摘要
选用2020年辽宁省286个气象站点地面10 m风场数据与中国气象局陆面数据同化系统(CLDAS)10 m风场数据,统计逐小时CLDAS网格插值到站点的风速数据与站点观测风速数据的相关系数(COR)、平均偏差(ME)、均方根误差(RMSE)和平均绝对误差(MAE),进行CLDAS风场数据在辽宁省的适用性分析和评估。结果表明:CLDAS风场格点数据分辨率1 km较5 km更接近站点观测数据,邻近点插值法较双线性插值法偏差更小。辽宁省286个站点中,逐小时CLDAS风场数据与观测数据相关系数低于0.95的站数仅占总站数的1.7%。辽宁沿海低海拔地区和北部地区较其他内陆地区的CLDAS风速与站点风速偏差大。CLDAS风速与观测风速误差的平均值为负,其中,秋季平均偏差最小,夏季、冬两季次之,春季偏差最大;夜间夏季、秋季日变化偏差最小,冬季次之,春季最大;白天冬季偏差最小,夏季、秋季次之,春季最大。辽宁省3次大风个例分析均表明,CLDAS风场数据有较好的适用性。
Using the 10 m wind field data from 286 meteorological stations in Liaoning province in 2020 and the 10 m wind field data from the Land Data Assimilation System(CLDAS)of the China Meteorological Administration,the correlation coefficient(COR),mean bias(ME),root mean square error(RMSE)and mean absolute error(MAE)between the hourly wind speed data from CLDAS interpolated to the stations and the observed wind speed data at the stations were calculated to analyze and evaluate the applicability of the CLDAS wind field data in Liaoning province.The results show that the CLDAS gridded data with a 1 km resolution is closer to the observed data than those with a 5 km resolution,and the biases of the nearest neighbor interpolation method are smaller than those of the bilinear interpolation method.For the 286 stations in Liaoning province,the hourly CLDAS wind field data has a correlation coefficient below 0.95 with the observed data for only 1.7%of the total stations.The biases between the CLDAS and the observed wind speeds are larger in the coastal low-lying areas and northern Liaoning than in other inland areas.The mean biases between the CLDAS and the observed wind speeds are negative.Among the seasons,the mean bias is smallest in autumn,followed by summer and winter,and largest in spring.For the diurnal variation,the biases are smallest at night in summer and autumn,followed by winter,and largest in spring.During the daytime,the bias is smallest in winter,followed by summer and autumn,and largest in spring.Case studies of 3 strong wind events in Liaoning province all indicate that the CLDAS wind field data has good applicability.
作者
刘卫华
金巍
王会品
师春香
曲姝霖
韩国敬
于淼
LIU Weihua;JIN Wei;WANG Huipin;SHI Chunxiang;QU Shulin;HAN Guojing;YU Miao(Dalian Weather Modification Office,Dalian 116000,China;Sate Key Laboratory of Space Weather,Chinese Academy of Sciences,Beijing 100190,China;Anshan Meteorological Service,Anshan 114004,China;Dalian Meteorological Information Center,Dalian 116000,China;National Meteorological Information Center,Beijing 100086,China;College of Atmospheric Sciences,Lanzhou University,Lanzhou 730000,China)
出处
《气象与环境学报》
2023年第6期61-68,共8页
Journal of Meteorology and Environment
基金
国家重点研发计划项目(2018YFC1506601)
国家气象信息中心结余资金项目(NMICJY202106)
辽宁省气象局人才计划科技活动资助项目(RC202201)
空间天气学国家重点实验室专项基金资助项目共同资助。
关键词
CLDAS
风速预报
相关系数
CLDAS
Wind speed forecast
Correlation coefficients