 using the SEEPS score. Mon Wea Rev,2012,140:2720-2733.
[27]黄丽萍,陈德辉,邓莲堂,等.GRAPES_Meso V4. 0主要技术改进和预报效果检验.应用气象学报,2017,28(1):25-37.
[2] Weisman M L,Davis C.Wang W,et al. Experiences with O-36-h explicit convective forecasts with the WRF-ARW model. Wea Forecasting, 2008,23:407-437.
[3]沈学顺,苏勇,胡江林,等.GRAPES_GFS全球中期预报系统的研发和业务化.应用气象学报,2017,28(1):1-10.
[4]谭桂容,范艺媛,牛若芸.江淮地区强降水分型及其环流演变.应用气象学报,2018,29(4):396-409.
[5]张萌,于海鹏,黄建平,等.GRAPES_GFS2.0模式系统误差评估.应用气象学报,2018,29(5):571-583.
[6]杨挺,端义宏,徐晶,等.城市效应对登陆热带气旋妮妲降水影响的模拟.应用气象学报,2018,29(4):410-422.
[7] Murphy A H. A note on the ranked probability score. J Appl Meteor, 1971,10:155-156.
[8]洪伟,郑玉兰.基于ECMWF产品福建省前汛期短时强降水预报方法.应用气象学报,2018,29(5):584-595.
[9] Ahijevych D,Gilleland E, Barbara G B,et al. Application of spatial verification methods to idealized and NWP-gridded precipitation forecasts. Wea Forecasting, 2009,24:1485-1497.
[10] Brill K F,Mesinger F. Applying a general analytic,method for assessing bias sensitivity to bias-adjusted threat, and equitable threat scores. Wea Forecasting,2009,24:1748-1754.
[11] Baldwin M E,Kain J S. Sensitivity of several performance measures to displacement error,bias,and event frequency. Wea Forecasting,2006,21:636-648.
[12] Elizabeth E. Neighborhood verification:A strategy for rewardingclose forecasts. Wea Forecasting,2009,24:1498-1510.
[13] Casati B. New developments of the intensity-scale technique within the spatial verification methods intercomparison project. Wea Forecasting,2010,25:113-143.
[14] Zepeda-Arce J, Foufoula-Georgiou E, Droegemeier K K. Spacetime rainfall organization and its role in validating quantitative precipitation forecasts. J Geophys Res, 2000,105(8):10129-10146.
[15] Yates E,Anquetin S,Ducrocq V,et al. Point and areal validation of forecast precipitation fields. Meleorol Appl,2006, 13:1-20.
[16] Roberts N M,Lean H W. Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events. Mon Wea Rev, 2008,136:78-97.
[17] Zhao B,Zhang B. Assessing hourly precipitation forecast skill with the fractions skill score. J Meteor Res,2018,32(1):135-145.
[18]赵滨,张博.邻域空间检验方法在降水评估中的应用.暴雨灾害,2018,37(1):1-7.
[19] Skok G,Roberts N. Analysis of Fractions Skill Score properties for random precipitation fields and ECMWF forecasts. Q J R Meteor Soc,2016,142:2599-2610.
[20]唐文苑,郑永光,张小雯.基于FSS的高分辨率模式华北对流预报能力评估.应用气象学报,2018,29(5):513-523.
[21]毕宝贵,代刊,王毅,等.定量降水预报技术进展.应用气象学报,2016,27(5):534-549.
[22] Davies B M, Thomson D J. Comparisons of some parameterizations of wind direction variability with observations. Atmos Environ,1999,33:4909-4917.
[23] Mahrt L. Surface wind direction variability. J Appl Meteor Climatol,2011,50:144-152.
[24] Dorninger M, Mittermaier M P,Gilleland E, et al. MesoVICT:Mesoscale Verification Inter-Comparison over Complex Terrain. NCAR Technical Note NCAR/TN-505+STR, 2013:1-23.
[25] Rodwell M J,Richardson D S,IIewson T D,et al. A new equitable score suitable for verifying precipitation in numerical weather prediction. Quart J Roy Meteor Soc,2010,136:1344-1363.
[26] Haiden T M, Rodwell M J, Richardson D S. Intercomparison of global model precipitation forecast skill in 2010/11 using the SEEPS score. Mon Wea Rev,2012,140:2720-2733.
[27]黄丽萍,陈德辉,邓莲堂,等.GRAPES_Meso V4. 0主要技术改进和预报效果检验.应用气象学报,2017,28(1):25-37.