雷达径向风观测在华北区域数值预报系统中的实时三维变分同化应用试验

重构GRAPES（Global/Regional Assimilation and Prediction System）全球、区域一体化变分同化系统中的极小化控制变量，提升中、小尺度同化分析能力，为中国气象局业务区域数值预报系统CMA-MESO提供千米尺度适用的同化方案。新方案用纬向风速（u）和经向风速（v）替代原有流函数和势函数作为新的风场控制变量，采用温度和地面气压（T，p_s）替代原有非平衡无量纲气压作为新的质量场控制变量，同时不再考虑准地转平衡约束，而是采用连续方程弱约束保证分析平衡。背景误差参数统计和数值试验结果表明，采用重构后的极小化控制变量，观测信息传播更加局地，分析结构更加合理，避免了原方案在中、小尺度应用时存在的虚假相关问题。连续方程弱约束的引入，限制了同化分析中辐合、辐散的不合理增长，帮助新方案在分析更加局地的同时保证分析平衡。为期1个月的连续同化循环和预报试验结果表明，新方案可以减小风场和质量场分析误差，CMA-MESO系统地面降水和10 m风场的预报评分显著提升。

The real-time radar radial velocity 3DVar assimilation experiments for application to an operational forecast model in North China

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, p_s) 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.