主办单位：中国气象局沈阳大气环境研究所
国际刊号：ISSN 1673-503X
国内刊号：CN 21-1531/P
气象与环境学报

气象与环境学报 ›› 2023, Vol. 39 ›› Issue (6): 61-68.doi: 10.3969/j.issn.1673-503X.2023.06.008

• 论文 • 上一篇    下一篇

2020年CLDAS风场数据在辽宁省的适用性分析

刘卫华1,2(),金巍2,3,*(),王会品4,师春香5,曲姝霖6,韩国敬3,于淼3   

  1. 1. 大连市人工影响天气办公室，辽宁大连 116000
    2. 中国科学院空间天气学国家重点实验室，北京 100190
    3. 鞍山市气象局，辽宁鞍山 114004
    4. 大连市气象信息中心，辽宁大连 116000
    5. 国家气象信息中心, 北京 100086
    6. 兰州大学大气科学学院，甘肃兰州 730000
  • 收稿日期:2023-03-24 出版日期:2023-12-28 发布日期:2024-01-27
  • 通讯作者: 金巍 E-mail:liuweihua2002@163.com;lnyk_jw@163.com
  • 作者简介:刘卫华, 男, 1985年生, 高级工程师, 主要从事气象数据处理方面的研究, E-mail: liuweihua2002@163.com
  • 基金资助:
    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

Analysis of the applicability of CLDAS wind field data in Liaoning province in 2020

Weihua LIU1,2(),Wei JIN2,3,*(),Huipin WANG4,Chunxiang SHI5,Shulin QU6,Guojing HAN3,Miao YU3   

  1. 1. Dalian Weather Modification Office, Dalian 116000, China
    2. Sate Key Laboratory of Space Weather, Chinese Academy of Sciences, Beijing 100190, China
    3. Anshan Meteorological Service, Anshan 114004, China
    4. Dalian Meteorological Information Center, Dalian 116000, China
    5. National Meteorological Information Center, Beijing 100086, China
    6. College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
  • Received:2023-03-24 Online:2023-12-28 Published:2024-01-27
  • Contact: Wei JIN E-mail:liuweihua2002@163.com;lnyk_jw@163.com

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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。

关键词: CLDAS, 风速预报, 相关系数

Abstract:

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.

Key words: CLDAS, Wind speed forecast, Correlation coefficients

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