一种控制变量循环扰动和多参数化方案的混合同化方法

敏感性试验表明集合变换卡尔曼滤波(Ensemble Transform Kalman Filter,ETKF)方法在混合(Hybrid)同化过程中易受观测资料数量变化的影响而产生较大程度的协方差震荡,从而可能导致系统不稳定。为设计一种简便、稳定的Hybrid同化系统,构建了一种基于物理控制变量扰动及多物理参数化方案的Hybrid同化及预报系统。本系统随着循环的进行,不断对Hybrid同化分析场进行控制变量扰动得到集合成员初始场,并且对各集合成员采用不同物理参数化方案以更合理地表征背景场的误差特征。连续10 d的循环同化及预报试验表明,本文同化方案效果明显优于三维变分方案,动力场的整体同化和预报效果与ETKF方案基本相当。本方案相比于ETKF方法不受观测波动影响,在没有经任何参数调试情况下,取得了良好同化和预报效果,为Hybrid同化的便捷运行提供了一种稳定可靠的手段。

A hybrid assimilation method with control variable cycle perturbation and multi-physical parameterization

The ensemble members of WRFDA/Hybrid assimilation system come from ETKF(Ensemble Transform Kalman Filter) ensemble forecast system.Sensitivity analysis shows that the ETKF scheme often crashes in cycle assimilation because of oscillation of the covariance inflation factors, which is caused by large variation of observation in cycle assimilation.In order to design a simple and stable hybrid data assimilation system, a new hybrid assimilation method, with physical control variable perturbation and multi-physical parameterization(RCV-Hybrid), was proposed.In RCV-Hybrid, the initial state of ensemble forecast come from hybrid analysis perturbed by physical control variables.Meanwhile, different microphysics and cumulus parameterization schemes were used in ensemble forecast to represent model error.Results of 10 days cycle assimilation and forecast show that the RCV-Hybrid method performs better than 3DVar and is similar to ETKF.In addition, the RCV-Hybrid method can be easily operated without perturbing observation.Thus, it is a new option for hybrid data assimilation.