邵长亮,闵锦忠. 2019. 基于地面资料集合均方根滤波同化方案的京津冀暴雨模拟研究[J]. 气象学报, (0):-, doi:10.11676/qxxb2019.008
基于地面资料集合均方根滤波同化方案的京津冀暴雨模拟研究
A Scheme of terrain error of representativeness based on Assimilation of Surface AWS Data Using the Ensemble Square Root Filter.
投稿时间:2017-11-15  修订日期:2018-04-13
DOI:10.11676/qxxb2019.008
中文关键词:  自动站资料  资料同化  集合均方根滤波  地形代表性误差
英文关键词:AWS  data assimilation  EnSRF, terrain error of representativeness
基金项目:高校基金
作者单位E-mail
邵长亮 南京信息工程大学气象灾害预报预警与评估协同创新中心中国气象局气象探测中心 shchl1@163.com 
闵锦忠 南京信息工程大学气象灾害预报预警与评估协同创新中心 minjz@nuist.edu.cn 
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中文摘要:
      为了更加有效的同化地面自动站资料,针对模式地形与观测站地形存在的高度差异对同化效果的影响,提出了相应的解决方案。在同化系统的位温和露点观测误差中分别引入位温和露点地形代表性误差,在WRF 模式中应用集合均方根滤波方法(EnSRF)同化地面自动站资料,并对2016年一次京津冀暴雨个例进行数值试验。研究结果表明:同化地面资料后,同化阶段的均方根误差、预报阶段的降水TS评分和前13个时次各要素预报均有整体改进。在观测误差中引入地形代表性误差与引入前相比,风场均方根误差得到整体改进;位温和露点的均方根误差在前期表现并不稳定,在后期有所改进;预报阶段前24小时累计降水与后24小时累计降水TS评分在整体上均有所提高。新方案能够减少高度差异对同化效果的影响。
英文摘要:
      For put surface Automatic Weather Station (AWS) data into numerical models sufficiently, in this paper, a further improvement based on the Ensemble Square Root Filter(EnSRF) is proposed to solve the impact of assimilation results caused by elevation difference between observation site and model surface. Potential temperature and dewpoint temperature Terrain Error of Representativeness (TER) are added into temperature and dewpoint temperature error of surface observation data assimilation in WRF-EnSRF system respectively, and a numerical simulation of a Heavy Rain in Jing-Jin-Ji area in 2016 has been carried. Results show that the Root-Mean-Square Error (RMSE), threat score(TS) and The first 13 hours elements prediction have been improved generally. As terrain error of representativeness(TER) added, RMSE of wind is improved in general, which of potential temperature and dewpoint temperature is unstable in the earlier stage, but improved in later stage, and TS of the first and the later 24 h accumulated rainfall are overall improved, compared with no TER added.
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