冯业荣,薛纪善,李梦婕,戴光丰. 2021. 基于扰动模式的四维变分资料同化系统框架的设计完善和数值试验[J]. 气象学报, (0):-, doi:10.11676/qxxb2021.061
基于扰动模式的四维变分资料同化系统框架的设计完善和数值试验
The Framework of the 4D-Var Data Assimilation System Based on Perturbation Forecast Model:Development and Numerical Experiment
投稿时间:2021-02-10  修订日期:2021-06-02
DOI:10.11676/qxxb2021.061
中文关键词:  4D-Var四维变分资料同化,扰动预报模式,GRAPES区域模式
英文关键词:Four-dimensional data assimilation (4D-Var),Perturbation forecast model,Regional nonlinear GRAPES model
基金项目:国家自然科学基金联合基金项目(U1811464)和国家重点研发计划重点专项项目(2018YFC1506900)
作者单位邮编
冯业荣 中国气象局广州热带海洋气象研究所/广东省区域数值天气预报重点实验室 510641
薛纪善  510641
李梦婕  510641
戴光丰  510641
摘要点击次数: 20
全文下载次数: 22
中文摘要:
      为了建立一个应用于区域数值预报的四维变分资料同化(4D-Var)系统,在近期开发的扰动预报模式GRAPES_PF基础上,开发完善了增量四维变分同化(4D-Var)系统框架。该框架中暂不包含物理过程(长短波辐射、边界层过程、对流参数化和云微物理等)。对比业务使用的GRAPES-3DVar系统,增加了温度控制变量。将无量纲Exner气压与流函数的线性风压平衡方程直接在地形追随垂直坐标面上求解,且通过广义共轭余差法(GCR)求解扰动亥姆霍兹(Helmholtz)伴随方程。利用人造探空资料对2015年10月南海台风“彩虹”进行了理想数值试验。试验表明,所开发的扰动四维变分同化框架得到了预期的结果,即同化更多资料并反复受到模式约束的四维变分同化系统能有效改善初值质量,进而改善数值预报。本研究建立了一个合理可行的区域四维变分同化框架,为进一步发展包含完整物理过程的区域四维变分同化系统奠定了研究基础。
英文摘要:
      To develop the four-dimensional variational data assimilation (4D-Var) system to be used in regional numerical weather prediction, the framework of the incremental 4D-Var, based on the recently developed perturbation forecast model GRAPES_PF,is developed in this study. At the present stage, this 4D-Var framework does not include physical schemes for short-wave and long-wave radiation, planetary boundary layer, cumulus convection and cloud microphysics. Compared to the operational GRAPES-3DVar system,air temperature is chosen as an extra analysis control variable in the new framework. The linear balance equation, which relates balanced Exner pressure with stream function, is deduced and solved numerically on the terrain-following vertical coordinate. The adjoint of perturbation Helmholtz equation is solved by the iterative generalized conjugate residual (GCR) approach. To evaluate the validity of this framework, a set of idealized numerical experiments using pseudo radiosonde data were carried out on the typhoon Mujigae which occurred over South China Sea in October 2015. The experiments reveal that the 4D-Var framework offers results in line with theoretical expectations, i.e., by ingesting more observations in time and through the constraint of perturbation forecast model, the 4D-Var contributes more obvious improvements than the 3D-Var in both analysis and forecast. This study provides a reasonable framework of four-dimensional variational data assimilation, which can be further implemented with full linear physical package soon.
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