中文摘要：

把地区性的整体预言系统和 3DVAR 基于 GRAPES (全球/地区性的吸收和预言系统)(三维变化)数据吸收系统，在中国气象学的管理的数字天气预言中心操作上被实现，一个基于整体的 3DVAR ( En-3DVAR )混血儿为 GRAPES_Meso 的数据吸收系统(地区性的 mesoscale 葡萄的数字预言系统)被使用扩大控制变量技术到开发就系统的基于整体的数据吸收部分的问题而言，与 3DVAR 数据吸收相比在在变量，和非光滑的分析增长和它的显然更小的尺寸之间的因地球自转而引起的平衡的度包括减小，相应措施被采取优化并且改善系统。因此，单个压力观察基于整体的数据吸收实验被进行保证系统的基于整体的数据吸收部分正确、合理。基于整体的数据吸收的很多本地化规模敏感测试也被进行决定最适当的本地化规模。然后，很多个混合数据吸收实验被执行。结果证明在实验设置估计整体的协变性的重量因素是 0.8 是很适当的。与 3DVAR 数据吸收相比，混合数据吸收实验的 geopotential 高度预报几乎没改善，但是风预报在每预报时间稍微改善了，特别在 300 hPa 上。总的来说，混合数据吸收在 3DVAR 数据吸收上表明一些优点。

英文摘要：

Based on the GRAPES（Global/Regional Assimilation and Prediction System） regional ensemble prediction system and 3DVAR（three-dimensional variational） data assimilation system,which are implemented operationally at the Numerical Weather Prediction Center of the China Meteorological Administration,an ensemble-based 3DVAR（En-3DVAR） hybrid data assimilation system for GRAPES-Meso（the regional mesoscale numerical prediction system of GRAPES） was developed by using the extended control variable technique to implement a hybrid background error covariance that combines the climatological covariance and ensemble-estimated covariance.Considering the problems of the ensemble-based data assimilation part of the system,including the reduction in the degree of geostrophic balance between variables,and the non-smooth analysis increment and its obviously smaller size compared with the 3DVAR data assimilation,corresponding measures were taken to optimize and ameliorate the system.Accordingly,a single pressure observation ensemble-based data assimilation experiment was conducted to ensure that the ensemble-based data assimilation part of the system is correct and reasonable.A number of localization-scale sensitivity tests of the ensemble-based data assimilation were also conducted to determine the most appropriate localization scale.Then,a number of hybrid data assimilation experiments were carried out.The results showed that it was most appropriate to set the weight factor of the ensemble-estimated covariance in the experiments to be 0.8.Compared with the 3DVAR data assimilation,the geopotential height forecast of the hybrid data assimilation experiments improved very little,but the wind forecast improved slightly at each forecast time,especially over 300 hPa.Overall,the hybrid data assimilation demonstrates some advantages over the3 DVAR data assimilation.