摘要
基于欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)、中国国家气象中心业务运行的中尺度数值预报系统(Global/Regional Assimilation and Prediction Enhanced System Meso,GRAPES-Meso)、美国国家环境预报中心(National Centers for Environmental Prediction,NCEP)的全球预报系统(Global Forecast System,GFS)、GRAPES全球预报系统(GRAPES-GFS)4个模式风场预报资料,利用双线性、反距离加权、三次样条、克里格等插值方法对华东及周边地区(110°~130°E,20°~40°N)2020年1—4月逐日地面和高空风0~72 h集合预报资料进行降尺度处理,得到满足机场及终端区气象保障的精细化风场预报。此外,还对精细化风场预报做多模式集成。结果表明,对于风场的精细化格点预报,反距离加权插值方法误差最小,为最优水平插值方法。基于扩展复卡尔曼滤波的多模式集成(Augmented Complex Extended Kalman Filter,ACEKF)可进一步减小风场预报的误差。对华东地区上海、青岛和厦门3个机场地面和高空风的多模式集成风场精细化预报的分析表明,ACEKF多模式集成预报不但均方根误差较BREM、ECMWF和GRAPES-GFS的预报误差小,且随高度变化也不如单模式预报的大,其预报性能更为稳定。
This study focuses on the generation of high-resolution wind forecasts over East China and its surrounding regions(110°—130°E,20°—40°N)for the period from January to April 2020,utilizing wind forcast data from the European Centre for Medium-Range Weather Forecasts(ECMWF),the Mesoscale component of the Global and Regional Assimilation and Prediction Enhanced System(GRAPES-Meso),the Global Forecast System(GFS)of the National Centers for Environmental Prediction(NCEP),and the Global Forecast System of the Global and Regional Assimilation and Prediction System(GRAPES-GFS).Various interpolation techniques,including bilinear interpolation,inverse distance weighted interpolation,kriging interpolation,and cubic spline interpolation,were employed to create downscaling forecasts spanning from 0 to 72 hours.These high-resolution forecasts aim to cater to the specific needs of airports and their terminal areas.Furthermore,this study encompasses multimodel ensemble forecasts of high-resolution wind fields.The results reveal that inverse-distance weighted interpolation outperforms other interpolation schemes for horizontal wind forecast interpolation.Leveraging the augmented complex extended Kalman Filter(ACEKF)for multimodel ensemble forecasts substantially reduces root-mean-square errors(RMSEs)in wind field predictions.Notably,whether concerning surface winds or high-level winds,the ACEKF forecasts exhibit significant superiority compared bias-removed ensemble mean(BREM)forecasts and individual models,as evidenced by lower RMSEs.Examining wind forecasts at three prominent airports in East China—Shanghai,Qingdao,and Xiamen—reveals that ACEKF forecasts not only feature reduced RMSEs compared to BREM,ECMWF,and GRAPES-GFS forecasts but also display consistent performance across varying altitudes.This heightened forecast stability distinguishes ACEKF forecasts from BREM and individual model forecasts.
作者
智协飞
吴柏莹
罗忠红
曹晴
ZHI Xiefei;WU Baiying;LUO Zhonghong;CAO Qing(Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD)/Key Laboratory of Meteorological Disasters,Ministry of Education(KLME),Nanjing University of Information Science&Technology,Nanjing 210044,China;WeatherOnline Institute of Meteorological Applications,Wuxi 214000,China;CAAC Xiamen Air Traffic Management Station,Xiamen 361006,China;CAAC East China Air Traffic Management Bureau,Shanghai 200335,China)
出处
《大气科学学报》
CSCD
北大核心
2023年第6期917-927,共11页
Transactions of Atmospheric Sciences
基金
中国民用航空华东地区管理局研发项目“华东地区机场及终端区风场预报预警系统研究”
国家自然科学基金重大研究计划集成项目(91937301)。
关键词
插值
风场预报
扩展复卡尔曼滤波
高分辨率
多模式集成
interpolation
wind forecast
augmented complex extended Kalman Filter
high-resolution
multimodel ensemble
