气象学报2024，Vol.82Issue(2)：208-221,14.DOI:10.11676/qxxb2024.20230076
CMA-MESO千米尺度变分同化系统中极小化控制变量的重构
A reformulation of the minimization control variables in the CMA-MESO km-scale variational assimilation system
摘要
Abstract
In order to improve the analysis capability of micro-and meso-scale flows and provide a kilometer-scale applicable assimilation scheme for the China Meteorological Administration(CMA)operational regional numerical weather prediction system CMA-MESO,a new formulation of the minimization control variables in the GRAPES(Global/Regional Assimilation and Prediction System)variational assimilation system has been developed.The new scheme uses eastward velocity u and northward velocity v to replace the original stream function and velocity potential as the new momentum control variables,and uses temperature and surface pressure(T,ps)to replace the original unbalanced dimensionless pressure as the new mass field control variable.In addition,the new scheme no longer introduces quasi-geostrophic balance constraint but uses a weak mass continuity constraint to ensure analysis balance.Results of background error statistics and numerical experiments show that the adoption of the reformulated control variables results in a more local propagation of observational information and a more reasonable analysis,avoiding the spurious correlation problem of the original scheme when applied at micro-and meso-scale analysis.The introduction of the weak mass continuity constraint suppresses unrealistic convergence and divergence in the analysis,making the new analysis more balanced.Results of one-month assimilation cycles and forecasts show that the new scheme can reduce analysis errors in wind and mass fields,which in turn significantly improves precipitation and 10 m wind field forecast scores of the CMA-MESO system.关键词
CMA-MESO/千米尺度变分同化/控制变量/平衡约束Key words
CMA-MESO/Kilometer scale variational assimilation/Control variables/Balance constraint分类
大气科学引用本文复制引用
王瑞春,龚建东,孙健..CMA-MESO千米尺度变分同化系统中极小化控制变量的重构[J].气象学报,2024,82(2):208-221,14.基金项目
国家重点研发计划项目(2017YFC1502001)、气象能力提升联合研究专项(23NLTSQ005). （2017YFC1502001）
