龚建东,王瑞春. 2021. 集合预报误差在GRAPES全球四维变分同化中的应用研究Ⅰ:局地化方案设计与动力平衡特征分析[J]. 气象学报, 79(1):31-47, doi:10.11676/qxxb2021.006
集合预报误差在GRAPES全球四维变分同化中的应用研究Ⅰ:局地化方案设计与动力平衡特征分析
The study of introducing ensemble forecast errors into the GRAPES global 4DVar Part Ⅰ:Localization scheme and dynamic balance analysis
投稿时间:2020-04-24  修订日期:2020-10-13
DOI:10.11676/qxxb2021.006
中文关键词:  集合预报误差  水平与垂直局地化方案  分析场地转平衡特征  GRAPES全球4DVar  数值试验
英文关键词:Ensemble forecast errors  Horizontal and vertical localization  Geostrophic balance of analysis  GRAPES global 4DVar  Numerical experiment
基金项目:国家重点研发计划项目(2018YFC1506700、2018YFC1506705)
作者单位
龚建东 国家气象中心, 北京, 100081
中国气象局数值预报中心, 北京, 100081 
王瑞春 国家气象中心, 北京, 100081
中国气象局数值预报中心, 北京, 100081 
摘要点击次数: 77
全文下载次数: 59
中文摘要:
      通过引入流依赖的集合预报误差,使得同化分析与天气形势紧密相关,是改善初值分析质量的重要途径。文中在GRAPES(Global Regional Assimilation and PrEdiction System)全球四维变分资料同化(4DVar)中研究了如何有效应用集合预报误差,包括增加扩展控制变量时如何降低其计算消耗以及如何在局地化过程中保持不同变量之间的动力平衡。利用高斯分布的谱滤波实现水平局地化,利用垂直正交经验函数分解实现垂直局地化,并采用前8个主导特征模态来限制控制变量空间维数增加。引入20至180个集合样本,在水平二维局地化情形下,控制变量总数的增长可以限制在1.1—1.8倍,而在三维局地化情形下,控制变量总数的增长限制在1.7—7.1倍。对60个集合样本和1°水平分辨率内循环,4DVar引入扩展控制变量后墙钟时间增加了约30%。进一步,通过采用在非平衡分析变量上进行水平局地化,然后再将风压地转平衡关系重新叠加到非平衡分析变量上,使得分析更好地保持了风压平衡关系,初始场地面气压倾向变化减小。此外,虽然垂直局地化对分析平衡影响较大,但依靠目标函数中的数字滤波弱约束,分析变量之间仍能较好满足动力平衡关系。结果表明,GRAPES全球4DVar中发展的增加扩展控制变量、谱滤波实现水平局地化、非平衡分析变量进行水平局地化等有效应用集合预报误差的方法,适合集合样本数超过100个的情况,在分析质量改善的同时,4DVar系统的计算和存储消耗没有显著增加。
英文摘要:
      Introducing flow-dependent ensemble forecast errors into the assimilation system to make the assimilation closely related to weather situations is an important way to improve the quality of the initial analysis field. In this paper, we investigate how to effectively apply ensemble forecast errors into the global four-dimensional variational data assimilation (4DVar) of GRAPES (Global Regional Assimilation and PrEdiction System), including how to reduce the computational cost when adding extended alpha control variables and how to maintain the dynamic balance between variables during localization. This paper uses the Gaussian shape spectral filter to realize horizontal localization, uses the EOF decomposition to realize vertical localization, and uses the first 8 leading eigenvectors to limit the increase of extended alpha control variable dimension. With the introduction of 20 to 180 ensemble members, the increase in the total number of control variables can be limited to about 1.1 to 1.8 times in the two-dimensional horizontal localization case, and about 1.7 to 7.1 times in the three-dimensional localization case. For 60 ensemble samples and 1.0° horizontal resolution inner loop, after the introduction of the extended alpha control variables, the wall clock time of 4DVar run time increases by about 30%. Furthermore, by performing horizontal localization on unbalanced analysis variables and then adding the geostrophic balance back to the unbalanced analytical variables, this study allows the analysis to better maintain geostrophic balance and the surface pressure tendency of the initial field is reduced. In addition, although the vertical localization has a large impact on the analysis balance, the analysis can well keep geostrophic balance due to the weak constraint formulation of a digital filter in the cost function. The methods developed in GRAPES Global 4DVar such as adding extended alpha control variables, spectral filtering for horizontal localization and localization on unbalanced analysis variables are suitable for the case where the number of ensemble samples exceeds 100, and the quality of analysis is improved without significantly increasing the computational and storage cost of the 4DVar system.
HTML   查看全文   查看/发表评论  下载PDF阅读器
分享按钮